# data[].attributes.body
Summarizes the legendary fight between a chess grand master and a computer program that attempted to beat him. Although he won in the end the article can show how artificial intelligence is slowly being developed to beat humans at certain tasks. "For the first time in the history of mankind, I saw something similar to an artificial intellect,” Kasparov said. “I know very few chess players who could take this heat.

# data[].attributes.body
The video shows how a robot is able to pick itself up in an unfamiliar environment. This can demonstrate how robots are already "thinking" for the essay. Since they haven't been to this environment they have to use past expirences.
"Robots can pick themselves up after a fall, even in an unfamiliar environment, thanks to an artificially intelligent controller that can adapt to new scenarios. "

# data[].attributes.attachment_link.readable_text
Robots can pick themselves up after a fall, even in an unfamiliar environment, thanks to an artificially intelligent controller that can adapt to new scenarios. It could make four-legged robots more useful in responding to natural disasters, such as earthquakes.

Zhibin (Alex) Li at the University of Edinburgh, UK and his colleagues used an AI technique called deep reinforcement learning to teach four-legged robots a set of basic skills, such as trotting, steering and fall recovery. This involves the robots experimenting with different ways of moving and being rewarded with a numerical score for achieving a certain goal, such as standing up after a fall, and penalised for failing. This lets the AI recognise which actions are desired and repeat them in the similar situations in the future.

The team then tested the robots in a range of environments to see if they could combine these basic skill and quickly react to new scenarios. The robots could adapt their movements to stairs, slippery surfaces and gravel, despite not being programmed to navigate these complex environments. Li's team also tested the robots' ability to recover after being repeatedly pushed over, as shown in the video above.

"It is like how a human might know how to throw a ball and how to catch a ball, and then by experimenting with these together, they learn how to juggle," says Edward Johns at Imperial College London, who was not involved in the research.

Advertisement

The robot's autonomy could prove useful in emergency situations like earthquakes and fires. "We can deploy these robots to do the search and rescue for us when it is too dangerous for humans," says Li.

Journal reference: Science Robotics, DOI: 10.1126/scirobotics.abb2174

# data[].attributes.attachment_link.title
Robots learn to get back up after a fall in an unfamiliar environment

# data[].attributes.attachment_link.description
Robots can pick themselves up after a fall, even in an unfamiliar environment, thanks to an artificially intelligent controller that can adapt to new scenarios. It could make four-legged robots more useful in responding to natural disasters, such as earthquakes.

# data[].attributes.body
The article, How AI and Neuroscience Drive Each Other Forwards written by Neil Savage, discusses how both fields push each other forward. Initially, the author describes an experiment that required a brain researcher to use a computer to reproduce a result. Many similar instances required artificial intelligence to study data or produce similar effects of experiments. Next, he talks about artificial neural networks and how they have been proven to help study the brain. Then Neil savage finally talks about replicating sense into a computer, which is vital to achieving a brilliant computer. "To master image recognition, for example, they might be shown images from ImageNet, a database of more than 14 million photographs of objects that have been categorized and annotated by people."

# data[].attributes.attachment_link.readable_text
Chethan Pandarinath wants to enable people with paralysed limbs to reach out and grasp with a robotic arm as naturally as they would their own. To help him meet this goal, he has collected recordings of brain activity in people with paralysis. His hope, which is shared by many other researchers, is that he will be able to identify the patterns of electrical activity in neurons that correspond to a person's attempts to move their arm in a particular way, so that the instruction can then be fed to a prosthesis. Essentially, he wants to read their minds.

"It turns out, that's a really challenging problem," says Pandarinath, a biomedical engineer at Emory University and the Georgia Institute of Technology, both in Atlanta. "These signals from the brain - they're really complicated." In search of help, he turned to artificial intelligence (AI). He fed his brain-activity recordings to an artificial neural network, a computer architecture that is inspired by the brain, and tasked it with learning how to reproduce the data.

The recordings came from a small subset of neurons in the brain - around 200 of the 10 million to 100 million neurons that are required for arm movement in humans. To make sense of such a small sample, the computer had to find the underlying structure of the data. This can be described by patterns that the researchers call latent factors, which control the overall behaviour of the recorded activity. The effort revealed the brain's temporal dynamics - the way that its pattern of neural activity changes from one moment to the next - thereby providing a more fine-grained set of instructions for arm movement than did previous methods. "Now, we can very precisely say, on an almost millisecond-by-millisecond basis, right now the animal is trying to move at this precise angle," Pandarinath explains. "That's exactly what we need to know to control a robotic arm."

His work is just one example of the growing interaction between AI and cognitive science. AI, with its ability to identify patterns in large, complex data sets, has seen remarkable successes in the past decade, in part by emulating how the brain performs certain computations. Artificial neural networks that are analogous to the networks of neurons that comprise the brain have given computers the ability to distinguish an image of a cat from one of a coconut, to spot pedestrians with enough accuracy to direct a self-driving car, and to recognize and respond to the spoken word. Now, cognitive science is beginning to benefit from the power of AI, both as a model for developing and testing ideas about how the brain performs computations, and as a tool for processing the complex data sets that researchers such as Pandarinath are producing. "The technology is coming full circle and being applied back to understand the brain," he says. That cycle of mutual reinforcement is likely to continue. As AI enables neuroscientists to obtain further insights into how computation works in the brain, the effort might lead to machines that can take on more human-like intelligence.

It's only natural that the two disciplines would fit together, says Maneesh Sahani, a theoretical neuroscientist and machine-learning researcher at the Gatsby Computational Neuroscience Unit at University College London. "We're effectively studying the same thing. In the one case, we're asking how to solve this learning problem mathematically so it can be implemented efficiently in a machine. In the other case, we're looking at the sole existing proof that it can be solved - which is the brain."

A brain analogue

The successes of AI owe much to the arrival of more powerful processors and ever-growing quantities of training data. But the concept that underlies these advances is the artificial neural network. These networks consist of layers of nodes that are analogous to neurons. Nodes in the input layer are connected to nodes in a hidden layer by a series of mathematical weights that act like the synapses between neurons. The hidden layer is similarly connected to an output layer. Input data for a task such as facial recognition could be an array of numbers that describe each pixel in an image of a face in terms of where it falls on a 100-point scale from white to black, or whether it is red, green or blue. Data are fed in, the hidden layer then multiplies those values by the weights of the connections, and an answer comes out. To train the system to produce the correct answer, this output is compared with what it should have been if the output were an exact match for the input, and the difference is used to adjust the weights between the nodes. A more complex version of this process, called a deep neural network, has many hidden layers. It's this kind of system that London-based AI research company DeepMind Technologies, which is owned by Google's parent company, Alphabet, used to build the computer that beat a professional human player at the board game Go in 2015 - a victory widely hailed as a triumph for machine intelligence.

An artificial neural network is only a rough analogy of how the brain works, says David Sussillo, a computational neuroscientist with the Google Brain Team in San Francisco, California, who collaborated with Pandarinath on his work on latent factors. For instance, it models synapses as numbers in a matrix, when in reality they are complex pieces of biological machinery that use both chemical and electrical activity to send or terminate signals, and that interact with their neighbours in dynamic patterns. "You couldn't get further from the truth of what a synapse actually is than a single number in a matrix," Sussillo says.

Nonetheless, artificial neural networks have proved useful for studying the brain. If such a system can produce a pattern of neural activity that resembles the pattern that is recorded from the brain, scientists can examine how the system generates its output and then make inferences about how the brain does the same thing. This approach can be applied to any cognitive task of interest to neuroscientists, including processing an image. "If you can train a neural network to do it," says Sussillo, "then perhaps you can understand how that network functions, and then use that to understand the biological data."

Dealing with data

AI techniques come in handy not just for making models and generating ideas, but as a tool for handling data. "Neural data are terribly complicated, and so often we will be using techniques from machine learning simply in order to look for structure," Sahani says. Machine learning's main strength lies in recognizing patterns that might be too subtle or too buried in huge data sets for people to spot.

Functional magnetic resonance imaging, for example, generates snapshots of activity throughout the brain at a resolution of 1-2 millimetres every second or so, potentially for hours. "The challenge of cognitive neuroscience is how you find the signal in images that are very, very large," says Nicholas Turk-Browne, a cognitive neuroscientist at Yale University in New Haven, Connecticut. Turk-Browne is leading one of several projects that are looking for fresh insights at the intersection of data science and neuroscience.

Using a machine to analyse these data is speeding up the research. "It's a huge change in how neuroscience is done," Sussillo says. "The grad students don't need to do as much sort of mindless work - they can focus on bigger questions. You can automate a lot of it, and you may get more accurate results."

Reproducing senses

Building an artificial system that would reproduce brain data was the approach taken by Daniel Yamins, a computational neuroscientist at the Wu Tsai Neurosciences Institute at Stanford University in California. In 2014, while Yamins was a postdoctoral researcher at the Massachusetts Institute of Technology in Cambridge, he and his colleagues trained a deep neural network to predict the brain activity of a monkey when it was recognizing certain objects. Object recognition in humans and monkeys is performed by a brain system called the ventral visual stream, which has two main architectural features. First, it is retinotopic, which means that the visual-processing pathways in the brain are organized in a way that reflects how the eye takes in visual information. Second, the system is hierarchical; specific areas of the cortex perform increasingly complex tasks, from a layer that identifies only the outlines of objects to a higher one that recognizes a whole object, such as a car or a face. The details of how the higher layers work are poorly understood, but the result is that the brain can recognize an object in various positions and under different lighting conditions, when it seems bigger or smaller on the basis of its distance, and even when it is partially hidden. Computers are often flummoxed by such obstacles.

Yamins and his colleagues constructed their deep neural network according to the same retinotopic, hierarchical architecture as the brain and showed it thousands of images of 64 objects that varied in characteristics such as their size and position. As the network learnt to recognize the objects, it produced several possible patterns of neural activity. The researchers then compared these computer-generated patterns with patterns recorded from the neurons of monkeys while they performed a similar task. It turned out that the versions of the network that were best at recognizing objects were the ones with patterns of activity that most closely matched those of the monkey brain. "What you find is that the structure of the neurons is mimicked in the structure of the network," Yamins says. The researchers were able to match areas of their network to areas of the brain with about 70% accuracy.

The results confirmed that the architecture of the ventral visual stream is important for its processing ability. In 2018, Yamins and his colleagues performed a similar feat using the auditory cortex, in which they created a deep neural network that was able to identify words and genres of music from 2-second clips with the same accuracy as a human. It helped researchers to identify which areas of the cortex perform speech recognition and which recognize music - a small step towards understanding the auditory system.

Neuroscientists are still a long way from understanding how the brain goes about a task such as distinguishing jazz from rock music, but machine learning does give them a way of constructing models with which to explore such questions. If researchers can design systems that perform similarly to the brain, Yamins says, their design can inform ideas about how the brain solves such tasks. That's important, because scientists often don't have a working hypothesis for how the brain operates. Making a machine perform a particular task will give them at least one possible explanation for how the brain achieves the same thing.

After researchers have built a hypothesis, the next step is to test it. Once again, AI models can help, by providing a representation of brain activity that can be tweaked to see which factors might be important in accomplishing a specific task. Researchers are limited by ethical considerations in terms of how much they can intervene in processes in the healthy human brain, so many recordings of neural activity in people come from the brains of those with epilepsy who are due to have brain tissue removed. This is because it is permissible to implant electrodes in brain tissue that will be excised anyway. Animal models enable researchers to use more invasive procedures, but there are human behaviours, notably speech, that cannot be replicated in other species. AI systems that can mimic human behaviour and be perturbed without ethical problems will provide scientists with extra tools for exploring how the brain works: researchers could teach a network to reproduce speech, and then impair that speech to observe what happens, for instance.

Common concerns

Computer science and cognitive science are tackling some big questions, and working out how to answer them in either of these fields could drive both forwards. One such question is exactly how learning occurs. Neural networks mostly perform supervised learning. To master image recognition, for example, they might be shown images from ImageNet, a database of more than 14 million photographs of objects that have been categorized and annotated by people. The networks develop a statistical understanding of what images with the same label - 'cat', for instance - have in common. When shown a new image, the networks examine it for similar numerical attributes; if they find a match, they will declare the image to be that of a cat.

That's obviously not how babies learn, says Tomaso Poggio, a computational neuroscientist at the Center for Brains, Minds and Machines, which is part of the Massachusetts Institute of Technology. "A baby sees something on the order of a billion images in the first two years of life," he says. But few of these images are labelled - only a small proportion of objects will be actively pointed out and named. "We don't know how to deal with that," says Poggio. "We don't know how to have machines that learn from mostly unlabelled data."

His laboratory is in the initial stages of a project that would enable a neural network to perform unsupervised learning, by inferring patterns from unlabelled videos. "We know biology can do that," Poggio says. "The question is how."

Yamins is tackling unsupervised learning by devising programs that behave like babies at play, who interrogate their environment through random interactions and slowly develop an understanding of how the world works. He essentially codes in curiosity to motivate the computer to explore, in the hope that new behaviours will emerge.

Another outstanding question is whether some aspects of intelligence are 'installed' by evolution. For instance, people seem to be predisposed to recognizing a face as a face; babies can do so from the first hours of life. It might be, Poggio suggests, that our genes encode a mechanism for learning that task quickly and early in development. Deciphering whether that idea is correct could enable computer scientists to work out one way to help machines to learn. And other researchers are studying the neural basis of morality. "People are afraid of 'evil' machines," Poggio says. "We'd probably better know how our moral behaviour arises if we want to build good machines, ethical machines."

Yamins says that it is difficult to see how neuroscience alone will be able to uncover how unsupervised learning works. "If you don't have an AI solution, if you have nothing that works artificially, you can't possibly have a model of the brain," he says. It's more probable, he thinks, that computer scientists will come up with one or more solutions that neuroscientists can then test. "It might turn out that they're wrong," he says, "but that's why you check them out."

Answering these riddles could create more intelligent machines that are capable of learning from their environments and that can combine the speed and processing power of computers with more human abilities. The data-crunching and modelling abilities of computers are already bringing about advances in brain science that researchers say are likely to grow. "AI is going to have a huge impact on neuroscience," Sussillo says, "and I want to be a part of that."

# data[].attributes.attachment_link.title
How AI and neuroscience drive each other forwards

# data[].attributes.attachment_link.description
Chethan Pandarinath wants to enable people with paralysed limbs to reach out and grasp with a robotic arm as naturally as they would their own. To help him meet this goal, he has collected recordings of brain activity in people with paralysis.

# data[].attributes.body
The article From Brain Science to Artificial Intelligence discusses the connection between the two subjects.  First, the paper talks about the immeasurable gap between human intelligence and Ai. Then the article talks about the origin of AI development and how it was created to simulate the human brain. The list of many things that the computer has already been able to manufacture and other items is still too far away for it to achieve. Next, the authors go over some projects that have been conducted on the brain. Finally, they talk about instrumental bridges between brain research and artificial intelligence.
The journal links brain research with the main idea of essay. It also includes information on brain memory and thinking.
"The working memory that was discovered from functional magnetic resonance imaging (fMRI) results inspired the memory module in machine learning models that led to the development of long short-term memory (LSTM).”

# data[].attributes.attachment_link.readable_text
Abstract

Reviewing the history of the development of artificial intelligence (AI) clearly reveals that brain science has resulted in breakthroughs in AI, such as deep learning. At present, although the developmental trend in AI and its applications has surpassed expectations, an insurmountable gap remains between AI and human intelligence. It is urgent to establish a bridge between brain science and AI research, including a link from brain science to AI, and a connection from knowing the brain to simulating the brain. The first steps toward this goal are to explore the secrets of brain science by studying new brain-imaging technology; to establish a dynamic connection diagram of the brain; and to integrate neuroscience experiments with theory, models, and statistics. Based on these steps, a new generation of AI theory and methods can be studied, and a subversive model and working mode from machine perception and learning to machine thinking and decision-making can be established. This article discusses the opportunities and challenges of adapting brain science to AI.

# data[].attributes.attachment_link.title
From Brain Science to Artificial Intelligence

# data[].attributes.attachment_link.description
Reviewing the history of the development of artificial intelligence (AI) clearly reveals that brain science has resulted in breakthroughs in AI, such ...

# data[].attributes.body
Discusses where AI accelerates compared to humans in the stock market. Computers are better at taking in large amounts of data and analyzing it to determine large amounts of trades. Although it could be seen as problematic using such programs if the market had not had time to react. This supports my claim because it shows that computers already perform better than people at certain tasks. It may even affect certain jobs at one point. "AI is already superior to human intelligence in some skills important to trading. It is certainly much quicker, completing complex analyses and executing vast numbers of trades in fractions of a second. This can be viewed as cheating if traders are able to take advantage of information about trades by large institutional investors before the market has had time to react to them. Nonlinear sensitivities to change and interaction between programs can also cause problems. "

# data[].attributes.attachment_link.title
The Path to More General Artificial Intelligence

# data[].attributes.body
Ways of measuring with instrumentation systems are improving, allowing you to get better precisions. Artificial intelligence allows for these tools to improve and creates more. This also demonstrates how artificial intelligence can improve everyday things. While also help developed specialized tools. "Changes in analog systems to digital ones increasingly improve the precision of instrumentation systems. The development of artificial intelligence (AI) adds the complexity of instrumentation systems but presents smarter ones and opens wide opportunities for more specialized use and autonomous instrumentation."

# data[].attributes.attachment_link.title
Topics and Trends in Artificial Intelligence Assisted Human Brain Research.

# data[].attributes.body
Can a computer think the same way a human can? I don't think they can yet, but they almost certainly will in the future with more funding and time.

# data[].attributes.body
The book was to explains who people are behind their physicals selves. It explains why many people do certain things and the brain functions behind them. Helps compare many ideas about the brain to computers. “Rather than memory being an accurate video recording of a moment in your life, it is a fragile brain state from a bygone time that must be resurrected for you to remember.”

# data[].attributes.attachment_link.description
Read 1,725 reviews from the world's largest community for readers. Locked in the silence and darkness of your skull, your brain fashions the rich narrative...

# data[].attributes.body
The article Study of human brain yields intelligent robots - Technology - International Herald Tribune, written by John Markoff, was about technological advancements in artificial intelligence. The article starts by listing a few companies that have created different programs and machines to do new and impressive things. Next, the author writes how the studies in the brain have improved the development of AI. Finally, the author noted how artificial intelligence has also worked to improve brain research.  This essay could show the progress made so far in achieving thinking machines with many different examples as well as give more background information on the subject. “A half-century after the term was coined, both scientists and engineers say they are making rapid progress in simulating the human brain, and their work is finding its way into a new wave of real-world products.”

# data[].attributes.attachment_link.readable_text
Robot cars drive themselves across the desert, electronic eyes perform lifeguard duty in swimming pools and virtual enemies with human-like behavior battle video game players.

These are some of the fruits of the research field known as artificial intelligence. A half-century after the term was coined, both scientists and engineers say they are making rapid progress in simulating the human brain, and their work is finding its way into a new wave of real-world products.

The advances can also be seen in the emergence of bold new projects intended to create more ambitious machines that can improve safety and security, entertain and inform, or just perform everyday tasks. At Stanford University, for instance, computer scientists are developing a robot that can use a hammer and a screwdriver to assemble an Ikea bookshelf (a project beyond the reach of many humans) as well as tidy up after a party, load a dishwasher or take out the trash.

One pioneer in the field is building an electronic butler that could hold a conversation with its master - like HAL in the movie "2001: A Space Odyssey" - or order more pet food.

Though most of the truly futuristic projects are probably years from the commercial market, scientists say that, after a lull, artificial intelligence has rapidly grown far more sophisticated. Some scientists are beginning to use the term cognitive computing, to distinguish their research from an earlier generation of artificial intelligence work. What sets the new researchers apart is a wealth of new biological data on how the human brain functions.

"There's definitely been a palpable upswing in methods, competence and boldness," said Eric Horvitz, a Microsoft researcher who is president- elect of the American Association for Artificial Intelligence. "At conferences you are hearing the phrase 'human- level AI,' and people are saying that without blushing."

Cognitive computing is still more of a research discipline than an industry that can be measured in revenue or profit. It is pursued in various pockets of academia and the business world. And despite some of the more dramatic achievements, improvements in the field are measured largely in increments - voice recognition systems with decreasing failure rates, or computerized cameras that can recognize more faces and objects than before.

Artificial intelligence had its origins in 1950, when the mathematician Alan Turing proposed a test to determine whether a machine could think. The test involved having a person face two computer terminals, only one of which had a human behind it. If the human judge could not tell which terminal was controlled by the human, the machine could be said to be intelligent.

In the late 1950s a field of study emerged that attempted to build systems replicating human capabilities like speech, hearing, manual tasks and reasoning.

During the 1960s and 1970s, the original artificial intelligence researchers began designing computer software programs they called "expert systems," which were essentially databases accompanied by a set of logical rules. They were handicapped both by underpowered computers and by the absence of the wealth of data that researchers have now amassed about the structure and function of the biological brain.

Those shortcomings led to the failure of a first generation of artificial intelligence companies during the 1980s, which became known as the "AI Winter." Recently, however, researchers have begun to speak about an AI Spring, which is emerging from the work of scientists putting forth theories on the workings of the human mind. They are being aided by the exponential increase in processing power, which has created computers millions of times more powerful than those available to researchers in the 1960s - at consumer prices.

"There is a new synthesis of four fields, including mathematics, neuroscience, computer science and psychology," said Dharmendra Modha, a computer scientist at International Business Machines. "The implication of this is amazing. What you are seeing is that cognitive computing is at a cusp where it's knocking on the door of potentially mainstream applications."

The advances have spurred rapid innovations in many areas: Robot cars can now drive autonomously for hundreds of miles, voice control systems are standard features in mid-priced automobiles, and advanced artificial reasoning techniques are routinely used in inexpensive video games to make the characters' actions more lifelike.

A French company called Poseidon Technologies sells underwater vision systems for swimming pools that function as lifeguard assistants, issuing alerts when people are drowning, and the system has saved lives in Europe.

Last October, a robot car designed by a team of Stanford engineers covered 132 miles, or 210 kilometers, of desert road without human intervention to capture a $2 million prize offered by the Pentagon's Defense Advanced Research Projects Agency. The feat was particularly striking because 18 months earlier, during the first such competition, the best vehicle got no farther than seven miles, getting stuck after driving off a mountain road.

Now the Pentagon agency has raised the stakes: Next year the robots will be back on the road, this time in traffic. It is being called the "urban challenge."

At Microsoft, researchers are working on the idea of "predestination." They envision a software program that guesses where you are traveling based on previous trips, and then offers information that might be useful based on where the software thinks you are going.

Tellme Networks, a company in Mountain View, California, that provides voice recognition services for both customer service and telephone directory applications, is a good indicator of the progress that is being made in relatively constrained situations, like looking up a phone number or transferring a call.

The company supplies the system that automates directory information for toll-free business listings. When the service was first introduced in 2001, it could correctly answer fewer than 37 percent of phone calls without a human operator's help. As the system has been refined, the figure has now risen to 74 percent.

Machine intelligence is also now being deployed to map the human brain - with the findings, in turn, helping to build better computers.

Researchers at the École Polytechnique Fédérale de Lausanne in Switzerland are building large-scale computer models to study how the brain works; they have used an IBM parallel supercomputer to create the most detailed three-dimensional model to date of a column of 10,000 neurons in the neocortex.

"The goal of my lab in the past 10 to 12 years has been to go inside these little columns and try to figure out how they are built with exquisite detail," said Henry Markram, a research scientist who is head of the project, called Blue Brain. "You can really now zoom in on single cells and watch the electrical activity emerging."

Blue Brain researchers say they believe the simulation will provide fundamental insights that can be applied by scientists who are attempting to simulate brain functions.

Another well-known researcher is Robert Hecht-Nielsen, who is seeking to build an electronic butler called Chancellor that would be capable of listening, speaking and providing in-home concierge services. He contends that, with adequate resources, he could create such a machine within five years.

In 1986, Hecht-Nielsen founded HNC Software, which sold systems to detect credit card fraud using neural network technology, which was designed to mimic biological circuits in the brain. HNC was sold in 2002 to Fair Isaac Corp., where Hecht-Nielsen is a vice president and leads a small research group.

Last year he began speaking publicly about his theory of "confabulation," a hypothesis about the way the brain makes decisions. At a recent IBM symposium, Hecht-Nielsen showed off a model of confabulation, demonstrating how his software program could read two sentences from The Detroit Free Press and create a third sentence that both made sense and was a natural extension of the previous text.

For example, the program read: "He started his goodbyes with a morning audience with Queen Elizabeth II at Buckingham Palace, sharing coffee, tea, cookies and his desire for a golf rematch with her son, Prince Andrew. The visit came after Clinton made the rounds through Ireland and Northern Ireland to offer support for the flagging peace process there."

The program then generated a sentence that read: "The two leaders also discussed bilateral cooperation in various fields."

At Stanford, meanwhile, researchers are hoping to make fundamental progress in mobile robotics, building machines that can carry out tasks around the home, like the current generation of robotic floor vacuums but more advanced. The field has recently been dominated by Japan and South Korea, but the Stanford researchers have sketched out a three-year plan to bring the United States to parity.

At the moment, the Stanford team is working on the first steps necessary to make the robot they are building feel comfortable in an American household - a prerequisite, presumably, to proficiency in Ikea assembly. The team is focusing on systems that will consistently recognize standard doorknobs and building robot hands that can open doors.

"It's time to build an AI robot," said Andrew Ng, a Stanford computer scientist and a leader of the project called the Stanford Artificial Intelligence Robot, or Stair. "The dream is to put a robot in every home."

# data[].attributes.attachment_link.title
Study of human brain yields intelligent robots - Technology - International Herald Tribune (Published 2006)

# data[].attributes.attachment_link.description
Robot cars drive themselves across the desert, electronic eyes perform lifeguard duty in swimming pools and virtual enemies with human-like behavior battle video game players. These are some of the fruits of the research field known as artificial intelligence.