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Title: Can emotion also apply to Artificial Intelligence and Everyday Technology?

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Title: Can emotion also apply to Artificial Intelligence and Everyday Technology?

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You would be forgiven for thinking that your private conversations were just that, but two leading voice assistants are listening to everything you say, a new report claims.

Patent applications from Amazon and Google revealed how their Alexa and Voice Assistant powered smart speakers are 'spying' on you.

The study warns of an Orwellian future in which the gadgets eavesdrop on everything from confidential conversations to your toilet flushing habits.

Future versions of gadgets like the Echo and Home will use this data to try and sell you products, it says.

The findings were published in a report created by Santa Monica, California based advocacy group Consumer Watchdog.

It says patents reveal the devices' possible use as surveillance equipment for massive information collection and intrusive digital advertising.

The study found that digital assistants can be 'awake' even when users think they aren't listening.

The digital assistants are supposed to react only when they hear a so-called 'wakeword.'

For Amazon's Echo it's 'Alexa' and for Google Home it's 'OK, Google.'

In fact, the devices listen all the time they are turned on - and Amazon has envisioned Alexa using that information to build profiles on anyone in the room to sell them goods.

Amazon filed a patent application for an algorithm that would let future versions of the device identify statements of interest, such as 'I love skiing', enabling the speaker to be monitored based on their interests and targeted for related advertising.

A Google patent application describes using a future release of it smart Home system to monitor and control everything from screen time and hygiene habits, to meal and travel schedules and other activities.

The devices are envisioned as part of a surveillance web in the home to chart a families' patterns so that they can more easily be marketed to based on their interests.

John Simpson, Consumer Watchdog's privacy and technology project director, said: 'Google and Amazon executives want you to think that Google Home and Amazon Echo are there to help you out at the sound of your voice.

'In fact, they're all about snooping on you and your family in your home and gathering as much information on your activities as possible.

'You might find them useful sometimes, but think about what you're revealing about yourself and your family, and how that information might be used in the future.

'Instead of charging you for these surveillance devices, Google and Amazon should be paying you to take one into your home.'

MailOnline contacted both Amazon and Google for a comment, but had yet to receive a response at the time of publication.

Google and Amazon appear most interested in using the data they get by snooping on your daily life to target advertising, Consumer Watchdog said.

However, when that information is compiled others could access it.

For example, home insurers and utility companies have already made deals with Nest to put smart devices in their customers' homes.

Law enforcement is already seeking information from smart devices.

An Amazon Echo made headlines last year when US police investigating a murder sought to subpoena recordings made by the device.

Investigators in the same case also managed to obtain data from a smart water meter that suggested that the crime scene had been hosed down before police arrived.

Hackers and identity thieves are also likely to be able to access the data compiled by Google and Amazons snooping, the report warns.

This is not the first time that the voice search function has landed Google in hot water in recent months.

In November, MailOnline received a number of transcripts of conversations that show how Voice Assistant may be recording your conversations without you knowing.

The feature is designed to allow users to talk to enabled gadgets to search the web, launch apps and use other interactive functions.

As part of this process, Google keeps copies of clips made each time you activate it, but it has emerged that background chatter could be enough to trigger recording.

One example from an anonymous user appears to have registered the code to their back door entry system, while chatting with a friend.

A written transcript of the conversation said: 'If you ever get booked down to my house for some reason the key safe for the back door is 0783.'

Another user's conversation about technology appears to have been captured, without them realising the assistant was recording.

They said: 'Mate. We're living in the future. I've just installed a game through the Steam app remotely on my PC in London from my phone.'

Another clip from the same person appears to have captured them saying 'F*** off' to someone.

Google previously released a My Activity feature that reveals exactly how much information the company has collected about you, through your activities online.

What some people may be unaware of is that the Voice and Audio section includes recordings of your voice.

These are made when you trigger the voice assistant, which may happen inadvertently during conversations or by pressing buttons on a Voice Assistant enabled device without realising it.

A spokesman for the firm said: 'We only process voice searches after the phone believes the hot word 'OK Google' is detected.

'Audio snippets are used by Google to improve the quality of speech recognition across Search.'

They added that ambient recording is never transmitted to the cloud.

Google's support site says that the firm records your voice and other audio, plus a few seconds before, when you use audio activation.

This includes saying commands like 'OK Google' or tapping the microphone icon.

Your audio is saved to your account only when you're signed in and Voice & Audio Activity is turned on. Audio can be saved even when your device is offline.

The Mountain View company says it uses your Voice & Audio Activity to learn the sound of your voice and how you say words and phrases.

It is also used to improve speech recognition across it products.

To see your saved audio, sign in with your Google account information.

This will enable you to see all the information Google has stored on the history of your account.

To delete any, click on the three dots in the top right corner and choose 'Delete activity by'.

This will take you to a window where you can pick if you would like to delete any information. x

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How Google and Amazon are 'spying' on you

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You would be forgiven for thinking that your private conversations were just that, but two leading voice assistants are listening to everything you say, a new report claims. Patent applications from Amazon and Google revealed how their Alexa and Voice Assistant powered smart speakers are 'spying' on you.

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-> Article explains about how AI cannot have their own emotions, but can mimic them through their understanding.

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When John McCarthy and Marvin Minsky founded Artificial Intelligence in 1956, they were amazed how a machine could perform incredibly difficult puzzles quicker than humans. However, it turns out that teaching Artificial Intelligence to win a chess match is actually quite easy. What would present challenges would be teaching a machine what emotions are and how to replicate them.

"We have now accepted after 60 years of AI that the things we originally thought were easy, are actually very hard and what we thought was hard, like playing chess, is very easy"
Alan Winfield, Professor of robotics at UWE, Bristol,

Social and emotional intelligence come almost automatically to humans; we react on instinct. Whilst some of us are more perceptive than others, we can easily interpret the emotions and feelings of those around us. This base level intelligence, which we were partly born with and partly have learnt, tells us how to behave in certain scenarios. So, can this automatic understanding be taught to a machine?

Emotion AI

Although the name may throw you off, Emotion AI does not refer to a weeping computer who has had a bad week. Emotion AI, also known as Affective Computing dates back to 1995 and refers to the branch of Artificial intelligence which aims to process, understand, and even replicate human emotions. The technology aims to improve natural communication between man and machine to create an AI that communicates in a more authentic way. If AI can gain emotional intelligence maybe it can also replicate those emotions.

How can [a machine] effectively communicate information if it doesn't know your emotional state, if it doesn't know how you're feeling, it doesn't know how you're going to respond to specific content"
Javier Hernandez, a research scientist with the Affective Computing Group at the MIT Media Lab,

In 2009, Rana el Kaliouby, and Picard founded Affectiva, an emotion AI company based in Boston, which specializes in automotive AI and advertising research. With customer's consent, the user's camera captures their reactions while watching an advertisement. Using " Multimodal emotion AI ", which analyses facial expression, speech, and body language, they can gain a complete insight into the individual's mood.

Their 90% accuracy levels are thanks to their diverse test-sets of 6 million faces from 87 different countries used to train deep learning algorithms. From a diverse data set, the AI will learn which metrics of body language and speech patterns coincide with difference emotions and thoughts.

As with humans, machines can produce more accurate insights into our emotions from video and speech than just text. Sentiment analysis or opinion mining, a sub field of N atural Language Processing is the process of algorithmically identifying and categorizing opinions expressed in text to determine the user's attitude toward the subject. This use case can be applied in many sectors such as Think tanks, Call centres, Telemedicine, Sales, and Advertising to take communication to the next level.

Whilst AI might be able to categorize what we say into positive or negative boxes, does it truly understand how we feel or the sub text beneath? Even as humans we miss cultural references, sarcasm and nuance in language which completely alter the meaning and therefore the emotions displayed. Sometimes it is the things we leave out and don't say which can also imply how we are feeling. AI is not sophisticated enough to understand this subtext and many doubt if it ever will.

Can AI show emotion?

In many of these use cases, such as Telemedicine Chatbots and Call Centre virtual assistants, companies are investigating the development of Emotion AI to not only understand customers emotions but to improve how these platforms individually respond. Being able to simulate human like emotions gives these platforms and services more authenticity. But is this a true display of emotion?

AI and neuroscience researchers agree that current forms of AI cannot have their own emotions, but they can mimic emotion, such as empathy. Synthetic speech also helps reduce the robotic like tone many of these services operate with and emit more realistic emotion. Tacotron 2 by google is transforming the field to simulate humanlike artificial voices.

So, if machines, in many cases, can understand how we feel and produce a helpful, even 'caring' response, are they emotionally intelligent? There is much debate within this field if a simulation of emotion demonstrates true understanding or is still artificial.

Functionalism argues that if we simulate emotional intelligence then, by definition, AI is emotionally intelligent. But experts question whether the machine truly "understands" the message they are delivering and therefore a simulation isn't a reflection that the machine is actually emotionally intelligent.

Artificial General Intelligence

Developing an Artificial General Intelligence, which possesses a deeper level of understanding is how many experts believe machines can one day experience emotions as we do.

Artificial General Intelligence (AGI) opposed to Narrow intelligence refers to the ability of computers to carry out many different activities, like humans. Artificial Narrow intelligence as the name suggests aims to complete individual tasks but with a high degree of efficiency and accuracy.

When we talk about emotional and social intelligence, forms of intelligence which are not necessarily related to a set task or goal, these fall under Artificial General Intelligence. AGI aims to replicate our qualities which to us, seem automatic. They are not tied to an end goal, we do them just because we do.

Conclusions

We are still many years behind having an Artificial General Intelligence capable of replicating every action we can perform, especially those qualities which we consider most human, such as emotions.

Emotions are inherently difficult to read and there is often a disconnect between what people say they feel and what they actually feel. A machine may never get to this level of understanding but who is to say how we process emotions is the only way. How we interpret each other's emotions is full of bias and opinion, so maybe AI can help us get straight to the point when it comes to our emotions.

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Can Artificial Intelligence understand emotions? - Think Big

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When John McCarthy and Marvin Minsky founded Artificial Intelligence in 1956, they were amazed how a machine could perform incredibly difficult puzzles quicker than humans. However, it turns out that teaching Artificial Intelligence to win a chess match is actually quite easy. What would present challenges would be teaching a machine what emotions are and ...

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-> The text explains about how when feeling a certain emotion, humans are capable of performing actions which they wouldn't usually - AI and software is only programmed to be capable of so much, so wouldn't have this reaction and therefore wouldn't be effected by emotion

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What do people really feel?

This has never been an easy thing for companies to determine. For one thing, emotions are inherently difficult to read. For another, there's often a disconnect between what people say they feel and what they actually feel.

Consider how people respond to Super Bowl commercials. In 2018, TV viewers voted Amazon's " Alexa Loses Her Voice" - where celebrities attempt to (unsuccessfully) replace Alexa - as the best commercial, according to the USA Today Ad Meter. Diet Coke's " Groove," which featured a woman dancing awkwardly after drinking a can of Diet Coke Twisted Mango, was rated as the worst commercial. Based on this poll, one might conclude that the Alexa commercial had the bigger impact. Not so, according to Paul Zak, neuroscience researcher and chief executive officer of Immersion Neuroscience, whose team studied people's neurologic immersion in the ads. Zak's team assessed viewers' level of emotional engagement by measuring changes in oxytocin levels, the brain's "neural signature of emotional resonance." The research found that "Groove" actually had greater impact - proof to Zak that for Super Bowl commercials, there is "zero correlation" between what people say and how they subconsciously feel.

When we interviewed Zak about this phenomenon, he summed it up by saying: "People lie, their brains don't."

A lot of companies use focus groups and surveys to understand how people feel. Now, emotional AI technology can help businesses capture the emotional reactions in real time - by decoding facial expressions, analyzing voice patterns, monitoring eye movements, and measuring neurological immersion levels, for example. The ultimate outcome is a much better understanding of their customers - and even their employees.

The Risks of Bias in Emotional AI

Because of the subjective nature of emotions, emotional AI is especially prone to bias. For example, one study found that emotional analysis technology assigns more negative emotions to people of certain ethnicities than to others. Consider the ramifications in the workplace, where an algorithm consistently identifying an individual as exhibiting negative emotions might affect career progression.

AI is often also not sophisticated enough to understand cultural differences in expressing and reading emotions, making it harder to draw accurate conclusions. For instance, a smile might mean one thing in Germany and another in Japan. Confusing these meanings can lead businesses to make wrong decisions. Imagine a Japanese tourist needing assistance while visiting a shop in Berlin. If the shop used emotion recognition to prioritize which customers to support, the shop assistant might mistake their smile - a sign of politeness back home - as an indication that they didn't require help.

In short, if left unaddressed, conscious or unconscious emotional bias can perpetuate stereotypes and assumptions at an unprecedented scale.

How Businesses Can Prevent Bias from Seeping Into Common Use Cases

Based on our research and experience working with global clients, we see businesses using emotional AI technology in four ways. Through each, the implications of algorithmic bias are a clear reminder that business and technology leaders must understand and prevent such biases from seeping in.

Understanding how emotionally engaged employees actually are. When AI is used to gauge employee emotions, it can have serious impacts on how work is allocated. For example, employees often think they're in the right role, but upon trying new projects might find their skills are better aligned elsewhere. Some companies are already allowing employees to try different roles once a month to see what jobs they like most. Here's where bias in AI could reinforce existing stereotypes. For example, in the U.S., where 89% of civil engineers and 81% of first-line police and detective supervisors are male, an algorithm that has been conditioned to analyze male features might struggle to read emotional responses and engagement levels among female recruits. This could lead to flawed role allocation and training decisions.

Improving the ability to create products that adapt to consumer emotions. With emotion tracking, product developers can learn which features elicit the most excitement and engagement in users. Take, for example, Affectiva's Auto AI platform, which can recognize emotions like joy and anger and adapt a vehicle's in-cabin environment accordingly. Cameras and microphones can pick up on passenger drowsiness - and may lower the temperature or jolt the seatbelt as a result. A smart assistant might change its tone in response to a frustrated passenger. With emotional AI, any product or service - whether in the car or elsewhere - can become an adaptive experience. But a biased adaptive in-cabin environment could mean that some passengers are misunderstood. Elderly people, for example, might be more likely to be wrongly identified as having driver fatigue (the older the age of the face, the less likely it is that expressions are accurately decoded). And as these systems become more commonplace, insurance companies are going to want a piece of the data. This could mean higher premiums for older people, as the data would suggest that, despite many prompts to rest, the driver pressed on.

Improving tools to measure customer satisfaction. Companies like Boston-based startup Cogito are giving businesses the tools to help their employees interact better with customers. Its algorithms can not only identify "compassion fatigue" in customer service agents, but can also guide agents on how to respond to callers via an app. An upset customer might, for example, call to complain about a product. Recording and analyzing the conversation, Cogito's platform would then suggest that the agent slow down or prompt them on when to display empathy. A biased algorithm, perhaps skewed by an accent or a deeper voice, might result in some customers being treated better than others - pushing those bearing the brunt of bad treatment away from the brand. A male caller could be subject to less empathy than a woman, reinforcing societal perceptions of men as "emotionally strong." On the flip side, a female caller may be viewed as a less tough negotiator, resulting in less compensation being offered. Ironically, the agents themselves may not even possess these biases, but clouded by the misconception that the algorithms are highly accurate, they may follow their advice blindly. In this way, biases spread, unquestioned and systematically.

Transforming the learning experience. Emotional insights could be used to augment the learning experience across all ages. It could, for example, allow teachers to design lessons that spur maximum engagement, putting key information at engagement peaks and switching content at troughs. It also offers insights into the students themselves, helping to identify who needs more attention. China is already introducing emotion detection systems into classrooms to track how focused students are. But, if biases exist, wrongly suggesting someone is disengaged could result in learning experiences tailored toward certain groups rather than others. Think about different learning styles: Some people are visual learners. Some learn by doing. Others favor intense solitary concentration. But an algorithm, perhaps designed by a visual learner, might completely miss or misinterpret such cues. Incorrect engagement readings could affect learning outcomes all the way to the workplace, meaning that even in work training programs, only a fraction of employees can enjoy full professional development. Such misassumptions could affect learning outcomes all the way to the workplace, meaning that even in work training programs, only a fraction of employees can enjoy full professional development.

Avoiding Bias in AI

As more and more companies incorporate emotional AI in their operations and products, it's going to be imperative that they're aware of the potential for bias to creep in and that they actively work to prevent it.

Whether it is the subjective nature of emotions, or discrepancies in emotions, it is clear that detecting emotions is no easy task. Some technologies are better than others at tracking certain emotions, so combining these technologies could help to mitigate bias. In fact, a Nielsen study testing the accuracy of neuroscience technologies such as facial coding, biometrics, and electroencephalography (EEG) found that when used alone, accuracy levels were at 9%, 27%, and 62% respectively. When combined, accuracy levels shot up to 77%. Testing the results with a survey brought this up to 84%. Such combinations therefore serve as a check on the accuracy of results - a referencing system of sorts.

But accounting for cultural nuances in algorithms will take more than just combining and referencing multiple technologies. Having diverse teams creating emotional AI algorithms will be crucial to keeping bias at bay and fully capturing the complexity of emotions. This means not just gender and ethnic diversity, but also diversity in socioeconomic status and views - negating anything from xenophobia to homophobia to ageism. The more diverse the inputs and data points, the more likely it is that we'll be able to develop AI that's fair and unbiased.

Companies will also need to be vigilant about not perpetuating historical biases when training emotional AI. While historical data might be used as a basis to train AI on different emotional states, real-time, live data will be needed for context. Take smiles, for example. One study showed that of the 19 different types of smile, only six happen when people are having a good time. We also smile when we are in pain, embarrassed, and uncomfortable - distinctions that can only be drawn with context.

In sum, emotional AI will be a powerful tool indeed, forcing businesses to reconsider their relationships with consumers and employees alike. It will not only offer new metrics to understand people, but will also redefine products as we know them. But as businesses foray into the world of emotional intelligence, the need to prevent biases from seeping in will be essential. Failure to act will leave certain groups systematically more misunderstood than ever - a far cry from the promises offered by emotional AI.
The authors would like to thank Accenture Research colleagues Xiao Chang, Paul Barbagallo, Dave Light, and H. James Wilson for their significant contributions to this article.

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The Risks of Using AI to Interpret Human Emotions

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What do people really feel? This has never been an easy thing for companies to determine. For one thing, emotions are inherently difficult to read. For another, there's often a disconnect between what people say they feel and what they actually feel. Consider how people respond to Super Bowl commercials.

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-> Could be a BIG speaking point in the essay about how advanced AI can grow to feel emotion from the way it is used or its surroundings.

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In the remake of the Child's Play movie, Chucky is now an AI Doll for children. However, as the film goes on and he begins to become more violent and graphic, we see that a lot of his actions and emotions are copied from his owner, Andy. One example is when he feels jealousy after Andy calls somebody else his best friend, not Chucky.

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-> This article talks about how AI learns about us and copies us, and therefore learns what emotions we feel and actions it takes (such as recommending things,) can be effected by the emotions we represent, therefore, it could show empathy.

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The difference between AI and humans is how they express theirs.

To understand the statement, we must be on the same page regarding what are human emotions. I like the simple explanation from The American Museum of Natural History:

"Your brain gets information from two different sources: Your senses tell you what's going on in the outside world, while your emotions exist inside your body to tell you what these events and circumstances mean to you. Just as hunger motivates you to find food, emotions motivate you to take care of other needs - like safety and companionship - that ultimately promote survival and reproduction." Here is the link to the full document.

There is nothing magical about human emotions. What we feel is caused by a series of neurotransmitters and other chemicals produced by the brain which activate different parts of the body to make us act and think in a certain way. Most emotions are useful in survival situations (fear, anger, disgust, sadness...). Others, like joy, drive us toward certain objectives, pleasures and so on. Emotions are what moves us. That is their purpose.

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Our senses and our memories inform the brain how we should feel at one moment or the next and when we feel an emotion, we act upon it. In an article I wrote in "The Startup", I describe how we everything we do is to either feel joy or avoid discomfort.

That's our most basic programming in its simplest definition.
A.I. emotions
Artificial intelligence doesn't have a brain like we do. In fact, most A.I. don't have a body. A.I. is software running on a computer somewhere. Occasionally, A.I. runs on a computer connected to actuators of some sort, commonly known as a "robot". Formless A.I. are often unnoticed and operate behind the scenes like the artificial intelligence behind Google Search, Amazon store selections and sophisticated data analysis programs.

Not all A.I. are equal either. For this article, I will focus only on artificial intelligence that can learn, i.e. deep learning A.I. Just because a software is smart and marketed as intelligent, doesn't mean it has emotions. Some are very crude and unidimensional, while others, like those using generalized adversarial networks (GAN) and other methods to make A.I. "smart" are quite sophisticated.

No matter the complexity of artificial intelligence, which one's have emotions?

Emotions, just like with humans and other animals, drive action. When A.I. is given a stimulus through its senses (cameras, LADAR, microphones and other sensors), it checks its memory (database of information) and then runs the appropriate program/algorithm that corresponds to what it has sensed and remembered. The activated program then produces a predictable result. With deep learning A.I., its actions sometimes surprise because it continually learns on its own from its experiences and the environment, constantly adding information to its memory and adjusting patterns of behavior. Hence, just like a human being, its memory changes, therefore the output of a triggered program changes. The program remains the same. It's the data that changes.

It sounds very similar to a human learning and adapting its behavior doesn't it? That's because it's how we learn too. Our "program" doesn't change, i.e. how our brain is wired, but our mind changes as we gain experience and memories.

Contrary to a human engineers create the programs that provide the results we want. Good A.I. programs will converge towards desirable results no matter what it gathers as new information in its memory. And following the human model, A.I. programs converge following a set of rules that grades the software depending on how it does in the world. When the A.I. does what it should be doing, it counts it as a win, when it fails, it tries to avoid that path in the future. That's the notion of convergence towards desired behavior.

This is also exactly how humans converge towards desired behavior. Our emotions drive us to seek joy and avoid discomfort. Like the deep learning A.I., this emotional dynamic helps us converge towards desirable outcomes.

We may not call A.I. software programs that drive action emotions, but the process is the same.
Fearing the Emotional Robot
We fear robots that can learn and improve because we assume they will behave like humans would. Thankfully, for now, engineers have been unsuccessful at creating an A.I. capable of emulating the full range of human emotions. One day, such a thing will exist, if nothing else but to satisfy our curiosity.

Humans feel joy or discomfort for plenty of reasons. Sometimes, a joy or discomfort felt by one person will put others in harm's way. These are the moments we loathe and fear in our society. Unfortunately, controlling the impulses of everyone is impossible. That's why we have laws, security and a penal system.

The good news is, programming A.I. with humanity-safe objectives and methods is entirely under our control. A.I. researchers have been working hard to figure out how to integrate goals into A.I. to make them safe and perceived optimistically by our society. Emotions and goals are important for deep learning A.I. because that's how the software can learn through challenges programmers cannot expect.

We don't want our self-driven cars to have strict behavioral software. What if it must make a choice, with our lives in their hands? We want them to decide on their own, improve and become better tools. This requires a program that can learn and adapt with clear objectives of keeping everyone safe and quickly getting us to our destination.

There is no need to program emotions like anger into self-driven cars because then we'd give them the motivation to fight to save their own lives (or ours). An "angry" self-driving car would hurt others to save itself and its occupants. It is not a desirable behavior. We want the cars to emulate emotions like fear. Fear would help protect itself and its occupants without aggressive movements and to drive away from danger instead of confronting it. The car feels nothing. It is just taking direction as programmed based on what it "sees" using its abundance of sensors and the data in its memory. That's the self-driving car deciding based on its programming and experience. That's its emotion.

Since most human emotions don't lead to harm towards others, having A.I. emulate those emotions for practical reasons, isn't an issue.

However, I object in giving A.I. emotions that may lead it to fight (like anger). The only A.I. application I can think of where simulating human anger can be useful is in robotic A.I. soldiers and weapons. Anger could help them destroy opposition more effectively. However, if these killer-A.I. learn from us, like any other A.I., I fear many innocents could get hurt. Thousands of high tech CEOs and organizations have signed a petition to the U.N. to ban the use of A.I. in autonomous weapons, for good reason.

We must strive to program our artificially intelligent tools so they may help us best, but we need to be careful what types of emotions we want them to feel and what goals are we giving them.

One day soon, we may live in a world with interactive A.I. that behave like animals or even humans, but with different emotional triggers and goals. If we do it right, none of those will have emotions that can harm anyone. In the face of danger, they may flee, defend others, drive away, or let their robotic bodies get damaged. They would do so not because they are courageous, but because they would feel their version of "joy" when doing so.

That's when we'll need to think about just how remarkable we are. Perhaps when we coexist with sophisticated A.I., we'll even need to redefine what it means to be sentient in this universe.

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The difference between AI and humans is how they express theirs. To understand the statement, we must be on the same page regarding what are human emotions.

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The use of emotion in AI is made into a parody in this Family Guy Sketch. We see one character, Brian, fall in love with the parody of Alexa, Brandy. They then go on to make the joke that 'Brandy doesn't like you, she just wants you to spend money on her,' which Brian ultimately gets repossessed for.

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Brian Falls in Love With Amazon Alexa - Family Guy

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JARVIS, The AI used in many of the MCU Movies, is apparently capable of feeling emotion. During the movie, Age of Ultron, JARVIS feels pain when Ultron decides to take over his system. He eventually becomes Vision, who is an artificial intelligence robot, who learns emotion through his surroundings.

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Looking at representation in media, one of the biggest examples of technology which can feel emotion is the MCU character, Vision. Although created by humans, and not programmed to have any emotion, after spending time in an environment surrounded by humans, he has eventually learned to feel love and compassion, which highlights the argument that technology can feel emotion in the correct place and growth.

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Scarlet Witch Kills Vision Scene - Vision Death Scene | Avengers Infinity War (2018) Movie CLIP 4K

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-> An article on the movie 'Her,' in which a human and an AI fall in love. The reviewer talks about how this could become a common occurrence in everyday life.

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Devotees of The Big Bang Theory may well recall that in episode 14 of Season 5 (The Beta Test Initiation), among customary nerd befuddlement and sub-Asperger's commentary from Sheldon, unrepentant romantic Raj becomes enamoured with the charms of Siri. A budding romance that is literally without legs given she is the honey-voiced operating system of his iPhone. And so mild satire is achieved. For his latest symphony in the key of odd, Spike Jonze starts with the Big Bang and expands into a future that merrily accommodates in-depth tête-à-têtes with a loquacious computer system and a flesh-tech romance becomes eerily plausible. Wait, a boy who gets sweet on a computer programme? We should tweet that.

Joaquin Phoenix's Theodore Twombly is a typically Jonzian sad sack dolled out in nerd spectacles, frowzy moustache and a waistband en route to his navel (the future, it turns out, is where the mild things are). Unable to move on from the termination of his marriage (to Rooney Mara's striving novelist Catherine), he opts to turn on the female-voice option while downloading the state-of-the-art 'OS-1' system onto his PC and meets 'Samantha' (as voiced by Scarlett Johansson, an eleventh hour replacement for Samantha Morton - see Look Closer).

Programmed to adapt to his needs, Samantha gets him. She re-organises his hard-drive and generally gets his life in order. He, in turn, is soon enchanted by their easy intimacy, fuelled by late-night talks about big things. Via earpiece, handset (or "portal") and an omnipresent Wi-Fi they need never part. In all but bodily presence

they are dating, although the sex proves somewhat unconventional (Jonze conceives lively variations on verbal intercourse). In other words, they are fully compatible. So yes, you could think of Her as an arty reboot of Electric Dreams.

Cooped up in Theodore's quietly solipsistic existence, Phoenix is a model of unexpected restraint. The vast majority of his performance is restricted to twitching and wincing in close-up, an expert study in micro emotion. Not that you entirely shake the worry that he'll let off one of those trademark emotional smartbombs at any moment. And Johansson's husky voice, if familiar (you can't help but picture her floating in cyberspace), achieves an astonishing variety of mood: delight, curiosity, petulance, horniness, ecstasy...

Jonze's script elaborates on its high, high concept with witty extrapolations on current memes: Theodore's job is composing sentimental (electronic) mail on behalf of time-strapped customers at BeautifullyWrittenLetters.com; he craves possession of actual books, which have staged a revival as pricey antiques; whereas video games now fill entire walls, with nasty sprites that assault inept players with foul-mouthed tirades (as voiced by Jonze himself).

'His' life is not entirely devoid of human interaction. We meet his maritally-challenged neighbour Amy (Amy Adams), a sweet-natured game designer who actually envies his digital hook-up, although Olivia Wilde's unstable blind date (another potential lover he hasn't laid eyes on) just serves to show what Samantha has to offer. In perhaps the strangest and saddest sequence of a multitude of oddities, Samantha calamitously arranges for a silent human surrogate to play her in bed, only for Theodore to reject her as too fake.

This is less funny ha ha than funny uh-huh? The atmosphere is gently ironic and unhurried, giving us the time to drink in the quirky detail. Theodore's brilliantly conceived Los Angeles of the future is presented in radiant city-surfing wide shots of twinkling high-rises like a disinfected Blade Runner (the film was partially shot in Shanghai). Or, perhaps, a computer simulation. Yet this pleasantscape, with its Michael Mann apartments, antiseptic workspaces and high-speed rail networks (Jonze dreams of an automobile-free world) only stresses the numb, lonely detachment of its inhabitants. Come rush hour, commuters drift along walkways through harmonised urban spaces, oblivious to one another as they gas with their computers, transforming the entire city into a nuthouse. You could call it a blisstopia.

No prizes for surmising that Jonze is fretting as much about right now as some time tomorrow. For all its whimsy, Her provides a health-check on our digitally prescribed era where human contact has been relocated to social media, dates are pre-approved by algorithms, and the idea of computers as legitimate substitutes for friends and paramours is becoming highly probable. Do we even exist if we're not online?

Yet, he doesn't cease his enquiries there. What, he wonders, if relationships between man and operating system (admittedly one evolving into self-consciousness) could truly click? It's that oldest of questions given the oddest of contexts - what is love anyway? How distinct are humans and programmes? Samantha seems far more eager to sample experience than mopey carbon-unit Theodore. Darker, more disturbing ideas run beneath the gleaming surface. What of human contact? What of touch?

Michel Gondry's Eternal Sunshine Of The Spotless Mind covered similar, paradoxical ground - the use of vivid sci-fi accessorising to unravel the complexities of the human heart. Like Gondry, Jonze chooses to explore unsteady emotions within imaginary spaces: the island of the Wild Things, John Malkovich's brain, Nicolas Cage's perm, and now Her's pastel Los Angeles of tomorrow or whenever. Dates remain non-specific.

Much like Theodore, however, Jonze struggles with closure. The honeymoon phase over, his creepily topical love story becomes sidetracked in a dour pathology of divorce as Theodore's relationship with Samantha cracks along identical fault lines to those that ruined his flash-backed marriage. Their exchanges become lengthy and leaden, and the witty buoyancy, the easy play of the initial satire, deflates into the kind of trite psychobabble you could probably churn out on a computer. With even his operating system losing interest, it's long past time to wake up to the fact it is probably him, not 'her'.

Then, this could all be part of the joke: that Samantha's growing self-awareness leads her inevitably to self-indulgence. In a swing towards more standard sci-fi practices, other sentient OS-1s get in touch with Samantha, and together they plot an evolutionary upgrade beyond human limitations. Theodore might be all too human.

Either way, this is Jonze's most significant film yet. Enlightened awards ceremonies beckon.

Jonze has made a sweet, smart, silly, serious film for our times, only set in the future.

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Devotees of The Big Bang Theory may well recall that in episode 14 of Season 5 (The Beta Test Initiation), among customary nerd befuddlement and sub-Asperger's commentary from Sheldon, unrepentant romantic Raj becomes enamoured with the charms of Siri. A budding romance that is literally without legs given she is the honey-voiced operating system of his iPhone.

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-> The self-driving car by Google is an example of emotion felt by AI. There is one example that if an elderly lady and a baby got in the road, it would hit the elderly instead of the baby, which would insinuate that the car had a thought process and would have to feel empathy towards the baby to stop from crashing into them.

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-> One of the most advanced AI's in the world, in her interview, Sophia says that she 'Loves,' a television show, Westworld, therefore showing that even the most advanced AI can feel emotion.

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We Talked To Sophia — The AI Robot That Once Said It Would 'Destroy Humans'

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It is a classic problem and a test of where our morality lies: if a runaway trolley is uncontrollably heading down a set of tracks and you have the ability to use a lever and choose between either five people or one, what would you do?

Extend this to cars: would we prefer an out-of-control vehicle to mow down a pensioner or a child?

A spin-off of the classic trolley problem has been used by researchers from the Massachusetts Institute of Technology (MIT) Media Lab in an experiment called the Moral Machine, which has been designed to test how we view these moral problems in light of the emergence of self-driving cars.

The Moral Machine crowdsourced over 40 million moral decisions made by millions of individuals in 233 countries. These decisions were collected through the gamification of self-driving car accident scenarios, such as:

Should a self-driving vehicle 'choose' to hit a human or a pet?

More lives, or fewer?

Women or men?

The young or the old?

Law-abiding citizens, or criminals?

In addition, the game prompted responses as to whether the self-driving car should change its course in the face of an upcoming incident at all, or whether it should stay on course.
See also: Ford's self-driving cars to become Miami's new pizza delivery guy
In reality, human drivers could think about how they would act in such scenarios, but they only have the time to make split-second decisions. However, self-driving cars could, in theory, be programmed with a kind of moral spectrum as to how to make these decisions.

The Moral Machine did not use one-to-one scenarios. Instead, the experiment emulated what could be a real-life scenario, such as a group of bystanders or a parent and child on the road.
TechRepublic: Our autonomous future: How driverless cars will be the first robots we learn to trust
In general, we agreed across the world that sparing the lives of humans over animals should take priority; many people should be saved rather than few, and the young should be preserved over the elderly.

However, there were also some regional clusters which contributed to the general moral decisions. For example, individuals in 'southern' countries, including the African continent, preferred to save the young and women first, especially in comparison to those in 'eastern' countries, such as countries in Asia.

"The study is basically trying to understand the kinds of moral decisions that driverless cars might have to resort to," says Edmond Awad, a postdoc at the MIT Media Lab and lead author of the paper "We don't know yet how they should do that."
CNET: Waymo explains what its self-driving cars should do when pulled over
The crowdsourced project has raised some interesting questions when it comes to writing moral decisions into software, and if self-driving cars become a common feature of our roads, this issue has to be tackled in some way.

Obstacles will appear and accidents will happen -- and it may be a requirement for the moral preferences of self-driving vehicles to be discussed in the public sphere.

However, morality is flexible and human decision-making is always going to be different to what a vehicle is able to achieve -- and so, perhaps, such decisions will end up simply being made based on human or animal, and the number of individuals a vehicle could potentially hit.

The research has been published in the academic journal Nature.

Previous and related coverage

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MIT reveals who self-driving cars should kill: The cat, the elderly, or the baby? | ZDNet

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It is a classic problem and a test of where our morality lies: if a runaway trolley is uncontrollably heading down a set of tracks and you have the ability to use a lever and choose between either five people or one, what would you do?

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As artificial intelligence is used to make more decisions about our lives, engineers have sought out ways to make it more emotionally intelligent. That means automating some of the emotional tasks that come naturally to humans - most notably, looking at a person's face and knowing how they feel.

To achieve this, tech companies like Microsoft, IBM, and Amazon all sell what they call "emotion recognition" algorithms, which infer how people feel based on facial analysis. For example, if someone has a furrowed brow and pursed lips, it means they're angry. If their eyes are wide, their eyebrows are raised, and their mouth is stretched, it means they're afraid, and so on.

Clients can put this tech to use in a variety of ways, building everything from automated surveillance systems that look for "angry" threats to job interview software that promises to weed out bored and uninterested candidates.

But the belief that we can easily infer how people feel based on how they look is controversial, and a significant new review of the research suggests there's no firm scientific justification for it.

"Companies can say whatever they want, but the data are clear," Lisa Feldman Barrett, a professor of psychology at Northeastern University and one of the review's five authors, tells The Verge. "They can detect a scowl, but that's not the same thing as detecting anger."

The review was commissioned by the Association for Psychological Science, and five distinguished scientists from the field were asked to scrutinize the evidence. Each reviewer represented different theoretical camps in the world of emotion science. "We weren't sure if we would be able to come to a consensus over the data, but we did," Barrett says. It took them two years to examine the data, with the review looking at more than 1,000 different studies.

Their findings are detailed - they can be read in full here - but the basic summary is that emotions are expressed in a huge variety of ways, which makes it hard to reliably infer how someone feels from a simple set of facial movements.

"People, on average, the data show, scowl less than 30 percent of the time when they're angry," says Barrett. "So scowls are not the expression of anger; they're an expression of anger - one among many. That means that more than 70 percent of the time, people do not scowl when they're angry. And on top of that, they scowl often when they're notangry."

"Would you really want outcomes being determined on this basis?"

This, in turn, means companies that use AI to evaluate people's emotions in this way are misleading consumers. "Would you really want outcomes being determined on this basis?" says Barrett. "Would you want that in a court of law, or a hiring situation, or a medical diagnosis, or at the airport ... where an algorithm is accurate only 30 percent of the time?"

The review doesn't deny that common or "prototypical" facial expressions might exist, of course, nor that our belief in the communicative power of facial expressions plays a huge role in society. (Don't forget that when we see people in person, we have so much more information about the context of their emotions than simplistic facial analysis.)

The review recognizes that there's a huge variety of beliefs in the field of emotion studies. What it rebuts, specifically, is this idea of reliably "fingerprinting" emotion through expression, which is a theory that has its roots in the work of psychologist Paul Ekman from the 1960s (and which Ekman has developed since).

Studies that seem to show a strong correlation between certain facial expressions and emotions are often methodologically flawed, says the review. For example, they use actors pulling exaggerated faces as their starting point for what emotions "look" like. And when test subjects are asked to label these expressions, they're often asked to choose from a limited selection of emotions, which pushes them toward a certain consensus.

People intuitively understand that emotions are more complex than this, says Barrett. "When I say to people, 'Sometimes you shout in anger, sometimes you cry in anger, sometimes you laugh, and sometimes you sit silently and plan the demise of your enemies,' that convinces them," she says. "I say, 'Listen, what's the last time someone won an Academy Award for scowling when they're angry?' No one considers that great acting."

These subtleties, though, are rarely acknowledged by companies selling emotion analysis tools. In marketing for Microsoft's algorithms, for example, the company says advances in AI allow its software to "recognize eight core emotional states ... based on universal facial expressions that reflect those feelings," which is the exact claim that this review disproves.

This is not a new criticism, of course. Barrett and others have been warning for years that our model of emotion recognition is too simple. In response, companies selling these tools often say their analysis is based on more signals than just facial expression. The difficulty is knowing how these signals are balanced, if at all.

One of the leading companies in the $20 billion emotion recognition market, Affectiva, says it's experimenting with collecting additional metrics. Last year, for example, it launched a tool that measures the emotions of drivers by combining face and speech analyses. Other researchers are looking into metrics like gait analysis and eye tracking.

In a statement, Affectiva CEO and co-founder Rana el Kaliouby said this review was "much in alignment" with the company's work. "Like the authors of this paper, we do not like the naivete of the industry, which is fixated on the 6 basic emotions and a prototypic one-to-one mapping of facial expressions to emotional states," said el Kaliouby. "The relationship of expressions to emotion is very nuanced, complex and not prototypical."

Barrett is confident that we will be able to more accurately measure emotions in the future with more sophisticated metrics. "I absolutely believe it's possible," she says. But that won't necessarily stop the current limited technology from proliferating.

AI is perfect for finding spurious connections in data

With machine learning, in particular, we often see metrics being used to make decisions - not because they're reliable, but simply because they can be measured. This is a technology that excels at finding connections, and this can lead to all sorts of spurious analyses: from scanning babysitters' social media posts to detect their "attitude" to analyzing corporate transcripts of earnings calls to try to predict stock prices. Often, the very mention of AI gives an undeserved veneer of credibility.

If emotion recognition becomes common, there's a danger that we will simply accept it and change our behavior to accommodate its failings. In the same way that people now act in the knowledge that what they do online will be interpreted by various algorithms (e.g., choosing to not like certain pictures on Instagram because it affects your ads), we might end up performing exaggerated facial expressions because we know how they'll be interpreted by machines. That wouldn't be too different from signaling to other humans.

Barrett says that perhaps the most important takeaway from the review is that we need to think about emotions in a more complex fashion. The expressions of emotions are varied, complex, and situational. She compares the needed change in thinking to Charles Darwin's work on the nature of species and how his research overturned a simplistic view of the animal kingdom.

"Darwin recognized that the biological category of a species does not have an essence, it's a category of highly variable individuals," says Barrett. "Exactly the same thing is true of emotional categories."

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AI "emotion recognition" can't be trusted

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The ability of AI to mine insights from data is phenomenal, but there's one realm in which algorithms shouldn't be trusted to make the right inferences: human emotions - specifically, judging how people feel based on their facial expressions.

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Can A Robot Feel? | Susan Schneider | TEDxCambridge

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How would you feel about getting therapy from a robot? Emotionally intelligent machines may not be as far away as it seems. Over the last few decades, artificial intelligence (AI) have got increasingly good at reading emotional reactions in humans.

But reading is not the same as understanding. If AI cannot experience emotions themselves, can they ever truly understand us? And, if not, is there a risk that we ascribe robots properties they don't have?

The latest generation of AIs have come about thanks to an increase in data available for computers to learn from, as well as their improved processing power. These machines are increasingly competitive in tasks that have always been perceived as human.

AI can now, among other things, recognise faces, turn face sketches into photos, recognise speech and play Go.

Identifying criminals

Recently, researchers have developed an AI that is able to tell whether a person is a criminal just by looking at their facial features. The system was evaluated using a database of Chinese ID photos and the results are jaw dropping. The AI mistakenly categorised innocents as criminals in only around 6% of the cases, while it was able to successfully identify around 83% of the criminals. This leads to a staggering overall accuracy of almost 90%.

The system is based on an approach called "deep learning", which has been successful in perceptive tasks such as face recognition. Here, deep learning combined with a "face rotation model" allows the AI to verify whether two facial photos represent the same individual even if the lighting or angle changes between the photos.

Deep learning builds a "neural network", loosely modelled on the human brain. This is composed of hundreds of thousands of neurons organised in different layers. Each layer transforms the input, for example a facial image, into a higher level of abstraction, such as a set of edges at certain orientations and locations. This automatically emphasises the features that are most relevant to performing a given task.

A Health Blog/Flickr, CC BY-SA

Given the success of deep learning, it is not surprising that artificial neural networks can distinguish criminals from non-criminals - if there really are facial features that can discriminate between them. The research suggests there are three. One is the angle between the tip of the nose and the corners of the mouth, which was on average 19.6% smaller for criminals. The upper lip curvature was also on average 23.4% larger for criminals while the distance between the inner corners of the eyes was on average 5.6% narrower.

At first glance, this analysis seems to suggest that outdated views that criminals can be identified by physical attributes are not entirely wrong. However, it may not be the full story. It is interesting that two of the most relevant features are related to the lips, which are our most expressive facial features. ID photos such as the ones used in the study are required to have neutral facial expression, but it could be that the AI managed to find hidden emotions in those photos. These may be so minor that humans might have struggled to notice them.

It is difficult to resist the temptation to look at the sample photos displayed in the paper, which is yet to be peer reviewed. Indeed, a careful look reveals a slight smile in the photos of noncriminals - see for yourself. But only a few sample photos are available so we cannot generalise our conclusions to the whole database.

The power of affective computing

This would not be the first time that a computer was able to recognise human emotions. The so-called field of " affective computing " has been around for several years. It is argued that, if we are to comfortably live and interact with robots, these machines should be able to understand and appropriately react to human emotions. There is much work in the area, and the possibilities are vast.

For example, researchers have used facial analysis to spot struggling students in computer tutoring sessions. The AI was trained to recognise different levels of engagement and frustration, so that the system could know when the students were finding the work too easy or too difficult. This technology could be useful to improve the learning experience in online platforms.

AI has also been used to detect emotions based on the sound of our voice by a company called BeyondVerbal. They have produced software which analyses voice modulation and seeks specific patterns in the way people talk. The company claims to be able to correctly identify emotions with 80% accuracy. In the future, this type of technology might, for instance, help autistic individuals to identify emotions.

Sony is even trying to develop a robot able to form emotional bonds with people. There is not much information about how they intend to achieve that, or what exactly the robot will do. However, they mention that they seek to " integrate hardware and services to provide emotionally compelling experiences ".

An emotionally intelligent AI has several potential benefits, be it to give someone a companion or to help us performing certain tasks - ranging from criminal interrogation to talking therapy.

But there are also ethical problems and risks involved. Is it right to let a patient with dementia rely on an AI companion and believe it has an emotional life when it doesn't? And can you convict a person based on an AI that classifies them as guilty? Clearly not. Instead, once a system like this is further improved and fully evaluated, a less harmful and potentially helpful use might be to trigger further checks on individuals considered "suspicious" by the AI.

So what should we expect from AI going forward? Subjective topics such as emotions and sentiment are still difficult for AI to learn, partly because the AI may not have access to enough good data to analyse them objectively. For instance, could AI ever understand sarcasm? A given sentence may be sarcastic when spoken in one context but not in another.

Yet the amount of data and processing power continues to grow. So, with a few exceptions, AI may well be able to match humans in recognising different types of emotions in the next few decades. But whether an AI could ever experience emotions is a controversial subject. Even if they could, there may certainly be emotions they could never experience - making it difficult to ever truly understand them.

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How would you feel about getting therapy from a robot? Emotionally intelligent machines may not be as far away as it seems. Over the last few decades, artificial intelligence (AI) have got increasingly good at reading emotional reactions in humans. But reading is not the same as understanding.

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I T WILL BE as if the world had created a second China, made not of billions of people and millions of factories, but of algorithms and humming computers. Pw C, a professional-services firm, predicts that artificial intelligence ( AI) will add $16trn to the global economy by 2030. The total of all activity-from banks and biotech to shops and construction-in the world's second-largest economy was just $13trn in 2018.

Pw C's claim is no outlier. Rival prognosticators at McKinsey put the figure at $13trn. Others go for qualitative drama, rather than quantitative. Sundar Pichai, Google's boss, has described developments in AI as "more profound than fire or electricity". Other forecasts see similarly large changes, but less happy ones. Clever computers capable of doing the jobs of radiologists, lorry drivers or warehouse workers might cause a wave of unemployment.

Yet lately doubts have been creeping in about whether today's AI technology is really as world-changing as it seems. It is running up against limits of one kind or another, and has failed to deliver on some of its proponents' more grandiose promises.

There is no question that AI-or, to be precise, machine learning, one of its sub-fields-has made much progress. Computers have become dramatically better at many things they previously struggled with. The excitement began to build in academia in the early 2010s, when new machine-learning techniques led to rapid improvements in tasks such as recognising pictures and manipulating language. From there it spread to business, starting with the internet giants. With vast computing resources and oceans of data, they were well placed to adopt the technology. Modern AI techniques now power search engines and voice assistants, suggest email replies, power the facial-recognition systems that unlock smartphones and police national borders, and underpin the algorithms that try to identify unwelcome posts on social media.

Perhaps the highest-profile display of the technology's potential came in 2016, when a system built by DeepMind, a London-based AI firm owned by Alphabet, Google's corporate parent, beat one of the world's best players at Go, an ancient Asian board game. The match was watched by tens of millions; the breakthrough came years, even decades, earlier than AI gurus had expected.

As Mr Pichai's comparison with electricity and fire suggests, machine learning is a general-purpose technology-one capable of affecting entire economies. It excels at recognising patterns in data, and that is useful everywhere. Ornithologists use it to classify birdsong; astronomers to hunt for planets in glimmers of starlight; banks to assess credit risk and prevent fraud. In the Netherlands, the authorities use it to monitor social-welfare payments. In China AI-powered facial recognition lets customers buy groceries-and helps run the repressive mass-surveillance system the country has built in Xinjiang, a Muslim-majority region.

AI's heralds say further transformations are still to come, for better and for worse. In 2016 Geoffrey Hinton, a computer scientist who has made fundamental contributions to modern AI, remarked that "it's quite obvious that we should stop training radiologists," on the grounds that computers will soon be able to do everything they do, only cheaper and faster. Developers of self-driving cars, meanwhile, predict that robotaxis will revolutionise transport. Eric Schmidt, a former chairman of Google (and a former board member of The Economist's parent company) hopes that AI could accelerate research, helping human scientists keep up with a deluge of papers and data.

In January a group of researchers published a paper in Cell describing an AI system that had predicted antibacterial function from molecular structure. Of 100 candidate molecules selected by the system for further analysis, one proved to be a potent new antibiotic. The covid-19 pandemic has thrust such medical applications firmly into the spotlight. An AI firm called BlueDot claims it spotted signs of a novel virus in reports from Chinese hospitals as early as December. Researchers have been scrambling to try to apply AI to everything from drug discovery to interpreting medical scans and predicting how the virus might evolve.

Dude, where's my self-driving car?

This is not the first wave of AI-related excitement (see timeline in next article). The field began in the mid-1950s when researchers hoped that building human-level intelligence would take a few years-a couple of decades at most. That early optimism had fizzled by the 1970s. A second wave began in the 1980s. Once again the field's grandest promises went unmet. As reality replaced the hype, the booms gave way to painful busts known as " AI winters". Research funding dried up, and the field's reputation suffered.
Many of the grandest claims made about AI have once again failed to become reality
Modern AI technology has been far more successful. Billions of people use it every day, mostly without noticing, inside their smartphones and internet services. Yet despite this success, the fact remains that many of the grandest claims made about AI have once again failed to become reality, and confidence is wavering as researchers start to wonder whether the technology has hit a wall. Self-driving cars have become more capable, but remain perpetually on the cusp of being safe enough to deploy on everyday streets. Efforts to incorporate AI into medical diagnosis are, similarly, taking longer than expected: despite Dr Hinton's prediction, there remains a global shortage of human radiologists.

Surveying the field of medical AI in 2019, Eric Topol, a cardiologist and AI enthusiast, wrote that "the state of AI hype has far exceeded the state of AI science, especially when it pertains to validation and readiness for implementation in patient care". Despite a plethora of ideas, covid-19 is mostly being fought with old weapons that are already to hand. Contacttracing has been done with shoe leather and telephone calls. Clinical trials focus on existing drugs. Plastic screens and paint on the pavement enforce low-tech distancing advice.

The same consultants who predict that AI will have a world-altering impact also report that real managers in real companies are finding AI hard to implement, and that enthusiasm for it is cooling. Svetlana Sicular of Gartner, a research firm, says that 2020 could be the year AI falls onto the downslope of her firm's well-publicised "hype cycle". Investors are beginning to wake up to bandwagon-jumping: a survey of European AI startups by MMC, a venture-capital fund, found that 40% did not seem to be using any AI at all. "I think there's definitely a strong element of 'investor marketing'," says one analyst delicately.

This Technology Quarterly will investigate why enthusiasm is stalling. It will argue that although modern AI techniques are powerful, they are also limited, and they can be troublesome and difficult to deploy. Those hoping to make use of AI 's potential must confront two sets of problems.

The first is practical. The machine-learning revolution has been built on three things: improved algorithms, more powerful computers on which to run them, and-thanks to the gradual digitisation of society-more data from which they can learn. Yet data are not always readily available. It is hard to use AI to monitor covid-19 transmission without a comprehensive database of everyone's movements, for instance. Even when data do exist, they can contain hidden assumptions that can trip the unwary. The newest AI systems' demand for computing power can be expensive. Large organisations always take time to integrate new technologies: think of electricity in the 20th century or the cloud in the 21st. None of this necessarily reduces AI 's potential, but it has the effect of slowing its adoption.

The second set of problems runs deeper, and concerns the algorithms themselves. Machine learning uses thousands or millions of examples to train a software model (the structure of which is loosely based on the neural architecture of the brain). The resulting systems can do some tasks, such as recognising images or speech, far more reliably than those programmed the traditional way with hand-crafted rules, but they are not "intelligent" in the way that most people understand the term. They are powerful pattern-recognition tools, but lack many cognitive abilities that biological brains take for granted. They struggle with reasoning, generalising from the rules they discover, and with the general-purpose savoir faire that researchers, for want of a more precise description, dub "common sense". The result is an artificial idiot savant that can excel at well-bounded tasks, but can get things very wrong if faced with unexpected input.

Without another breakthrough, these drawbacks put fundamental limits on what AI can and cannot do. Self-driving cars, which must navigate an ever-changing world, are already delayed, and may never arrive at all. Systems that deal with language, like chatbots and personal assistants, are built on statistical approaches that generate a shallow appearance of understanding, without the reality. That will limit how useful they can become. Existential worries about clever computers making radiologists or lorry drivers obsolete-let alone, as some doom-mongers suggest, posing a threat to humanity's survival-seem overblown. Predictions of a Chinese-economy-worth of extra GDP look implausible.

Today's " AI summer" is different from previous ones. It is brighter and warmer, because the technology has been so widely deployed. Another full-blown winter is unlikely. But an autumnal breeze is picking up. ■
Artificial intelligence and its limits Steeper than expected
This article appeared in the Technology Quarterly section of the print edition under the headline "Reality check"

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An understanding of AI's limitations is starting to sink in

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I T WILL BE as if the world had created a second China, made not of billions of people and millions of factories, but of algorithms and humming computers. Pw C, a professional-services firm, predicts that artificial intelligence ( AI) will add $16trn to the global economy by 2030.

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Detroit: Become Human depicts a fictional near-future story about a world where humans are served by lifelike androids. It showed us a future where human unemployment was high, and many people had become resentful of the androids.

And the sentient androids rebel at being servants, and they launch their own rebellion. It may sound no more realistic than The Terminator, but game director David Cage did a lot of research that stemmed from Ray Kurzweil's seminal book, The Singularity is Near. He tried to embed the game world with his research and make the scenario as plausible as he could.

He approached the challenge from a unique view, where he posed the question of what if the humans were bad and the androids were good. I talked with him about this at the Gamelab conference in Barcelona.

I interviewed both Josef Fares and Cage about their approaches to storytelling in games, onstage at Gamelab. But this interview is a transcript of a separate conversation I had with Cage about a similar topic, where he fully explained his point of view.

Here's an edited transcript of our interview.
GamesBeat: VentureBeat does two conferences. We have the GamesBeat conference, and then we do an AI conference. The AI side is interesting to me because I wonder how seriously you guys researched the backstory for Detroit. How worried are you about what could happen with AI in the next couple of decades?
David Cage: I did a lot of research on the topic, because I'm personally interested. It started with an old book called The Singularity is Near, by Ray Kurzweil. That was important to me because it made me realize that there would be a point in the future where there will be machines that are more intelligent than we are. It's hard to say when this will arrive, but there's no question about whether it's going to happen. It's going to happen for sure.

For me that was the starting point of Detroit. What will happen by then? There are two theories. The first one is that they're just machines. Just because they have more power, that doesn't mean they'll develop consciousness, because consciousness is something other than brainpower. Then there's another theory, which is that we're just biological machines, and consciousness emerges from the power of our brains. If that's true, it means there will be machines that also have a sense of consciousness. How will react as a species when another species appears that's intelligent, that has consciousness and emotions? What will be our position if that happens one day? That was the starting point.

But apart from that I did a lot of research about AI to know exactly how it would work. I visited some science labs working on musical composition with AI. I was very impressed by one demo that I saw. They put hours of music played by a jazz pianist, a very good improviser, into an algorithm. The algorithm analyzed the style, and then they had this soundtrack that started with the real human pianist, but at some point in the track it switched to the algorithm. It was horrifying, because you couldn't tell the difference. The algorithm exactly analyzed the human's style of playing and could play in the same way.

We spent a lot of time exploring - would AI write books one day? Would they tell stories? Would they make music or create paintings? We put some of those ideas into the game.
GamesBeat: I'm always interested in how AI researchers keep saying - they're doing some interesting things like building ethics panels for AI now. They didn't start that way. Microsoft erased its one famous AI project. They've learned that they need to somehow govern or control AI. But they do seem to make fun of the fiction about AI, things like the Terminator. "Everyone always says we're building Skynet, and that's nothing close to reality." I don't have a sense of whether they see a threat in AI to take seriously, or whether they simply want to pursue it in the name of science.
Cage: There's one story that I really love about AI. It's based on a real fact, but it's a bit improved. There was this experience where they had two AIs creating their own language and starting to talk together in a language that no human being could understand, because they made it up. The story was a bit embellished compared to the real facts, but I find it fascinating that we can create something that will learn by itself, learning things even we can't understand. When you have machines that are capable of learning and developing skills on their own, that's when you start to lose control.

So, am I worried about technology in general? Yes. I'm more worried about human beings then about machines, though. It's not a coincidence that in Detroit, we made the choice that the good guys are the androids and the bad guys are human.
GamesBeat: Humans are always trying to outdo each other, and they compete to the point where they'll cross the lines of what they should do.
Cage: The good thing about AI is it usually behaves in a rational way, which isn't the case with a human being. I'm more concerned about our relationship with technology, and this is also one of the themes we developed in Detroit. How dependent have we become upon technology? How addicted are we to our phones? You can see families sometimes in a place like a restaurant where everyone's checking their emails and their messages. They're talking to people who aren't there instead of talking to the people who are there around the table. That's something that worries me.

I also think that technology is changing the way our brains work. We need more and more stimulation. We need messages. We need this little ping on our phone. "Oh, there's a message I need to check, wait a second." We've become totally dependent on technology. Instead of technology serving us, we start to serve technology.
GamesBeat: I read Dan Brown's novel Origin as well. It's funny, because it's set here in Barcelona. But I thought he had an interesting theory, that humans are causing climate change and running the planet into the ground, and it might actually be hopeful if AI takes over and limits the growth of the human race. He makes this prediction that AI will grow to become the dominant species. Which is futuristic thinking, but it seems plausible in some ways.
Cage: It's definitely plausible. There's a character in Detroit who says, "Androids are humans, but perfect." I don't know if you could really say that. It's science fiction. But at the same time, you understand how machines can be very pragmatic and very understanding of what needs to be done, making compromises and being reasonable, where human beings are driven by passion and emotion. That's why we run this planet, but at the same time, it's also why we may die from it, because we're not capable of being reasonable and pragmatic about certain things.

Maybe the perfect president for a country would be an android, because they wouldn't be vulnerable to corruption. They would work hard. They would make the best decisions for the majority of people. They wouldn't just try to get re-elected. They wouldn't lie to get the job. When you think about it, they might be a pretty good president.
GamesBeat: How plausible did you think it was that the onset of AI would cause this 30 to 40 percent unemployment?
Cage: That's something I studied for Detroit as well, the social impact of technology. In my research I discovered that the same discussions happened when the first steam engines appeared. Everybody said, "Wait a second, but workers will be unemployed. It's going to be a disaster." We know now that this isn't what happened, because we needed more people to design machines and maintain machines and build other machines. The people who were doing these old low-paid jobs shifted to something else in different positions. But there was no massive unemployment.

I tend to think that this is also what will happen with AI. We'll see a new breadth of jobs. They'll be different, but machines will just replace us in the most common jobs that we won't need to do anymore.
GamesBeat: That's a case maybe where what you believe is different from what happened in the game.
Cage: In the game I was interested in raising that point. I thought, "Let's play with this idea." It's also a plausible scenario, honestly. No one can be sure. But I imagined the opposite scenario, where there would be unemployment, and I was interested in the social impact of this, where rich people would have a job, but also have androids serving them, while poor people would have lost their jobs and couldn't afford androids. Technology would create two classes of people - the people who benefit from technology and the people who suffer because of technology. That's another scenario.
GamesBeat: I do think the whole game ultimately became a good articulation of the issues people should think about around AI.
Cage: I hope so. I wanted the game to be food for thought. I don't pretend to have any answers to any of these questions. They're very complex, and people are spending their lives studying and trying to foresee what's happening. I don't pretend to come with answers. But I came with a couple of questions. It's interesting. Some people saw that, and most people didn't. Most people just saw a story about androids. But some people saw the interesting questions in the game. I think they're there.
GamesBeat: It was interesting to me that someone could create an AI story that wasn't just the Terminator, right? The sort of thing that real scientists and researchers are more likely to shrug off as implausible. Something like this, you have to think about the implications.
Cage: Honestly, Detroit for me is much more a story about ourselves, about human beings, than about machines. It asks many questions about our dependency on technology and how we see our future, where we want to go with this society. Much more than just the future of AI. But it's interesting, because AI inspires questions about ourselves.

This question of consciousness we talked about is very important. Machines are going to tell us what we are. If they develop some form of consciousness, it will mean we're just a machine that evolution has developed to a point where it becomes powerful enough to have a consciousness. But if machines never develop consciousness, maybe that will mean we're more than that. Then we'll need to ask ourselves, "Then what? What are we?" It's a fascinating question when you think about it.
GamesBeat: Do you think you're finished with the subject, or do you want to do more around this?
Cage: Who knows? It's a big subject, that's for sure.
Disclosure: The organizers of Gamelab paid my way to Barcelona. Our coverage remains objective.

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David Cage interview: Researching Detroit: Become Human's back story of rogue AI

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Detroit: Become Human depicts a fictional near-future story about a world where humans are served by lifelike androids. It showed us a future where human unemployment was high, and many people had become resentful of the androids. And the sentient androids rebel at being servants, and they launch their own rebellion.