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Artificial Intelligence | Latest News, Photos & Videos | WIRED

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Find the latest Artificial Intelligence news from WIRED. See related science and technology articles, photos, slideshows and videos.

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One Hundred Year Study on Artificial Intelligence (AI100) |

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One Hundred Year Study of Artificial Intelligence (AI) has its roots in a one-year study on Long-term AI Futures that we commissioned during my term of service as president of the Association for the Advancement of Artificial Intelligence (AAAI) in 2008-2009.

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AI (artificial intelligence) is the simulation of human intelligence processes by machines, especially computer systems. These processes include learning (the acquisition of information and rules for using the information), reasoning (using the rules to reach approximate or definite conclusions) and self-correction. Particular applications of AI include expert systems, speech recognition and machine vision.

AI was coined by John McCarthy, an American computer scientist, in 1956 at The Dartmouth Conference where the discipline was born. Today, it is an umbrella term that encompasses everything from robotic process automation to actual robotics. It has gained prominence recently due, in part, to big data, or the increase in speed, size and variety of data businesses are now collecting. AI can perform tasks such as identifying patterns in the data more efficiently than humans, enabling businesses to gain more insight out of their data.

Types of artificial intelligence

AI can be categorized in any number of ways, but here are two examples.

The first classifies AI systems as either weak AI or strong AI. Weak AI, also known as narrow AI, is an AI system that is designed and trained for a particular task. Virtual personal assistants, such as Apple's Siri, are a form of weak AI.

Strong AI, also known as artificial general intelligence, is an AI system with generalized human cognitive abilities so that when presented with an unfamiliar task, it has enough intelligence to find a solution. The Turing Test, developed by mathematician Alan Turing in 1950, is a method used to determine if a computer can actually think like a human, although the method is controversial.

The second example is from Arend Hintze, an assistant professor of integrative biology and computer science and engineering at Michigan State University. He categorizes AI into four types, from the kind of AI systems that exist today to sentient systems, which do not yet exist. His categories are as follows:

Type 1: Reactive machines. An example is Deep Blue, the IBM chess program that beat Garry Kasparov in the 1990s. Deep Blue can identify pieces on the chess board and make predictions, but it has no memory and cannot use past experiences to inform future ones. It analyzes possible moves -- its own and its opponent -- and chooses the most strategic move. Deep Blue and Google's AlphaGO were designed for narrow purposes and cannot easily be applied to another situation.

Type 2: Limited memory. These AI systems can use past experiences to inform future decisions. Some of the decision-making functions in autonomous vehicles have been designed this way. Observations used to inform actions happening in the not-so-distant future, such as a car that has changed lanes. These observations are not stored permanently.

Type 3: Theory of mind. This is a psychology term. It refers to the understanding that others have their own beliefs, desires and intentions that impact the decisions they make. This kind of AI does not yet exist.

Type 4: Self-awareness. In this category, AI systems have a sense of self, have consciousness. Machines with self-awareness understand their current state and can use the information to infer what others are feeling. This type of AI does not yet exist.

Examples of AI technology

Automation is the process of making a system or process function automatically. Robotic process automation, for example, can be programmed to perform high-volume, repeatable tasks normally performed by humans. RPA is different from IT automation in that it can adapt to changing circumstances.

Machine learning is the science of getting a computer to act without programming. Deep learning is a subset of machine learning that, in very simple terms, can be thought of as the automation of predictive analytics. There are three types of machine learning algorithms: supervised learning, in which data sets are labeled so that patterns can be detected and used to label new data sets; unsupervised learning, in which data sets aren't labeled and are sorted according to similarities or differences; and reinforcement learning, in which data sets aren't labeled but, after performing an action or several actions, the AI system is given feedback.

Machine vision is the science of making computers see. Machine vision captures and analyzes visual information using a camera, analog-to-digital conversion and digital signal processing. It is often compared to human eyesight, but machine vision isn't bound by biology and can be programmed to see through walls, for example. It is used in a range of applications from signature identification to medical image analysis. Computer vision, which is focused on machine-based image processing, is often conflated with machine vision.

Natural language processing (NLP) is the processing of human -- and not computer -- language by a computer program. One of the older and best known examples of NLP is spam detection, which looks at the subject line and the text of an email and decides if it's junk. Current approaches to NLP are based on machine learning. NLP tasks include text translation, sentiment analysis and speech recognition.

Pattern recognition is a branch of machine learning that focuses on identifying patterns in data. The term, today, is dated.

Robotics is a field of engineering focused on the design and manufacturing of robots. Robots are often used to perform tasks that are difficult for humans to perform or perform consistently. They are used in assembly lines for car production or by NASA to move large objects in space. More recently, researchers are using machine learning to build robots that can interact in social settings.

AI applications

AI in healthcare. The biggest bets are on improving patient outcomes and reducing costs. Companies are applying machine learning to make better and faster diagnoses than humans. One of the best known healthcare technologies is IBM Watson. It understands natural language and is capable of responding to questions asked of it. The system mines patient data and other available data sources to form a hypothesis, which it then presents with a confidence scoring schema. Other AI applications include chatbots, a computer program used online to answer questions and assist customers, to help schedule follow-up appointments or aiding patients through the billing process, and virtual health assistants that provide basic medical feedback.

AI in business. Robotic process automation is being applied to highly repetitive tasks normally performed by humans. Machine learning algorithms are being integrated into analytics and CRM platforms to uncover information on how to better serve customers. Chatbots have been incorporated into websites to provide immediate service to customers. Automation of job positions has also become a talking point among academics and IT consultancies such as Gartner and Forrester.

AI in education. AI can automate grading, giving educators more time. AI can assess students and adapt to their needs, helping them work at their own pace. AI tutors can provide additional support to students, ensuring they stay on track. AI could change where and how students learn, perhaps even replacing some teachers.

AI in finance. AI applied to personal finance applications, such as Mint or Turbo Tax, is upending financial institutions. Applications such as these could collect personal data and provide financial advice. Other programs, IBM Watson being one, have been applied to the process of buying a home. Today, software performs much of the trading on Wall Street.

AI in law. The discovery process, sifting through of documents, in law is often overwhelming for humans. Automating this process is a better use of time and a more efficient process. Startups are also building question-and-answer computer assistants that can sift programmed-to-answer questions by examining the taxonomy and ontology associated with a database.

AI in manufacturing. This is an area that has been at the forefront of incorporating robots into the workflow. Industrial robots used to perform single tasks and were separated from human workers, but as the technology advanced that changed.

Continue Reading About AI (artificial intelligence)

Margaret Rouse asks:

How is your company adapting AI to the enterprise?

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What is AI (artificial intelligence)? - Definition from WhatIs.com

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AI (artificial intelligence) is the simulation of human intelligence processes by machines, especially computer systems. These processes include learning (the acquisition of information and rules for using the information), reasoning (using the rules to reach approximate or definite conclusions) and self-correction. Particular applications of AI include expert systems, speech recognition and machine vision.

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Years ago I had coffee with a friend who ran a startup. He had just turned 40. His father was ill, his back was sore, and he found himself overwhelmed by life. "Don't laugh at me," he said, "but I was counting on the singularity."

My friend worked in technology; he'd seen the changes that faster microprocessors and networks had wrought. It wasn't that much of a step for him to believe that before he was beset by middle age, the intelligence of machines would exceed that of humans-a moment that futurists call the singularity. A benevolent superintelligence might analyze the human genetic code at great speed and unlock the secret to eternal youth. At the very least, it might know how to fix your back.

But what if it wasn't so benevolent? Nick Bostrom, a philosopher who directs the Future of Humanity Institute at the University of Oxford, describes the following scenario in his book Superintelligence, which has prompted a great deal of debate about the future of artificial intelligence. Imagine a machine that we might call a "paper-clip maximizer"-that is, a machine programmed to make as many paper clips as possible. Now imagine that this machine somehow became incredibly intelligent. Given its goals, it might then decide to create new, more efficient paper-clip-manufacturing machines-until, King Midas style, it had converted essentially everything to paper clips.

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No worries, you might say: you could just program it to make exactly a million paper clips and halt. But what if it makes the paper clips and then decides to check its work? Has it counted correctly? It needs to become smarter to be sure. The superintelligent machine manufactures some as-yet-uninvented raw-computing material (call it "computronium") and uses that to check each doubt. But each new doubt yields further digital doubts, and so on, until the entire earth is converted to computronium. Except for the million paper clips.

Things Reviewed

"Superintelligence: Paths, Dangers, Strategies" By Nick Bostrom
Oxford University Press, 2014

Bostrom does not believe that the paper-clip maximizer will come to be, exactly; it's a thought experiment, one designed to show how even careful system design can fail to restrain extreme machine intelligence. But he does believe that superintelligence could emerge, and while it could be great, he thinks it could also decide it doesn't need humans around. Or do any number of other things that destroy the world. The title of chapter 8 is: "Is the default outcome doom?"

If this sounds absurd to you, you're not alone. Critics such as the robotics pioneer Rodney Brooks say that people who fear a runaway AI misunderstand what computers are doing when we say they're thinking or getting smart. From this perspective, the putative superintelligence Bostrom describes is far in the future and perhaps impossible.

Yet a lot of smart, thoughtful people agree with Bostrom and are worried now. Why?
Volition
The question "Can a machine think?" has shadowed computer science from its beginnings. Alan Turing proposed in 1950 that a machine could be taught like a child; John McCarthy, inventor of the programming language LISP, coined the term "artificial intelligence" in 1955. As AI researchers in the 1960s and 1970s began to use computers to recognize images, translate between languages, and understand instructions in normal language and not just code, the idea that computers would eventually develop the ability to speak and think-and thus to do evil-bubbled into mainstream culture. Even beyond the oft-referenced HAL from 2001: A Space Odyssey, the 1970 movie Colossus: The Forbin Project featured a large blinking mainframe computer that brings the world to the brink of nuclear destruction; a similar theme was explored 13 years later in WarGames. The androids of 1973's Westworld went crazy and started killing.

Extreme AI predictions are "comparable to seeing more efficient internal combustion engines... and jumping to the conclusion that the warp drives are just around the corner," Rodney Brooks writes.

When AI research fell far short of its lofty goals, funding dried up to a trickle, beginning long "AI winters." Even so, the torch of the intelligent machine was carried forth in the 1980s and '90s by sci-fi authors like Vernor Vinge, who popularized the concept of the singularity; researchers like the roboticist Hans Moravec, an expert in computer vision; and the engineer/entrepreneur Ray Kurzweil, author of the 1999 book The Age of Spiritual Machines. Whereas Turing had posited a humanlike intelligence, Vinge, Moravec, and Kurzweil were thinking bigger: when a computer became capable of independently devising ways to achieve goals, it would very likely be capable of introspection-and thus able to modify its software and make itself more intelligent. In short order, such a computer would be able to design its own hardware.

As Kurzweil described it, this would begin a beautiful new era. Such machines would have the insight and patience (measured in picoseconds) to solve the outstanding problems of nanotechnology and spaceflight; they would improve the human condition and let us upload our consciousness into an immortal digital form. Intelligence would spread throughout the cosmos.

You can also find the exact opposite of such sunny optimism. Stephen Hawking has warned that because people would be unable to compete with an advanced AI, it "could spell the end of the human race." Upon reading Superintelligence, the entrepreneur Elon Musk tweeted: "Hope we're not just the biological boot loader for digital superintelligence. Unfortunately, that is increasingly probable." Musk then followed with a $10 million grant to the Future of Life Institute. Not to be confused with Bostrom's center, this is an organization that says it is "working to mitigate existential risks facing humanity," the ones that could arise "from the development of human-level artificial intelligence."

No one is suggesting that anything like superintelligence exists now. In fact, we still have nothing approaching a general-purpose artificial intelligence or even a clear path to how it could be achieved. Recent advances in AI, from automated assistants such as Apple's Siri to Google's driverless cars, also reveal the technology's severe limitations; both can be thrown off by situations that they haven't encountered before. Artificial neural networks can learn for themselves to recognize cats in photos. But they must be shown hundreds of thousands of examples and still end up much less accurate at spotting cats than a child.

This is where skeptics such as Brooks, a founder of iRobot and Rethink Robotics, come in. Even if it's impressive-relative to what earlier computers could manage-for a computer to recognize a picture of a cat, the machine has no volition, no sense of what cat-ness is or what else is happening in the picture, and none of the countless other insights that humans have. In this view, AI could possibly lead to intelligent machines, but it would take much more work than people like Bostrom imagine. And even if it could happen, intelligence will not necessarily lead to sentience. Extrapolating from the state of AI today to suggest that superintelligence is looming is "comparable to seeing more efficient internal combustion engines appearing and jumping to the conclusion that warp drives are just around the corner," Brooks wrote recently on Edge.org. "Malevolent AI" is nothing to worry about, he says, for a few hundred years at least.
Insurance policy
Even if the odds of a superintelligence arising are very long, perhaps it's irresponsible to take the chance. One person who shares Bostrom's concerns is Stuart J. Russell, a professor of computer science at the University of California, Berkeley. Russell is the author, with Peter Norvig (a peer of Kurzweil's at Google), of Artificial Intelligence: A Modern Approach, which has been the standard AI textbook for two decades.

"There are a lot of supposedly smart public intellectuals who just haven't a clue," Russell told me. He pointed out that AI has advanced tremendously in the last decade, and that while the public might understand progress in terms of Moore's Law (faster computers are doing more), in fact recent AI work has been fundamental, with techniques like deep learning laying the groundwork for computers that can automatically increase their understanding of the world around them.

Bostrom's book proposes ways to align computers with human needs. We're basically telling a god how we'd like to be treated.

Because Google, Facebook, and other companies are actively looking to create an intelligent, "learning" machine, he reasons, "I would say that one of the things we ought not to do is to press full steam ahead on building superintelligence without giving thought to the potential risks. It just seems a bit daft." Russell made an analogy: "It's like fusion research. If you ask a fusion researcher what they do, they say they work on containment. If you want unlimited energy you'd better contain the fusion reaction." Similarly, he says, if you want unlimited intelligence, you'd better figure out how to align computers with human needs.

Bostrom's book is a research proposal for doing so. A superintelligence would be godlike, but would it be animated by wrath or by love? It's up to us (that is, the engineers). Like any parent, we must give our child a set of values. And not just any values, but those that are in the best interest of humanity. We're basically telling a god how we'd like to be treated. How to proceed?

Bostrom draws heavily on an idea from a thinker named Eliezer Yudkowsky, who talks about "coherent extrapolated volition"-the consensus-derived "best self" of all people. AI would, we hope, wish to give us rich, happy, fulfilling lives: fix our sore backs and show us how to get to Mars. And since humans will never fully agree on anything, we'll sometimes need it to decide for us-to make the best decisions for humanity as a whole. How, then, do we program those values into our (potential) superintelligences? What sort of mathematics can define them? These are the problems, Bostrom believes, that researchers should be solving now. Bostrom says it is "the essential task of our age."

For the civilian, there's no reason to lose sleep over scary robots. We have no technology that is remotely close to superintelligence. Then again, many of the largest corporations in the world are deeply invested in making their computers more intelligent; a true AI would give any one of these companies an unbelievable advantage. They also should be attuned to its potential downsides and figuring out how to avoid them.

This somewhat more nuanced suggestion-without any claims of a looming AI-mageddon-is the basis of an open letter on the website of the Future of Life Institute, the group that got Musk's donation. Rather than warning of existential disaster, the letter calls for more research into reaping the benefits of AI "while avoiding potential pitfalls." This letter is signed not just by AI outsiders such as Hawking, Musk, and Bostrom but also by prominent computer scientists (including Demis Hassabis, a top AI researcher). You can see where they're coming from. After all, if they develop an artificial intelligence that doesn't share the best human values, it will mean they weren't smart enough to control their own creations.
Paul Ford, a freelance writer in New York, wrote about Bitcoin in March/April 2014.

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Illustration by Jacob Escobedo

Paul Ford is a writer and computer programmer who lives in Brooklyn. He is writing a book of essays about Web pages.

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Are We Smart Enough to Control Artificial Intelligence?

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Years ago I had coffee with a friend who ran a startup. He had just turned 40. His father was ill, his back was sore, and he found himself overwhelmed by life. "Don't laugh at me," he said, "but I was counting on the singularity."

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Dmitry Malioutov can't say much about what he built.

As a research scientist at IBM, Malioutov spends part of his time building machine learning systems that solve difficult problems faced by IBM's corporate clients. One such program was meant for a large insurance corporation. It was a challenging assignment, requiring a sophisticated algorithm. When it came time to describe the results to his client, though, there was a wrinkle. "We couldn't explain the model to them because they didn't have the training in machine learning."

In fact, it may not have helped even if they were machine learning experts. That's because the model was an artificial neural network, a program that takes in a given type of data-in this case, the insurance company's customer records-and finds patterns in them. These networks have been in practical use for over half a century, but lately they've seen a resurgence, powering breakthroughs in everything from speech recognition and language translation to Go-playing robots and self-driving cars.

Michael Nielsen / NeuralNetworksandDeepLearning.com

As exciting as their performance gains have been, though, there's a troubling fact about modern neural networks: Nobody knows quite how they work. And that means no one can predict when they might fail.

Take, for example, an episode recently reported by machine learning researcher Rich Caruana and his colleagues. They described the experiences of a team at the University of Pittsburgh Medical Center who were using machine learning to predict whether pneumonia patients might develop severe complications. The goal was to send patients at low risk for complications to outpatient treatment, preserving hospital beds and the attention of medical staff. The team tried several different methods, including various kinds of neural networks, as well as software-generated decision trees that produced clear, human-readable rules.

The neural networks were right more often than any of the other methods. But when the researchers and doctors took a look at the human-readable rules, they noticed something disturbing: One of the rules instructed doctors to send home pneumonia patients who already had asthma, despite the fact that asthma sufferers are known to be extremely vulnerable to complications.

The model did what it was told to do: Discover a true pattern in the data. The poor advice it produced was the result of a quirk in that data. It was hospital policy to send asthma sufferers with pneumonia to intensive care, and this policy worked so well that asthma sufferers almost never developed severe complications. Without the extra care that had shaped the hospital's patient records, outcomes could have been dramatically different.

The hospital anecdote makes clear the practical value of interpretability. "If the rule-based system had learned that asthma lowers risk, certainly the neural nets had learned it, too," wrote Caruana and colleagues-but the neural net wasn't human-interpretable, and its bizarre conclusions about asthma patients might have been difficult to diagnose. ¹ If there hadn't been an interpretable model, Malioutov cautions, "you could accidentally kill people."

This is why so many are reluctant to gamble on the mysteries of neural networks. When Malioutov presented his accurate but inscrutable neural network model to his own corporate client, he also offered them an alternative, rule-based model whose workings he could communicate in simple terms. This second, interpretable, model was less accurate than the first, but the client decided to use it anyway-despite being a mathematically sophisticated insurance company for which every percentage point of accuracy mattered. "They could relate to [it] more," Malioutov says. "They really value intuition highly."

Even governments are starting to show concern about the increasing influence of inscrutable neural-network oracles. The European Union recently proposed to establish a "right to explanation," which allows citizens to demand transparency for algorithmic decisions. ² The legislation may be difficult to implement, however, because the legislators didn't specify exactly what "transparency" means. It's unclear whether this omission stemmed from ignorance of the problem, or an appreciation of its complexity.

Some researchers hope to eliminate the need to choose-to let us have our many-layered cake, and understand it, too.

In fact, some believe that such a definition might be impossible. At the moment, though we can know everything there is to know about what neural networks are doing-they are, after all, just computer programs-we can discern very little about how or why they are doing it. The networks are made up of many, sometimes millions, of individual units, called neurons. Each neuron converts many numerical inputs into a single numerical output, which is then passed on to one or more other neurons. As in brains, these neurons are divided into "layers," groups of cells that take input from the layer below and send their output to the layer above.

Neural networks are trained by feeding in data, then adjusting the connections between layers until the network's calculated output matches the known output (which usually consists of categories) as closely as possible. The incredible results of the past few years are thanks to a series of new techniques that make it possible to quickly train deep networks, with many layers between the first input and the final output. One popular deep network called AlexNet is used to categorize photographs-labeling them according to such fine distinctions as whether they contain a Shih Tzu or a Pomeranian. It consists of over 60 million "weights," each of which tell each neuron how much attention to pay to each of its inputs. "In order to say you have some understanding of the network," says Jason Yosinski, a computer scientist affiliated with Cornell University and Geometric Intelligence, "you'd have to have some understanding of these 60 million numbers."

Even if it were possible to impose this kind of interpretability, it may not always be desirable. The requirement for interpretability can be seen as another set of constraints, preventing a model from a "pure" solution that pays attention only to the input and output data it is given, and potentially reducing accuracy. At a DARPA conference early this year, program manager David Gunning summarized the trade-off in a chart that shows deep networks as the least understandable of modern methods. At the other end of the spectrum are decision trees, rule-based systems that tend to prize explanation over efficacy.

Darpa

The result is that modern machine learning offers a choice among oracles: Would we like to know what will happen with high accuracy, or why something will happen, at the expense of accuracy? The "why" helps us strategize, adapt, and know when our model is about to break. The "what" helps us act appropriately in the immediate future.

It can be a difficult choice to make. But some researchers hope to eliminate the need to choose-to allow us to have our many-layered cake, and understand it, too. Surprisingly, some of the most promising avenues of research treat neural networks as experimental objects-after the fashion of the biological science that inspired them to begin with-rather than analytical, purely mathematical objects. Yosinski, for example, says he is trying to understand deep networks "in the way we understand animals, or maybe even humans." He and other computer scientists are importing techniques from biological research that peer inside networks after the fashion of neuroscientists peering into brains: probing individual components, cataloguing how their internals respond to small changes in inputs, and even removing pieces to see how others compensate.

Having built a new intelligence from scratch, scientists are now taking it apart, applying to these virtual organisms the digital equivalents of a microscope and scalpel.

Yosinski sits at a computer terminal, talking into a webcam. The data from the webcam is fed into a deep neural net, while the net itself is being analyzed, in real time, using a software toolkit Yosinski and his colleagues developed called the Deep Visualization toolkit. Clicking through several screens, Yosinski zooms in on one neuron in the network. "This neuron seems to respond to faces," he says in a video record of the interaction. ³ Human brains are also known to have such neurons, many of them clustered in a region of the brain called the fusiform face area. This region, discovered over the course of multiple studies beginning in 1992, 4, 5 has become one of the most reliable observations in human neuroscience. But where those studies required advanced techniques like positron emission tomography, Yosinski can peer at his artificial neurons through code alone.

Left: Jason Yosinski, et al. Understanding Neural Networks Through Deep Visualization. Deep Learning Workshop, International Conference on Machine Learning (ICML) (2015). Right: Maximilian Riesenhuber, Georgetown University Medical Center

This approach lets him map certain artificial neurons to human-understandable ideas or objects, like faces, which could help turn neural networks into intuitive tools. His program can also highlight which aspects of a picture are most important to stimulating the face neuron. "We can see that it would respond even more strongly if we had darker eyes, and rosier lips," he says.

To Cynthia Rudin, professor of computer science and electrical and computer engineering at Duke University, these "post-hoc" interpretations are by nature problematic. Her research focuses on building rule-based machine learning systems applied to domains like prison sentencing and medical diagnosis, where human-readable interpretations are possible-and critically important. But for problems in areas like vision, she says, "Interpretations are completely within the eye of the beholder." We can simplify a network response by identifying a face neuron, but how can we be certain that's really what it's looking for? Rudin's concerns echo the famous dictum that there may be no simpler model of the visual system than the visual system itself. "You could have many explanations for what a complex model is doing," she says. "Do you just pick the one you 'want' to be correct?"

Yosinski's toolkit can, in part, counter these concerns by working backward, discovering what the network itself "wants" to be correct-a kind of artificial ideal. The program starts with raw static, then adjusts it, pixel by pixel, tinkering with the image using the reverse of the process that trained the network. Eventually it finds a picture that elicits the maximum possible response of a given neuron. When this method is applied to AlexNet neurons, it produces caricatures that, while ghostly, unquestionably evoke the labeled categories.

Jason Yosinski, et al. Understanding Neural Networks Through Deep Visualization. Deep Learning Workshop, International Conference on Machine Learning (ICML) (2015).

This seems to support his claim that the face neurons are indeed looking for faces, in some very general sense. But there's a catch. To generate those pictures, Yosinski's procedure relies on a statistical constraint (called a natural image prior) that confines it to producing images that match the sorts of structure that one finds in pictures of real-world objects. When he removes those rules, the toolkit still settles on an image that it labels with maximum confidence, but that image is pure static. In fact, Yosinski has shown that in many cases, the majority of images that AlexNet neurons prefer appear to humans as static. He readily admits that "it's pretty easy to figure out how to make the networks say something extreme."

To avoid some of these pitfalls, Dhruv Batra, an assistant professor of electrical and computer engineering at Virginia Tech, takes a higher-level experimental approach to interpreting deep networks. Rather than trying to find patterns in their internal structure-"people smarter than me have worked on it," he demurs-he probes how the networks behave using, essentially, a robotic version of eye tracking. His group, in a project led by graduate students Abhishek Das and Harsh Agrawal, asks a deep net questions about an image, like whether there are drapes on a window in a given picture of a room. ⁶ Unlike AlexNet or similar systems, Das' network is designed to focus on only a small patch of an image at a time. It moves its virtual eyes around the picture until it has decided it has enough information to answer the question. After sufficient training, the deep network performs extremely well, answering about as accurately as the best humans.

Trained machines are exquisitely well suited to their environment-and ill-adapted to any other.

Das, Batra, and their colleagues then try to get a sense of how the network makes its decisions by investigating where in the pictures it chooses to look. What they have found surprises them: When answering the question about drapes, the network doesn't even bother looking for a window. Instead, it first looks to the bottom of the image, and stops looking if it finds a bed. It seems that, in the dataset used to train this neural net, windows with drapes may be found in bedrooms.

While this approach does reveal some of the inner workings of the deep net, it also reinforces the challenge presented by interpretability. "What machines are picking up on are not facts about the world," Batra says. "They're facts about the dataset." That the machines are so tightly tuned to the data they are fed makes it difficult to extract general rules about how they work. More importantly, he cautions, if you don't know how it works, you don't know how it will fail. And when they do they fail, in Batra's experience, "they fail spectacularly disgracefully."

Some of the obstacles faced by researchers like Yosinski and Batra will be familiar to scientists studying the human brain. Questions over the interpretations of neuroimaging, for example, are common to this day, if not commonly heeded. In a 2014 review of the field, cognitive neuroscientist Martha Farah wrote that "the worry ... is that [functional brain] images are more researcher inventions than researcher observations." ⁷ The appearance of these issues in very different realizations of intelligent systems suggests that they could be obstacles, not to the study of this or that kind of brain, but to the study of intelligence itself.

Is chasing interpretability a fool's errand? In a 2015 blog post entitled "The Myth of Model Interpretability," Zachary Lipton, of the University of California, San Diego, offered a critical perspective on both the motivations behind interpreting neural networks, and the value of building interpretable machine learning models for huge datasets in the first place. He submitted a provocative paper on this subject to a workshop (organized by Malioutov and two of his colleagues) on Human Interpretability at this year's International Conference on Machine Learning (ICML). ⁸

Lipton points out that many scholars disagree over the very concept of interpretability, which suggests to him either that interpretability is poorly understood-or that there are many equally valid meanings. In either case, chasing interpretability may not satisfy our desire for a straightforward, plain-English description of a neural net output. In his blog post, Lipton argued that, when it comes to enormous datasets, researchers have the option to resist the impulse to interpret and could, instead, "place faith in empirical success." One purpose of the field, he argued, is to "build models which can learn from a greater number of features than any human could consciously account for," and interpretability could keep such models from reaching their full potential.

But this ability is both feature and failing: If we don't understand how network output is generated, then we can't know what aspects of the input were necessary, or even what might be considered input at all. Case in point: In 1996, Adrian Thompson of Sussex University used software to design a circuit by applying techniques similar to those that train deep networks today. The circuit was to perform a straightforward task: discriminate between two audio tones. After thousands of iterations, shuffling and rearranging circuit components, the software found a configuration that performed the task nearly perfectly.

Thompson was surprised, however, to discover that the circuit used fewer components than any human engineer would have used-including several that were not physically connected to the rest, and yet were somehow still necessary for the circuit to work properly.

He took to dissecting the circuit. After several experiments, he learned that its success exploited subtle electromagnetic interference between adjacent components. The disconnected elements influenced the circuit by causing small fluctuations in local electrical fields. Human engineers usually guard against these interactions, because they are unpredictable. Sure enough, when Thompson copied the same circuit layout to another batch of components-or even changed the ambient temperature-it failed completely.

The circuit exhibited a hallmark feature of trained machines: They are as compact and simplified as they can be, exquisitely well suited to their environment-and ill-adapted to any other. They pick up on patterns invisible to their engineers; but can't know which of those patterns exist nowhere else. Machine learning researchers go to great lengths to avoid this phenomenon, called "overfitting," but as these algorithms are used in more and more dynamic situations, their brittleness will inevitably be exposed.

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To Sanjeev Arora, a professor of computer science at Princeton University, this fact is the primary motivation to seek interpretable models that allow humans to intervene and adjust the networks. Arora points to two problems that could represent hard limits on the capabilities of machines in the absence of interpretability. One is "composability"-when the task at hand involves many different decisions (as with Go, or self-driving cars), networks can't efficiently learn which are responsible for a failure. "Usually when we design things, we understand the different components and then we put them together," he says. This allows humans to adjust components that aren't appropriate for a given environment.

The other problem with leaving interpretability unsolved is what Arora calls "domain adaptability"-the ability to flexibly apply knowledge learned in one setting to another. This is a task human learners do very well, but machines can fail at in surprising ways. Arora describes how programs can be catastrophically incapable of adjusting to even subtle contextual shifts of the sort that humans handle with ease. For instance, a network trained to parse human language by reading formal documents, like Wikipedia, can fail completely in more vernacular settings, like Twitter.

By this view, interpretability seems essential. But do we understand what we mean by the word? Pioneering computer scientist Marvin Minsky coined the phrase "suitcase word" to describe many of the terms-such as "consciousness" or "emotion"-we use when we talk about our own intelligence. ⁹ These words, he proposed, reflect the workings of many different underlying processes, which are locked inside the "suitcase." As long as we keep investigating these words as stand-ins for the more fundamental concepts, the argument went, our insight will be limited by our language. In the study of intelligence, could interpretability itself be such a suitcase word?

While many of the researchers I spoke to are optimistic that theorists will someday unlock the suitcase and discover a single, unifying set of principles or laws that govern machine (and, perhaps, human) learning, akin to Newton's Principia, others warn that there is little reason to expect this. Massimo Pigliucci, a professor of philosophy at City University of New York, cautions that "understanding" in the natural sciences-and, by extension, in artificial intelligence-might be what Ludwig Wittgenstein, anticipating Minsky, called a "cluster concept," one which could admit many, partly distinct, definitions. If "understanding" in this field does come, he says, it could be of the sort found not in physics, but evolutionary biology. Rather than Principia, he says, we might expect Origin of the Species.

This doesn't mean, of course, that deep networks are a harbinger of some new kind of autonomous life. But they could turn out to be just as difficult to understand as life. The field's incremental, experimental approaches and post-hoc interpretations may not be some kind of desperate feeling around in the dark, hoping for theory to shine a light. They could, instead, be the only kind of light we can expect. Interpretability may emerge piecemeal, as a set of prototypic examples of "species" arranged in a taxonomy defined by inference and contingent, context-specific explanations.

At the ICML workshop's close, some of the presenters appeared on a panel to try and define "interpretability." There were as many responses as there were panelists. After some discussion the group seemed to find consensus that "simplicity" was necessary for a model to be interpretable. But, when pressed to define simplicity, the group again diverged. Is the "simplest" model the one that relies on the fewest number of features? The one that makes the sharpest distinctions? Is it the smallest program? The workshop closed without an agreed-upon answer, leaving the definition of one inchoate concept replaced by another.

As Malioutov puts it, "Simplicity is not so simple."
Aaron M. Bornstein is a researcher at the Princeton Neuroscience Institute. His research investigates how we use memories to make sense of the present and plan for the future.

References
1. Caruana, R., et. al Intelligible models for healthcare: Predicting pneumonia risk and hospital 30-day readmission. Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 1721-1730 (2015).

2. Metz, C. Artificial Intelligence Is Setting Up the Internet for a Huge Clash with Europe. Wired.com (2016).

3. Yosinski, J., Clune, J., Nguyen, A., Fuchs, T., & Lipson, H. Understanding neural networks through deep visualization. arXiv:1506.06579 (2015).

4. Sergent, J., Ohta, S., & MacDonald, B. Functional neuroanatomy of face and object processing. A positron emission tomography study. Brain 115, 15-36 (1992).

5. Kanwisher. N., McDermott, J., & Chun, M.M. The fusiform face area: A module in human extrastriate cortex specialized for face perception. The Journal of Neuroscience 17, 4302-4311 (1997).

6. Das, A., Agrawal, H., Zitnick, C.L., Parikh, D., & Batra, D. Human attention in visual question answering: Do humans and deep networks look at the same regions? Conference on Empirical Methods in Natural Language Processing (2016).

7. Farah, M.J. Brain images, babies, and bathwater: Critiquing critiques of functional neuroimaging. Interpreting Neuroimages: An Introduction to the Technology and Its Limits 45, S19-S30 (2014).

8. Lipton, Z.C. The mythos of model interpretability. arXiv:1606.03490 (2016).

9. Brockman, J. Consciousness Is a Big Suitcase: A talk with Marvin Minsky. Edge.org (1998).

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Is Artificial Intelligence Permanently Inscrutable? - Issue 40: Learning - Nautilus

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Dmitry Malioutov can't say much about what he built. As a research scientist at IBM, Malioutov spends part of his time building machine learning systems that solve difficult problems faced by IBM's corporate clients. One such program was meant for a large insurance corporation. It was a challenging assignment, requiring a sophisticated algorithm.

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Association for the Advancement of Artificial Intelligence

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Founded in 1979, the Association for the Advancement of Artificial Intelligence (AAAI) (formerly the American Association for Artificial Intelligence) is a nonprofit scientific society devoted to advancing the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines. AAAI aims to promote research in, and responsible use of, artificial intelligence.

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Scientists know that the first signs of autism can appear in early childhood, but reliably predicting that at very young ages is difficult. A behavior questionnaire is a crapshoot at 12 months. However, artificial intelligence might just be the key to making an accurate call. University of North Carolina researchers have developed a deep learning algorithm that can predict autism in babies with a relatively high 81 percent accuracy and 88 percent sensitivity. The team trained the algorithm to recognize early hints of autism by feeding it brain scans and asking it to watch for three common factors: the brain's surface area, its volume and the child's gender (as boys are more likely to have autism). In tests, the AI could spot the telltale increase in surface area as early as 6 months, and a matching increase in volume as soon as 12 months -- it wasn't a surprise that most of these babies were formally diagnosed with autism at 2 years old.

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AI can predict autism through babies' brain scans

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Scientists know that the first signs of autism can appear in early childhood, but reliably predicting that at very young ages is difficult. A behavior questionnaire is a crapshoot at 12 months. However, artificial intelligence might just be the key to making an accurate call.

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Artificial Intelligence ideas and resources.

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The latest insights, ideas, and tools for building solutions that rely on machine intelligence.

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I write about technology, entrepreneurs and innovation. Opinions expressed by Forbes Contributors are their own.

The market for artificial intelligence (AI) technologies is flourishing. Beyond the hype and the heightened media attention, the numerous startups and the internet giants racing to acquire them, there is a significant increase in investment and adoption by enterprises. A Narrative Science survey found last year that 38% of enterprises are already using AI, growing to 62% by 2018. Forrester Research predicted a greater than 300% increase in investment in artificial intelligence in 2017 compared with 2016. IDC estimated that the AI market will grow from $8 billion in 2016 to more than $47 billion in 2020.

Coined in 1955 to describe a new computer science sub-discipline, "Artificial Intelligence" today includes a variety of technologies and tools, some time-tested, others relatively new. To help make sense of what's hot and what's not, Forrester just published a TechRadar report on Artificial Intelligence (for application development professionals), a detailed analysis of 13 technologies enterprises should consider adopting to support human decision-making.

Based on Forrester's analysis, here's my list of the 10 hottest AI technologies:

1. Natural Language Generation: Producing text from computer data. Currently used in customer service, report generation, and summarizing business intelligence insights. Sample vendors: Attivio, Automated Insights, Cambridge Semantics, Digital Reasoning, Lucidworks, Narrative Science, SAS, Yseop.

2. Speech Recognition: Transcribe and transform human speech into format useful for computer applications. Currently used in interactive voice response systems and mobile applications. Sample vendors: NICE, Nuance Communications, OpenText, Verint Systems.

3. Virtual Agents: "The current darling of the media," says Forrester (I believe they refer to my evolving relationships with Alexa), from simple chatbots to advanced systems that can network with humans. Currently used in customer service and support and as a smart home manager. Sample vendors: Amazon, Apple, Artificial Solutions, Assist AI, Creative Virtual, Google, IBM, IPsoft, Microsoft, Satisfi.

4. Machine Learning Platforms: Providing algorithms, APIs, development and training toolkits, data, as well as computing power to design, train, and deploy models into applications, processes, and other machines. Currently used in a wide range of enterprise applications, mostly `involving prediction or classification. Sample vendors: Amazon, Fractal Analytics, Google, H2O.ai, Microsoft, SAS, Skytree.

5. AI-optimized Hardware: Graphics processing units (GPU) and appliances specifically designed and architected to efficiently run AI-oriented computational jobs. Currently primarily making a difference in deep learning applications. Sample vendors: Alluviate, Cray, Google, IBM, Intel, Nvidia.

6. Decision Management: Engines that insert rules and logic into AI systems and used for initial setup/training and ongoing maintenance and tuning. A mature technology, it is used in a wide variety of enterprise applications, assisting in or performing automated decision-making. Sample vendors: Advanced Systems Concepts, Informatica, Maana, Pegasystems, UiPath.

7. Deep Learning Platforms: A special type of machine learning consisting of artificial neural networks with multiple abstraction layers. Currently primarily used in pattern recognition and classification applications supported by very large data sets. Sample vendors: Deep Instinct, Ersatz Labs, Fluid AI, MathWorks, Peltarion, Saffron Technology, Sentient Technologies.

8. Biometrics: Enable more natural interactions between humans and machines, including but not limited to image and touch recognition, speech, and body language. Currently used primarily in market research. Sample vendors: 3VR, Affectiva, Agnitio, FaceFirst, Sensory, Synqera, Tahzoo.

9. Robotic Process Automation: Using scripts and other methods to automate human action to support efficient business processes. Currently used where it's too expensive or inefficient for humans to execute a task or a process. Sample vendors: Advanced Systems Concepts, Automation Anywhere, Blue Prism, UiPath, WorkFusion.

10. Text Analytics and NLP: Natural language processing (NLP) uses and supports text analytics by facilitating the understanding of sentence structure and meaning, sentiment, and intent through statistical and machine learning methods. Currently used in fraud detection and security, a wide range of automated assistants, and applications for mining unstructured data. Sample vendors: Basis Technology, Coveo, Expert System, Indico, Knime, Lexalytics, Linguamatics, Mindbreeze, Sinequa, Stratifyd, Synapsify.

There are certainly many business benefits gained from AI technologies today, but according to a survey Forrester conducted last year, there are also obstacles to AI adoption as expressed by companies with no plans of investing in AI:

There is no defined business case 42%

Not clear what AI can be used for 39%

Don't have the required skills 33%

Need first to invest in modernizing data mgt platform 29%

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Partnership on Artificial Intelligence to Benefit People and Society

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We are at an inflection point in the development and application of AI technologies. The upswing in AI competencies, fueled by data, computation, and advances in algorithms for machine learning, perception, planning, and natural language, promise great value to people and society. However, with successes come new concerns and challenges based on the effects of those technologies on people's lives...

Support research, discussions, identification, sharing, and recommendation of best practices in the research, development, testing, and fielding of AI technologies. Address such areas as fairness and inclusivity, explanation and transparency, security and privacy, values and ethics, collaboration between people and AI systems, interoperability of systems, and of the trustworthiness, reliability, containment, safety, and robustness of the technology.

Create and support opportunities for AI researchers and key stakeholders, including people in technology, law, policy, government, civil liberties, and the greater public, to communicate directly and openly with each other about relevant issues to AI and its influences on people and society. Ensure that key stakeholders have the knowledge, resources, and overall capacity to participate fully.

Advance public understanding and awareness of AI by multiple constituencies, including writing and other communications on core technologies, potential benefits, and costs. Act as a trusted and expert point of contact as questions, concerns, and aspirations arise from the public and others in the area of AI. Regularly update key constituents on the current state of AI progress.

Seek out, support, celebrate, and highlight aspirational efforts in AI for socially benevolent applications. Identify areas of untapped opportunity, including promising technologies and applications not being explored by academia and industry R&D.

We believe that artificial intelligence technologies hold great promise for raising the quality of people's lives and can be leveraged to help humanity address important global challenges such as climate change, food, inequality, health, and education.

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That mythology, in turn, has spurred a reactionary, perpetual spasm from people who are horrified by what they hear. You'll have a figure say, "The computers will take over the Earth, but that's a good thing, because people had their chance and now we should give it to the machines." Then you'll have other people say, "Oh, that's horrible, we must stop these computers." Most recently, some of the most beloved and respected figures in the tech and science world, including Stephen Hawking and Elon Musk, have taken that position of: "Oh my God, these things are an existential threat. They must be stopped."

In the past, all kinds of different figures have proposed that this kind of thing will happen, using different terminology. Some of them like the idea of the computers taking over, and some of them don't. What I'd like to do here today is propose that the whole basis of the conversation is itself askew, and confuses us, and does real harm to society and to our skills as engineers and scientists.

A good starting point might be the latest round of anxiety about artificial intelligence, which has been stoked by some figures who I respect tremendously, including Stephen Hawking and Elon Musk. And the reason it's an interesting starting point is that it's one entry point into a knot of issues that can be understood in a lot of different ways, but it might be the right entry point for the moment, because it's the one that's resonating with people.

The usual sequence of thoughts you have here is something like: "so-and-so," who's a well-respected expert, is concerned that the machines will become smart, they'll take over, they'll destroy us, something terrible will happen. They're an existential threat, whatever scary language there is. My feeling about that is it's a kind of a non-optimal, silly way of expressing anxiety about where technology is going. The particular thing about it that isn't optimal is the way it talks about an end of human agency.

But it's a call for increased human agency, so in that sense maybe it's functional, but I want to go little deeper in it by proposing that the biggest threat of AI is probably the one that's due to AI not actually existing, to the idea being a fraud, or at least such a poorly constructed idea that it's phony. In other words, what I'm proposing is that if AI was a real thing, then it probably would be less of a threat to us than it is as a fake thing.

What do I mean by AI being a fake thing? That it adds a layer of religious thinking to what otherwise should be a technical field. Now, if we talk about the particular technical challenges that AI researchers might be interested in, we end up with something that sounds a little duller and makes a lot more sense.

For instance, we can talk about pattern classification. Can you get programs that recognize faces, that sort of thing? And that's a field where I've been active. I was the chief scientist of the company Google bought that got them into that particular game some time ago. And I love that stuff. It's a wonderful field, and it's been wonderfully useful.

But when you add to it this religious narrative that's a version of the Frankenstein myth, where you say well, but these things are all leading to a creation of life, and this life will be superior to us and will be dangerous ... when you do all of that, you create a series of negative consequences that undermine engineering practice, and also undermine scientific method, and also undermine the economy.

The problem I see isn't so much with the particular techniques, which I find fascinating and useful, and am very positive about, and should be explored more and developed, but the mythology around them which is destructive. I'm going to go through a couple of layers of how the mythology does harm.

The most obvious one, which everyone in any related field can understand, is that it creates this ripple every few years of what have sometimes been called AI winters, where there's all this overpromising that AIs will be about to do this or that. It might be to become fully autonomous driving vehicles instead of only partially autonomous, or it might be being able to fully have a conversation as opposed to only having a useful part of a conversation to help you interface with the device.

This kind of overpromise then leads to disappointment because it was premature, and then that leads to reduced funding and startups crashing and careers destroyed, and this happens periodically, and it's a shame. It hurt a lot of careers. It has helped other careers, but that has been kind of random; depending on where you fit in the phase of this process as you're coming up. It's just immature and ridiculous, and I wish that cycle could be shut down. And that's a widely shared criticism. I'm not saying anything at all unusual.

Let's go to another layer of how it's dysfunctional. And this has to do with just clarity of user interface, and then that turns into an economic effect. People are social creatures. We want to be pleasant, we want to get along. We've all spent many years as children learning how to adjust ourselves so that we can get along in the world. If a program tells you, well, this is how things are, this is who you are, this is what you like, or this is what you should do, we have a tendency to accept that.

Since our economy has shifted to what I call a surveillance economy, but let's say an economy where algorithms guide people a lot, we have this very odd situation where you have these algorithms that rely on big data in order to figure out who you should date, who you should sleep with, what music you should listen to, what books you should read, and on and on and on. And people often accept that because there's no empirical alternative to compare it to, there's no baseline. It's bad personal science. It's bad self-understanding.

I'll give you a few examples of what I mean by that. Maybe I'll start with Netflix. The thing about Netflix is that there isn't much on it. There's a paucity of content on it. If you think of any particular movie you might want to see, the chances are it's not available for streaming, that is; that's what I'm talking about. And yet there's this recommendation engine, and the recommendation engine has the effect of serving as a cover to distract you from the fact that there's very little available from it. And yet people accept it as being intelligent, because a lot of what's available is perfectly fine.

The one thing I want to say about this is I'm not blaming Netflix for doing anything bad, because the whole point of Netflix is to deliver theatrical illusions to you, so this is just another layer of theatrical illusion-more power to them. That's them being a good presenter. What's a theater without a barker on the street? That's what it is, and that's fine. But it does contribute, at a macro level, to this overall atmosphere of accepting the algorithms as doing a lot more than they do. In the case of Netflix, the recommendation engine is serving to distract you from the fact that there's not much choice anyway.

There are other cases where the recommendation engine is not serving that function, because there is a lot of choice, and yet there's still no evidence that the recommendations are particularly good. There's no way to compare them to an alternative, so you don't know what might have been. If you want to put the work into it, you can play with that; you can try to erase your history, or have multiple personas on a site to compare them. That's the sort of thing I do, just to get a sense. I've also had a chance to work on the algorithms themselves, on the back side, and they're interesting, but they're vastly, vastly overrated.

I want to get to an even deeper problem, which is that there's no way to tell where the border is between measurement and manipulation in these systems. For instance, if the theory is that you're getting big data by observing a lot of people who make choices, and then you're doing correlations to make suggestions to yet more people, if the preponderance of those people have grown up in the system and are responding to whatever choices it gave them, there's not enough new data coming into it for even the most ideal or intelligent recommendation engine to do anything meaningful.

In other words, the only way for such a system to be legitimate would be for it to have an observatory that could observe in peace, not being sullied by its own recommendations. Otherwise, it simply turns into a system that measures which manipulations work, as opposed to which ones don't work, which is very different from a virginal and empirically careful system that's trying to tell what recommendations would work had it not intervened. That's a pretty clear thing. What's not clear is where the boundary is.

If you ask: is a recommendation engine like Amazon more manipulative, or more of a legitimate measurement device? There's no way to know. At this point there's no way to know, because it's too universal. The same thing can be said for any other big data system that recommends courses of action to people, whether it's the Google ad business, or social networks like Facebook deciding what you see, or any of the myriad of dating apps. All of these things, there's no baseline, so we don't know to what degree they're measurement versus manipulation.

Dating always has an element of manipulation; shopping always has an element of manipulation; in a sense, a lot of the things that people use these things for have always been a little manipulative. There's always been a little bit of nonsense. And that's not necessarily a terrible thing, or the end of the world.

But it's important to understand it if this is becoming the basis of the whole economy and the whole civilization. If people are deciding what books to read based on a momentum within the recommendation engine that isn't going back to a virgin population, that hasn't been manipulated, then the whole thing is spun out of control and doesn't mean anything anymore. It's not so much a rise of evil as a rise of nonsense. It's a mass incompetence, as opposed to Skynet from the Terminator movies. That's what this type of AI turns into. But I'm going to get back to that in a second.

To go yet another rung deeper, I'll revive an argument I've made previously, which is that it turns into an economic problem. The easiest entry point for understanding the link between the religious way of confusing AI with an economic problem is through automatic language translation. If somebody has heard me talk about that before, my apologies for repeating myself, but it has been the most readily clear example.

For three decades, the AI world was trying to create an ideal, little, crystalline algorithm that could take two dictionaries for two languages and turn out translations between them. Intellectually, this had its origins particularly around MIT and Stanford. Back in the 50s, because of Chomsky's work, there had been a notion of a very compact and elegant core to language. It wasn't a bad hypothesis, it was a legitimate, perfectly reasonable hypothesis to test. But over time, the hypothesis failed because nobody could do it.

Finally, in the 1990s, researchers at IBM and elsewhere figured out that the way to do it was with what we now call big data, where you get a very large example set, which interestingly, we call a corpus-call it a dead person. That's the term of art for these things. If you have enough examples, you can correlate examples of real translations phrase by phrase with new documents that need to be translated. You mash them all up, and you end up with something that's readable. It's not perfect, is not artful, it's not necessarily correct, but suddenly it's usable. And you know what? It's fantastic. I love the idea that you can take some memo, and instead of having to find a translator and wait for them to do the work, you can just have something approximate right away, because that's often all you need. That's a benefit to the world. I'm happy it's been done. It's a great thing.

The thing that we have to notice though is that, because of the mythology about AI, the services are presented as though they are these mystical, magical personas. IBM makes a dramatic case that they've created this entity that they call different things at different times-Deep Blue and so forth. The consumer tech companies, we tend to put a face in front of them, like a Cortana or a Siri. The problem with that is that these are not freestanding services.

In other words, if you go back to some of the thought experiments from philosophical debates about AI from the old days, there are lots of experiments, like if you have some black box that can do something-it can understand language-why wouldn't you call that a person? There are many, many variations on these kinds of thought experiments, starting with the Turing test, of course, through Mary the color scientist, and a zillion other ones that have come up.

This is not one of those. What this is, is behind the curtain, is literally millions of human translators who have to provide the examples. The thing is, they didn't just provide one corpus once way back. Instead, they're providing a new corpus every day, because the world of references, current events, and slang does change every day. We have to go and scrape examples from literally millions of translators, unbeknownst to them, every single day, to help keep those services working.

The problem here should be clear, but just let me state it explicitly: we're not paying the people who are providing the examples to the corpora-which is the plural of corpus-that we need in order to make AI algorithms work. In order to create this illusion of a freestanding autonomous artificial intelligent creature, we have to ignore the contributions from all the people whose data we're grabbing in order to make it work. That has a negative economic consequence.

This, to me, is where it becomes serious. Everything up to now, you can say, "Well, look, if people want to have an algorithm tell them who to date, is that any stupider than how we decided who to sleep with when we were young, before the Internet was working?" Doubtful, because we were pretty stupid back then. I doubt it could have that much negative consequence.

This is all of a sudden a pretty big deal. If you talk to translators, they're facing a predicament, which is very similar to some of the other early victim populations, due to the particular way we digitize things. It's similar to what's happened with recording musicians, or investigative journalists-which is the one that bothers me the most-or photographers. What they're seeing is a severe decline in how much they're paid, what opportunities they have, their long-term prospects. They're seeing certain opportunities for continuing, particularly in real-time translation... but I should point out that's going away soon too. We're going to have real-time translation on Skype soon.

The thing is, they're still needed. There's an impulse, a correct impulse, to be skeptical when somebody bemoans what's been lost because of new technology. For the usual thought experiments that come up, a common point of reference is the buggy whip: You might say, "Well, you wouldn't want to preserve the buggy whip industry."

But translators are not buggy whips, because they're still needed for the big data scheme to work. They're the opposite of a buggy whip. What's happened here is that translators haven't been made obsolete. What's happened instead is that the structure through which we receive the efforts of real people in order to make translations happen has been optimized, but those people are still needed.

This pattern-of AI only working when there's what we call big data, but then using big data in order to not pay large numbers of people who are contributing-is a rising trend in our civilization, which is totally non-sustainable. Big data systems are useful. There should be more and more of them. If that's going to mean more and more people not being paid for their actual contributions, then we have a problem.

The usual counterargument to that is that they are being paid in the sense that they too benefit from all the free stuff and reduced-cost stuff that comes out of the system. I don't buy that argument, because you need formal economic benefit to have a civilization, not just informal economic benefit. The difference between a slum and the city is whether everybody gets by on day-to-day informal benefits or real formal benefits.

The difference between formal and informal has to do with whether it's strictly real-time or not. If you're living on informal benefits and you're a musician, you have to play a gig every day. If you get sick, or if you have a sick kid, or whatever and you can't do it, suddenly you don't get paid that day. Everything's real-time. If we were all perfect, immortal robots, that would be fine. As real people, we can't do it, so informal benefits aren't enough. And that's precisely why things, like employment, savings, real estate, and ownership of property and all these things were invented-to acknowledge the truth of the fragility of the human condition, and that's what made civilization.

If you talk about AI as a set of techniques, as a field of study in mathematics or engineering, it brings benefits. If we talk about AI as a mythology of creating a post-human species, it creates a series of problems that I've just gone over, which include acceptance of bad user interfaces, where you can't tell if you're being manipulated or not, and everything is ambiguous. It creates incompetence, because you don't know whether recommendations are coming from anything real or just self-fulfilling prophecies from a manipulative system that spun off on its own, and economic negativity, because you're gradually pulling formal economic benefits away from the people who supply the data that makes the scheme work.

For all those reasons, the mythology is the problem, not the algorithms. To back up again, I've given two reasons why the mythology of AI is stupid, even if the actual stuff is great. The first one is that it results in periodic disappointments that cause damage to careers and startups, and it's a ridiculous, seasonal disappointment and devastation that we shouldn't be randomly imposing on people according to when they happen to hit the cycle. That's the AI winter problem. The second one is that it causes unnecessary negative benefits to society for technologies that are useful and good. The mythology brings the problems, not the technology.

Having said all that, let's address directly this problem of whether AI is going to destroy civilization and people, and take over the planet and everything. Here I want to suggest a simple thought experiment of my own. There are so many technologies I could use for this, but just for a random one, let's suppose somebody comes up with a way to 3-D print a little assassination drone that can go buzz around and kill somebody. Let's suppose that these are cheap to make.

I'm going to give you two scenarios. In one scenario, there's suddenly a bunch of these, and some disaffected teenagers, or terrorists, or whoever start making a bunch of them, and they go out and start killing people randomly. There's so many of them that it's hard to find all of them to shut it down, and there keep on being more and more of them. That's one scenario; it's a pretty ugly scenario.

There's another one where there's so-called artificial intelligence, some kind of big data scheme, that's doing exactly the same thing, that is self-directed and taking over 3-D printers, and sending these things off to kill people. The question is, does it make any difference which it is?

The truth is that the part that causes the problem is the actuator. It's the interface to physicality. It's the fact that there's this little killer drone thing that's coming around. It's not so much whether it's a bunch of teenagers or terrorists behind it or some AI, or even, for that matter, if there's enough of them, it could just be an utterly random process. The whole AI thing, in a sense, distracts us from what the real problem would be. The AI component would be only ambiguously there and of little importance.

This notion of attacking the problem on the level of some sort of autonomy algorithm, instead of on the actuator level is totally misdirected. This is where it becomes a policy issue. The sad fact is that, as a society, we have to do something to not have little killer drones proliferate. And maybe that problem will never take place anyway. What we don't have to worry about is the AI algorithm running them, because that's speculative. There isn't an AI algorithm that's good enough to do that for the time being. An equivalent problem can come about, whether or not the AI algorithm happens. In a sense, it's a massive misdirection.

This idea that some lab somewhere is making these autonomous algorithms that can take over the world is a way of avoiding the profoundly uncomfortable political problem, which is that if there's some actuator that can do harm, we have to figure out some way that people don't do harm with it. There are about to be a whole bunch of those. And that'll involve some kind of new societal structure that isn't perfect anarchy. Nobody in the tech world wants to face that, so we lose ourselves in these fantasies of AI. But if you could somehow prevent AI from ever happening, it would have nothing to do with the actual problem that we fear, and that's the sad thing, the difficult thing we have to face.

I haven't gone through a whole litany of reasons that the mythology of it AI does damage. There's a whole other problem area that has to do with neuroscience, where if we pretend we understand things before we do, we do damage to science, not just because we raise expectations and then fail to meet them repeatedly, but because we confuse generations of young scientists. Just to be absolutely clear, we don't know how most kinds of thoughts are represented in the brain. We're starting to understand a little bit about some narrow things. That doesn't mean we never will, but we have to be honest about what we understand in the present.

A retort to that caution is that there's some exponential increase in our understanding, so we can predict that we'll understand everything soon. To me, that's crazy, because we don't know what the goal is. We don't know what the scale of achieving the goal would be... So to say, "Well, just because I'm accelerating, I know I'll reach my goal soon," is absurd if you don't know the basic geography which you're traversing. As impressive as your acceleration might be, reality can also be impressive in the obstacles and the challenges it puts up. We just have no idea.

This is something I've called, in the past, "premature mystery reduction," and it's a reflection of poor scientific mental discipline. You have to be able to accept what your ignorances are in order to do good science. To reject your own ignorance just casts you into a silly state where you're a lesser scientist. I don't see that so much in the neuroscience field, but it comes from the computer world so much, and the computer world is so influential because it has so much money and influence that it does start to bleed over into all kinds of other things. A great example is the Human Brain Project in Europe, which is a lot of public money going into science that's very influenced by this point of view, and it has upset some in the neuroscience community for precisely the reason I described.

There is a social and psychological phenomenon that has been going on for some decades now: A core of technically proficient, digitally-minded people reject traditional religions and superstitions. They set out to come up with a better, more scientific framework. But then they re-create versions of those old religious superstitions! In the technical world these superstitions are just as confusing and just as damaging as before, and in similar ways.

To my mind, the mythology around AI is a re-creation of some of the traditional ideas about religion, but applied to the technical world. All of the damages are essentially mirror images of old damages that religion has brought to science in the past.

There's an anticipation of a threshold, an end of days. This thing we call artificial intelligence, or a new kind of personhood... If it were to come into existence it would soon gain all power, supreme power, and exceed people.

The notion of this particular threshold-which is sometimes called the singularity, or super-intelligence, or all sorts of different terms in different periods-is similar to divinity. Not all ideas about divinity, but a certain kind of superstitious idea about divinity, that there's this entity that will run the world, that maybe you can pray to, maybe you can influence, but it runs the world, and you should be in terrified awe of it.

That particular idea has been dysfunctional in human history. It's dysfunctional now, in distorting our relationship to our technology. It's been dysfunctional in the past in exactly the same way. Only the words have changed.

In the history of organized religion, it's often been the case that people have been disempowered precisely to serve what were perceived to be the needs of some deity or another, where in fact what they were doing was supporting an elite class that was the priesthood for that deity.

That looks an awful lot like the new digital economy to me, where you have (natural language) translators and everybody else who contributes to the corpora that allow the data schemes to operate, contributing mostly to the fortunes of whoever runs the top computers. The new elite might say, "Well, but they're helping the AI, it's not us, they're helping the AI." It reminds me of somebody saying, "Oh, build these pyramids, it's in the service of this deity," but, on the ground, it's in the service of an elite. It's an economic effect of the new idea. The effect of the new religious idea of AI is a lot like the economic effect of the old idea, religion.

There is an incredibly retrograde quality to the mythology of AI. I know I said it already, but I just have to repeat that this is not a criticism of the particular algorithms. To me, what would be ridiculous is for somebody to say, "Oh, you mustn't study deep learning networks," or "you mustn't study theorem provers," or whatever technique you're interested in. Those things are incredibly interesting and incredibly useful. It's the mythology that we have to become more self-aware of.

This is analogous to saying that in traditional religion there was a lot of extremely interesting thinking, and a lot of great art. And you have to be able to kind of tease that apart and say this is the part that's great, and this is the part that's self-defeating. We have to do it exactly the same thing with AI now.

This is a hard topic to talk about, because the accepted vocabulary undermines you at every turn. This is also similar to a problem traditional religion. If I talk about AI, am I talking about the particular technical work, or the mythology that influences how we integrate that into our world, into our society? Well, the vocabulary that we typically use doesn't give us an easy way to distinguish those things. And it becomes very confusing.

If AI means this mythology of this new creature we're creating, then it's just a stupid mess that's confusing everybody, and harming the future of the economy. If what we're talking about is a set of algorithms and actuators that we can improve and apply in useful ways, then I'm very interested, and I'm very much a participant in the community that's improving those things.

Unfortunately, the standard vocabulary that people use doesn't give us a great way to distinguish those two entirely different items that one might reference. I could try to coin some phrases, but for the moment, I'll just say these are two entirely different things that deserve to have entirely distinguishing vocabulary. Once again, this vocabulary problem is entirely retrograde and entirely characteristic of traditional religions.

Maybe it's worse today, because in the old days, at least we had the distinction between, say, ethics and morality, where you could talk about two similar things, where one was a little bit more engaged with the mythology of religion, and one is a little less engaged. We don't quite have that yet for our new technical world, and we certainly need it.

Having said all this, I'll mention one other similarity, which is that just because a mythology has a ridiculous quality that can undermine people in many cases doesn't mean that the people who adhere to it are necessarily unsympathetic or bad people. A lot of them are great. In the religious world, there are lots of people I love. We have a cool Pope now, there are a lot of cool rabbis in the world. A lot of people in the religious world are just great, and I respect and like them. That goes hand-in-hand with my feeling that some of the mythology in big religion still leads us into trouble that we impose on ourselves and don't need.

In the same way, if you think of the people who are the most successful in the new economy in this digital world-I'm probably one of them; it's been great to me-they're, in general, great. I like the people who've done well in the cloud computer economy. They're cool. But that doesn't detract from all of the things I just said.

That does create yet another layer of potential confusion and differentiation that becomes tedious to state over and over again, but it's important to say.

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That mythology, in turn, has spurred a reactionary, perpetual spasm from people who are horrified by what they hear. You'll have a figure say, "The computers will take over the Earth, but that's a good thing, because people had their chance and now we should give it to the machines."

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Four days later, a couple of hundred journalists, entrepreneurs and advertisers from all over the world gathered in Google's London engineering office for a special announcement. Guests were greeted with Translate-branded fortune cookies. Their paper slips had a foreign phrase on one side - mine was in Norwegian - and on the other, an invitation to download the Translate app. Tables were set with trays of doughnuts and smoothies, each labeled with a placard that advertised its flavor in German ( zitrone), Portuguese ( baunilha) or Spanish ( manzana). After a while, everyone was ushered into a plush, dark theater.

Sadiq Khan, the mayor of London, stood to make a few opening remarks. A friend, he began, had recently told him he reminded him of Google. "Why, because I know all the answers?" the mayor asked. "No," the friend replied, "because you're always trying to finish my sentences." The crowd tittered politely. Khan concluded by introducing Google's chief executive, Sundar Pichai, who took the stage.

Pichai was in London in part to inaugurate Google's new building there, the cornerstone of a new "knowledge quarter" under construction at King's Cross, and in part to unveil the completion of the initial phase of a company transformation he announced last year. The Google of the future, Pichai had said on several occasions, was going to be "A.I. first." What that meant in theory was complicated and had welcomed much speculation. What it meant in practice, with any luck, was that soon the company's products would no longer represent the fruits of traditional computer programming, exactly, but "machine learning."

A rarefied department within the company, Google Brain, was founded five years ago on this very principle: that artificial "neural networks" that acquaint themselves with the world via trial and error, as toddlers do, might in turn develop something like human flexibility. This notion is not new - a version of it dates to the earliest stages of modern computing, in the 1940s - but for much of its history most computer scientists saw it as vaguely disreputable, even mystical. Since 2011, though, Google Brain has demonstrated that this approach to artificial intelligence could solve many problems that confounded decades of conventional efforts. Speech recognition didn't work very well until Brain undertook an effort to revamp it; the application of machine learning made its performance on Google's mobile platform, Android, almost as good as human transcription. The same was true of image recognition. Less than a year ago, Brain for the first time commenced with the gut renovation of an entire consumer product, and its momentous results were being celebrated tonight.

Translate made its debut in 2006 and since then has become one of Google's most reliable and popular assets; it serves more than 500 million monthly users in need of 140 billion words per day in a different language. It exists not only as its own stand-alone app but also as an integrated feature within Gmail, Chrome and many other Google offerings, where we take it as a push-button given - a frictionless, natural part of our digital commerce. It was only with the refugee crisis, Pichai explained from the lectern, that the company came to reckon with Translate's geopolitical importance: On the screen behind him appeared a graph whose steep curve indicated a recent fivefold increase in translations between Arabic and German. (It was also close to Pichai's own heart. He grew up in India, a land divided by dozens of languages.) The team had been steadily adding new languages and features, but gains in quality over the last four years had slowed considerably.

Until today. As of the previous weekend, Translate had been converted to an A.I.-based system for much of its traffic, not just in the United States but in Europe and Asia as well: The rollout included translations between English and Spanish, French, Portuguese, German, Chinese, Japanese, Korean and Turkish. The rest of Translate's hundred-odd languages were to come, with the aim of eight per month, by the end of next year. The new incarnation, to the pleasant surprise of Google's own engineers, had been completed in only nine months. The A.I. system had demonstrated overnight improvements roughly equal to the total gains the old one had accrued over its entire lifetime.

Pichai has an affection for the obscure literary reference; he told me a month earlier, in his office in Mountain View, Calif., that Translate in part exists because not everyone can be like the physicist Robert Oppenheimer, who learned Sanskrit to read the Bhagavad Gita in the original. In London, the slide on the monitors behind him flicked to a Borges quote: " Uno no es lo que es por lo que escribe, sino por lo que ha leído."

Grinning, Pichai read aloud an awkward English version of the sentence that had been rendered by the old Translate system: "One is not what is for what he writes, but for what he has read."

To the right of that was a new A.I.-rendered version: "You are not what you write, but what you have read."

It was a fitting remark: The new Google Translate was run on the first machines that had, in a sense, ever learned to read anything at all.

Google's decision to reorganize itself around A.I. was the first major manifestation of what has become an industrywide machine-learning delirium. Over the past four years, six companies in particular - Google, Facebook, Apple, Amazon, Microsoft and the Chinese firm Baidu - have touched off an arms race for A.I. talent, particularly within universities. Corporate promises of resources and freedom have thinned out top academic departments. It has become widely known in Silicon Valley that Mark Zuckerberg, chief executive of Facebook, personally oversees, with phone calls and video-chat blandishments, his company's overtures to the most desirable graduate students. Starting salaries of seven figures are not unheard-of. Attendance at the field's most important academic conference has nearly quadrupled. What is at stake is not just one more piecemeal innovation but control over what very well could represent an entirely new computational platform: pervasive, ambient artificial intelligence.

The phrase "artificial intelligence" is invoked as if its meaning were self-evident, but it has always been a source of confusion and controversy. Imagine if you went back to the 1970s, stopped someone on the street, pulled out a smartphone and showed her Google Maps. Once you managed to convince her you weren't some oddly dressed wizard, and that what you withdrew from your pocket wasn't a black-arts amulet but merely a tiny computer more powerful than the one that guided Apollo missions, Google Maps would almost certainly seem to her a persuasive example of "artificial intelligence." In a very real sense, it is. It can do things any map-literate human can manage, like get you from your hotel to the airport - though it can do so much more quickly and reliably. It can also do things that humans simply and obviously cannot: It can evaluate the traffic, plan the best route and reorient itself when you take the wrong exit.

Practically nobody today, however, would bestow upon Google Maps the honorific "A.I.," so sentimental and sparing are we in our use of the word "intelligence." Artificial intelligence, we believe, must be something that distinguishes HAL from whatever it is a loom or wheelbarrow can do. The minute we can automate a task, we downgrade the relevant skill involved to one of mere mechanism. Today Google Maps seems, in the pejorative sense of the term, robotic: It simply accepts an explicit demand (the need to get from one place to another) and tries to satisfy that demand as efficiently as possible. The goal posts for "artificial intelligence" are thus constantly receding.

When he has an opportunity to make careful distinctions, Pichai differentiates between the current applications of A.I. and the ultimate goal of "artificial general intelligence." Artificial general intelligence will not involve dutiful adherence to explicit instructions, but instead will demonstrate a facility with the implicit, the interpretive. It will be a general tool, designed for general purposes in a general context. Pichai believes his company's future depends on something like this. Imagine if you could tell Google Maps, "I'd like to go to the airport, but I need to stop off on the way to buy a present for my nephew." A more generally intelligent version of that service - a ubiquitous assistant, of the sort that Scarlett Johansson memorably disembodied three years ago in the Spike Jonze film "Her"- would know all sorts of things that, say, a close friend or an earnest intern might know: your nephew's age, and how much you ordinarily like to spend on gifts for children, and where to find an open store. But a truly intelligent Maps could also conceivably know all sorts of things a close friend wouldn't, like what has only recently come into fashion among preschoolers in your nephew's school - or more important, what its users actually want. If an intelligent machine were able to discern some intricate if murky regularity in data about what we have done in the past, it might be able to extrapolate about our subsequent desires, even if we don't entirely know them ourselves.

The new wave of A.I.-enhanced assistants - Apple's Siri, Facebook's M, Amazon's Echo - are all creatures of machine learning, built with similar intentions. The corporate dreams for machine learning, however, aren't exhausted by the goal of consumer clairvoyance. A medical-imaging subsidiary of Samsung announced this year that its new ultrasound devices could detect breast cancer. Management consultants are falling all over themselves to prep executives for the widening industrial applications of computers that program themselves. DeepMind, a 2014 Google acquisition, defeated the reigning human grandmaster of the ancient board game Go, despite predictions that such an achievement would take another 10 years.

In a famous 1950 essay, Alan Turing proposed a test for an artificial general intelligence: a computer that could, over the course of five minutes of text exchange, successfully deceive a real human interlocutor. Once a machine can translate fluently between two natural languages, the foundation has been laid for a machine that might one day "understand" human language well enough to engage in plausible conversation. Google Brain's members, who pushed and helped oversee the Translate project, believe that such a machine would be on its way to serving as a generally intelligent all-encompassing personal digital assistant.

What follows here is the story of how a team of Google researchers and engineers - at first one or two, then three or four, and finally more than a hundred - made considerable progress in that direction. It's an uncommon story in many ways, not least of all because it defies many of the Silicon Valley stereotypes we've grown accustomed to. It does not feature people who think that everything will be unrecognizably different tomorrow or the next day because of some restless tinkerer in his garage. It is neither a story about people who think technology will solve all our problems nor one about people who think technology is ineluctably bound to create apocalyptic new ones. It is not about disruption, at least not in the way that word tends to be used.

It is, in fact, three overlapping stories that converge in Google Translate's successful metamorphosis to A.I. - a technical story, an institutional story and a story about the evolution of ideas. The technical story is about one team on one product at one company, and the process by which they refined, tested and introduced a brand-new version of an old product in only about a quarter of the time anyone, themselves included, might reasonably have expected. The institutional story is about the employees of a small but influential artificial-intelligence group within that company, and the process by which their intuitive faith in some old, unproven and broadly unpalatable notions about computing upended every other company within a large radius. The story of ideas is about the cognitive scientists, psychologists and wayward engineers who long toiled in obscurity, and the process by which their ostensibly irrational convictions ultimately inspired a paradigm shift in our understanding not only of technology but also, in theory, of consciousness itself.

The first story, the story of Google Translate, takes place in Mountain View over nine months, and it explains the transformation of machine translation. The second story, the story of Google Brain and its many competitors, takes place in Silicon Valley over five years, and it explains the transformation of that entire community. The third story, the story of deep learning, takes place in a variety of far-flung laboratories - in Scotland, Switzerland, Japan and most of all Canada - over seven decades, and it might very well contribute to the revision of our self-image as first and foremost beings who think.

All three are stories about artificial intelligence. The seven-decade story is about what we might conceivably expect or want from it. The five-year story is about what it might do in the near future. The nine-month story is about what it can do right this minute. These three stories are themselves just proof of concept. All of this is only the beginning.

Part I: Learning Machine

1. The Birth of Brain

Jeff Dean, though his title is senior fellow, is the de facto head of Google Brain. Dean is a sinewy, energy-efficient man with a long, narrow face, deep-set eyes and an earnest, soapbox-derby sort of enthusiasm. The son of a medical anthropologist and a public-health epidemiologist, Dean grew up all over the world - Minnesota, Hawaii, Boston, Arkansas, Geneva, Uganda, Somalia, Atlanta - and, while in high school and college, wrote software used by the World Health Organization. He has been with Google since 1999, as employee 25ish, and has had a hand in the core software systems beneath nearly every significant undertaking since then. A beloved artifact of company culture is Jeff Dean Facts, written in the style of the Chuck Norris Facts meme: "Jeff Dean's PIN is the last four digits of pi." "When Alexander Graham Bell invented the telephone, he saw a missed call from Jeff Dean." "Jeff Dean got promoted to Level 11 in a system where the maximum level is 10." (This last one is, in fact, true.)

One day in early 2011, Dean walked into one of the Google campus's "microkitchens" - the "Googley" word for the shared break spaces on most floors of the Mountain View complex's buildings - and ran into Andrew Ng, a young Stanford computer-science professor who was working for the company as a consultant. Ng told him about Project Marvin, an internal effort (named after the celebrated A.I. pioneer Marvin Minsky) he had recently helped establish to experiment with "neural networks," pliant digital lattices based loosely on the architecture of the brain. Dean himself had worked on a primitive version of the technology as an undergraduate at the University of Minnesota in 1990, during one of the method's brief windows of mainstream acceptability. Now, over the previous five years, the number of academics working on neural networks had begun to grow again, from a handful to a few dozen. Ng told Dean that Project Marvin, which was being underwritten by Google's secretive X lab, had already achieved some promising results.

Dean was intrigued enough to lend his "20 percent" - the portion of work hours every Google employee is expected to contribute to programs outside his or her core job - to the project. Pretty soon, he suggested to Ng that they bring in another colleague with a neuroscience background, Greg Corrado. (In graduate school, Corrado was taught briefly about the technology, but strictly as a historical curiosity. "It was good I was paying attention in class that day," he joked to me.) In late spring they brought in one of Ng's best graduate students, Quoc Le, as the project's first intern. By then, a number of the Google engineers had taken to referring to Project Marvin by another name: Google Brain.

Since the term "artificial intelligence" was first coined, at a kind of constitutional convention of the mind at Dartmouth in the summer of 1956, a majority of researchers have long thought the best approach to creating A.I. would be to write a very big, comprehensive program that laid out both the rules of logical reasoning and sufficient knowledge of the world. If you wanted to translate from English to Japanese, for example, you would program into the computer all of the grammatical rules of English, and then the entirety of definitions contained in the Oxford English Dictionary, and then all of the grammatical rules of Japanese, as well as all of the words in the Japanese dictionary, and only after all of that feed it a sentence in a source language and ask it to tabulate a corresponding sentence in the target language. You would give the machine a language map that was, as Borges would have had it, the size of the territory. This perspective is usually called "symbolic A.I." - because its definition of cognition is based on symbolic logic - or, disparagingly, "good old-fashioned A.I."

There are two main problems with the old-fashioned approach. The first is that it's awfully time-consuming on the human end. The second is that it only really works in domains where rules and definitions are very clear: in mathematics, for example, or chess. Translation, however, is an example of a field where this approach fails horribly, because words cannot be reduced to their dictionary definitions, and because languages tend to have as many exceptions as they have rules. More often than not, a system like this is liable to translate "minister of agriculture" as "priest of farming." Still, for math and chess it worked great, and the proponents of symbolic A.I. took it for granted that no activities signaled "general intelligence" better than math and chess.

There were, however, limits to what this system could do. In the 1980s, a robotics researcher at Carnegie Mellon pointed out that it was easy to get computers to do adult things but nearly impossible to get them to do things a 1-year-old could do, like hold a ball or identify a cat. By the 1990s, despite punishing advancements in computer chess, we still weren't remotely close to artificial general intelligence.

There has always been another vision for A.I. - a dissenting view - in which the computers would learn from the ground up (from data) rather than from the top down (from rules). This notion dates to the early 1940s, when it occurred to researchers that the best model for flexible automated intelligence was the brain itself. A brain, after all, is just a bunch of widgets, called neurons, that either pass along an electrical charge to their neighbors or don't. What's important are less the individual neurons themselves than the manifold connections among them. This structure, in its simplicity, has afforded the brain a wealth of adaptive advantages. The brain can operate in circumstances in which information is poor or missing; it can withstand significant damage without total loss of control; it can store a huge amount of knowledge in a very efficient way; it can isolate distinct patterns but retain the messiness necessary to handle ambiguity.

There was no reason you couldn't try to mimic this structure in electronic form, and in 1943 it was shown that arrangements of simple artificial neurons could carry out basic logical functions. They could also, at least in theory, learn the way we do. With life experience, depending on a particular person's trials and errors, the synaptic connections among pairs of neurons get stronger or weaker. An artificial neural network could do something similar, by gradually altering, on a guided trial-and-error basis, the numerical relationships among artificial neurons. It wouldn't need to be preprogrammed with fixed rules. It would, instead, rewire itself to reflect patterns in the data it absorbed.

This attitude toward artificial intelligence was evolutionary rather than creationist. If you wanted a flexible mechanism, you wanted one that could adapt to its environment. If you wanted something that could adapt, you didn't want to begin with the indoctrination of the rules of chess. You wanted to begin with very basic abilities - sensory perception and motor control - in the hope that advanced skills would emerge organically. Humans don't learn to understand language by memorizing dictionaries and grammar books, so why should we possibly expect our computers to do so?

Google Brain was the first major commercial institution to invest in the possibilities embodied by this way of thinking about A.I. Dean, Corrado and Ng began their work as a part-time, collaborative experiment, but they made immediate progress. They took architectural inspiration for their models from recent theoretical outlines - as well as ideas that had been on the shelf since the 1980s and 1990s - and drew upon both the company's peerless reserves of data and its massive computing infrastructure. They instructed the networks on enormous banks of "labeled" data - speech files with correct transcriptions, for example - and the computers improved their responses to better match reality.

"The portion of evolution in which animals developed eyes was a big development," Dean told me one day, with customary understatement. We were sitting, as usual, in a whiteboarded meeting room, on which he had drawn a crowded, snaking timeline of Google Brain and its relation to inflection points in the recent history of neural networks. "Now computers have eyes. We can build them around the capabilities that now exist to understand photos. Robots will be drastically transformed. They'll be able to operate in an unknown environment, on much different problems." These capacities they were building may have seemed primitive, but their implications were profound.

2. The Unlikely Intern

In its first year or so of existence, Brain's experiments in the development of a machine with the talents of a 1-year-old had, as Dean said, worked to great effect. Its speech-recognition team swapped out part of their old system for a neural network and encountered, in pretty much one fell swoop, the best quality improvements anyone had seen in 20 years. Their system's object-recognition abilities improved by an order of magnitude. This was not because Brain's personnel had generated a sheaf of outrageous new ideas in just a year. It was because Google had finally devoted the resources - in computers and, increasingly, personnel - to fill in outlines that had been around for a long time.

A great preponderance of these extant and neglected notions had been proposed or refined by a peripatetic English polymath named Geoffrey Hinton. In the second year of Brain's existence, Hinton was recruited to Brain as Andrew Ng left. (Ng now leads the 1,300-person A.I. team at Baidu.) Hinton wanted to leave his post at the University of Toronto for only three months, so for arcane contractual reasons he had to be hired as an intern. At intern training, the orientation leader would say something like, "Type in your LDAP" - a user login - and he would flag a helper to ask, "What's an LDAP?" All the smart 25-year-olds in attendance, who had only ever known deep learning as the sine qua non of artificial intelligence, snickered: "Who is that old guy? Why doesn't he get it?"

"At lunchtime," Hinton said, "someone in the queue yelled: 'Professor Hinton! I took your course! What are you doing here?' After that, it was all right."

A few months later, Hinton and two of his students demonstrated truly astonishing gains in a big image-recognition contest, run by an open-source collective called ImageNet, that asks computers not only to identify a monkey but also to distinguish between spider monkeys and howler monkeys, and among God knows how many different breeds of cat. Google soon approached Hinton and his students with an offer. They accepted. "I thought they were interested in our I.P.," he said. "Turns out they were interested in us."

Hinton comes from one of those old British families emblazoned like the Darwins at eccentric angles across the intellectual landscape, where regardless of titular preoccupation a person is expected to make sideline contributions to minor problems in astronomy or fluid dynamics. His great-great-grandfather was George Boole, whose foundational work in symbolic logic underpins the computer; another great-great-grandfather was a celebrated surgeon, his father a venturesome entomologist, his father's cousin a Los Alamos researcher; the list goes on. He trained at Cambridge and Edinburgh, then taught at Carnegie Mellon before he ended up at Toronto, where he still spends half his time. (His work has long been supported by the largess of the Canadian government.) I visited him in his office at Google there. He has tousled yellowed-pewter hair combed forward in a mature Noel Gallagher style and wore a baggy striped dress shirt that persisted in coming untucked, and oval eyeglasses that slid down to the tip of a prominent nose. He speaks with a driving if shambolic wit, and says things like, "Computers will understand sarcasm before Americans do."

Hinton had been working on neural networks since his undergraduate days at Cambridge in the late 1960s, and he is seen as the intellectual primogenitor of the contemporary field. For most of that time, whenever he spoke about machine learning, people looked at him as though he were talking about the Ptolemaic spheres or bloodletting by leeches. Neural networks were taken as a disproven folly, largely on the basis of one overhyped project: the Perceptron, an artificial neural network that Frank Rosenblatt, a Cornell psychologist, developed in the late 1950s. The New York Times reported that the machine's sponsor, the United States Navy, expected it would "be able to walk, talk, see, write, reproduce itself and be conscious of its existence." It went on to do approximately none of those things. Marvin Minsky, the dean of artificial intelligence in America, had worked on neural networks for his 1954 Princeton thesis, but he'd since grown tired of the inflated claims that Rosenblatt - who was a contemporary at Bronx Science - made for the neural paradigm. (He was also competing for Defense Department funding.) Along with an M.I.T. colleague, Minsky published a book that proved that there were painfully simple problems the Perceptron could never solve.

Minsky's criticism of the Perceptron extended only to networks of one "layer," i.e., one layer of artificial neurons between what's fed to the machine and what you expect from it - and later in life, he expounded ideas very similar to contemporary deep learning. But Hinton already knew at the time that complex tasks could be carried out if you had recourse to multiple layers. The simplest description of a neural network is that it's a machine that makes classifications or predictions based on its ability to discover patterns in data. With one layer, you could find only simple patterns; with more than one, you could look for patterns of patterns. Take the case of image recognition, which tends to rely on a contraption called a "convolutional neural net." (These were elaborated in a seminal 1998 paper whose lead author, a Frenchman named Yann LeCun, did his postdoctoral research in Toronto under Hinton and now directs a huge A.I. endeavor at Facebook.) The first layer of the network learns to identify the very basic visual trope of an "edge," meaning a nothing (an off-pixel) followed by a something (an on-pixel) or vice versa. Each successive layer of the network looks for a pattern in the previous layer. A pattern of edges might be a circle or a rectangle. A pattern of circles or rectangles might be a face. And so on. This more or less parallels the way information is put together in increasingly abstract ways as it travels from the photoreceptors in the retina back and up through the visual cortex. At each conceptual step, detail that isn't immediately relevant is thrown away. If several edges and circles come together to make a face, you don't care exactly where the face is found in the visual field; you just care that it's a face.

The issue with multilayered, "deep" neural networks was that the trial-and-error part got extraordinarily complicated. In a single layer, it's easy. Imagine that you're playing with a child. You tell the child, "Pick up the green ball and put it into Box A." The child picks up a green ball and puts it into Box B. You say, "Try again to put the green ball in Box A." The child tries Box A. Bravo.

Now imagine you tell the child, "Pick up a green ball, go through the door marked 3 and put the green ball into Box A." The child takes a red ball, goes through the door marked 2 and puts the red ball into Box B. How do you begin to correct the child? You cannot just repeat your initial instructions, because the child does not know at which point he went wrong. In real life, you might start by holding up the red ball and the green ball and saying, "Red ball, green ball." The whole point of machine learning, however, is to avoid that kind of explicit mentoring. Hinton and a few others went on to invent a solution (or rather, reinvent an older one) to this layered-error problem, over the halting course of the late 1970s and 1980s, and interest among computer scientists in neural networks was briefly revived. "People got very excited about it," he said. "But we oversold it." Computer scientists quickly went back to thinking that people like Hinton were weirdos and mystics.

These ideas remained popular, however, among philosophers and psychologists, who called it "connectionism" or "parallel distributed processing." "This idea," Hinton told me, "of a few people keeping a torch burning, it's a nice myth. It was true within artificial intelligence. But within psychology lots of people believed in the approach but just couldn't do it." Neither could Hinton, despite the generosity of the Canadian government. "There just wasn't enough computer power or enough data. People on our side kept saying, 'Yeah, but if I had a really big one, it would work.' It wasn't a very persuasive argument."

3. A Deep Explanation of Deep Learning

When Pichai said that Google would henceforth be "A.I. first," he was not just making a claim about his company's business strategy; he was throwing in his company's lot with this long-unworkable idea. Pichai's allocation of resources ensured that people like Dean could ensure that people like Hinton would have, at long last, enough computers and enough data to make a persuasive argument. An average brain has something on the order of 100 billion neurons. Each neuron is connected to up to 10,000 other neurons, which means that the number of synapses is between 100 trillion and 1,000 trillion. For a simple artificial neural network of the sort proposed in the 1940s, the attempt to even try to replicate this was unimaginable. We're still far from the construction of a network of that size, but Google Brain's investment allowed for the creation of artificial neural networks comparable to the brains of mice.

To understand why scale is so important, however, you have to start to understand some of the more technical details of what, exactly, machine intelligences are doing with the data they consume. A lot of our ambient fears about A.I. rest on the idea that they're just vacuuming up knowledge like a sociopathic prodigy in a library, and that an artificial intelligence constructed to make paper clips might someday decide to treat humans like ants or lettuce. This just isn't how they work. All they're doing is shuffling information around in search of commonalities - basic patterns, at first, and then more complex ones - and for the moment, at least, the greatest danger is that the information we're feeding them is biased in the first place.

If that brief explanation seems sufficiently reassuring, the reassured nontechnical reader is invited to skip forward to the next section, which is about cats. If not, then read on. (This section is also, luckily, about cats.)

Imagine you want to program a cat-recognizer on the old symbolic-A.I. model. You stay up for days preloading the machine with an exhaustive, explicit definition of "cat." You tell it that a cat has four legs and pointy ears and whiskers and a tail, and so on. All this information is stored in a special place in memory called Cat. Now you show it a picture. First, the machine has to separate out the various distinct elements of the image. Then it has to take these elements and apply the rules stored in its memory. If(legs=4) and if(ears=pointy) and if(whiskers=yes) and if(tail=yes) and if(expression=supercilious), then(cat=yes). But what if you showed this cat-recognizer a Scottish Fold, a heart-rending breed with a prized genetic defect that leads to droopy doubled-over ears? Our symbolic A.I. gets to (ears=pointy) and shakes its head solemnly, "Not cat." It is hyperliteral, or "brittle." Even the thickest toddler shows much greater inferential acuity.

Now imagine that instead of hard-wiring the machine with a set of rules for classification stored in one location of the computer's memory, you try the same thing on a neural network. There is no special place that can hold the definition of "cat." There is just a giant blob of interconnected switches, like forks in a path. On one side of the blob, you present the inputs (the pictures); on the other side, you present the corresponding outputs (the labels). Then you just tell it to work out for itself, via the individual calibration of all of these interconnected switches, whatever path the data should take so that the inputs are mapped to the correct outputs. The training is the process by which a labyrinthine series of elaborate tunnels are excavated through the blob, tunnels that connect any given input to its proper output. The more training data you have, the greater the number and intricacy of the tunnels that can be dug. Once the training is complete, the middle of the blob has enough tunnels that it can make reliable predictions about how to handle data it has never seen before. This is called "supervised learning."

The reason that the network requires so many neurons and so much data is that it functions, in a way, like a sort of giant machine democracy. Imagine you want to train a computer to differentiate among five different items. Your network is made up of millions and millions of neuronal "voters," each of whom has been given five different cards: one for cat, one for dog, one for spider monkey, one for spoon and one for defibrillator. You show your electorate a photo and ask, "Is this a cat, a dog, a spider monkey, a spoon or a defibrillator?" All the neurons that voted the same way collect in groups, and the network foreman peers down from above and identifies the majority classification: "A dog?"

You say: "No, maestro, it's a cat. Try again."

Now the network foreman goes back to identify which voters threw their weight behind "cat" and which didn't. The ones that got "cat" right get their votes counted double next time - at least when they're voting for "cat." They have to prove independently whether they're also good at picking out dogs and defibrillators, but one thing that makes a neural network so flexible is that each individual unit can contribute differently to different desired outcomes. What's important is not the individual vote, exactly, but the pattern of votes. If Joe, Frank and Mary all vote together, it's a dog; but if Joe, Kate and Jessica vote together, it's a cat; and if Kate, Jessica and Frank vote together, it's a defibrillator. The neural network just needs to register enough of a regularly discernible signal somewhere to say, "Odds are, this particular arrangement of pixels represents something these humans keep calling 'cats.' " The more "voters" you have, and the more times you make them vote, the more keenly the network can register even very weak signals. If you have only Joe, Frank and Mary, you can maybe use them only to differentiate among a cat, a dog and a defibrillator. If you have millions of different voters that can associate in billions of different ways, you can learn to classify data with incredible granularity. Your trained voter assembly will be able to look at an unlabeled picture and identify it more or less accurately.

Part of the reason there was so much resistance to these ideas in computer-science departments is that because the output is just a prediction based on patterns of patterns, it's not going to be perfect, and the machine will never be able to define for you what, exactly, a cat is. It just knows them when it sees them. This wooliness, however, is the point. The neuronal "voters" will recognize a happy cat dozing in the sun and an angry cat glaring out from the shadows of an untidy litter box, as long as they have been exposed to millions of diverse cat scenes. You just need lots and lots of the voters - in order to make sure that some part of your network picks up on even very weak regularities, on Scottish Folds with droopy ears, for example - and enough labeled data to make sure your network has seen the widest possible variance in phenomena.

It is important to note, however, that the fact that neural networks are probabilistic in nature means that they're not suitable for all tasks. It's no great tragedy if they mislabel 1 percent of cats as dogs, or send you to the wrong movie on occasion, but in something like a self-driving car we all want greater assurances. This isn't the only caveat. Supervised learning is a trial-and-error process based on labeled data. The machines might be doing the learning, but there remains a strong human element in the initial categorization of the inputs. If your data had a picture of a man and a woman in suits that someone had labeled "woman with her boss," that relationship would be encoded into all future pattern recognition. Labeled data is thus fallible the way that human labelers are fallible. If a machine was asked to identify creditworthy candidates for loans, it might use data like felony convictions, but if felony convictions were unfair in the first place - if they were based on, say, discriminatory drug laws - then the loan recommendations would perforce also be fallible.

Image-recognition networks like our cat-identifier are only one of many varieties of deep learning, but they are disproportionately invoked as teaching examples because each layer does something at least vaguely recognizable to humans - picking out edges first, then circles, then faces. This means there's a safeguard against error. For instance, an early oddity in Google's image-recognition software meant that it could not always identify a barbell in isolation, even though the team had trained it on an image set that included a lot of exercise categories. A visualization tool showed them the machine had learned not the concept of "dumbbell" but the concept of "dumbbell+arm," because all the dumbbells in the training set were attached to arms. They threw into the training mix some photos of solo barbells. The problem was solved. Not everything is so easy.

Over the course of its first year or two, Brain's efforts to cultivate in machines the skills of a 1-year-old were auspicious enough that the team was graduated out of the X lab and into the broader research organization. (The head of Google X once noted that Brain had paid for the entirety of X's costs.) They still had fewer than 10 people and only a vague sense for what might ultimately come of it all. But even then they were thinking ahead to what ought to happen next. First a human mind learns to recognize a ball and rests easily with the accomplishment for a moment, but sooner or later, it wants to ask for the ball. And then it wades into language.

The first step in that direction was the cat paper, which made Brain famous.

What the cat paper demonstrated was that a neural network with more than a billion "synaptic" connections - a hundred times larger than any publicized neural network to that point, yet still many orders of magnitude smaller than our brains - could observe raw, unlabeled data and pick out for itself a high-order human concept. The Brain researchers had shown the network millions of still frames from YouTube videos, and out of the welter of the pure sensorium the network had isolated a stable pattern any toddler or chipmunk would recognize without a moment's hesitation as the face of a cat. The machine had not been programmed with the foreknowledge of a cat; it reached directly into the world and seized the idea for itself. (The researchers discovered this with the neural-network equivalent of something like an M.R.I., which showed them that a ghostly cat face caused the artificial neurons to "vote" with the greatest collective enthusiasm.) Most machine learning to that point had been limited by the quantities of labeled data. The cat paper showed that machines could also deal with raw unlabeled data, perhaps even data of which humans had no established foreknowledge. This seemed like a major advance not only in cat-recognition studies but also in overall artificial intelligence.

The lead author on the cat paper was Quoc Le. Le is short and willowy and soft-spoken, with a quick, enigmatic smile and shiny black penny loafers. He grew up outside Hue, Vietnam. His parents were rice farmers, and he did not have electricity at home. His mathematical abilities were obvious from an early age, and he was sent to study at a magnet school for science. In the late 1990s, while still in school, he tried to build a chatbot to talk to. He thought, How hard could this be?

"But actually," he told me in a whispery deadpan, "it's very hard."

He left the rice paddies on a scholarship to a university in Canberra, Australia, where he worked on A.I. tasks like computer vision. The dominant method of the time, which involved feeding the machine definitions for things like edges, felt to him like cheating. Le didn't know then, or knew only dimly, that there were at least a few dozen computer scientists elsewhere in the world who couldn't help imagining, as he did, that machines could learn from scratch. In 2006, Le took a position at the Max Planck Institute for Biological Cybernetics in the medieval German university town of Tübingen. In a reading group there, he encountered two new papers by Geoffrey Hinton. People who entered the discipline during the long diaspora all have conversion stories, and when Le read those papers, he felt the scales fall away from his eyes.

"There was a big debate," he told me. "A very big debate." We were in a small interior conference room, a narrow, high-ceilinged space outfitted with only a small table and two whiteboards. He looked to the curve he'd drawn on the whiteboard behind him and back again, then softly confided, "I've never seen such a big debate."

He remembers standing up at the reading group and saying, "This is the future." It was, he said, an "unpopular decision at the time." A former adviser from Australia, with whom he had stayed close, couldn't quite understand Le's decision. "Why are you doing this?" he asked Le in an email.

"I didn't have a good answer back then," Le said. "I was just curious. There was a successful paradigm, but to be honest I was just curious about the new paradigm. In 2006, there was very little activity." He went to join Ng at Stanford and began to pursue Hinton's ideas. "By the end of 2010, I was pretty convinced something was going to happen."

What happened, soon afterward, was that Le went to Brain as its first intern, where he carried on with his dissertation work - an extension of which ultimately became the cat paper. On a simple level, Le wanted to see if the computer could be trained to identify on its own the information that was absolutely essential to a given image. He fed the neural network a still he had taken from YouTube. He then told the neural network to throw away some of the information contained in the image, though he didn't specify what it should or shouldn't throw away. The machine threw away some of the information, initially at random. Then he said: "Just kidding! Now recreate the initial image you were shown based only on the information you retained." It was as if he were asking the machine to find a way to "summarize" the image, and then expand back to the original from the summary. If the summary was based on irrelevant data - like the color of the sky rather than the presence of whiskers - the machine couldn't perform a competent reconstruction. Its reaction would be akin to that of a distant ancestor whose takeaway from his brief exposure to saber-tooth tigers was that they made a restful swooshing sound when they moved. Le's neural network, unlike that ancestor, got to try again, and again and again and again. Each time it mathematically "chose" to prioritize different pieces of information and performed incrementally better. A neural network, however, was a black box. It divined patterns, but the patterns it identified didn't always make intuitive sense to a human observer. The same network that hit on our concept of cat also became enthusiastic about a pattern that looked like some sort of furniture-animal compound, like a cross between an ottoman and a goat.

Le didn't see himself in those heady cat years as a language guy, but he felt an urge to connect the dots to his early chatbot. After the cat paper, he realized that if you could ask a network to summarize a photo, you could perhaps also ask it to summarize a sentence. This problem preoccupied Le, along with a Brain colleague named Tomas Mikolov, for the next two years.

In that time, the Brain team outgrew several offices around him. For a while they were on a floor they shared with executives. They got an email at one point from the administrator asking that they please stop allowing people to sleep on the couch in front of Larry Page and Sergey Brin's suite. It unsettled incoming V.I.P.s. They were then allocated part of a research building across the street, where their exchanges in the microkitchen wouldn't be squandered on polite chitchat with the suits. That interim also saw dedicated attempts on the part of Google's competitors to catch up. (As Le told me about his close collaboration with Tomas Mikolov, he kept repeating Mikolov's name over and over, in an incantatory way that sounded poignant. Le had never seemed so solemn. I finally couldn't help myself and began to ask, "Is he ... ?" Le nodded. "At Facebook," he replied.)

They spent this period trying to come up with neural-network architectures that could accommodate not only simple photo classifications, which were static, but also complex structures that unfolded over time, like language or music. Many of these were first proposed in the 1990s, and Le and his colleagues went back to those long-ignored contributions to see what they could glean. They knew that once you established a facility with basic linguistic prediction, you could then go on to do all sorts of other intelligent things - like predict a suitable reply to an email, for example, or predict the flow of a sensible conversation. You could sidle up to the sort of prowess that would, from the outside at least, look a lot like thinking.

Part II: Language Machine

The hundred or so current members of Brain - it often feels less like a department within a colossal corporate hierarchy than it does a club or a scholastic society or an intergalactic cantina - came in the intervening years to count among the freest and most widely admired employees in the entire Google organization. They are now quartered in a tiered two-story eggshell building, with large windows tinted a menacing charcoal gray, on the leafy northwestern fringe of the company's main Mountain View campus. Their microkitchen has a foosball table I never saw used; a Rock Band setup I never saw used; and a Go kit I saw used on a few occasions. (I did once see a young Brain research associate introducing his colleagues to ripe jackfruit, carving up the enormous spiky orb like a turkey.)

When I began spending time at Brain's offices, in June, there were some rows of empty desks, but most of them were labeled with Post-it notes that said things like "Jesse, 6/27." Now those are all occupied. When I first visited, parking was not an issue. The closest spaces were those reserved for expectant mothers or Teslas, but there was ample space in the rest of the lot. By October, if I showed up later than 9:30, I had to find a spot across the street.

Brain's growth made Dean slightly nervous about how the company was going to handle the demand. He wanted to avoid what at Google is known as a "success disaster" - a situation in which the company's capabilities in theory outpaced its ability to implement a product in practice. At a certain point he did some back-of-the-envelope calculations, which he presented to the executives one day in a two-slide presentation.

"If everyone in the future speaks to their Android phone for three minutes a day," he told them, "this is how many machines we'll need." They would need to double or triple their global computational footprint.

"That," he observed with a little theatrical gulp and widened eyes, "sounded scary. You'd have to" - he hesitated to imagine the consequences - "build new buildings."

There was, however, another option: just design, mass-produce and install in dispersed data centers a new kind of chip to make everything faster. These chips would be called T.P.U.s, or "tensor processing units," and their value proposition - counterintuitively - is that they are deliberately less precise than normal chips. Rather than compute 12.246 times 54.392, they will give you the perfunctory answer to 12 times 54. On a mathematical level, rather than a metaphorical one, a neural network is just a structured series of hundreds or thousands or tens of thousands of matrix multiplications carried out in succession, and it's much more important that these processes be fast than that they be exact. "Normally," Dean said, "special-purpose hardware is a bad idea. It usually works to speed up one thing. But because of the generality of neural networks, you can leverage this special-purpose hardware for a lot of other things."

Just as the chip-design process was nearly complete, Le and two colleagues finally demonstrated that neural networks might be configured to handle the structure of language. He drew upon an idea, called "word embeddings," that had been around for more than 10 years. When you summarize images, you can divine a picture of what each stage of the summary looks like - an edge, a circle, etc. When you summarize language in a similar way, you essentially produce multidimensional maps of the distances, based on common usage, between one word and every single other word in the language. The machine is not "analyzing" the data the way that we might, with linguistic rules that identify some of them as nouns and others as verbs. Instead, it is shifting and twisting and warping the words around in the map. In two dimensions, you cannot make this map useful. You want, for example, "cat" to be in the rough vicinity of "dog," but you also want "cat" to be near "tail" and near "supercilious" and near "meme," because you want to try to capture all of the different relationships - both strong and weak - that the word "cat" has to other words. It can be related to all these other words simultaneously only if it is related to each of them in a different dimension. You can't easily make a 160,000-dimensional map, but it turns out you can represent a language pretty well in a mere thousand or so dimensions - in other words, a universe in which each word is designated by a list of a thousand numbers. Le gave me a good-natured hard time for my continual requests for a mental picture of these maps. "Gideon," he would say, with the blunt regular demurral of Bartleby, "I do not generally like trying to visualize thousand-dimensional vectors in three-dimensional space."

Still, certain dimensions in the space, it turned out, did seem to represent legible human categories, like gender or relative size. If you took the thousand numbers that meant "king" and literally just subtracted the thousand numbers that meant "queen," you got the same numerical result as if you subtracted the numbers for "woman" from the numbers for "man." And if you took the entire space of the English language and the entire space of French, you could, at least in theory, train a network to learn how to take a sentence in one space and propose an equivalent in the other. You just had to give it millions and millions of English sentences as inputs on one side and their desired French outputs on the other, and over time it would recognize the relevant patterns in words the way that an image classifier recognized the relevant patterns in pixels. You could then give it a sentence in English and ask it to predict the best French analogue.

The major difference between words and pixels, however, is that all of the pixels in an image are there at once, whereas words appear in a progression over time. You needed a way for the network to "hold in mind" the progression of a chronological sequence - the complete pathway from the first word to the last. In a period of about a week, in September 2014, three papers came out - one by Le and two others by academics in Canada and Germany - that at last provided all the theoretical tools necessary to do this sort of thing. That research allowed for open-ended projects like Brain's Magenta, an investigation into how machines might generate art and music. It also cleared the way toward an instrumental task like machine translation. Hinton told me he thought at the time that this follow-up work would take at least five more years.

Le's paper showed that neural translation was plausible, but he had used only a relatively small public data set. (Small for Google, that is - it was actually the biggest public data set in the world. A decade of the old Translate had gathered production data that was between a hundred and a thousand times bigger.) More important, Le's model didn't work very well for sentences longer than about seven words.

Mike Schuster, who then was a staff research scientist at Brain, picked up the baton. He knew that if Google didn't find a way to scale these theoretical insights up to a production level, someone else would. The project took him the next two years. "You think," Schuster says, "to translate something, you just get the data, run the experiments and you're done, but it doesn't work like that."

Schuster is a taut, focused, ageless being with a tanned, piston-shaped head, narrow shoulders, long camo cargo shorts tied below the knee and neon-green Nike Flyknits. He looks as if he woke up in the lotus position, reached for his small, rimless, elliptical glasses, accepted calories in the form of a modest portion of preserved acorn and completed a relaxed desert decathlon on the way to the office; in reality, he told me, it's only an 18-mile bike ride each way. Schuster grew up in Duisburg, in the former West Germany's blast-furnace district, and studied electrical engineering before moving to Kyoto to work on early neural networks. In the 1990s, he ran experiments with a neural-networking machine as big as a conference room; it cost millions of dollars and had to be trained for weeks to do something you could now do on your desktop in less than an hour. He published a paper in 1997 that was barely cited for a decade and a half; this year it has been cited around 150 times. He is not humorless, but he does often wear an expression of some asperity, which I took as his signature combination of German restraint and Japanese restraint.

The issues Schuster had to deal with were tangled. For one thing, Le's code was custom-written, and it wasn't compatible with the new open-source machine-learning platform Google was then developing, TensorFlow. Dean directed to Schuster two other engineers, Yonghui Wu and Zhifeng Chen, in the fall of 2015. It took them two months just to replicate Le's results on the new system. Le was around, but even he couldn't always make heads or tails of what they had done.

As Schuster put it, "Some of the stuff was not done in full consciousness. They didn't know themselves why they worked."

This February, Google's research organization - the loose division of the company, roughly a thousand employees in all, dedicated to the forward-looking and the unclassifiable - convened their leads at an offsite retreat at the Westin St. Francis, on Union Square, a luxury hotel slightly less splendid than Google's own San Francisco shop a mile or so to the east. The morning was reserved for rounds of "lightning talks," quick updates to cover the research waterfront, and the afternoon was idled away in cross-departmental "facilitated discussions." The hope was that the retreat might provide an occasion for the unpredictable, oblique, Bell Labs-ish exchanges that kept a mature company prolific.

At lunchtime, Corrado and Dean paired up in search of Macduff Hughes, director of Google Translate. Hughes was eating alone, and the two Brain members took positions at either side. As Corrado put it, "We ambushed him."

"O.K.," Corrado said to the wary Hughes, holding his breath for effect. "We have something to tell you."

They told Hughes that 2016 seemed like a good time to consider an overhaul of Google Translate - the code of hundreds of engineers over 10 years - with a neural network. The old system worked the way all machine translation has worked for about 30 years: It sequestered each successive sentence fragment, looked up those words in a large statistically derived vocabulary table, then applied a battery of post-processing rules to affix proper endings and rearrange it all to make sense. The approach is called "phrase-based statistical machine translation," because by the time the system gets to the next phrase, it doesn't know what the last one was. This is why Translate's output sometimes looked like a shaken bag of fridge magnets. Brain's replacement would, if it came together, read and render entire sentences at one draft. It would capture context - and something akin to meaning.

The stakes may have seemed low: Translate generates minimal revenue, and it probably always will. For most Anglophone users, even a radical upgrade in the service's performance would hardly be hailed as anything more than an expected incremental bump. But there was a case to be made that human-quality machine translation is not only a short-term necessity but also a development very likely, in the long term, to prove transformational. In the immediate future, it's vital to the company's business strategy. Google estimates that 50 percent of the internet is in English, which perhaps 20 percent of the world's population speaks. If Google was going to compete in China - where a majority of market share in search-engine traffic belonged to its competitor Baidu - or India, decent machine translation would be an indispensable part of the infrastructure. Baidu itself had published a pathbreaking paper about the possibility of neural machine translation in July 2015.

And in the more distant, speculative future, machine translation was perhaps the first step toward a general computational facility with human language. This would represent a major inflection point - perhaps the major inflection point - in the development of something that felt like true artificial intelligence.

Most people in Silicon Valley were aware of machine learning as a fast-approaching horizon, so Hughes had seen this ambush coming. He remained skeptical. A modest, sturdily built man of early middle age with mussed auburn hair graying at the temples, Hughes is a classic line engineer, the sort of craftsman who wouldn't have been out of place at a drafting table at 1970s Boeing. His jeans pockets often look burdened with curious tools of ungainly dimension, as if he were porting around measuring tapes or thermocouples, and unlike many of the younger people who work for him, he has a wardrobe unreliant on company gear. He knew that various people in various places at Google and elsewhere had been trying to make neural translation work - not in a lab but at production scale - for years, to little avail.

Hughes listened to their case and, at the end, said cautiously that it sounded to him as if maybe they could pull it off in three years.

Dean thought otherwise. "We can do it by the end of the year, if we put our minds to it." One reason people liked and admired Dean so much was that he had a long record of successfully putting his mind to it. Another was that he wasn't at all embarrassed to say sincere things like "if we put our minds to it."

Hughes was sure the conversion wasn't going to happen any time soon, but he didn't personally care to be the reason. "Let's prepare for 2016," he went back and told his team. "I'm not going to be the one to say Jeff Dean can't deliver speed."

A month later, they were finally able to run a side-by-side experiment to compare Schuster's new system with Hughes's old one. Schuster wanted to run it for English-French, but Hughes advised him to try something else. "English-French," he said, "is so good that the improvement won't be obvious."

It was a challenge Schuster couldn't resist. The benchmark metric to evaluate machine translation is called a BLEU score, which compares a machine translation with an average of many reliable human translations. At the time, the best BLEU scores for English-French were in the high 20s. An improvement of one point was considered very good; an improvement of two was considered outstanding.

The neural system, on the English-French language pair, showed an improvement over the old system of seven points.

Hughes told Schuster's team they hadn't had even half as strong an improvement in their own system in the last four years.

To be sure this wasn't some fluke in the metric, they also turned to their pool of human contractors to do a side-by-side comparison. The user-perception scores, in which sample sentences were graded from zero to six, showed an average improvement of 0.4 - roughly equivalent to the aggregate gains of the old system over its entire lifetime of development.

In mid-March, Hughes sent his team an email. All projects on the old system were to be suspended immediately.

Until then, the neural-translation team had been only three people - Schuster, Wu and Chen - but with Hughes's support, the broader team began to coalesce. They met under Schuster's command on Wednesdays at 2 p.m. in a corner room of the Brain building called Quartz Lake. The meeting was generally attended by a rotating cast of more than a dozen people. When Hughes or Corrado were there, they were usually the only native English speakers. The engineers spoke Chinese, Vietnamese, Polish, Russian, Arabic, German and Japanese, though they mostly spoke in their own efficient pidgin and in math. It is not always totally clear, at Google, who is running a meeting, but in Schuster's case there was no ambiguity.

The steps they needed to take, even then, were not wholly clear. "This story is a lot about uncertainty - uncertainty throughout the whole process," Schuster told me at one point. "The software, the data, the hardware, the people. It was like" - he extended his long, gracile arms, slightly bent at the elbows, from his narrow shoulders - "swimming in a big sea of mud, and you can only see this far." He held out his hand eight inches in front of his chest. "There's a goal somewhere, and maybe it's there."

Most of Google's conference rooms have videochat monitors, which when idle display extremely high-resolution oversaturated public Google+ photos of a sylvan dreamscape or the northern lights or the Reichstag. Schuster gestured toward one of the panels, which showed a crystalline still of the Washington Monument at night.

"The view from outside is that everyone has binoculars and can see ahead so far."

The theoretical work to get them to this point had already been painstaking and drawn-out, but the attempt to turn it into a viable product - the part that academic scientists might dismiss as "mere" engineering - was no less difficult. For one thing, they needed to make sure that they were training on good data. Google's billions of words of training "reading" were mostly made up of complete sentences of moderate complexity, like the sort of thing you might find in Hemingway. Some of this is in the public domain: The original Rosetta Stone of statistical machine translation was millions of pages of the complete bilingual records of the Canadian Parliament. Much of it, however, was culled from 10 years of collected data, including human translations that were crowdsourced from enthusiastic respondents. The team had in their storehouse about 97 million unique English "words." But once they removed the emoticons, and the misspellings, and the redundancies, they had a working vocabulary of only around 160,000.

Then you had to refocus on what users actually wanted to translate, which frequently had very little to do with reasonable language as it is employed. Many people, Google had found, don't look to the service to translate full, complex sentences; they translate weird little shards of language. If you wanted the network to be able to handle the stream of user queries, you had to be sure to orient it in that direction. The network was very sensitive to the data it was trained on. As Hughes put it to me at one point: "The neural-translation system is learning everything it can. It's like a toddler. 'Oh, Daddy says that word when he's mad!' " He laughed. "You have to be careful."

More than anything, though, they needed to make sure that the whole thing was fast and reliable enough that their users wouldn't notice. In February, the translation of a 10-word sentence took 10 seconds. They could never introduce anything that slow. The Translate team began to conduct latency experiments on a small percentage of users, in the form of faked delays, to identify tolerance. They found that a translation that took twice as long, or even five times as long, wouldn't be registered. An eightfold slowdown would. They didn't need to make sure this was true across all languages. In the case of a high-traffic language, like French or Chinese, they could countenance virtually no slowdown. For something more obscure, they knew that users wouldn't be so scared off by a slight delay if they were getting better quality. They just wanted to prevent people from giving up and switching over to some competitor's service.

Schuster, for his part, admitted he just didn't know if they ever could make it fast enough. He remembers a conversation in the microkitchen during which he turned to Chen and said, "There must be something we don't know to make it fast enough, but I don't know what it could be."

He did know, though, that they needed more computers - "G.P.U.s," graphics processors reconfigured for neural networks - for training.

Hughes went to Schuster to ask what he thought. "Should we ask for a thousand G.P.U.s?"

Schuster said, "Why not 2,000?"

Ten days later, they had the additional 2,000 processors.

By April, the original lineup of three had become more than 30 people - some of them, like Le, on the Brain side, and many from Translate. In May, Hughes assigned a kind of provisional owner to each language pair, and they all checked their results into a big shared spreadsheet of performance evaluations. At any given time, at least 20 people were running their own independent weeklong experiments and dealing with whatever unexpected problems came up. One day a model, for no apparent reason, started taking all the numbers it came across in a sentence and discarding them. There were months when it was all touch and go. "People were almost yelling," Schuster said.

By late spring, the various pieces were coming together. The team introduced something called a "word-piece model," a "coverage penalty," "length normalization." Each part improved the results, Schuster says, by maybe a few percentage points, but in aggregate they had significant effects. Once the model was standardized, it would be only a single multilingual model that would improve over time, rather than the 150 different models that Translate currently used. Still, the paradox - that a tool built to further generalize, via learning machines, the process of automation required such an extraordinary amount of concerted human ingenuity and effort - was not lost on them. So much of what they did was just gut. How many neurons per layer did you use? 1,024 or 512? How many layers? How many sentences did you run through at a time? How long did you train for?

"We did hundreds of experiments," Schuster told me, "until we knew that we could stop the training after one week. You're always saying: When do we stop? How do I know I'm done? You never know you're done. The machine-learning mechanism is never perfect. You need to train, and at some point you have to stop. That's the very painful nature of this whole system. It's hard for some people. It's a little bit an art - where you put your brush to make it nice. It comes from just doing it. Some people are better, some worse."

By May, the Brain team understood that the only way they were ever going to make the system fast enough to implement as a product was if they could run it on T.P.U.s, the special-purpose chips that Dean had called for. As Chen put it: "We did not even know if the code would work. But we did know that without T.P.U.s, it definitely wasn't going to work." He remembers going to Dean one on one to plead, "Please reserve something for us." Dean had reserved them. The T.P.U.s, however, didn't work right out of the box. Wu spent two months sitting next to someone from the hardware team in an attempt to figure out why. They weren't just debugging the model; they were debugging the chip. The neural-translation project would be proof of concept for the whole infrastructural investment.

One Wednesday in June, the meeting in Quartz Lake began with murmurs about a Baidu paper that had recently appeared on the discipline's chief online forum. Schuster brought the room to order. "Yes, Baidu came out with a paper. It feels like someone looking through our shoulder - similar architecture, similar results." The company's BLEU scores were essentially what Google achieved in its internal tests in February and March. Le didn't seem ruffled; his conclusion seemed to be that it was a sign Google was on the right track. "It is very similar to our system," he said with quiet approval.

The Google team knew that they could have published their results earlier and perhaps beaten their competitors, but as Schuster put it: "Launching is more important than publishing. People say, 'Oh, I did something first,' but who cares, in the end?"

This did, however, make it imperative that they get their own service out first and better. Hughes had a fantasy that they wouldn't even inform their users of the switch. They would just wait and see if social media lit up with suspicions about the vast improvements.

"We don't want to say it's a new system yet," he told me at 5:36 p.m. two days after Labor Day, one minute before they rolled out Chinese-to-English to 10 percent of their users, without telling anyone. "We want to make sure it works. The ideal is that it's exploding on Twitter: 'Have you seen how awesome Google Translate got?' "

The only two reliable measures of time in the seasonless Silicon Valley are the rotations of seasonal fruit in the microkitchens - from the pluots of midsummer to the Asian pears and Fuyu persimmons of early fall - and the zigzag of technological progress. On an almost uncomfortably warm Monday afternoon in late September, the team's paper was at last released. It had an almost comical 31 authors. The next day, the members of Brain and Translate gathered to throw themselves a little celebratory reception in the Translate microkitchen. The rooms in the Brain building, perhaps in homage to the long winters of their diaspora, are named after Alaskan locales; the Translate building's theme is Hawaiian.

The Hawaiian microkitchen has a slightly grainy beach photograph on one wall, a small lei-garlanded thatched-hut service counter with a stuffed parrot at the center and ceiling fixtures fitted to resemble paper lanterns. Two sparse histograms of bamboo poles line the sides, like the posts of an ill-defended tropical fort. Beyond the bamboo poles, glass walls and doors open onto rows of identical gray desks on either side. That morning had seen the arrival of new hooded sweatshirts to honor 10 years of Translate, and many team members went over to the party from their desks in their new gear. They were in part celebrating the fact that their decade of collective work was, as of that day, en route to retirement. At another institution, these new hoodies might thus have become a costume of bereavement, but the engineers and computer scientists from both teams all seemed pleased.

Google's neural translation was at last working. By the time of the party, the company's Chinese-English test had already processed 18 million queries. One engineer on the Translate team was running around with his phone out, trying to translate entire sentences from Chinese to English using Baidu's alternative. He crowed with glee to anybody who would listen. "If you put in more than two characters at once, it times out!" (Baidu says this problem has never been reported by users.)

When word began to spread, over the following weeks, that Google had introduced neural translation for Chinese to English, some people speculated that it was because that was the only language pair for which the company had decent results. Everybody at the party knew that the reality of their achievement would be clear in November. By then, however, many of them would be on to other projects.

Hughes cleared his throat and stepped in front of the tiki bar. He wore a faded green polo with a rumpled collar, lightly patterned across the midsection with dark bands of drying sweat. There had been last-minute problems, and then last-last-minute problems, including a very big measurement error in the paper and a weird punctuation-related bug in the system. But everything was resolved - or at least sufficiently resolved for the moment. The guests quieted. Hughes ran efficient and productive meetings, with a low tolerance for maundering or side conversation, but he was given pause by the gravity of the occasion. He acknowledged that he was, perhaps, stretching a metaphor, but it was important to him to underline the fact, he began, that the neural translation project itself represented a "collaboration between groups that spoke different languages."

Their neural-translation project, he continued, represented a "step function forward" - that is, a discontinuous advance, a vertical leap rather than a smooth curve. The relevant translation had been not just between the two teams but from theory into reality. He raised a plastic demi-flute of expensive-looking Champagne.

"To communication," he said, "and cooperation!"

The engineers assembled looked around at one another and gave themselves over to little circumspect whoops and applause.

Jeff Dean stood near the center of the microkitchen, his hands in his pockets, shoulders hunched slightly inward, with Corrado and Schuster. Dean saw that there was some diffuse preference that he contribute to the observance of the occasion, and he did so in a characteristically understated manner, with a light, rapid, concise addendum.

What they had shown, Dean said, was that they could do two major things at once: "Do the research and get it in front of, I dunno, half a billion people."

Everyone laughed, not because it was an exaggeration but because it wasn't.

Epilogue: Machines Without Ghosts

Perhaps the most famous historic critique of artificial intelligence, or the claims made on its behalf, implicates the question of translation. The Chinese Room argument was proposed in 1980 by the Berkeley philosopher John Searle. In Searle's thought experiment, a monolingual English speaker sits alone in a cell. An unseen jailer passes him, through a slot in the door, slips of paper marked with Chinese characters. The prisoner has been given a set of tables and rules in English for the composition of replies. He becomes so adept with these instructions that his answers are soon "absolutely indistinguishable from those of Chinese speakers." Should the unlucky prisoner be said to "understand" Chinese? Searle thought the answer was obviously not. This metaphor for a computer, Searle later wrote, exploded the claim that "the appropriately programmed digital computer with the right inputs and outputs would thereby have a mind in exactly the sense that human beings have minds."

For the Google Brain team, though, or for nearly everyone else who works in machine learning in Silicon Valley, that view is entirely beside the point. This doesn't mean they're just ignoring the philosophical question. It means they have a fundamentally different view of the mind. Unlike Searle, they don't assume that "consciousness" is some special, numinously glowing mental attribute - what the philosopher Gilbert Ryle called the "ghost in the machine." They just believe instead that the complex assortment of skills we call "consciousness" has randomly emerged from the coordinated activity of many different simple mechanisms. The implication is that our facility with what we consider the higher registers of thought are no different in kind from what we're tempted to perceive as the lower registers. Logical reasoning, on this account, is seen as a lucky adaptation; so is the ability to throw and catch a ball. Artificial intelligence is not about building a mind; it's about the improvement of tools to solve problems. As Corrado said to me on my very first day at Google, "It's not about what a machine 'knows' or 'understands' but what it 'does,' and - more importantly - what it doesn't do yet."

Where you come down on "knowing" versus "doing" has real cultural and social implications. At the party, Schuster came over to me to express his frustration with the paper's media reception. "Did you see the first press?" he asked me. He paraphrased a headline from that morning, blocking it word by word with his hand as he recited it: GOOGLE SAYS A.I. TRANSLATION IS INDISTINGUISHABLE FROM HUMANS'. Over the final weeks of the paper's composition, the team had struggled with this; Schuster often repeated that the message of the paper was "It's much better than it was before, but not as good as humans." He had hoped it would be clear that their efforts weren't about replacing people but helping them.

And yet the rise of machine learning makes it more difficult for us to carve out a special place for us. If you believe, with Searle, that there is something special about human "insight," you can draw a clear line that separates the human from the automated. If you agree with Searle's antagonists, you can't. It is understandable why so many people cling fast to the former view. At a 2015 M.I.T. conference about the roots of artificial intelligence, Noam Chomsky was asked what he thought of machine learning. He pooh-poohed the whole enterprise as mere statistical prediction, a glorified weather forecast. Even if neural translation attained perfect functionality, it would reveal nothing profound about the underlying nature of language. It could never tell you if a pronoun took the dative or the accusative case. This kind of prediction makes for a good tool to accomplish our ends, but it doesn't succeed by the standards of furthering our understanding of why things happen the way they do. A machine can already detect tumors in medical scans better than human radiologists, but the machine can't tell you what's causing the cancer.

Then again, can the radiologist?

Medical diagnosis is one field most immediately, and perhaps unpredictably, threatened by machine learning. Radiologists are extensively trained and extremely well paid, and we think of their skill as one of professional insight - the highest register of thought. In the past year alone, researchers have shown not only that neural networks can find tumors in medical images much earlier than their human counterparts but also that machines can even make such diagnoses from the texts of pathology reports. What radiologists do turns out to be something much closer to predictive pattern-matching than logical analysis. They're not telling you what caused the cancer; they're just telling you it's there.

Once you've built a robust pattern-matching apparatus for one purpose, it can be tweaked in the service of others. One Translate engineer took a network he put together to judge artwork and used it to drive an autonomous radio-controlled car. A network built to recognize a cat can be turned around and trained on CT scans - and on infinitely more examples than even the best doctor could ever review. A neural network built to translate could work through millions of pages of documents of legal discovery in the tiniest fraction of the time it would take the most expensively credentialed lawyer. The kinds of jobs taken by automatons will no longer be just repetitive tasks that were once - unfairly, it ought to be emphasized - associated with the supposed lower intelligence of the uneducated classes. We're not only talking about three and a half million truck drivers who may soon lack careers. We're talking about inventory managers, economists, financial advisers, real estate agents. What Brain did over nine months is just one example of how quickly a small group at a large company can automate a task nobody ever would have associated with machines.

The most important thing happening in Silicon Valley right now is not disruption. Rather, it's institution-building - and the consolidation of power - on a scale and at a pace that are both probably unprecedented in human history. Brain has interns; it has residents; it has "ninja" classes to train people in other departments. Everywhere there are bins of free bike helmets, and free green umbrellas for the two days a year it rains, and little fruit salads, and nap pods, and shared treadmill desks, and massage chairs, and random cartons of high-end pastries, and places for baby-clothes donations, and two-story climbing walls with scheduled instructors, and reading groups and policy talks and variegated support networks. The recipients of these major investments in human cultivation - for they're far more than perks for proles in some digital salt mine - have at hand the power of complexly coordinated servers distributed across 13 data centers on four continents, data centers that draw enough electricity to light up large cities.

But even enormous institutions like Google will be subject to this wave of automation; once machines can learn from human speech, even the comfortable job of the programmer is threatened. As the party in the tiki bar was winding down, a Translate engineer brought over his laptop to show Hughes something. The screen swirled and pulsed with a vivid, kaleidoscopic animation of brightly colored spheres in long looping orbits that periodically collapsed into nebulae before dispersing once more.

Hughes recognized what it was right away, but I had to look closely before I saw all the names - of people and files. It was an animation of the history of 10 years of changes to the Translate code base, every single buzzing and blooming contribution by every last team member. Hughes reached over gently to skip forward, from 2006 to 2008 to 2015, stopping every once in a while to pause and remember some distant campaign, some ancient triumph or catastrophe that now hurried by to be absorbed elsewhere or to burst on its own. Hughes pointed out how often Jeff Dean's name expanded here and there in glowing spheres.

Hughes called over Corrado, and they stood transfixed. To break the spell of melancholic nostalgia, Corrado, looking a little wounded, looked up and said, "So when do we get to delete it?"

"Don't worry about it," Hughes said. "The new code base is going to grow. Everything grows."

Correction: December 22, 2016

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Four days later, a couple of hundred journalists, entrepreneurs and advertisers from all over the world gathered in Google's London engineering office for a special announcement. Guests were greeted with Translate-branded fortune cookies. Their paper slips had a foreign phrase on one side - mine was in Norwegian - and on the other, an invitation to download the Translate app.

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Ai Research - Creating a new form of life

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Artificial Intelligence Ltd. (Ai) develops conversational software - technology that enables machines to converse with humans in natural language.

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AI Experiments | Experiments with Google

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AI Experiments | Experiments with Google

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A.i from wiki.

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Artificial intelligence ( AI) is the intelligence of machines or software, as opposed to the intelligence of living beings, primarily of humans. It is a field of study in computer science that develops and studies intelligent machines. Such machines may be called AIs.

AI technology is widely used throughout industry, government, and science. Some high-profile applications are: advanced web search engines (e.g., Google Search), recommendation systems (used by YouTube, Amazon, and Netflix), interacting via human speech (such as Google Assistant, Siri, and Alexa), self-driving cars (e.g., Waymo), generative and creative tools ( ChatGPT and AI art), and superhuman play and analysis in strategy games (such as chess and Go).

Alan Turing was the first person to conduct substantial research in the field that he called machine intelligence. Artificial intelligence was founded as an academic discipline in 1956. The field went through multiple cycles of optimism, followed by periods of disappointment and loss of funding, known as AI winter. Funding and interest vastly increased after 2012 when deep learning surpassed all previous AI techniques, and after 2017 with the transformer architecture. This led to the AI spring of the early 2020s, with companies, universities, and laboratories overwhelmingly based in the United States pioneering significant advances in artificial intelligence.

The growing use of artificial intelligence in the 21st century is influencing a societal and economic shift towards increased automation, data-driven decision-making, and the integration of AI systems into various economic sectors and areas of life, impacting job markets, healthcare, government, industry, and education. This raises questions about the ethical implications and risks of AI, prompting discussions about regulatory policies to ensure the safety and benefits of the technology.

The various sub-fields of AI research are centered around particular goals and the use of particular tools. The traditional goals of AI research include reasoning, knowledge representation, planning, learning, natural language processing, perception, and support for robotics. General intelligence (the ability to complete any task performable by a human) is among the field's long-term goals.

To solve these problems, AI researchers have adapted and integrated a wide range of problem-solving techniques, including search and mathematical optimization, formal logic, artificial neural networks, and methods based on statistics, operations research, and economics. AI also draws upon psychology, linguistics, philosophy, neuroscience and other fields.

The general problem of simulating (or creating) intelligence has been broken into sub-problems. These consist of particular traits or capabilities that researchers expect an intelligent system to display. The traits described below have received the most attention and cover the scope of AI research.

Reasoning, problem-solving

Early researchers developed algorithms that imitated step-by-step reasoning that humans use when they solve puzzles or make logical deductions. By the late 1980s and 1990s, methods were developed for dealing with uncertain or incomplete information, employing concepts from probability and economics.

Many of these algorithms are insufficient for solving large reasoning problems because they experience a "combinatorial explosion": they became exponentially slower as the problems grew larger. Even humans rarely use the step-by-step deduction that early AI research could model. They solve most of their problems using fast, intuitive judgments. Accurate and efficient reasoning is an unsolved problem.

Knowledge representation

An ontology represents knowledge as a set of concepts within a domain and the relationships between those concepts.

Knowledge representation and knowledge engineering allow AI programs to answer questions intelligently and make deductions about real-world facts. Formal knowledge representations are used in content-based indexing and retrieval, scene interpretation, clinical decision support, knowledge discovery (mining "interesting" and actionable inferences from large databases), and other areas.

A knowledge base is a body of knowledge represented in a form that can be used by a program. An ontology is the set of objects, relations, concepts, and properties used by a particular domain of knowledge. Knowledge bases need to represent things such as: objects, properties, categories and relations between objects; situations, events, states and time; causes and effects; knowledge about knowledge (what we know about what other people know); default reasoning (things that humans assume are true until they are told differently and will remain true even when other facts are changing); and many other aspects and domains of knowledge.

Among the most difficult problems in knowledge representation are: the breadth of commonsense knowledge (the set of atomic facts that the average person knows is enormous); and the sub-symbolic form of most commonsense knowledge (much of what people know is not represented as "facts" or "statements" that they could express verbally). There is also the difficulty of knowledge acquisition, the problem of obtaining knowledge for AI applications.

Planning and decision making

An "agent" is anything that perceives and takes actions in the world. A rational agent has goals or preferences and takes actions to make them happen. In automated planning, the agent has a specific goal. In automated decision making, the agent has preferences - there are some situations it would prefer to be in, and some situations it is trying to avoid. The decision making agent assigns a number to each situation (called the " utility") that measures how much the agent prefers it. For each possible action, it can calculate the " expected utility": the utility of all possible outcomes of the action, weighted by the probability that the outcome will occur. It can then choose the action with the maximum expected utility.

In some problems, the agent's preferences may be uncertain, especially if there are other agents or humans involved. These can be learned (e.g., with inverse reinforcement learning) or the agent can seek information to improve its preferences. Information value theory can be used to weigh the value of exploratory or experimental actions. The space of possible future actions and situations is typically intractably large, so the agents must take actions and evaluate situations while being uncertain what the outcome will be.

A Markov decision process has a transition model that describes the probability that a particular action will change the state in a particular way, and a reward function that supplies the utility of each state and the cost of each action. A policy associates a decision with each possible state. The policy could be calculated (e.g. by iteration), be heuristic, or it can be learned.

There are several kinds of machine learning. Unsupervised learning analyzes a stream of data and finds patterns and makes predictions without any other guidance. Supervised learning requires a human to label the input data first, and comes in two main varieties: classification (where the program must learn to predict what category the input belongs in) and regression (where the program must deduce a numeric function based on numeric input).

In reinforcement learning the agent is rewarded for good responses and punished for bad ones. The agent learns to choose responses that are classified as "good". Transfer learning is when the knowledge gained from one problem is applied to a new problem. Deep learning is a type of machine learning that runs inputs through biologically inspired artificial neural networks for all of these types of learning.

Natural language processing

Natural language processing (NLP) allows programs to read, write and communicate in human languages such as English. Specific problems include speech recognition, speech synthesis, machine translation, information extraction, information retrieval and question answering.

Early work, based on Noam Chomsky's generative grammar and semantic networks, had difficulty with word-sense disambiguation unless restricted to small domains called " micro-worlds" (due to the common sense knowledge problem). Margaret Masterman believed that it was meaning, and not grammar that was the key to understanding languages, and that thesauri and not dictionaries should be the basis of computational language structure.

Modern deep learning techniques for NLP include word embedding (representing words, typically as vectors encoding their meaning), transformers (a deep learning architecture using an attention mechanism), and others. In 2019, generative pre-trained transformer (or "GPT") language models began to generate coherent text, and by 2023 these models were able to get human-level scores on the bar exam, SAT test, GRE test, and many other real-world applications.

Perception

The field includes speech recognition, image classification, facial recognition, object recognition, and robotic perception.

Social intelligence

Kismet, a robot head which was made in the 1990s; a machine that can recognize and simulate emotions.

Affective computing is an interdisciplinary umbrella that comprises systems that recognize, interpret, process or simulate human feeling, emotion and mood. For example, some virtual assistants are programmed to speak conversationally or even to banter humorously; it makes them appear more sensitive to the emotional dynamics of human interaction, or to otherwise facilitate human-computer interaction.

However, this tends to give naïve users an unrealistic conception of the intelligence of existing computer agents. Moderate successes related to affective computing include textual sentiment analysis and, more recently, multimodal sentiment analysis, wherein AI classifies the affects displayed by a videotaped subject.

General intelligence

Techniques

AI research uses a wide variety of techniques to accomplish the goals above.

Search and optimization

AI can solve many problems by intelligently searching through many possible solutions. There are two very different kinds of search used in AI: state space search and local search.

State space search

State space search searches through a tree of possible states to try to find a goal state. For example, planning algorithms search through trees of goals and subgoals, attempting to find a path to a target goal, a process called means-ends analysis.

Simple exhaustive searches are rarely sufficient for most real-world problems: the search space (the number of places to search) quickly grows to astronomical numbers. The result is a search that is too slow or never completes. " Heuristics" or "rules of thumb" can help to prioritize choices that are more likely to reach a goal.

Local search

Illustration of gradient descent for 3 different starting points. Two parameters (represented by the plan coordinates) are adjusted in order to minimize the loss function (the height).

Local search uses mathematical optimization to find a solution to a problem. It begins with some form of guess and refines it incrementally.

Gradient descent is a type of local search that optimizes a set of numerical parameters by incrementally adjusting them to minimize a loss function. Variants of gradient descent are commonly used to train neural networks.

Another type of local search is evolutionary computation, which aims to iteratively improve a set of candidate solutions by "mutating" and "recombining" them, selecting only the fittest to survive each generation.

Distributed search processes can coordinate via swarm intelligence algorithms. Two popular swarm algorithms used in search are particle swarm optimization (inspired by bird flocking) and ant colony optimization (inspired by ant trails).

Formal Logic is used for reasoning and knowledge representation. Formal logic comes in two main forms: propositional logic (which operates on statements that are true or false and uses logical connectives such as "and", "or", "not" and "implies") and predicate logic (which also operates on objects, predicates and relations and uses quantifiers such as " Every X is a Y" and "There are some Xs that are Y s").

Logical inference (or deduction) is the process of proving a new statement ( conclusion) from other statements that are already known to be true (the premises). A logical knowledge base also handles queries and assertions as a special case of inference. An inference rule describes what is a valid step in a proof. The most general inference rule is resolution. Inference can be reduced to performing a search to find a path that leads from premises to conclusions, where each step is the application of an inference rule. Inference performed this way is intractable except for short proofs in restricted domains. No efficient, powerful and general method has been discovered.

Fuzzy logic assigns a "degree of truth" between 0 and 1. It can therefore handle propositions that are vague and partially true. Non-monotonic logics are designed to handle default reasoning. Other specialized versions of logic have been developed to describe many complex domains (see knowledge representation above).

Probabilistic methods for uncertain reasoning

A simple Bayesian network, with the associated conditional probability tables

Bayesian networks are a very general tool that can be used for many problems, including reasoning (using the Bayesian inference algorithm), learning (using the expectation-maximization algorithm), planning (using decision networks) and perception (using dynamic Bayesian networks).

Precise mathematical tools have been developed that analyze how an agent can make choices and plan, using decision theory, decision analysis, and information value theory. These tools include models such as Markov decision processes, dynamic decision networks, game theory and mechanism design.

Expectation-maximization clustering of Old Faithful eruption data starts from a random guess but then successfully converges on an accurate clustering of the two physically distinct modes of eruption.

Classifiers and statistical learning methods

The simplest AI applications can be divided into two types: classifiers (e.g. "if shiny then diamond"), on one hand, and controllers (e.g. "if diamond then pick up"), on the other hand. Classifiers are functions that use pattern matching to determine the closest match. They can be fine-tuned based on chosen examples using supervised learning. Each pattern (also called an " observation") is labeled with a certain predefined class. All the observations combined with their class labels are known as a data set. When a new observation is received, that observation is classified based on previous experience.

There are many kinds of classifiers in use. The decision tree is the simplest and most widely used symbolic machine learning algorithm. K-nearest neighbor algorithm was the most widely used analogical AI until the mid-1990s, and Kernel methods such as the support vector machine (SVM) displaced k-nearest neighbor in the 1990s. The naive Bayes classifier is reportedly the "most widely used learner" at Google, due in part to its scalability. Neural networks are also used as classifiers.

Artificial neural networks

A neural network is an interconnected group of nodes, akin to the vast network of neurons in the human brain.

Learning algorithms for neural networks use local search to choose the weights that will get the right output for each input during training. The most common training technique is the backpropagation algorithm. Neural networks learn to model complex relationships between inputs and outputs and find patterns in data. In theory, a neural network can learn any function.

In feedforward neural networks the signal passes in only one direction. Recurrent neural networks feed the output signal back into the input, which allows short-term memories of previous input events. Long short term memory is the most successful network architecture for recurrent networks. Perceptrons use only a single layer of neurons, deep learning uses multiple layers. Convolutional neural networks strengthen the connection between neurons that are "close" to each other - this is especially important in image processing, where a local set of neurons must identify an "edge" before the network can identify an object.

Deep learning

Deep learning uses several layers of neurons between the network's inputs and outputs. The multiple layers can progressively extract higher-level features from the raw input. For example, in image processing, lower layers may identify edges, while higher layers may identify the concepts relevant to a human such as digits or letters or faces.

Deep learning has profoundly improved the performance of programs in many important subfields of artificial intelligence, including computer vision, speech recognition, natural language processing, image classification and others. The reason that deep learning performs so well in so many applications is not known as of 2023. The sudden success of deep learning in 2012-2015 did not occur because of some new discovery or theoretical breakthrough (deep neural networks and backpropagation had been described by many people, as far back as the 1950s) but because of two factors: the incredible increase in computer power (including the hundred-fold increase in speed by switching to GPUs) and the availability of vast amounts of training data, especially the giant curated datasets used for benchmark testing, such as ImageNet.

Generative pre-trained transformers (GPT) are large language models that are based on the semantic relationships between words in sentences ( natural language processing). Text-based GPT models are pre-trained on a large corpus of text which can be from the internet. The pre-training consists in predicting the next token (a token being usually a word, subword, or punctuation). Throughout this pre-training, GPT models accumulate knowledge about the world, and can then generate human-like text by repeatedly predicting the next token. Typically, a subsequent training phase makes the model more truthful, useful and harmless, usually with a technique called reinforcement learning from human feedback (RLHF). Current GPT models are still prone to generating falsehoods called " hallucinations", although this can be reduced with RLHF and quality data. They are used in chatbots, which allow you to ask a question or request a task in simple text.

Current models and services include: Gemini (formerly Bard), ChatGPT, Grok, Claude, Copilot and LLaMA. Multimodal GPT models can process different types of data ( modalities) such as images, videos, sound and text.

Specialized hardware and software

In the late 2010s, graphics processing units (GPUs) that were increasingly designed with AI-specific enhancements and used with specialized TensorFlow software, had replaced previously used central processing unit (CPUs) as the dominant means for large-scale (commercial and academic) machine learning models' training. Historically, specialized languages, such as Lisp, Prolog, Python and others, had been used.

Applications

AI and machine learning technology is used in most of the essential applications of the 2020s, including: search engines (such as Google Search), targeting online advertisements, recommendation systems (offered by Netflix, YouTube or Amazon), driving internet traffic, targeted advertising ( AdSense, Facebook), virtual assistants (such as Siri or Alexa), autonomous vehicles (including drones, ADAS and self-driving cars), automatic language translation ( Microsoft Translator, Google Translate), facial recognition ( Apple's Face ID or Microsoft's DeepFace and Google's FaceNet) and image labeling (used by Facebook, Apple's iPhoto and TikTok).

Health and Medicine

The application of AI in medicine and medical research has the potential to increase patient care and quality of life. Through the lens of the Hippocratic Oath, medical professionals are ethically compelled to use AI, if applications can more accurately diagnose and treat patients.

For medical research, AI is an important tool for processing and integrating Big Data. This is particularly important for organoid and tissue engineering development which use microscopy imaging as a key technique in fabrication. It has been suggested that AI can overcome discrepancies in funding allocated to different fields of research. New AI tools can deepen our understanding of biomedically relevant pathways. For example, AlphaFold 2 (2021) demonstrated the ability to approximate, in hours rather than months, the 3D structure of a protein. In 2023 it was reported that AI guided drug discovery helped find a class of antibiotics capable of killing two different types of drug-resistant bacteria.

Game playing programs have been used since the 1950s to demonstrate and test AI's most advanced techniques. Deep Blue became the first computer chess-playing system to beat a reigning world chess champion, Garry Kasparov, on 11 May 1997. In 2011, in a quiz show exhibition match, IBM's question answering system, Watson, defeated the two greatest Jeopardy! champions, Brad Rutter and Ken Jennings, by a significant margin. In March 2016, AlphaGo won 4 out of 5 games of Go in a match with Go champion Lee Sedol, becoming the first computer Go-playing system to beat a professional Go player without handicaps. Then in 2017 it defeated Ke Jie, who was the best Go player in the world. Other programs handle imperfect-information games, such as the poker-playing program Pluribus. DeepMind developed increasingly generalistic reinforcement learning models, such as with MuZero, which could be trained to play chess, Go, or Atari games. In 2019, DeepMind's AlphaStar achieved grandmaster level in StarCraft II, a particularly challenging real-time strategy game that involves incomplete knowledge of what happens on the map. In 2021 an AI agent competed in a Playstation Gran Turismo competition, winning against four of the world's best Gran Turismo drivers using deep reinforcement learning.

Various countries are deploying AI military applications. The main applications enhance command and control, communications, sensors, integration and interoperability. Research is targeting intelligence collection and analysis, logistics, cyber operations, information operations, and semiautonomous and autonomous vehicles. AI technologies enable coordination of sensors and effectors, threat detection and identification, marking of enemy positions, target acquisition, coordination and deconfliction of distributed Joint Fires between networked combat vehicles involving manned and unmanned teams. AI was incorporated into military operations in Iraq and Syria.

Generative AI

Vincent van Gogh in watercolour created by generative AI software

In the early 2020s, generative AI gained widespread prominence. In March 2023, 58% of US adults had heard about ChatGPT and 14% had tried it. The increasing realism and ease-of-use of AI-based text-to-image generators such as Midjourney, DALL-E, and Stable Diffusion sparked a trend of viral AI-generated photos. Widespread attention was gained by a fake photo of Pope Francis wearing a white puffer coat, the fictional arrest of Donald Trump, and a hoax of an attack on the Pentagon, as well as the usage in professional creative arts.

Industry Specific Tasks

There are also thousands of successful AI applications used to solve specific problems for specific industries or institutions. In a 2017 survey, one in five companies reported they had incorporated "AI" in some offerings or processes. A few examples are energy storage, medical diagnosis, military logistics, applications that predict the result of judicial decisions, foreign policy, or supply chain management.

In agriculture, AI has helped farmers identify areas that need irrigation, fertilization, pesticide treatments or increasing yield. Agronomists use AI to conduct research and development. AI has been used to predict the ripening time for crops such as tomatoes, monitor soil moisture, operate agricultural robots, conduct predictive analytics, classify livestock pig call emotions, automate greenhouses, detect diseases and pests, and save water.

Artificial intelligence is used in astronomy to analyze increasing amounts of available data and applications, mainly for "classification, regression, clustering, forecasting, generation, discovery, and the development of new scientific insights" for example for discovering exoplanets, forecasting solar activity, and distinguishing between signals and instrumental effects in gravitational wave astronomy. It could also be used for activities in space such as space exploration, including analysis of data from space missions, real-time science decisions of spacecraft, space debris avoidance, and more autonomous operation.

Anyone looking to use machine learning as part of real-world, in-production systems needs to factor ethics into their AI training processes and strive to avoid bias. This is especially true when using AI algorithms that are inherently unexplainable in deep learning.

Risks and harm

Privacy and copyright

Machine learning algorithms require large amounts of data. The techniques used to acquire this data have raised concerns about privacy, surveillance and copyright.

Technology companies collect a wide range of data from their users, including online activity, geolocation data, video and audio. For example, in order to build speech recognition algorithms, Amazon have recorded millions of private conversations and allowed temporary workers to listen to and transcribe some of them. Opinions about this widespread surveillance range from those who see it as a necessary evil to those for whom it is clearly unethical and a violation of the right to privacy.

AI developers argue that this is the only way to deliver valuable applications. and have developed several techniques that attempt to preserve privacy while still obtaining the data, such as data aggregation, de-identification and differential privacy. Since 2016, some privacy experts, such as Cynthia Dwork, began to view privacy in terms of fairness. Brian Christian wrote that experts have pivoted "from the question of 'what they know' to the question of 'what they're doing with it'.".

Generative AI is often trained on unlicensed copyrighted works, including in domains such as images or computer code; the output is then used under a rationale of " fair use". Also website owners who do not wish to have their copyrighted content be AI indexed or 'scraped' can add code to their site, as you would, if you did not want your website to be indexed by a search engine which is currently available to certain services such as OpenAI. Experts disagree about how well, and under what circumstances, this rationale will hold up in courts of law; relevant factors may include "the purpose and character of the use of the copyrighted work" and "the effect upon the potential market for the copyrighted work". In 2023, leading authors (including John Grisham and Jonathan Franzen) sued AI companies for using their work to train generative AI.

Misinformation

YouTube, Facebook and others use recommender systems to guide users to more content. These AI programs were given the goal of maximizing user engagement (that is, the only goal was to keep people watching). The AI learned that users tended to choose misinformation, conspiracy theories, and extreme partisan content, and, to keep them watching, the AI recommended more of it. Users also tended to watch more content on the same subject, so the AI led people into filter bubbles where they received multiple versions of the same misinformation. This convinced many users that the misinformation was true, and ultimately undermined trust in institutions, the media and the government. The AI program had correctly learned to maximize its goal, but the result was harmful to society. After the U.S. election in 2016, major technology companies took steps to mitigate the problem.

In 2022, generative AI began to create images, audio, video and text that are indistinguishable from real photographs, recordings, films or human writing. It is possible for bad actors to use this technology to create massive amounts of misinformation or propaganda. AI pioneer Geoffrey Hinton expressed concern about AI enabling "authoritarian leaders to manipulate their electorates" on a large scale, among other risks.

Algorithmic bias and fairness

Machine learning applications will be biased if they learn from biased data. The developers may not be aware that the bias exists. Bias can be introduced by the way training data is selected and by the way a model is deployed. If a biased algorithm is used to make decisions that can seriously harm people (as it can in medicine, finance, recruitment, housing or policing) then the algorithm may cause discrimination. Fairness in machine learning is the study of how to prevent the harm caused by algorithmic bias. It has become serious area of academic study within AI. Researchers have discovered it is not always possible to define "fairness" in a way that satisfies all stakeholders.

On June 28, 2015, Google Photos's new image labeling feature mistakenly identified Jacky Alcine and a friend as "gorillas" because they were black. The system was trained on a dataset that contained very few images of black people, a problem called "sample size disparity". Google "fixed" this problem by preventing the system from labelling anything as a "gorilla". Eight years later, in 2023, Google Photos still could not identify a gorilla, and neither could similar products from Apple, Facebook, Microsoft and Amazon.

COMPAS is a commercial program widely used by U.S. courts to assess the likelihood of a defendant becoming a recidivist. In 2016, Julia Angwin at ProPublica discovered that COMPAS exhibited racial bias, despite the fact that the program was not told the races of the defendants. Although the error rate for both whites and blacks was calibrated equal at exactly 61%, the errors for each race were different-the system consistently overestimated the chance that a black person would re-offend and would underestimate the chance that a white person would not re-offend. In 2017, several researchers showed that it was mathematically impossible for COMPAS to accommodate all possible measures of fairness when the base rates of re-offense were different for whites and blacks in the data.

A program can make biased decisions even if the data does not explicitly mention a problematic feature (such as "race" or "gender"). The feature will correlate with other features (like "address", "shopping history" or "first name"), and the program will make the same decisions based on these features as it would on "race" or "gender". Moritz Hardt said "the most robust fact in this research area is that fairness through blindness doesn't work."

Criticism of COMPAS highlighted a deeper problem with the misuse of AI. Machine learning models are designed to make "predictions" that are only valid if we assume that the future will resemble the past. If they are trained on data that includes the results of racist decisions in the past, machine learning models must predict that racist decisions will be made in the future. Unfortunately, if an application then uses these predictions as recommendations, some of these "recommendations" will likely be racist. Thus, machine learning is not well suited to help make decisions in areas where there is hope that the future will be better than the past. It is necessarily descriptive and not proscriptive.

Bias and unfairness may go undetected because the developers are overwhelmingly white and male: among AI engineers, about 4% are black and 20% are women.

Lack of transparency

Lidar testing vehicle for autonomous driving

Many AI systems are so complex that their designers cannot explain how they reach their decisions. Particularly with deep neural networks, in which there are a large amount of non- linear relationships between inputs and outputs. But some popular explainability techniques exist.

People who have been harmed by an algorithm's decision have a right to an explanation. Doctors, for example, are required to clearly and completely explain the reasoning behind any decision they make.[clarification needed ] Early drafts of the European Union's General Data Protection Regulation in 2016 included an explicit statement that this right exists. Industry experts noted that this is an unsolved problem with no solution in sight. Regulators argued that nevertheless the harm is real: if the problem has no solution, the tools should not be used.

DARPA established the XAI ("Explainable Artificial Intelligence") program in 2014 to try and solve these problems.

There are several potential solutions to the transparency problem. SHAP helps visualise the contribution of each feature to the output. LIME can locally approximate a model with a simpler, interpretable model. Multitask learning provides a large number of outputs in addition to the target classification. These other outputs can help developers deduce what the network has learned. Deconvolution, DeepDream and other generative methods can allow developers to see what different layers of a deep network have learned and produce output that can suggest what the network is learning.

Conflict, surveillance and weaponized AI

A lethal autonomous weapon is a machine that locates, selects and engages human targets without human supervision. By 2015, over fifty countries were reported to be researching battlefield robots. These weapons are considered especially dangerous for several reasons: if they kill an innocent person it is not clear who should be held accountable, it is unlikely they will reliably choose targets, and, if produced at scale, they are potentially weapons of mass destruction. In 2014, 30 nations (including China) supported a ban on autonomous weapons under the United Nations' Convention on Certain Conventional Weapons, however the United States and others disagreed.

AI provides a number of tools that are particularly useful for authoritarian governments: smart spyware, face recognition and voice recognition allow widespread surveillance; such surveillance allows machine learning to classify potential enemies of the state and can prevent them from hiding; recommendation systems can precisely target propaganda and misinformation for maximum effect; deepfakes and generative AI aid in producing misinformation; advanced AI can make authoritarian centralized decision making more competitive with liberal and decentralized systems such as markets.

AI facial recognition systems are used for mass surveillance, notably in China. In 2019, Bengaluru, India deployed AI-managed traffic signals. This system uses cameras to monitor traffic density and adjust signal timing based on the interval needed to clear traffic. Terrorists, criminals and rogue states can use weaponized AI such as advanced digital warfare and lethal autonomous weapons. Machine-learning AI is also able to design tens of thousands of toxic molecules in a matter of hours.

Technological unemployment

Economists have frequently highlighted the risks of redundancies from AI, and speculated about unemployment if there is no adequate social policy for full employment.

In the past, technology has tended to increase rather than reduce total employment, but economists acknowledge that "we're in uncharted territory" with AI. A survey of economists showed disagreement about whether the increasing use of robots and AI will cause a substantial increase in long-term unemployment, but they generally agree that it could be a net benefit if productivity gains are redistributed. Risk estimates vary; for example, in the 2010s, Michael Osborne and Carl Benedikt Frey estimated 47% of U.S. jobs are at "high risk" of potential automation, while an OECD report classified only 9% of U.S. jobs as "high risk". The methodology of speculating about future employment levels has been criticised as lacking evidential foundation, and for implying that technology, rather than social policy, creates unemployment, as opposed to redundancies.

Unlike previous waves of automation, many middle-class jobs may be eliminated by artificial intelligence; The Economist stated in 2015 that "the worry that AI could do to white-collar jobs what steam power did to blue-collar ones during the Industrial Revolution" is "worth taking seriously". Jobs at extreme risk range from paralegals to fast food cooks, while job demand is likely to increase for care-related professions ranging from personal healthcare to the clergy.

In April 2023, it was reported that 70% of the jobs for Chinese video game illustrators had been eliminated by generative artificial intelligence.

Existential risk

First, AI does not require human-like "sentience" to be an existential risk. Modern AI programs are given specific goals and use learning and intelligence to achieve them. Philosopher Nick Bostrom argued that if one gives almost any goal to a sufficiently powerful AI, it may choose to destroy humanity to achieve it (he used the example of a paperclip factory manager). Stuart Russell gives the example of household robot that tries to find a way to kill its owner to prevent it from being unplugged, reasoning that "you can't fetch the coffee if you're dead." In order to be safe for humanity, a superintelligence would have to be genuinely aligned with humanity's morality and values so that it is "fundamentally on our side".

Second, Yuval Noah Harari argues that AI does not require a robot body or physical control to pose an existential risk. The essential parts of civilization are not physical. Things like ideologies, law, government, money and the economy are made of language; they exist because there are stories that billions of people believe. The current prevalence of misinformation suggests that an AI could use language to convince people to believe anything, even to take actions that are destructive.

The opinions amongst experts and industry insiders are mixed, with sizable fractions both concerned and unconcerned by risk from eventual superintelligent AI. Personalities such as Stephen Hawking, Bill Gates, and Elon Musk have expressed concern about existential risk from AI.

In the early 2010s, experts argued that the risks are too distant in the future to warrant research or that humans will be valuable from the perspective of a superintelligent machine. However, after 2016, the study of current and future risks and possible solutions became a serious area of research.

AI pioneers including Fei-Fei Li, Geoffrey Hinton, Yoshua Bengio, Cynthia Breazeal, Rana el Kaliouby, Demis Hassabis, Joy Buolamwini, and Sam Altman have expressed concerns about the risks of AI. In 2023, many leading AI experts issued the joint statement that "Mitigating the risk of extinction from AI should be a global priority alongside other societal-scale risks such as pandemics and nuclear war".

Other researchers, however, spoke in favor of a less dystopian view. AI pioneer Juergen Schmidhuber did not sign the joint statement, emphasising that in 95% of all cases, AI research is about making "human lives longer and healthier and easier." While the tools that are now being used to improve lives can also be used by bad actors, "they can also be used against the bad actors." Andrew Ng also argued that "it's a mistake to fall for the doomsday hype on AI-and that regulators who do will only benefit vested interests." Yann LeCun "scoffs at his peers' dystopian scenarios of supercharged misinformation and even, eventually, human extinction."

Limiting AI

Possible options for limiting AI include: using Embedded Ethics or Constitutional AI where companies or governments can add a policy, restricting high levels of compute power in training, restricting the ability to rewrite its own code base, restrict certain AI techniques but not in the training phase, open-source (transparency) vs proprietary (could be more restricted), backup model with redundancy, restricting security, privacy and copyright, restricting or controlling the memory, real-time monitoring, risk analysis, emergency shut-off, rigorous simulation and testing, model certification, assess known vulnerabilities, restrict the training material, restrict access to the internet, issue terms of use.

Ethical machines and alignment

Machines with intelligence have the potential to use their intelligence to make ethical decisions. The field of machine ethics provides machines with ethical principles and procedures for resolving ethical dilemmas. The field of machine ethics is also called computational morality, and was founded at an AAAI symposium in 2005.

Other approaches include Wendell Wallach's "artificial moral agents" and Stuart J. Russell's three principles for developing provably beneficial machines.

Frameworks

RESPECT the dignity of individual people

CONNECT with other people sincerely, openly and inclusively

CARE for the wellbeing of everyone

PROTECT social values, justice and the public interest

Promotion of the wellbeing of the people and communities that these technologies affect requires consideration of the social and ethical implications at all stages of AI system design, development and implementation, and collaboration between job roles such as data scientists, product managers, data engineers, domain experts, and delivery managers.

Regulation

The first global AI Safety Summit was held in 2023 with a declaration calling for international co-operation.

The regulation of artificial intelligence is the development of public sector policies and laws for promoting and regulating artificial intelligence (AI); it is therefore related to the broader regulation of algorithms. The regulatory and policy landscape for AI is an emerging issue in jurisdictions globally. According to AI Index at Stanford, the annual number of AI-related laws passed in the 127 survey countries jumped from one passed in 2016 to 37 passed in 2022 alone. Between 2016 and 2020, more than 30 countries adopted dedicated strategies for AI. Most EU member states had released national AI strategies, as had Canada, China, India, Japan, Mauritius, the Russian Federation, Saudi Arabia, United Arab Emirates, US and Vietnam. Others were in the process of elaborating their own AI strategy, including Bangladesh, Malaysia and Tunisia. The Global Partnership on Artificial Intelligence was launched in June 2020, stating a need for AI to be developed in accordance with human rights and democratic values, to ensure public confidence and trust in the technology. Henry Kissinger, Eric Schmidt, and Daniel Huttenlocher published a joint statement in November 2021 calling for a government commission to regulate AI. In 2023, OpenAI leaders published recommendations for the governance of superintelligence, which they believe may happen in less than 10 years. In 2023, the United Nations also launched an advisory body to provide recommendations on AI governance; the body comprises technology company executives, governments officials and academics.

In a 2022 Ipsos survey, attitudes towards AI varied greatly by country; 78% of Chinese citizens, but only 35% of Americans, agreed that "products and services using AI have more benefits than drawbacks". A 2023 Reuters/Ipsos poll found that 61% of Americans agree, and 22% disagree, that AI poses risks to humanity. In a 2023 Fox News poll, 35% of Americans thought it "very important", and an additional 41% thought it "somewhat important", for the federal government to regulate AI, versus 13% responding "not very important" and 8% responding "not at all important".

In November 2023, the first global AI Safety Summit was held in Bletchley Park in the UK to discuss the near and far term risks of AI and the possibility of mandatory and voluntary regulatory frameworks. 28 countries including the United States, China, and the European Union issued a declaration at the start of the summit, calling for international co-operation to manage the challenges and risks of artificial intelligence.

The study of mechanical or "formal" reasoning began with philosophers and mathematicians in antiquity. The study of logic led directly to Alan Turing's theory of computation, which suggested that a machine, by shuffling symbols as simple as "0" and "1", could simulate both mathematical deduction and formal reasoning, which is known as the Church-Turing thesis. This, along with concurrent discoveries in cybernetics and information theory, led researchers to consider the possibility of building an "electronic brain".

Alan Turing was thinking about machine intelligence at least as early as 1941, when he circulated a paper on machine intelligence which could be the earliest paper in the field of AI - though it is now lost. The first available paper generally recognized as "AI" was McCullouch and Pitts design for Turing-complete "artificial neurons" in 1943 - the first mathematical model of a neural network. The paper was influenced by Turing's earlier paper ' On Computable Numbers' from 1936 using similar two-state boolean 'neurons', but was the first to apply it to neuronal function.

The term 'machine intelligence' was used by Alan Turing during his life which was later often referred to as 'artificial intelligence' after his death in 1954. In 1950 Turing published the best known of his papers ' Computing Machinery and Intelligence', the paper introduced his concept of what is now known as the Turing test to the general public. Then followed three radio broadcasts on AI by Turing, the lectures: 'Intelligent Machinery, A Heretical Theory', 'Can Digital Computers Think'? and the panel discussion 'Can Automatic Calculating Machines be Said to Think'. By 1956 computer intelligence had been actively pursued for more than a decade in Britain; the earliest AI programmes were written there in 1951-1952.

In 1951, using a Ferranti Mark 1 computer of the University of Manchester, checkers and chess programs were wrote where you could play against the computer. The field of American AI research was founded at a workshop at Dartmouth College in 1956. The attendees became the leaders of AI research in the 1960s. They and their students produced programs that the press described as "astonishing": computers were learning checkers strategies, solving word problems in algebra, proving logical theorems and speaking English. Artificial Intelligence laboratories were set up at a number of British and US Universities in the latter 1950s and early 1960s.

They had, however, underestimated the difficulty of the problem. Both the U.S. and British governments cut off exploratory research in response to the criticism of Sir James Lighthill and ongoing pressure from the U.S. Congress to fund more productive projects. Minsky's and Papert's book Perceptrons was understood as proving that artificial neural networks would never be useful for solving real-world tasks, thus discrediting the approach altogether. The " AI winter", a period when obtaining funding for AI projects was difficult, followed.

In the early 1980s, AI research was revived by the commercial success of expert systems, a form of AI program that simulated the knowledge and analytical skills of human experts. By 1985, the market for AI had reached over a billion dollars. At the same time, Japan's fifth generation computer project inspired the U.S. and British governments to restore funding for academic research. However, beginning with the collapse of the Lisp Machine market in 1987, AI once again fell into disrepute, and a second, longer-lasting winter began.

Many researchers began to doubt that the current practices would be able to imitate all the processes of human cognition, especially perception, robotics, learning and pattern recognition. A number of researchers began to look into "sub-symbolic" approaches. Robotics researchers, such as Rodney Brooks, rejected "representation" in general and focussed directly on engineering machines that move and survive. Judea Pearl, Lofti Zadeh and others developed methods that handled incomplete and uncertain information by making reasonable guesses rather than precise logic. But the most important development was the revival of " connectionism", including neural network research, by Geoffrey Hinton and others. In 1990, Yann LeCun successfully showed that convolutional neural networks can recognize handwritten digits, the first of many successful applications of neural networks.

AI gradually restored its reputation in the late 1990s and early 21st century by exploiting formal mathematical methods and by finding specific solutions to specific problems. This " narrow" and "formal" focus allowed researchers to produce verifiable results and collaborate with other fields (such as statistics, economics and mathematics). By 2000, solutions developed by AI researchers were being widely used, although in the 1990s they were rarely described as "artificial intelligence".

Deep learning began to dominate industry benchmarks in 2012 and was adopted throughout the field. For many specific tasks, other methods were abandoned. Deep learning's success was based on both hardware improvements ( faster computers, graphics processing units, cloud computing) and access to large amounts of data (including curated datasets, such as ImageNet).

Deep learning's success led to an enormous increase in interest and funding in AI. The amount of machine learning research (measured by total publications) increased by 50% in the years 2015-2019, and WIPO reported that AI was the most prolific emerging technology in terms of the number of patent applications and granted patents. According to 'AI Impacts', about $50 billion annually was invested in "AI" around 2022 in the US alone and about 20% of new US Computer Science PhD graduates have specialized in "AI"; about 800,000 "AI"-related US job openings existed in 2022. The large majority of the advances have occurred within the United States, with its companies, universities, and research labs leading artificial intelligence research.

Philosophy

Defining artificial intelligence

Alan Turing wrote in 1950 "I propose to consider the question 'can machines think'?" He advised changing the question from whether a machine "thinks", to "whether or not it is possible for machinery to show intelligent behaviour". He devised the Turing test, which measures the ability of a machine to simulate human conversation. Since we can only observe the behavior of the machine, it does not matter if it is "actually" thinking or literally has a "mind". Turing notes that we can not determine these things about other people but "it is usual to have a polite convention that everyone thinks"

Russell and Norvig agree with Turing that AI must be defined in terms of "acting" and not "thinking". However, they are critical that the test compares machines to people. " Aeronautical engineering texts," they wrote, "do not define the goal of their field as making 'machines that fly so exactly like pigeons that they can fool other pigeons.'" AI founder John McCarthy agreed, writing that "Artificial intelligence is not, by definition, simulation of human intelligence".

McCarthy defines intelligence as "the computational part of the ability to achieve goals in the world." Another AI founder, Marvin Minsky similarly defines it as "the ability to solve hard problems". These definitions view intelligence in terms of well-defined problems with well-defined solutions, where both the difficulty of the problem and the performance of the program are direct measures of the "intelligence" of the machine-and no other philosophical discussion is required, or may not even be possible.

Another definition has been adopted by Google, a major practitioner in the field of AI. This definition stipulates the ability of systems to synthesize information as the manifestation of intelligence, similar to the way it is defined in biological intelligence.

Evaluating approaches to AI

No established unifying theory or paradigm has guided AI research for most of its history. The unprecedented success of statistical machine learning in the 2010s eclipsed all other approaches (so much so that some sources, especially in the business world, use the term "artificial intelligence" to mean "machine learning with neural networks"). This approach is mostly sub-symbolic, soft and narrow (see below). Critics argue that these questions may have to be revisited by future generations of AI researchers.

Symbolic AI and its limits

Symbolic AI (or " GOFAI") simulated the high-level conscious reasoning that people use when they solve puzzles, express legal reasoning and do mathematics. They were highly successful at "intelligent" tasks such as algebra or IQ tests. In the 1960s, Newell and Simon proposed the physical symbol systems hypothesis: "A physical symbol system has the necessary and sufficient means of general intelligent action."

However, the symbolic approach failed on many tasks that humans solve easily, such as learning, recognizing an object or commonsense reasoning. Moravec's paradox is the discovery that high-level "intelligent" tasks were easy for AI, but low level "instinctive" tasks were extremely difficult. Philosopher Hubert Dreyfus had argued since the 1960s that human expertise depends on unconscious instinct rather than conscious symbol manipulation, and on having a "feel" for the situation, rather than explicit symbolic knowledge. Although his arguments had been ridiculed and ignored when they were first presented, eventually, AI research came to agree with him.

The issue is not resolved: sub-symbolic reasoning can make many of the same inscrutable mistakes that human intuition does, such as algorithmic bias. Critics such as Noam Chomsky argue continuing research into symbolic AI will still be necessary to attain general intelligence, in part because sub-symbolic AI is a move away from explainable AI: it can be difficult or impossible to understand why a modern statistical AI program made a particular decision. The emerging field of neuro-symbolic artificial intelligence attempts to bridge the two approaches.

Neat vs. scruffy

Soft vs. hard computing

Finding a provably correct or optimal solution is intractable for many important problems. Soft computing is a set of techniques, including genetic algorithms, fuzzy logic and neural networks, that are tolerant of imprecision, uncertainty, partial truth and approximation. Soft computing was introduced in the late 1980s and most successful AI programs in the 21st century are examples of soft computing with neural networks.

Narrow vs. general AI

Machine consciousness, sentience and mind

The philosophy of mind does not know whether a machine can have a mind, consciousness and mental states, in the same sense that human beings do. This issue considers the internal experiences of the machine, rather than its external behavior. Mainstream AI research considers this issue irrelevant because it does not affect the goals of the field: to build machines that can solve problems using intelligence. Russell and Norvig add that "[t]he additional project of making a machine conscious in exactly the way humans are is not one that we are equipped to take on." However, the question has become central to the philosophy of mind. It is also typically the central question at issue in artificial intelligence in fiction.

Consciousness

David Chalmers identified two problems in understanding the mind, which he named the "hard" and "easy" problems of consciousness. The easy problem is understanding how the brain processes signals, makes plans and controls behavior. The hard problem is explaining how this feels or why it should feel like anything at all, assuming we are right in thinking that it truly does feel like something (Dennett's consciousness illusionism says this is an illusion). Human information processing is easy to explain, however, human subjective experience is difficult to explain. For example, it is easy to imagine a color-blind person who has learned to identify which objects in their field of view are red, but it is not clear what would be required for the person to know what red looks like.

Computationalism and functionalism

AI welfare and rights

It is difficult or impossible to reliably evaluate whether an advanced AI is sentient (has the ability to feel), and if so, to what degree. But if there is a significant chance that a given machine can feel and suffer, then it may be entitled to certain rights or welfare protection measures, similarly to animals. Sapience (a set of capacities related to high intelligence, such as discernment or self-awareness) may provide another moral basis for AI rights. Robot rights are also sometimes proposed as a practical way to integrate autonomous agents into society.

In 2017, the European Union considered granting "electronic personhood" to some of the most capable AI systems. Similarly to the legal status of companies, it would have conferred rights but also responsibilities. Critics argued in 2018 that granting rights to AI systems would downplay the importance of human rights, and that legislation should focus on user needs rather than speculative futuristic scenarios. They also noted that robots lacked the autonomy to take part to society on their own.

Progress in AI increased interest in the topic. Proponents of AI welfare and rights often argue that AI sentience, if it emerges, would be particularly easy to deny. They warn that this may be a moral blind spot analogous to slavery or factory farming, which could lead to large-scale suffering if sentient AI is created and carelessly exploited.

Superintelligence and the singularity

If research into artificial general intelligence produced sufficiently intelligent software, it might be able to reprogram and improve itself. The improved software would be even better at improving itself, leading to what I. J. Good called an " intelligence explosion" and Vernor Vinge called a " singularity".

Transhumanism

Robot designer Hans Moravec, cyberneticist Kevin Warwick, and inventor Ray Kurzweil have predicted that humans and machines will merge in the future into cyborgs that are more capable and powerful than either. This idea, called transhumanism, has roots in Aldous Huxley and Robert Ettinger.

The word "robot" itself was coined by Karel Čapek in his 1921 play R.U.R., the title standing for "Rossum's Universal Robots".

Thought-capable artificial beings have appeared as storytelling devices since antiquity, and have been a persistent theme in science fiction.

A common trope in these works began with Mary Shelley's Frankenstein, where a human creation becomes a threat to its masters. This includes such works as Arthur C. Clarke's and Stanley Kubrick's 2001: A Space Odyssey (both 1968), with HAL 9000, the murderous computer in charge of the Discovery One spaceship, as well as The Terminator (1984) and (1999). In contrast, the rare loyal robots such as Gort from The Day the Earth Stood Still (1951) and Bishop from (1986) are less prominent in popular culture.

Several works use AI to force us to confront the fundamental question of what makes us human, showing us artificial beings that have the ability to feel, and thus to suffer. This appears in Karel Čapek's , the films A.I. Artificial Intelligence and , as well as the novel Do Androids Dream of Electric Sheep?, by Philip K. Dick. Dick considers the idea that our understanding of human subjectivity is altered by technology created with artificial intelligence.

Explanatory notes

References

1. ^ Copeland, J., ed. (2004). The Essential Turing: the ideas that gave birth to the computer age. Oxford, England: Clarendon Press. ISBN 0-19-825079-7.

2. Dartmouth workshop: The proposal:

3. Funding initiatives in the early 1980s: Fifth Generation Project (Japan), Alvey (UK), Microelectronics and Computer Technology Corporation (US), Strategic Computing Initiative (US):

4. First AI Winter, Lighthill report, Mansfield Amendment

5. Problem solving, puzzle solving, game playing and deduction: ^

Russell & Norvig (2021, chpt. 3-5)

Russell & Norvig (2021, chpt. 6) (constraint satisfaction)

Poole, Mackworth & Goebel (1998, chpt. 2, 3, 7, 9)

Luger & Stubblefield (2004, chpt. 3, 4, 6, 8)

Nilsson (1998, chpt. 7-12)

6. Representing categories and relations: ^ Semantic networks, description logics, inheritance (including frames and scripts):

7. Representing events and time:^ Situation calculus, event calculus, fluent calculus (including solving the frame problem):

8. Default reasoning, Frame problem, default logic, non-monotonic logics, circumscription, closed world assumption, abduction: (Pooleet al. places abduction under "default reasoning". Luger et al. places this under "uncertain reasoning").

9. ^ Sensorless or "conformant" planning, contingent planning, replanning (a.k.a online planning):

10. ^ Non-parameteric learning models such as K-nearest neighbor and support vector machines:

11. Gradient calculation in computational graphs, ^ backpropagation, automatic differentiation:

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The two most widely used textbooks in 2023. (See the Open Syllabus).

These were the four of the most widely used AI textbooks in 2008:

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Further reading

Ashish Vaswani, Noam Shazeer, Niki Parmar et al. "Attention is all you need." Advances in neural information processing systems 30 (2017). Seminal paper on transformers.

Autor, David H., "Why Are There Still So Many Jobs? The History and Future of Workplace Automation" (2015) 29(3) Journal of Economic Perspectives 3.

Boden, Margaret, Mind As Machine, Oxford University Press, 2006.

Cukier, Kenneth, "Ready for Robots? How to Think about the Future of AI", Foreign Affairs, vol. 98, no. 4 (July/August 2019), pp. 192-98. George Dyson, historian of computing, writes (in what might be called "Dyson's Law") that "Any system simple enough to be understandable will not be complicated enough to behave intelligently, while any system complicated enough to behave intelligently will be too complicated to understand." (p. 197.) Computer scientist Alex Pentland writes: "Current AI machine-learning algorithms are, at their core, dead simple stupid. They work, but they work by brute force." (p. 198.)

Domingos, Pedro, "Our Digital Doubles: AI will serve our species, not control it", Scientific American, vol. 319, no. 3 (September 2018), pp. 88-93. "AIs are like autistic savants and will remain so for the foreseeable future.... AIs lack common sense and can easily make errors that a human never would... They are also liable to take our instructions too literally, giving us precisely what we asked for instead of what we actually wanted." (p. 93.)

Gertner, Jon. (2023) "Wikipedia's Moment of Truth: Can the online encyclopedia help teach A.I. chatbots to get their facts right - without destroying itself in the process?" New York Times Magazine (July 18, 2023) online

Gleick, James, "The Fate of Free Will" (review of Kevin J. Mitchell, Free Agents: How Evolution Gave Us Free Will, Princeton University Press, 2023, 333 pp.), The New York Review of Books, vol. LXXI, no. 1 (18 January 2024), pp. 27-28, 30. "Agency is what distinguishes us from machines. For biological creatures, reason and purpose come from acting in the world and experiencing the consequences. Artificial intelligences - disembodied, strangers to blood, sweat, and tears - have no occasion for that." (p. 30.)

Hanna, Alex, and Emily M. Bender, "Theoretical AI Harms Are a Distraction: Fearmongering about artificial intelligence's potential to end humanity shrouds the real harm it already causes", Scientific American, vol 330, no. 2 (February 2024), pp. 69-70. "[H]ype [about "existential risks"] surrounds many AI firms, but their technology already enables myriad harms, including... discrimination in housing, criminal justice, and health care, as well as the spread of hate speech and misinformation... Large language models extrude... fluent... coherent-seeming text but have no understanding of what the text means, let alone the ability to reason.... (p. 69.) [T]hat output... becomes a noxious... insidious pollutant of our information ecosystem.... [T]oo many... publications [about] AI come from corporate labs or... academic groups that receive... industry funding. Many of these publications are based on junk science [that] is nonreproducible... is full of hype, and uses evaluation methods that do not measure what they purport to... Meanwhile 'AI doomers' cite this junk science... to [misdirect] attention [to] the fantasy of all-powerful machines possibly going rogue and destroying humanity." (p. 70.)

Hughes-Castleberry, Kenna, "A Murder Mystery Puzzle: The literary puzzle Cain's Jawbone, which has stumped humans for decades, reveals the limitations of natural-language-processing algorithms", Scientific American, vol. 329, no. 4 (November 2023), pp. 81-82. "This murder mystery competition has revealed that although NLP (natural-language processing) models are capable of incredible feats, their abilities are very much limited by the amount of context they receive. This [...] could cause [difficulties] for researchers who hope to use them to do things such as analyze ancient languages. In some cases, there are few historical records on long-gone civilizations to serve as training data for such a purpose." (p. 82.)

Immerwahr, Daniel, "Your Lying Eyes: People now use A.I. to generate fake videos indistinguishable from real ones. How much does it matter?", The New Yorker, 20 November 2023, pp. 54-59. "If by 'deepfakes' we mean realistic videos produced using artificial intelligence that actually deceive people, then they barely exist. The fakes aren't deep, and the deeps aren't fake. [...] A.I.-generated videos are not, in general, operating in our media as counterfeited evidence. Their role better resembles that of cartoons, especially smutty ones." (p. 59.)

Johnston, John (2008) The Allure of Machinic Life: Cybernetics, Artificial Life, and the New AI, MIT Press.

Jumper, John; Evans, Richard; Pritzel, Alexander; et al. (26 August 2021). "Highly accurate protein structure prediction with AlphaFold". Nature. 596 (7873): 583-589. Bibcode:2021Natur.596..583J. doi:10.1038/s41586-021-03819-2. PMC . PMID 34265844. S2CID 235959867.

LeCun, Yann; Bengio, Yoshua; Hinton, Geoffrey (28 May 2015). "Deep learning". Nature. 521 (7553): 436-444. Bibcode:2015Natur.521..436L. doi:10.1038/nature14539. PMID 26017442. S2CID 3074096. Archived from the original on 5 June 2023.

Marcus, Gary, "Am I Human?: Researchers need new ways to distinguish artificial intelligence from the natural kind", Scientific American, vol. 316, no. 3 (March 2017), pp. 61-63. Marcus points out a so far insuperable stumbling block to artificial intelligence: an incapacity for reliable disambiguation. "[V]irtually every sentence [that people generate] is ambiguous, often in multiple ways. Our brain is so good at comprehending language that we do not usually notice." A prominent example is the "pronoun disambiguation problem" ("PDP"): a machine has no way of determining to whom or what a pronoun in a sentence-such as "he", "she" or "it"-refers.

Marcus, Gary, "Artificial Confidence: Even the newest, buzziest systems of artificial general intelligence are stymmied by the same old problems", Scientific American, vol. 327, no. 4 (October 2022), pp. 42-45.

Mitchell, Melanie (2019). Artificial intelligence: a guide for thinking humans. New York: Farrar, Straus and Giroux. ISBN 9780374257835.

Mnih, Volodymyr; Kavukcuoglu, Koray; Silver, David; et al. (26 February 2015). "Human-level control through deep reinforcement learning". Nature. 518 (7540): 529-533. Bibcode:2015Natur.518..529M. doi:10.1038/nature14236. PMID 25719670. S2CID 205242740. Archived from the original on 19 June 2023. Introduced DQN, which produced human-level performance on some Atari games.

Press, Eyal, "In Front of Their Faces: Does facial-recognition technology lead police to ignore contradictory evidence?", The New Yorker, 20 November 2023, pp. 20-26.

Roivainen, Eka, "AI's IQ: ChatGPT aced a [standard intelligence] test but showed that intelligence cannot be measured by IQ alone", Scientific American, vol. 329, no. 1 (July/August 2023), p. 7. "Despite its high IQ, ChatGPT fails at tasks that require real humanlike reasoning or an understanding of the physical and social world.... ChatGPT seemed unable to reason logically and tried to rely on its vast database of... facts derived from online texts."

Serenko, Alexander; Michael Dohan (2011). "Comparing the expert survey and citation impact journal ranking methods: Example from the field of Artificial Intelligence" (PDF). Journal of Informetrics. 5 (4): 629-49. doi:10.1016/j.joi.2011.06.002. Archived (PDF) from the original on 4 October 2013.

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External links

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Artificial intelligence ( AI) is the intelligence of machines or software, as opposed to the intelligence of living beings, primarily of humans. It is a field of study in computer science that develops and studies intelligent machines. Such machines may be called AIs. AI technology is widely used throughout industry, government, and science.

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What was once just a figment of the imagination of some our most famous science fiction writers, artificial intelligence (AI) is taking root in our everyday lives. We're still a few years away from having robots at our beck and call, but AI has already had a profound impact in more subtle ways. Weather forecasts, email spam filtering, Google's search predictions, and voice recognition, such Apple's Siri, are all examples. What these technologies have in common are machine-learning algorithms that enable them to react and respond in real time. There will be growing pains as AI technology evolves, but the positive effect it will have on society in terms of efficiency is immeasurable.

A Lesson in History

AI isn't a new concept; its storytelling roots go as far back as Greek antiquity. However, it was less than a century ago that the technological revolution took off and AI went from fiction to very plausible reality. Alan Turing, British mathematician and WWII code-breaker, is widely credited as being one of the first people to come up with the idea of machines that think in 1950. He even created the Turing test, which is still used today, as a benchmark to determine a machine's ability to "think" like a human. Though his ideas were ridiculed at the time, they set the wheels in motion, and the term "artificial intelligence" entered popular awareness in the mid- 1950s, after Turing died.

American cognitive scientist Marvin Minsky picked up the AI torch and co-founded the Massachusetts Institute of Technology's AI laboratory in 1959, and he was one of the leading thinkers in the field through the 1960s and 1970s. He even advised Stanley Kubrick on "2001: A Space Odyssey," released in 1968, which gave the world one of the best representations of AI in the form of HAL 9000. The rise of the personal computer in the 1980s sparked even more interest in machines that think.

But it took a couple of decades for people to recognize the true power of AI. High-profile investors and physicists, like Elon Musk, founder of Tesla, and Stephen Hawking, are continuing the conversation about the potential for AI technology. While the discussion occasionally turns to potential doomsday scenarios, there is a consensus that when used for good, AI could radically change the course of human history. And that is especially true when it comes to big data.
[ Also in Insights: Google and Elon Musk to Decide What Is Good for Humanity ]
The very premise of AI technology is its ability to continually learn from the data it collects. The more data there is to collect and analyze through carefully crafted algorithms, the better the machine becomes at making predictions. Not sure what movie to watch tonight? Don't worry; Netflix has some suggestions for you based on your previous viewing experiences. Don't feel like driving? Google's working on a solution for that, too, racking up the miles on its driverless car prototype.

The Business Effect

Nowhere has AI had a greater impact in the early stages of the 21st century than in the office. Machine-learning technologies are driving increases in productivity never before seen. From workflow management tools to trend predictions and even the way brands purchase advertising, AI is changing the way we do business. In fact, a Japanese venture capital firm recently became the first company in history to nominate an AI board member for its ability to predict market trends faster than humans.

Big data is a goldmine for businesses, but companies are practically drowning in it. Yet, it's been a primary driver for AI advancements, as machine-learning technologies can collect and organize massive amounts of information to make predictions and insights that are far beyond the capabilities of manual processing. Not only does it increase organizational efficiency, but it dramatically reduces the likelihood that a critical mistake will be made. AI can detect irregular patterns, such as spam filtering or payment fraud, and alert businesses in real time about suspicious activities. Businesses can "train" AI machines to handle incoming customer support calls, reducing costs. It can even be used to optimize the sales funnel by scanning the database and searching the Web for prospects that exhibit the same buying patterns as existing customers.

There is so much potential for AI development that it's getting harder to imagine a future without it. We're already seeing an increase in workplace productivity thanks to AI advancements. By the end of the decade, AI will become commonplace in everyday life, whether it's self-driving cars, more accurate weather predictions, or space exploration. We will even see machine-learning algorithms used to prevent cyberterrorism and payment fraud, albeit with increasing public debate over privacy implications. AI will also have a strong impact in healthcare advancements due to its ability to analyze massive amounts of genomic data, leading to more accurate prevention and treatment of medical conditions on a personalized level.

But don't expect a machine takeover any time soon. As easy as it is for machine-learning technology to self-improve, what it lacks is intuition. There's a gut instinct that can't be replicated via algorithms, making humans an important piece of the puzzle. The best way forward is for humans and machines to live harmoniously, leaning on one another's strengths. Advertising is a perfect example, where machines are now doing much of the purchasing through programmatic exchanges to maximize returns on investment, allowing advertisers to focus on creating more engaging content.

While early science fiction writers might have expected more from AI at this stage, the rest of the world is generally satisfied with our progress. After all, not everyone is ready for humanoid robots or self-learning spaceships.
Or Shani is founder and CEO of Adgorithms.

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From Science Fiction to Reality: The Evolution of Artificial Intelligence

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What was once just a figment of the imagination of some our most famous science fiction writers, artificial intelligence (AI) is taking root in our everyday lives. We're still a few years away from having robots at our beck and call, but AI has already had a profound impact in more subtle ways.

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America and the World - Artificial Intelligence Documentary - US - Best Technology Documentaries

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America and the World - Artificial Intelligence Documentary - US - Best Technology Documentaries https://youtu.be/ZDiC4i4gfUM I created this video with the YouTube Video Editor (http://www.youtube.com/editor) Creative Commons Attribution license (reuse allowed) https://creativecommons.org/licenses/by/3.0/legalcode https://support.google.com/youtube/answer/2797468?hl=en Origin: https://www.youtube.com/watch?v=VBceREwF7SA Author: http://www.youtube.com/channel/UCw-kH-Od73XDAt7qtH9uBYA Description: http://www.weforum.org/ How close are technologies to simulating or overtaking human intelligence and what are the implications for industry and society?

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Advances in carpentry, metalworking and electronics have been adapted for military purposes.

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increasing number of farm bots are being developed that are capable of complex tasks that have not been possible with the large-scale agricultural machinery in the past.

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new materials and methods for manufacturing cars more efficiently, as well as creating safer, more accessible car technologies.

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How is AI being used for space exploration?

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How is AI being used for space exploration?

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help scientists react more quickly to emergencies during manned flights by allowing space-borne astronauts to spot and prevent problems before they happen.

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is being designed to assist doctors in the effort to reduce the mortality rate among patients.

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Some robots can interact socially.