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The Discontents of Artificial Intelligence in 2022

Recent years have seen a boom in the use of Artificial Intelligence. This review essay is divided into two parts: part I introduces contemporary AI, and part II discusses its implications. Part-II will be dedicated to the widespread and rapid adoption of artificial intelligence and its resulting crises.

In recent years, Artificial Intelligence or AI has flooded the world with applications outside of the research laboratory. There are now a number of standard Artificial Intelligence techniques, including face recognition, keyboard suggestions, Amazon recommendations, Twitter followers, image similarity search, and text translation. Artificial intelligence is also being applied in areas such as radiological diagnostics, pharmaceutical drug development, and drone navigation far removed from the ordinary user. Therefore, artificial intelligence is the new buzzword of the day and is seen as a portal to the future.

In 1956, John McCarthy and others conceptualized a summer research project aimed at replicating human activity. It is thought that this led to the discipline of artificial intelligence. In the beginning, these pioneers worked under the premise that every aspect of learning or intelligence could be so precisely described that it could be simulated by a machine.

Although the objective was ambitious, board games have often been used to test artificial intelligence methods due to pragmatic considerations. Board games have precise rules that can be encoded into a computational framework, so playing board games with skill is a hallmark of intelligence.

Earlier this year, a program called AlphaGo created a sensation by defeating the reigning Go champion. The program was developed by DeepMind, a Google company.

Gary Kasparov, then the world chess champion, was shocked by IBMs Deep Blue in a celebrated encounter between humans and machines in 1997. Kasparovs defeat was unnerving as it was the breach of a frontier in chess, which is traditionally thought of as a cerebral game. The notion that a machine could defeat the world champion at the board game of Go was considered to be an unlikely dream at the time. Based on this belief, the number of possible move sequences in Go is very much more significant than those in chess and Go played on a much larger board than chess.

Nevertheless, in 2016 a computer program made headlines after it defeated the reigning world Go champion, Lee Sedol, using a program developed by DeepMind, a company owned by Google. By 1997, commentators celebrated this victory as the beginning of a new era in which machines would eventually surpass humans in intelligence.

The reality was completely different. By any measure, AlphaGo was a sophisticated tool, but it could not be considered intelligent. While it was able to pick the best move at any time, the software did not understand the reasoning behind its choices.

In AI, a key lesson is that machines can be endowed with abilities previously possessed only by humans, although they do not have to be intelligent in the same way as sentient beings. The case of arithmetic computation is one non-AI example. The task of multiplying two large numbers was a difficult one throughout history.

Logarithm tables had to be painstakingly produced to accomplish these tasks, which required a lot of human effort. Even the most straightforward computer can now perform such calculations efficiently and reliably for many decades now. The same can be said about virtually any human task involving routine operations that can be solved with AI.

In addition, AI is beginning to make inroads into the domains of science and engineering, where domain knowledge is required. Healthcare is one such area.

Todays AI will be able to extend the above metaphor beyond simple, routine tasks to more sophisticated ones with unprecedented advances in computing power and data availability. Millions of people are already using AI tools. Nonetheless, AI is starting to make headway in areas like science and engineering, where domain knowledge is involved.

A place of universal relevance includes healthcare, where AI tools can be used to assess a persons health, provide a diagnosis based on clinical data, or analyze large-scale study data. Using artificial intelligence for solving highly complex problems such as protein folding or fluid dynamics has been developed recently in more esoteric fields. Such advances are expected to have a multitude of practical applications in the real world.

History

Many early AI works centred around symbolic reasoning laying out a set of propositions and logically deducing their implications. However, this enterprise soon ran into trouble as enumerating all the operational rules in a specific problem context was impossible.

A competing paradigm is a connectionism, which aims to overcome the difficulty of describing rules explicitly by inferring them implicitly through data. An artificial neural network is created based on the strength (weight) of connections between neurons, loosely based on the properties of neurons and their connectivity in the brain.

A number of leading figures have claimed a definitive solution to the problem of computational intelligence is imminent, based on one paradigms success or another. In spite of progress, the challenges proved far more complex, and the hype was typically followed by a period of profound disillusionment and a significant reduction in funding for American academics-a period referred to as the AI winter.

Thus, DeepMinds recent success should serve as an endorsement of its approaches as they could help society find answers to some of the worlds most pressing and fundamental scientific problems. If the reader is interested in the critical concepts in AI, as well as the background of the field and its boom-bust cycles, two recently published popular expositions written by long-term researchers may be of interest.

These are Melanie Mitchells Artificial Intelligence: A Guide for Thinking Humans (Pelican Books, 2019) and Michael Wooldridges The Road to Conscious Machines: The Story of Artificial Intelligence (Pelican Books, 2020).

Artificial Intelligence has been confronted with two issues of profound significance since its inception. While it is impressive to defeat a world champion at their own game, the real world is a much messier environment than the one in which ironclad rules govern everything.

Due to this reason, the successful AI methods developed to solve narrowly defined problems cannot be generalized to other situations involving diverse aspects of intelligence. Developing the ability to use ones hands for delicate tasks is an essential skill that a child learns effortlessly through robotics research.

Although AlphaGo worked out the winning moves, its human representative had to reposition the stones on the board, a seemingly mundane task. Intelligence isnt defined by a single skill like winning games because intelligence is a whole lot more than the sum of its parts. It encompasses, among other things, the ability to interact with ones environment, which is one of the essentials of embodied behaviour.

One of the most essential skills that a child develops effortlessly is that of using their hands to perform delicate tasks. Robotics has yet to develop this skill.

Moreover, the question of how to define intelligence itself looms more considerable and more significant than how AI tools can overcome the technical limitations. Researchers often assume that approaches developed to tackle narrowly defined problems like winning at Go can be used to solve more general intelligence problems. There has been scepticism towards this rather brash belief, both from those within the community as well as from older disciplines like philosophy and psychology.

Intelligence has been the subject of heavy debate regarding its ability to be substantially or entirely captured in a computational paradigm or whether it is irreducible and ineffable. Hubert Dreyfus well-known 1965 report entitled Alchemy and Artificial Intelligence reveal the disdain and hostility some people feel towards AI claims. Dreyfus views were called a budget of fallacies by a well-known AI researcher.

AI is also viewed with unbridled optimism that it can transcend biological limitations, a notion known as Singularity, thereby breaking all barriers. The futurist Ray Kurzweil claims that machine intelligence will overwhelm human intelligence as the capabilities of AI systems grow exponentially. Kurzweil has attracted a fervent following despite his ridiculous argument regarding exponential growth in technology. It is best to consider Singularity as a kind of technological rapture without intellectual severe foundations.

Intelligence has been a bone of contention for decades, primarily about whether it can be wholly or essentially captured through computations or if it has an ineffable, irreducible human core.

Stuart Russell, the first author of the most widely used textbook on artificial intelligence, is an AI researcher who does not shy away from defining intelligence. Humans are intelligent to the extent that they can be expected to reach their objectives (Russell, Human Compatible, 9). Machine intelligence can be defined in the same way. An approach such as this does help pin down the elusive notion of intelligence, but as anyone who has read about utility in economics can attest, it falls back on an accurate description of our goals to provide the meaning.

The style of Russell differs significantly from the writing of Mitchell and Wooldridge: he is terse and expects his readers to keep engaged; he gives no quarter. Although Human Compatible is a highly thought-provoking book, it also possesses a personal narrative that jumps from flowing argument to the abstruse hypothesis.

A recent study found that none of the hundreds of AI tools developed for detecting Covid was effective.

Additionally, Human Compatible differs significantly from other AI expositions by examining the dangers of future AI surpassing human capabilities. While Russell avoids evoking dystopian Hollywood imagery, he does argue that AI agents might combine to cause harm and accidents in the future. He points to the story of Leo Szilard, who figured out the physics of nuclear chains after Ernest Rutherford had argued that the idea of atomic power was moonshine and warned against the belief that such an eventuality was highly unlikely or impossible.

After that, nuclear warfare unleashed its horrors. Human Compatible focuses on guarding against the possibility of AI robots taking over the world. Wooldridges argument is not convincing here. The decades of AI research suggest that human-level AI differs from a nuclear chain reaction that can be described as a simple mechanism (Wooldridge, The Road to Consciousness, 244).

It is enriching but ultimately undecidable to debate the nature of intelligence and the fate of humanity in philosophy. Most researchers in AI research are focused on specific problems and are indifferent to more significant debates due to the two distinct tracks of cognitive science and engineering. Unfortunately, the objectives and claims of these two approaches are often conflated in the public discourse, leading to much confusion.

Relevantly, terms like neurons and learning have a mathematical meaning within the discipline. However, they are immediately associated with their commonsense connotation, leading to severe misunderstandings about the entire enterprise. The concept of a neural network is not the same as the concept of the human brain, and learning is a broad set of statistical principles and methods that are essentially sophisticated curve fitting and decision rule algorithms.

It has almost completely replaced other methods of machine learning since deep learning was discovered nearly a decade ago.

It was considered ineffective a few decades ago to develop neural networks that could learn from data. With the development of deep learning, neural networks garnered renewed interest in 2012, leading to significant improvements in image and speech recognition methods. Currently, successful AI methods such as AlphaGo and its successors and widely used tools such as Google Translate employ deep learning, in which the adjective does not signify profundity but rather a multiple layering of the network.

Deep understanding has been sweeping many disciplines since it was introduced over a decade ago, and it is now nearly wholly replacing other methods of machine learning. Three of its pioneers received the Turing Award in 2018, the highest honour in the field of computer science, anointing their paradigmatic dominance.

Success in AI is accompanied by hype and hubris. In 2016, Geoff Hinton, one of the Turing trio, stated: We should have ceased training radiologists by now, because it will become clear in five years that deep learning will provide better outcomes than radiologists. The failure to deliver us from flawed radiologists and other problems with the method did not hinder Hinton from stating in 2020 that deep learning will be able to do everything. In addition, a recent study concluded that none of the hundreds of AI tools developed for finding Covid was effective.

AI follows success with hype and hubris as an iron law.

Our understanding of the nature of contemporary learning-based AI tools will be enhanced by looking at how they are developed. As an example, consider detecting chairs from images. Various components of a chair can be observed: legs, backrests, armrests, cushions, etc. All of these combinations are recognizable as chairs, so there are potentially countless combinations of such elements.

Other things, such as bean bags, can trump any rule we may formulate about what a chair should contain. Methods such as deep learning seek to overcome precisely the limitations of symbolic, rule-based deduction. We may collect a number of images of chairs and other objects instead of trying to define rules that cover all of their varieties and feed these into a neural network along with the correct output (output of chair vs non-chair).

A deep learning approach would then modify the weights of the connections in the network in the training phase to mimic as best as possible the desired input-output relationships. Basically, the network will now be able to answer the question of whether previously unseen test images contain chairs if it has been done correctly.

For a chair-recognizer of this nature, many images of chairs of different shapes and sizes are needed. As an extension of that analogy, one may now consider any number of categories one can imagine, including chairs, tables, trees, people, and so on, all of which appear in the world in a variety of glorious but maddening variety. As a result, it is essential to acquire adequately representative images of objects.

It has been shown that deep learning methods can work extraordinarily well, but they are often unreliable and unpredictable.

A number of significant advances were made in 2012 in automatic image recognition thanks to the combination of relatively cheap and powerful hardware, as well as the rapid expansion of the internet, which enabled researchers to build a large dataset, known as ImageNet, containing millions of images labelled with thousands of categories.

Despite working well, deep learning methods are unreliable when it comes to their behaviour. Suppose, for example; an American school bus is mistaken for an ostrich due to tiny changes in images that cannot be seen by the human eye. Additionally, it is recognized that sometimes incorrect results can arise from spurious and unreliable statistical correlations rather than from any deep understanding.

When a boat is shown in an image that is surrounded by water, it is correctly recognized. A ship is not modelled or envisioned in the method. The limitations and problems of AI may have typically been academic concerns in the past. In this case, however, it is different since a number of AI tools have been taken from the laboratory and deployed into real life, often with grave consequences.

Due to a relentless push towards automation, a number of data-driven methods have already been developed and deployed locally, including in India, well before deep learning became a fad. Among the tools that have achieved extraordinary notoriety is COMPAS, which is used by US courts to determine the appropriate sentence length based upon the risk of recidivism.

A tool such as this uses statistics from existing criminal records to determine a defendants chances of committing a crime in the future. The device, even without explicitly biasing itself against black people, resulted in racial bias in a well-known investigation. When judges use artificial intelligence to predict sentence length, they discriminate based on race.

For biometric identification and authentication, fingerprints and face images are even more valuable. Many law enforcement agencies and other state agencies have adopted face recognition tools due to their utility in surveillance and forensics. Affective computing and other dubious techniques for detecting emotion have also been used in a number of contexts, including employment decisions as well as more intrusive surveillance methods.

A number of necessary studies have shown that many face recognition programs available in the commercial sector are profoundly flawed and discriminatory. A recent audit of commercially available tools revealed that black women could experience face recognition error rates as high as 35% higher than white women, causing growing calls for their halt. In India and China especially, face and emotion recognition is becoming more widespread and is having tremendous implications for human rights and welfare. This deserves a much more thorough discussion than the one presented here.

Various sources of bias result from relying on real-world data for decision making. Many of these sources can be grouped under the heading of bias. Face recognition suffers from a bias caused by the low number of people of colour in many datasets used to develop the tools. Another limitation is the limited relevance of the past for defining the contours of the society we want to build. If an AI algorithm relies on past records, as is done in the US recidivism modelling, it would disparately harm the poor since they have historically experienced higher incarceration rates.

Additionally, if one were to consider automating the hiring process for a professional position in India, models based on past hirings would automatically lead to caste bias, even if caste was not explicitly considered. As Cathy ONeil details in her famous book, Weapons of Math Destruction: How Big Data is Increasing Inequality and Threatening Democracy (Penguin Books, 2016), which details a number of such incidents in the American context, her argument here can be summarized as follows:

Likewise, models based on past hires in India would automatically result in caste bias if one were to automate hiring people for, say, a professional position.

Artificial intelligence methods do not learn from the world directly but from a dataset as a proxy. A lack of ethical oversight and the lack of design of data collection have long plagued AI research in academia. Scholars from a range of disciplines have put a great deal of effort into developing discussions of bias in AI tools and datasets, including their ramifications in society, particularly among those who are poor and traditionally discriminated against.

Additionally, many modern AI tools are impossible to reason about or interpret, in addition to bias. Since those who are affected by a decision often have a right to know the reasoning used to arrive at a conclusion, the problem of explainability has profound implications for transparency.

Within the computer science community broadly, there has been an interest in formalizing these problems, which has led to academic conferences and an online textbook in preparation. An essential result of this exercise has been a theoretical understanding of the impossibility of fairness, which is a result of multiple notions of fairness not all being possible to satisfy simultaneously.

Research and practice in AI should also consider the trade-offs involved in designing software and the societal implications of these choices. The second part of this essay will show, however, that these considerations are seldom adequate as the rapid expansion of contemporary AI technology from the research lab into daily life has unleashed a wide range of problems.

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The Discontents Of Artificial Intelligence In 2022 - Inventiva

He sees an opening on the left wing and immediately punishes them. After rushing down the side, he looks for his teammates in the center and quickly makes the cross in to finish it off!

Turn on any sports channel and youll hear something similar. Chances are you pictured Ronaldo or another star player running down a fresh pitch. In fact, this could actually describe a play from an artificial intelligence bot in a recent international tournament. Its time to shift our thinking as AI becomes the star player.

As we already know, using AI to enhance human athlete performance is becoming a pervasive practice. The next step for AI in sports is introducing AI players. In fact, we currently have AI agents smart enough to mimic high-level human tactics. They have the potential to revolutionize the sports industry while pushing the envelope regarding what AI can really do.

The immediate response from many people is that such a world will never come to be how could we enjoy watching machines? Many claim that playing against traditional AI can often be a repetitive and boring experience. Others cant imagine any joy from beating their machine opponents. To address this, lets start by examining why we like traditional sports and then outline how AI will come to meet these demands.

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Sports fan psychologists have nailed down eight core reasons why people love their sports.

Many of the motivations mentioned above arent unique to traditional sports. For example, getting together with friends and family to bond is about the people, not about the sport. As such, if the conditions are right, a similar variant involving AI could make inroads into the industry.

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The adoption of AI into the world of sports will be slower than other AI and software applications. Many of the motivations of sports relate to how others around an individual think and behave, so its not enough to change a few people; you need to change preconceptions around an entire industry to be truly effective. Here are four ways were already seeing AI infiltrate sports and how those applications appeal to our existing interest in sports:

Firstly, AI must be able to compete with humans for humans to get interested. We can already see AIs competitive edge with some of our most complex board games and e-sports. Here are some key cases:

These are all examples of deep learning AI, where strategies are not pre-programmed, but learned. Deep learning systems consist of up to billions of individual parameters which are layered together to create a complex network. Some goal is defined for the system, such as winning in a simple two-player game, which the system can begin to optimize toward. This optimization process happens through machine-based trial and error. The system plays millions of games with itself, each time learning about what works and what doesnt, and adjusting its parameters. After all these games, the system will have (hopefully) learned to play at or above its human counterparts, which is exactly what weve seen with the games mentioned above. This brings us to the wild world of e-sports.

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Our robotics capabilities are still somewhat limited, as seen in various robotic games such as soccer. It will still be some time before we can apply AI players to most traditional sports (though Boston Dynamics is getting there quickly). Instead, AI is likely to become most common in the world of e-sports.

E-sports is quickly becoming comparable (in terms of market share) to traditional sports. The industry has eclipsed $1 billion in revenue in 2021 and has a projected 15 percent year-over-year growth. The largest team in e-sports, Cloud 9, had a valuation of over $300 million, which equates to five percent of the worlds largest sports franchise, the Dallas Cowboys, at $7 billion. In prize pools, e-sports already exceed many, including the Golf Masters and Confederations Cup, at over $40 million.

The key thing to note is that e-sports are still relatively new. As opposed to traditional sports, some of which have franchises that are over a century old and have been big businesses for over 30 years, e-sports only began 25 years ago and the most popular game, Dota 2, was released just 10 years ago. The size of the prize pools contrasted with the young e-sports shows how quickly the industry has grown. Once this continued growth hits a critical mass and breaks into the mainstream, e-sports may provide similar family and group affiliation motivation that we see in traditional sports.

Consider that FIFA now runs an international tournament of e-sports for their very own games. For fans at home, the experience is largely the same, watching the same match on the same television with the same live commentary. Granted, the animation of the current games still has room for improvement, but it improves every year with new games. The rapidly advancing animations, along with the fact that theyre AI-generated, allow for far greater creativity. For example, you can watch in 3D and experience being in play or maybe even in the referee's shoes. The fact that the worlds most lucrative sport (soccer) is already moving into e-sports, so it wont be long before others follow.

There are other reasons e-sports make a good first choice for those interested in AI games, such as the ability to more efficiently train and improve AI. For a computer game, AI can play millions of games (e.g. 5 million games for AlphaGo) for training as opposed to traditional sports where AI must physically play the game to learn strategy and test its performance (and even this limitation is something OpenAI is working on).

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Right now, if someone asks you to watch two programs compete against each other inside another program, you might think theyre a little weird. This is a reasonable reaction, but like it or not, AI competitions are becoming more and more mainstream.

There are various competitions between AI that garner millions of viewers. Heres a list of various games and AI representations on YouTube which already have large audiences.

Overall, this is on the order of 100 million views on YouTube, which was only around two percent of one day of streaming(as of 2017). However, given the relatively small community this number is significant. Coupling the growth of AI bots with the growth in e-sports will create massive expansion in the genre as a whole. However, this growth wont be sustainable unless the AI stays interesting.

Once watching AI compete becomes common, well need to find new ways to keep viewers involved. In order to achieve this, its critical that we diversify our AI. People dont want to watch the same thing over and over again. As previously mentioned, one of the motivators for watching sport is entertainment which comes from the chance factor of not knowing who will walk out victorious on any given day. In order to achieve this, the agents must be capable of making various high-level, non-straightforward plays (which weve already seen with Dota 2 and Go, to name a few).

In fact, theres a common misconception that watching AI is a boring experience as they unintelligently copy humans or follow pre-described rulesets. Certainly that was true of machines of the past, but for many years now weve had AI that can act in creative and all-together astonishing ways.

One of the most interesting parts about Googles AlphaGo was its creativity and ways it played that game that were unexpected by humans. Along the same line, in the world of chess, when human players make moves that vary from the standard procedure, referees start to suspect players of using artificial intelligence systems as assistants. Put another way, in the game of chess, creativity is no longer the mark of a human, but that of a machine. Its the same in Go and as time passes, it will become true in other sports, too.

During the AlphaStar training, the Deepmind team observed that the bots adopted various good strategies. One might expect that the bots followed a specific strategy and got better and better at it in time. In fact, the bots could be clumped into various groups and each group had a different way to play the game (e.g. aggressive start, focus on a certain type of units, etc.). In a way, each bot had its own player personality. These personalities, with varied play-styles will keep AI sports both interesting and entertaining for human viewers.

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Once AI agents have become a regular part of our sporting experience, advancements in robotics will catch up, allowing them to play all of the games we usually play, not just for us, but with us. Soccer players will be able to practice against full teams of AI bots that are set to challenge them and help them grow. Theyll also be able to compete in human-robot leagues.

While human biology is relatively fixed, robotics will continue to advance. This means that sports can continue to evolve too. Imagine a game of soccer played at double the pace with a magnetic ball and speeds matching that of tennis? Sounds pretty exciting to me.

Finally, new games can be created that only AI can perfect. As previously mentioned, escape and aesthetic are two of the motivators for sports fans. Watching an AI empowered machine conquer and handle complex games will create a feeling of escape weve never experienced before.

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If the above story comes to be, there would naturally be significant impacts on sports and entertainment.

Sports organizations and related companies should start preparing for these changes before its too late. For the rest of us, likely not much will change. We cant hope to imitate Cristiano Ronaldos beautiful strikes or Federers impossible serves and I wont be able to match the feats of our robotic future athletes. If nothing else, it will be interesting to see how sports evolve in the wake of AI development. So for now, Ill sit back, pick a side and enjoy the game with my friends.

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Is AI the Future of Sports? - Built In

Hassabis has been thinking about proteins on and off for 25 years. He was introduced to the problem when he was an undergraduate at the University of Cambridge in the 1990s. A friend of mine there was obsessed with this problem, he says. He would bring it up at any opportunityin the bar, playing pooltelling me if we could just crack protein folding, it would be transformational for biology. His passion always stuck with me.

That friend was Tim Stevens, who is now a Cambridge researcher working on protein structures. Proteins are the molecular machines that make life on earth work, Stevens says.

Nearly everything your body does, it does with proteins: they digest food, contract muscles, fire neurons, detect light, power immune responses, and much more. Understanding what individual proteins do is therefore crucial for understanding how bodies work, what happens when they dont, and how to fix them.

A protein is made up of a ribbon of amino acids, which chemical forces fold up into a knot of complex twists and twirls. The resulting 3D shape determines what it does. For example, hemoglobin, a protein that ferries oxygen around the body and gives blood its red color, is shaped like a little pouch, which lets it pick up oxygen molecules in the lungs. The structure of SARS-CoV-2s spike protein lets the virus hook onto your cells.

COURTESY OF DEEPMIND

The catch is that its hard to figure out a proteins structureand thus its functionfrom the ribbon of amino acids. An unfolded ribbon can take 10^300 possible forms, a number on the order of all the possible moves in a game of Go.

Predicting this structure in a lab, using techniques such as x-ray crystallography, is painstaking work. Entire PhDs have been spent working out the folds of a single protein. The long-running CASP (Critical Assessment of Structure Prediction) competition was set up in 1994 to speed things up by pitting computerized prediction methods against each other every two years. But no technique ever came close to matching the accuracy of lab work. By 2016, progress had been flatlining for a decade.

Within months of its AlphaGo success in 2016, DeepMind hired a handful of biologists and set up a small interdisciplinary team to tackle protein folding. The first glimpse of what they were working on came in 2018, when DeepMind won CASP 13, outperforming other techniques by a significant margin. But beyond the world of biology, few paid much attention.

That changed when AlphaFold2 came out two years later. It won the CASP competition, marking the first time an AI had predicted protein structure with an accuracy matching that of models produced in an experimental laboften with margins of error just the width of an atom. Biologists were stunned by just how good it was.

Watching AlphaGo play in Seoul, Hassabis says, hed been reminded of an online game called FoldIt, which a team led by David Baker, a leading protein researcher at the University of Washington, released in 2008. FoldIt asked players to explore protein structures, represented as 3D images on their screens, by folding them up in different ways. With many people playing, the researchers behind the game hoped, some data about the probable shapes of certain proteins might emerge. It worked, and FoldIt players even contributed to a handful of new discoveries.

If we can mimic the pinnacle of intuition in Go, then why couldnt we map that across to proteins?

Hassabis played that game when he was a postdoc at MIT in his 20s. He was struck by the way basic human intuition could lead to real breakthroughs, whether making a move in Go or finding a new configuration in FoldIt.

I was thinking about what we had actually done with AlphaGo, says Hassabis. Wed mimicked the intuition of incredible Go masters. I thought, if we can mimic the pinnacle of intuition in Go, then why couldnt we map that across to proteins?

The two problems werent so different, in a way. Like Go, protein folding is a problem with such vast combinatorial complexity that brute-force computational methods are no match. Another thing Go and protein folding have in common is the availability of lots of data about how the problem could be solved. AlphaGo used an endless history of its own past games; AlphaFold used existing protein structures from the Protein Data Bank, an international database of solved structures that biologists have been adding to for decades.

AlphaFold2 uses attention networks, a standard deep-learning technique that lets an AI focus on specific parts of its input data. This tech underpins language models like GPT-3, where it directs the neural network to relevant words in a sentence. Similarly, AlphaFold2 is directed to relevant amino acids in a sequence, such as pairs that might sit together in a folded structure. They wiped the floor with the CASP competition by bringing together all these things biologists have been pushing toward for decades and then just acing the AI, says Stevens.

Over the past year, AlphaFold2 has started having an impact. DeepMind has published a detailed description of how the system works and released the source code. It has also set up a public database with the European Bioinformatics Institute that it is filling with new protein structures as the AI predicts them. The database currently has around 800,000 entries, and DeepMind says it will add more than 100 millionnearly every protein known to sciencein the next year.

A lot of researchers still dont fully grasp what DeepMind has done, says Charlotte Deane, chief scientist at Exscientia, an AI drug discovery company based in the UK, and head of the protein informatics lab at the University of Oxford. Deane was also one of the reviewers of the paper that DeepMind published on AlphaFold in the scientific journal Nature last year. Its changed the questions you can ask, she says.

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This is the reason Demis Hassabis started DeepMind - MIT Technology Review

An artificial intelligence system developed by a Sony Group Corp. subsidiary beat world-class players of the Gran Turismo Sport car racing game, overtaking opponents at high speed and avoiding crashes based on split-second decisions.

Gran Turismo Sophy defeated four Japanese players, some of whom had won world championships, in all races at an event held in Tokyo in 2021, according to an article published Feb. 10 in the online edition of the British science journal Nature.

Sony AI Inc. adopted an approach called deep reinforcement learning, and the system acquired driving skills, such as how to efficiently use acceleration and braking and how to respond when the way ahead is blocked by an opponent, based on vast amounts of data.

Gran Turismo Sophy used innovative ways to race its cars faster, and I could tell at a glance that its moves made perfect sense, said one player. There is so much to learn from what it did.

Technologies behind complex racing maneuvers could be applied to autonomous driving on public roads, an area that Sony Group is expected to work on through an electric vehicle venture to be set up this spring.

Similar know-how could also be used for drones as well as robots designed to work alongside humans.

Gran Turismo Sport, a popular simulation game for the PlayStation 4 home video-game console, allows players across the globe to compete online.

The e-sport platform offers faithful reproductions of racing cars, high-resolution imagery and realistic driving experiences. It has been adopted for racing events certified by the International Automobile Federation (FIA).

At the event in Tokyo, four AI-operated cars competed against the four players in races set along three tracks, including Frances Circuit de la Sarthe. The 13.629-kilometer course is the venue of the 24 Hours of Le Mans endurance race, a leg of the so-called Triple Crown of Motorsport.

The Red Bull X2019 Competition, a fictional car with a top speed in excess of 300 kph, was among the models featured in the virtual races.

Gran Turismo Sophy also beat three top-level players in time trial races on solo runs. The best finish time along the Circuit de la Sarthe was 193.080 seconds, about 1.8 seconds below the minimum of 194.888 seconds for humans.

AI has the potential to make racing games more exciting and help discover new maneuvers, said a member of the research team.

AI systems have already overwhelmed humans in board games.

In 1997, IBMs supercomputer, Deep Blue, defeated the world chess champion. Another system won a professional shogi player in 2013. An article published in Nature magazine in 2016 said AlphaGo, developed by a Google Inc. subsidiary, beat Europes go champion.

But things are far more complicated when it comes to racing games, where car movements are simulated in accordance with the laws of physics, especially when multiple players are involved.

Competitors have to know, for example, how to pass an opponent using tactical maneuvers and block a rival while avoiding excessive contact and incurring penalties.

Those techniques require complicated strategies, real-time decision-making and advanced car control skills all at the same time. That previously made it difficult for AI systems to get the better of humans.

But there is still room for improvement in AIs strategic decision-making abilities.

Gran Turismo Sophy sometimes failed to follow the racing line immediately after it had overtaken an opponent along a linear section of track, according to the research team.

The journal article can be read at (https://www.nature.com/articles/s41586-021-04357-7).

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Sony's AI system outraces some of the world's best e-sports drivers | The Asahi Shimbun: Breaking News, Japan News and Analysis - Asahi Shimbun

What is the most important product that comes out of the semiconductor industry?

Here is a hint: It is inherent to the market, but enhanced by a positively reinforcing feedback loop of history. Here is another hint: You cant hold it in your hand, like an A0 stepping of a device, and you cant point at it like a foundry with the most advanced manufacturing processes created from $15 billion to $20 billion worth of concrete, steel, and wafer etching equipment and a whole lotta people in bunny suits.

No, the most important thing that the semiconductor industry delivers and has consistently delivered for over five decades is optimism. And unlike a lot of chips these days, there is no shortage of it despite the serious challenges that the industry is facing.

By optimism we do not mean the kind of future poisoning that company founders and chief executives sometimes succumb to when they spend too much time in the future that is not yet here without seeing the consequences of the technologies they are in the process of inventing. And we certainly do not mean the zeal that others exhibit when they think that information technology can solve all of our problems. It cant, and it often makes some things worse as it is making other things better, as all technologies have done since humanity first picked up a stick. It is the arm that swings the stick both ways to plant a seed or to crush a skull. So it is with the Internet, social media, artificial intelligence, and so on.

The optimism that we are speaking of in the semiconductor industry is usually stripped bare of such consequences, with the benefits all emphasized and the drawbacks mostly ignored except possibly when considering the aspects of climate change and how compute, storage, and networking are an increasingly large part of our lives, and something that represents an ever-enlargening portion of business and personal budgets and consequently an embiggening part of the energy consumption on the planet. Semiconductor makers turn this drawback more computers requiring more power and cooling into a cause for driving innovation as hard as it can be done.

The irony is that we will need some of the most power-hungry systems the world has ever seen to simulate the conditions that will prove how climate change will affect us collectively and here is the important bit individually. How will you feel when you can drill down into a simulation, for a modest fee of course, and see a digital twin of your home being destroyed by a predicted hurricane two years from now? Or an earthquake, or a fire, or a tsunami? What is true of the Earth simulation will be as true for your body simulation and your consequent healthcare.

If the metaverse means anything, it means using HPC and AI to make general concepts extremely personal. We dont know that the world was hell bent to adopt the 24 hour news cycle and extreme entertainment optionality of cable television, or the Web, or social networks, but what we do know is that most of us ended up on these platforms anyway. And what seems clear is that immersive, simulated experiences are going to be normalized, are going to be a tool in all aspects of our lives, and that the race is on to develop the technologies that will get us there.

It would be hard to find someone more genuine and more optimistic about the future of the semiconductor industry than Aart de Geus, co-founder, chief executive officer, and chairman of electronic design automation tool maker Synopsys, who gave the opening keynote at the ISSCC 2022 chip conference, which was hosted online this week. We read the paper that de Geus presented and watched the keynote as well, and will do our best to summarize the tour de force in semiconductor history and prognostication as we enter in what de Geus called the SysMoore Era the confluence of Moores Law ambitions in transistor design and now packaging coupled to systemic complexity that together will bring about a 1,000X increase in compute across devices and systems of all kinds and lead to a smart everything world.

Here is de Geus showing the well familiar exponential plot of the transistor density of CPUs, starting with the Intel 4004 in 1971 and running all the way out five decades later to the Intel Ponte Vecchio GPU complex, with 47 chiplets lashing together 100 billion transistors, and the Cerebras WSE 2 wafer-scale processor, with 2.5 trillion transistors.

Thats the very familiar part of the SysMoore Era, of course. The Sys part needs a little explaining, but it is something that we have all been wrestling with in our next platforms. Moores Law improvements of 2X transistor density are taking bigger leaps to stay on track and are not yielding a 2X lowering in the cost of the transistors. This latter bit is what actually drives the semiconductor industry (aside from optimism), and we are now entering a time when the cost of transistors could rise a little with each generation, which is why we are resorting to chiplets and advanced packaging to glue them together side-by-side with 2.5D interposers or stacking them up in 3D fashion with vias or in many cases, a mix of the two approaches. Chiplets are smaller and have higher yield, but there is complexity and cost in the 2.5D and 3D packaging. The consensus, excepting Cerebras, is that this chiplet approach will yield the best tech-onomic results, to use a term from de Geus.

With SysMoore, we are moving from system on chip designs to system of chips designs, illustrated below, to bend up the semiconductor innovation curve that has been dominated by Moores Law for so long (with some help from Dennard scaling until 2000 or so, of course). Like this:

The one thing that is not on the charts that de Geus showed in the keynote, and that we want to inject as an idea, is that compute engines and other kinds of ASICsare definitely going to get more expensive even if the cost of packing up chiplets or building wafer-scale systems does not consume all of the benefits from higher yield that comes from using gangs of smaller chips or adding lots of redundancy into a circuit and never cutting it up.

By necessity, as the industry co-designs hardware and software together to wring the most performance per dollar per watt out of a system, we will move away from the volume economics of mass manufacturing. Up until now, a compute engine or network ASIC might have hundreds of thousands to millions of units, driving up yields over time and driving down manufacturing cost per unit. But in this SysMoore Era, volumes for any given semiconductor complex will go down because they are not general purpose, like the X86 processor in servers and PCs or the Arm system on chip was for smartphones and tablet have both been for the past decade and a half. If volumes per type of device go down by an order of magnitude, and the industry needs to make more types devices, this will put upward pressure on unit costs, too.

So what is the answer to these perplexing dilemmas that the semiconductor industry is facing? Artificial intelligence augmenting human expertise in designing these future system of chips complexes, of course. And it is interesting that the pattern that evolved to create machine learning for data analytics is being repeated in chip design.

EDA is relatively simple conceptually, explains de Geus. If you can capture data, you may be able to model it. If you can model it, maybe you can simulate. If you can simulate, maybe you can analyze. If you can analyze, maybe you can optimize. And if you can optimize, maybe you can automate. Actually, lets not forget the best automation is IP reuse it is the fastest, most efficient kind. Now its interesting to observe this because if you look at the bottom layers, what we have been doing in our field really for 50 years, is we have built digital twins of the thing that we are still building. And if we now say were going to deliver to our customers and the world that 1,000X more capability in chips, the notion of Metaverse some call it Omniverse, Neoverse, whatever you want to call it is becoming extremely powerful because it is a digital view of the world as a simulation of it.

The complexity that comprises a modern chip complex, full of chiplets and packaging, is mind-numbing and the pressure to create the most efficient implementation, across its many possible variations, is what is driving the next level of AI-assisted automation. We are moving from computer-aided design, where a workstation helped a chip designer, to electronic design automation, where synthesis of logic and the placing and routing of that logic and its memories and interconnects, is done by tools such as those supplied by Synopsys, to what we would call AIDA, short for Artificial Intelligence Design Automation, and making us think of Ada Lovelace, of course, the programmer on the Difference Engine from Charles Babbage.

This chart captures the scale of complexity in an interesting way, since the bottom two have been automated by computers IBMs Deep Blue using brute force algorithms to play chess and Googles AlphaGo using AI reinforcement learning to play Go.

Google has been using lessons learned from AlphaGo to do placement and routing of logic blocks on chips, as we reported two years ago from ISSCC 2020, and Synposys is embedding AI in all parts of its tool stack in something it is calling Design Space Optimization, or DSO. A chess match has a large number of possible moves, and Go has orders of magnitude more, but both are win-loss algorithms. Not so for route and placement of logic blocks or the possible ways to glue compute complexes together from myriad parts. These are not zero sum algorithms, but merely better or worse options, like going to the eye doctor and sitting behind that annoying machine with all the blasted lenses.

The possible combinations of logic elements and interconnects is a very large data space, and will itself require an immense amount of computation to add AI to the design stack. The amount has been increasing on a log scale since the first CAD tools became widely used:

But the good news is that the productivity gains from chip design tools have been growing at a log scale, too. Which means what you can do with one person and one workstation designing a chip is amazing here in the 2020s. And will very likely be downright amazing in the 2030s, if the vision of de Geus and his competitors comes to pass.

In the chart above, the Fusion block is significant, says de Geus, and it is implemented in something called the Fusion Compiler in the Synopsys toolchain, and this is the foundation for the next step, which is DSO. Fusion plugs all of these different tools together to share data as designers optimize a chip for power, performance, and area or PPA, in the lingo. These different tools work together, but they also fight, and they can be made to provide more optimal results than using the tools in a serial manner, as this shows:

The data shown above is an average of more than 1,000 chip designs, spanning from 40 nanometers down to 3 nanometers. With DSO, machine learning is embedded in all of the individual elements of the Fusion Compiler, and output from simulations is used to drive machine learning training that in turn is used to drive designs. The way we conceive of this and de Geus did not say this is that the more the Synopsys tools design chips and examine options in the design space, the faster it will learn what works and what does not and the better it will be at showing human chip designers how to push their designs.

Lets show some examples of how the early stages of DSO works with the Synopsys tools, beginning with a real microcontroller from a real customer:

De Geus highlighted the important parts of the design, with a baseline of the prior design and the target of the new design. A team of people were set loose on the problem using the Synopsys tools, and you can see that they beat the customer target on both power and timing by a little bit. Call it a day. But then Synopsys fired up the Fusion Compiler and its DSO AI extensions. Just using the DSO extensions to Fusion pushed the power draw down a lot and to the left a little, and then once AI trained algorithms were kicked on, the power was pushed down even further. You can see the banana curve for the DSO and DSO AI simulations, which allows designers to trade off power and timing on the chip along those curves.

Here is another design run that was done for an actual CPU as it was being designed a year ago:

A team of experts took months to balance out the power leakage versus the timing in the CPU design. The DSO extensions to the Fusion Compiler pushed it way over to the left and down a little, and when the AI trained models of the tool were switched on, a new set of power leakage and timing options were shown to be possible. A single engineer did the DSO design compared to a team using the Synopsys tools, and that single engineer was able to get a design that burned from 9 percent to 13 percent less power and had 30 percent less power leakage with anywhere from 2X to 5X faster time to design completion.

There were many more examples in the keynote of such advances after an injection of AI into the tools. But here is the thing, and de Geus emphasized this a number of times. The cumulative nature of these advances are not additive, but multiplicative. They will amplify much more than the percents of improvement on many different design vectors might imply. But it is more than that, according to de Geus.

The hand that develops the computer on which EDA is written can help develop the next computer to write better EDA, and so on, de Geus explained at the end of his talk. That circle has brought about exponential achievements. So often we say that success is the sum of our efforts. No, its not. It is the product of our efforts. A single zero, and we all sink. Great collaboration, and we all soar.

Continued here:
SysMoore: The Next 10 Years, The Next 1,000X In Performance - The Next Platform