What Today’s AI can and cannot do (Part 2)

1. Introduction

This is a sequel to the previous post, that was mostly about Kai-Fu Lee’s book “AI Superpowers”. Very simply stated, that book develops two threads of thoughts:

1. AI technology has reached a maturity level where it is ready for massive deployment and application. This should change the way we operate many processes/services/products in our world.
2. The AI tool box is now ready, what matters is scaling and engineering, more than new science. Hence the race will favour those ecosystems where speed and efficiency meet with software engineering and business skills.

The first part should come as no surprise. There are a number of other good books, such as “The Mathematical Corporation – where machine intelligence + human ingenuity achieve the impossible” and “Human+AI – reimagining work in the age of AI” that make the same point with lots of convincing examples. The report of the National Academy of Technologies of France on Artificial Intelligence and Machine Learning is saying exactly the same thing : we have reached a tipping point and the time to act is now.

The first part should come as no surprise. There are a number of other good books, such as “The Mathematical Corporation – where machine intelligence + human ingenuity achieve the impossible” and “Human+AI – reimagining work in the age of AI” that make the same point with lots of convincing examples. The report of the National Academy of Technologies of France on Artificial Intelligence and Machine Learning is saying exactly the same thing : we have reached a tipping point and the time to act is now.

The second part is more controversial. There has been a lot of heated reactions to Kai-Fu Lee’s statement about the state of AI and the chances of Europe to be part of the winning players in the years to come. This debate is included into a larger one about the hype and the fake statements about what is possible today. We may summarize the “AI paradoxes” or “open questions” as follows:

• Is today’s AI ready for wonders, or are there so many impossibilities today that many claims are hyped ?
• Is the next generation of  autonomous AI around the corner ? or is AGI a pure fiction that is totally out of reach ?
• Should one should just focus on data to build the best AI strategy (i.e., become your own business’ best data source), or is there more than data to AI mastery ?
• Will, as Kai-Fu Lee seems to suggest, only large massive players dominate, or should we expect to see some breakthrough from small players ?

To try to shed some light on those questions, I propose a short synthesis of Martin Ford’s book, “Architect of Intelligence - The Truth about AI from the People Building it”, where 25 world experts share their views about the future of AI. At the time of this writing,  this book is the best source to search for answers to the previous four open questions. The thesis of this post is that, while we have indeed reached a tipping point about AI and while the “current level of AI” technology enables a world race of investment and development, there is a larger field of “tomorrow’s AI” for which predictions are hazardous at best. Martin Ford’s book is an absolute must-read for anyone who is interested in AI. As told in the previous post, I find it a great source to explore the questions and issues raised by Kai-Fu Lee’s book, but there are many other topics addressed in this book that I will not touch today.

2. Architects of Intelligence

Martin Ford is a well-known futurist and author who has worked extensively on topics such as AI, automation, robots and the future of work. His previous book, “Rise of the Robots – Technology and the Threats of a Jobless Future” is a thought-provoking essay that addresses the issues of “AI and the future of work” and which I have made a personal reference on this topic. His new book, “Architects of Intelligence” is a list of 25 interviews with the world best-known scientists in the field of Artificial Intelligence and Machine Learning. You may think of it as an extended version of the Wired article “How to teach Artificial Intelligence common sense”. Although each interview is different, Martin Ford uses a common canvas of questions that have a clear intersection with the 4 introductory issues. The exceptional quality of the book comes both from the very distinguished list of scientists but also from the talent and knowledge of the interviewer.

In his first chapter Martin Ford says: “All would acknowledge the remarkable achievements of deep neural networks over the past decade, but they would likely argue that deep learning is just “one tool in the toolbox” and that continued progress will require integrating ideas from other spheres of artificial intelligence”. This book provides with a remarkable synthesis on the AI topic, but I should say beforehand that you should read it, because this post only covers a small part of the content. A summary is next to impossible since, even though there is a strong common thread of ideas that are shared by the majority of experts, there are also dissenting opinions. Therefore, what follows is my own synthesis that represents an editor’s choice both with the topics and the selected voices, even though I try to be as faithful as possible. Because of the multiple opinions and the dissenting topics, I have decided to include a fair number of quotes and to attribute them, consistently, to one of the interviewed scientists. A synthesis of so many different viewpoints is biased by nature. I try to stay faithful to the spirits both of the scientists to whom I borrow the quotations and to Martin Ford as the book editor, but you may disagree.

2.1 Even the best experts are very careful about what tomorrow’s AI will be an will do: we do not know what’s ahead.

This is one of the most consensual statement I will make in this synthesis: all experts are very careful about what the future of AI will look like. Yoshua Bengio insists that each new discovery changes the landscape of what will be possible next: “As we reach this satisfying improvement that we are getting in our techniques—we reach the top of the first hill—we also see the limitations, and then we see another hill that we have to climb, and once we climb that one we’ll see another one, and so on. It’s impossible to tell how many more breakthroughs or significant advances are going to be needed before we reach human-level intelligence.”  Fei-Fei Li explains that this is only the beginning, that convolutional networks and deep learning are not the final tools that will solve all problems. She warns us that “Dating when a breakthrough will come, is much harder to predict. I learned, as a scientist, not to predict scientific breakthroughs, because they come serendipitously, and they come when a lot of ingredients in history converge. But I’m very hopeful that in our lifetime we’ll be seeing a lot more AI breakthroughs given the incredible amount of global investment in this area”. Many other scientists use the same language: we don’t know, the path is unclear, etc. There is a strong worry about the hype and exaggeration that could cause a new winter or unsubstantiated fears, as said by Andrew Ng: “A lot of the hype about superintelligence and exponential growth were based on very naive and very simplistic extrapolations. It’s easy to hype almost anything. I don’t think that there is a significant risk of superintelligence coming out of nowhere and it happening in a blink of an eye, in the same way that I don’t see Mars becoming overpopulated overnight”. Rodney Brooks explains that hundreds of new algorithms need to be invented before we can address all the limitations of current AI. He also notices that even the technology trends may become more difficult to forecast when we enter the end of Moore’s Law: “We’re used to exponentials because we had exponentials in Moore’s Law, but Moore’s Law is slowing down because you can no longer halve the feature size. What it’s leading to though is a renaissance of computer architecture. For 50 years, you couldn’t afford to do anything out of the ordinary because the other guys would overtake you, just because of Moore’s Law”.

2.2 Even though there is no consensus of what “hybrid” may mean, it is most likely that a “system of systems” approach will prevail to solve the current challenges of AI.

Even the fathers of the modern deep learning are looking for a way to add structure and architecture to neural nets in order to address larger challenges than perception and recognition. Yoshua Bengio says: “Note that your brain is all neural networks. We have to come up with different architectures and different training frameworks that can do the kinds of things that classical AI was trying to do, like reasoning, inferring an explanation for what you’re seeing and planning”. When we look at the human brain, there seems to be much structure and specialization that occurs before the birth. Here is what Joshua Tenenbaum says: “Elizabeth Spelke is one of the most important people that anybody in AI should know if they’re going to look to humans. She has very famously shown that from the age of two to three months, babies already understand certain basic things about the world …. It used to be thought that that was something that kids came to and learned by the time they were one year old, but Spelke and others have shown that in many ways our brains are born already prepared to understand the world in terms of physical objects, and in terms of what we call intentional agents.” The debate starts when it comes to define what the best paradigm could be to add this structure. For Yann Lecun, “Everybody agrees that there is a need for some structure, the question is how much, and what kind of structure is needed. I guess when you say that some people believe that there should be structures such as logic and reasoning, you’re probably referring to Gary Marcus and maybe Oren Etzioni”.

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The majority of scientists advocate for a hybrid approach that combines different forms of AI. Stuart Russel explains that “Carnegie Mellon’s Libratus poker AI was another very impressive hybrid AI example: it was a combination of several different algorithmic contributions that were pieced together from research that’s happened over the last 10 or 15 years”. He explains the value of randomized algorithms, a technique applied universally from AI (such as AlphaGo and MCTS) to operations research, network and cryptography algorithms. According to him, “The only way that humans and robots can operate in the real world is to operate at multiple scales of abstraction. Andrew Ng acknowledges that hybrid combination is de facto a standard for many systems: “At Landing AI we use hybrids all the time to build solutions for industrial partners. There’s often a hybrid of deep learning tools together with, say, traditional computer vision tools because when your datasets are small, deep learning by itself isn’t always the best tool”. Judea Pearl makes a great argument about the constraints imposed by small data sets but then extends to the problem of understanding causality: “Even today, people are building hybrid systems when you have sparse data. There’s a limit, however, to how much you can extrapolate or interpolate sparse data if you want to get cause-effect relationships. Even if you have infinite data, you can’t tell the difference between A causes B and B causes A”. Evolutionary algorithms – where machine learning tries to simulate and reproduce evolution – are a plausible path to develop hybrid architectures for AI, as illustrated by Joshua Tenenbaum: “Evolution does a lot of architecture search; it designs machines. It builds very differently, structured machines across different species or over multiple generations. We can see this most obviously in bodies, but there’s no reason to think it’s any different in brains. The idea that evolution builds complex structures that have complex functions, and it does it by a process which is very different to gradient descent, but rather something more like search in the space of developmental programs, is very inspiring to me.”

2.3 Deep Learning is “the technology advance of the decade” and we are only seeing the beginning of the consequences, but it is not a universal problem solver technique. 

There is more to AI than deep learning, Stuart Russel recalls that Deep Learning is a strict subset of Machine Learning, which is only one kind of AI : “it would be a huge mistake for someone to think that deep learning is the same thing as artificial intelligence, because the ability to distinguish Dalmatian dogs from bowls of cherries is useful but it is still only a very small part of what we need to give an artificial intelligence in order for it to be successful”.  He recalls that AlphaGo is a hybrid of classical search-based randomized algorithm and a deep learning algorithm for position evaluation. This is also what Demmis Hassabis says : “Deep learning is amazing at scaling, so combining that with reinforcement learning allowed it to scale to these large problems that we’ve now tackled in AlphaGo and DQN—all of these things that people would have told you was impossible 10 years ago”.

Gary Marcus is famous for his position that we need more than Deep Learning, especially because it requires a huge amount of data and delivers low levels of abstraction from that data. This entails that these algorithms are well suited to “the few big common problems” – such as vision or speech - and less for the large number of less frequent ones: “Neural networks are able to capture a lot of the garden-variety cases, but if you think about a long-tail distribution, they’re very weak at the tail.”  Oren Etzioni also sees Deep Learning as one tool in the tool box, that is very good at what it does but with a rather narrow scope: “we really have a long way to go and there are many unsolved problems. In that sense, deep learning is very much overhyped. I think the reality is that deep learning, and neural networks are particularly nice tools in our toolbox, but it’s a tool that still leaves us with a number of problems like reasoning, background knowledge, common sense, and many others largely unsolved”. I would argue, differently, that the problems that Deep Learning allow us to solve – perception - had been plaguing the scientific community for decades and that solving them makes Deep Learning  more than a “really nice tool”. Like many other scientists, I believe that the availability of DL for perception is opening a new era of hybrid approaches (as is precisely demonstrated by AlphaGo).

2.4 The scientific investigation is running at full speed, many domains are progressing fast and multiple techniques are added constantly to the toolbox.

Natural Language Processing is a perfect instance of a field that is making constant progress and that is fueled with the lower-abstraction progresses of speech/text recognition brought by Deep Learning. We have made great progresses – translation being a great example – but the consensus is that we are reaching the barrier of semantics (there is only so much you can do with automatic translation without understanding). This is just critical for dialogues (think chatbots) as explained by Barbara Grosz : “If you consider any of the systems that purport to carry on dialogues, however, the bottom line is they essentially don’t work. They seem to do well if the dialogue system constrains the person to following a script, but people aren’t very good at following a script. There are claims that these systems can carry on a dialogue with a person, but in truth, they really can’t.” Yoshua Bengio explains the search of semantic understanding: “There’s a lot of research in grounded language learning now trying to build an understanding of language, even if it’s a small subset of the language, where the computer actually understands what those words mean, and it can act in correspondence to those words”. David Ferrucci, who was part of the Watson team, works along the same path: “Elemental Cognition is an AI research venture that’s trying to do real language understanding. It’s trying to deal with that area of AI that we still have not cracked, which is, can we create an AI that reads, dialogs, and builds understanding”.

It is well recognized that Deep Learning works better that we are able to explain or understand, maybe for good reasons. This opens a huge research field of better understanding and characterizing the deep learning and neural nets techniques. The work from Yoshua Bengio about autoencoders is a good example of the multiple possible applications of these techniques besides pure pattern recognition: “There are two parts to an autoencoder, an encoder and a decoder. The idea is that the encoder part takes an image, for example, and tries to represent it in a compressed way, such as a verbal description.” Better explainability is another hot research topic; for instance, James Manyika talks about LIME (Local Interpretable Model Agnostic Explanation) : “LIME tries to identify which particular data sets a trained model relies on most to make a prediction. Another promising technique is the use of Generalized Additive Models, or GAMs. These use single feature models additively and therefore limit interactions between features, and so changes in predictions cane be determined as features are added”.

Yann Lecun makes a very interesting distinction between three levels (in a continuum) of learning from reinforcement learning, supervised learning to self-supervised learning. Self-supervised learning supports the constant enrichment of the learning model, as shown in another example brought by Oren Etzioni : “Tom Mitchell’s work, with lifelong learning at CMU, is also very interesting—they’re trying to build a system that looks more like a person: it doesn’t just run through a dataset and build a model and then it’s done. Instead, it continually operates and continually tries to learn, and then learn based on that, over a longer extended period of time”. Yann Lecun is famous for his argument against the hype about reinforcement learning, saying that you could not learn how to drive this way without too many – possibly fatal - crashes. As a reinforcement learning practitioner, I find this argument biased: one could grow the speed very progressively while the evaluation function penalizes small deviations from safe situation, which is more or less the way we learn to drive.

Many large players such as Google or Salesforce are investing massively into AutoML, the addition of another layer of machine learning methods to automatically tune the parameters for lower level machine learning techniques. Fei-Feil Li says: “An example of what we’re doing is a product we created that’s called AutoML. This is a unique product on the market to really lower the entry barrier of AI as much as much as possible—so that AI can be delivered to people who don’t do AI “. AutoML is a critical tool for continuous learning, thus it is much more than simply saving time. It supports the continuous adaptation to an environment that may be changing.  This is why it is a core technology for Digital Manufacturing as show by a startup like TellMePlus. AutoML is clearly a technology to watch according to Jeffrey Dean : “We also have a suite of AutoML products, which are essentially designed for people who may not have as much machine learning expertise, but want a customized solution for a particular problem they have. Imagine if you have a set of images of parts that are going down your assembly line and there are 100 kinds of parts, and you want to be able to identify what part it is from the pixels in an image. There, we can actually train you a custom model without you having to know any machine learning through this technique called AutoML.”.

Last, I want to point out a key insight from Judea Pearl: there are many things that you cannot learn by simply watching, you have to act – causality cannot be learned though simple observation. I will return to this idea when I write about Judea Pearl’s book “The Book of Why – the new science of causes and effects”, which is probably the deepest book I have read in 2018. I believe that this insight will have a profound effect on future machine learning algorithms, especially for autonomous robots: “This is how a child learns causal structure, by playful manipulation, and this is how a scientist learns causal structure—playful manipulation. But we have to have the abilities and the template to store what we learn from this playful manipulation so we can use it, test it, and change it. Without the ability to store it in a parsimonious encoding, in some template in our mind, we cannot utilize it, nor can we change it or play around with it”. I see there a delightful parallel with Nassim Taleb’s idea that you cannot really learn without “skin in the game”.

2.5  Although the AGI concept is difficult to pin down, there is a consensus that a “spectacularly better level of AI” performance will be achieved this century.

Stuart Russel reminds us that the goal of AI has always been to create general-purpose intelligent machines. Because this is hard, most of the work has been applied to more specific subtasks and application tasks, but “arguably, some of the conceptual building blocks for AGI have already been here for decades. We just haven’t figured out yet how to combine those with the very impressive learning capacities of deep learning.”  Fei-Fei Li position is not that different: “So, let’s first define AGI, because this isn’t about AI versus AGI: it’s all on one continuum. We all recognize today’s AI is very narrow and task specific, focusing on pattern recognition with labeled data, but as we make AI more advanced, that is going to be relaxed, and so in a way, the future of AI and AGI is one blurred definition”. Some research scientists, like Demmis Hassabis, have no difficulty recognizing their end goal: “From the beginning, we were an AGI company, and we were very clear about that. Our mission statement of solving intelligence was there from the beginning. As you can imagine, trying to pitch that to standard venture capitalists was quite hard”.

As told in the first section, it is impossible to speculate about future scientific discoveries, so many scientists prefer to stay vague about the time line. For instance, Stuart Russel says: “So that is why most AI researchers have a feeling that AGI is something in the not-too-distant future. It’s not thousands of years in the future, and it’s probably not even hundreds of years in the future”. Yann Lecun has a similar position: “How much prior structure do we need to build into those systems for them to actually work appropriately and be stable, and for them to have intrinsic motivations so that they behave properly around humans? There’s a whole lot of problems that will absolutely pop up, so AGI might take 50 years, it might take 100 years, I’m not too sure”. One thing seems clear though, AGI is not around the corner, as told by Andrew Ng: “The honest answer is that I really don’t know. I would love to see AGI in my lifetime, but I think there’s a good chance it’ll be further out than that. … Frankly, I do not see much progress. Other than having faster computers and data, and progress at a very general level, I do not see specific progress toward AGI”.

It is hard to speculate about the “when”, but many scientists have their opinion about what the path towards AGI could look like. For Stuart Russel, “many of the conceptual building blocks needed for AGI or human-level intelligence are already here. But there are some missing pieces. One of them is a clear approach to how natural language can be understood to produce knowledge structures upon which reasoning processes can operate.” For Gary Marcus, capturing common sense requires adding other techniques than Deep Learning: “Another way to put it is that humans have all kinds of common-sense reasoning, and that has to be part of the solution. It’s not well captured by deep learning. In my view, we need to bring together symbol manipulation, which has a strong history in AI, with deep learning. They have been treated separately for too long, and it’s time to bring them together”. Joshua Tenenbaum makes a compelling argument that deep understanding of natural language (which has to be measured in other ways than the Turing test, which most scientists recognize as too easy to fool) is on the critical path towards AGI : “ Language is absolutely at the heart of human intelligence, but I think that we have to start with the earlier stages of intelligence that are there before language, but that language builds on. If I was to sketch out a high-level roadmap to building some form of AGI of the sort you’re talking about, I would say you could roughly divide it into three stages corresponding to three rough stages of human cognitive development”.

2.6 Our future will be strongly impacted by the constant progress of AI in applicability and performance.

In a way very similar to Kai-Fu Lee or the two books that I quoted in the introduction, most scientists interviewed in Martin Ford’s book see AI as a powerful transformation force. Start Russel says: “what’s likely to happen is that machines will far exceed human capabilities along various important dimensions. There may be other dimensions along which they’re fairly stunted and so they’re not going to look like humans in that sense”. Yoshua Bengio also foresees a very strong impact of AI to come: “I don’t think it’s overhyped. The part that is less clear is whether this is going to happen over a decade or three decades. What I can say is that even if we stop basic research in AI and deep learning tomorrow, the science has advanced enough that there’s already a huge amount of social and economic benefit to reap from it simply by engineering new services and new products from these ideas”. Geoffrey Hinton explains that there are many more dangerous technologies out there (such as molecular biology) compared to the threat of “ultra-intelligent systems”. He sees the probable outcome as positive even though he opens the need for social regulation which we will discuss later on: “I hope the rewards will outweigh the downsides, but I don’t know whether they will, and that’s an issue of social systems, not with the technology”. Andrew Ng is confident that AI technology has reached the critical mass for delivering value and that we should not experience a new AI winter: “In the earlier AI winters, there was a lot of hype about technologies that ultimately did not really deliver. The technologies that were hyped were really not that useful, and the amount of value created by those earlier generations of technology was vastly less than expected. I think that’s what caused the AI winters”.

Artificial Intelligence is seen very much as “Augmented Intelligence”, a technique that will help humans to be more efficient. For instance, Rana el Kaliouby says: “My thesis is that this kind of interface between humans and machines is going to become ubiquitous, that it will just be ingrained in the future human-machine interfaces, whether it’s our car, our phone or smart devices at our home or in the office. We will just be coexisting and collaborating with these new devices, and new kinds of interfaces“.  Barbara Grosz sees a similar pattern in the world of multi-agent systems: “This whole area of multi-agent systems now addresses a wide range of situations and problems. Some work focuses on strategic reasoning; other on teamwork. And, I’m thrilled to say, more recently, much of it is now really looking at how computer agents can work with people, rather than just with other computer agents”. David Ferrucci at Elemental Cognition envision a future where human and machine intelligence collaborate tightly and fluently: “Through thought-partnership with machines that can learn, reason, and communicate, humans can do more because they don’t need as much training and as much skill to get access to knowledge and to apply it effectively. In that collaboration, we are also training the computer to be smarter and more understanding of the way we think”.

A key point that is made by many scientists is that AI is part of a larger ecosystem – software, digitalization, connected objects and robots -, and will not move forward independently but together with other technology advances. For Daniela Rus, “the advances in navigation were enabled by hardware advances. When the LIDAR sensor—the laser scanner—was introduced, all of a sudden, the algorithms that didn’t work with sonar started working, and that was transformational”. Rodney Brooks insists that we need to take a larger perspective when want to understand the future transformation: “I don’t deny that, but what I do deny is when people say, oh that’s AI and robots doing that. As I say, I think this is more down to digitalization”.

2.7 Faced to the speed of change, it is likely that our mature countries will need adaptive mechanisms, similar to “universal basic income”.

The ideal mechanism to help the society adapt to an era of fast transformation due to accrued automation is yet to be determined, but there is a consensus among the scientists interviewed by Martin Ford that something is necessary. Yann Lecun says: “Economic disruption is clearly an issue. It’s not an issue without a solution, but it’s an issue with considerable political obstacles, particularly in cultures like the US where income and wealth redistribution are not something that’s culturally accepted”. James Manyika has a strong opinion on the possible scope of AI-fueled automation: “By looking at all this, we have concluded that on a task level in the US economy, roughly about 50% of activities—not jobs, but tasks, and it’s important to emphasize this—that people do now are, in principle, automatable”. Gary Marcus has a similar view, even if the timeline is stretched: “Driverless cars are harder than we thought, so paid drivers are safe for a while, but fast-food workers and cashiers are in deep trouble, and there’s a lot of them in the workplace. I do think these fundamental changes are going to happen. Some of them will be slow, but in the scale of, say, 100 years, if something takes an extra 20 years, it’s nothing”.  The conclusion, as pointed out by Daphne Koller, is that society needs to think hard about the disruption that is coming: “Yes, I think that we are looking at a big disruption on the economic side. The biggest risk/opportunity of this technology is that it will take a lot of jobs that are currently being done by humans and have those be taken over to a lesser or greater extent by machines. There are social obstacles to adoption in many cases, but as robust increased performance is demonstrated, it will follow the standard disruptive innovation cycle”.

For many scientists, the solution will look very much like Universal Basic Income (UBI), from Geoffrey Hinton : “Yes, I think a basic income is a very sensible idea” to Yoshua Bengio : “I think a basic income could work, but we have to take a scientific view on this to get rid of our moral priors that say if a person doesn’t work, then they shouldn’t have an income. I think it’s crazy”. Gary Markus sees UBI as the only solution to the inevitable job loss: “I see no real alternative. We will get there, but it’s a question of whether we get there peacefully through a universal agreement or whether there are riots on the street and people getting killed. I don’t know the method, but I don’t see any other ending”. Ray Kurzweil provides the optimistic version of this, since he thinks that the positive outcome of AI will have raised the overall revenue: “I made a prediction at TED that we will have universal basic income, which won’t actually need to be that much to provide a very high standard of living, as we get into the 2030s”.

The exact formula is not clear and some object to universal income as “inactivity subsidy”, but everyone is calling for something to soften the strength of the upcoming transformation. For instance, Demmis Hassabis says: “I think that’s the key thing, whether that’s universal basic income, or it’s done in some other form. There are lots of economists debating these things, and we need to think very carefully about how everyone in society will benefit from those presumably huge productivity gains, which must be coming in, otherwise it wouldn’t be so disruptive”. James Manyika likes the debate about UBI because he agrees that something has to happen, although he points out that work brings more than income, so the replacement of jobs should not be income alone. We need to supply as well “meaning, dignity, self-respect, purpose, community and social effects, and more” – hence UBI is not enough: “My issue with it is that I think it misses the wider role that work plays. Work is a complicated thing because while work provides income, it also does a whole bunch of other stuff. It provides meaning, dignity, self-respect, purpose, community and social effects, and more. By going to a UBI-based society, while that may solve the wage question, it won’t necessarily solve these other aspects of what work brings”. David Ferrucci also welcomes the need for regulation, while he points out how hard this is, to regulate without slowing down the advances and the benefits of technology for society. Eventually, what is needed is not only universal basic income, but universal contribution to society and universal training. Joshua Tenenbaum is more open about possible solutions in the future: “We should think about a basic income, yes, but I don’t think anything is inevitable. Humans are a resilient and flexible species. Yes, it might be that our abilities to learn and retrain ourselves have limitations to them. If technology keeps advancing, especially at this pace, it might be that we might have to do things like that. But again, we’ve seen that happen in previous eras of human history. It’s just unfolded more slowly”. Yann Lecun emphasizes the importance of training when society needs to undergo a strong technology shift : “ You would think that as technological progress accelerates, there’d be more and more people left behind, but what the economists say is that the speed at which a piece of technology disseminates in the economy is actually limited by the proportion of people who are not trained to use it”. A similar view is expressed by Andrew Ng: “I don’t support a universal basic income, but I do think a conditional basic income is a much better idea. There’s a lot about the dignity of work and I actually favor a conditional basic income in which unemployed individuals can be paid to study”.

2.8  Most AI experts are optimistic about the future that AI will make possible for our societies, despite the complex transformation journey.

 The consensus is rather positive about what AI will enable society to accomplish in the years to come. For instance, Stuart Russel says: “As an optimist, I can also see a future where AI systems are well enough designed that they’re saying to humans, “Don’t use us. Get on and learn stuff yourself. Keep your own capabilities, propagate civilization through humans, not through machines.” Stuart Russel explains that AI should be controllable and safe by design, otherwise it fails its purpose. For Yann Lecun, the rise of automation will create a premium on meaningful human interaction: “Everything that’s by done by machine is going to get a lot cheaper, and anything that’s done by humans is going to get more expensive. We’re going to pay more for authentic human experience, and the stuff that can be done by machine is going to get cheap”. For James Maniyika, the race for AI applications has already started (“the genie is out of the bottle”) and this is good because “we’re about to enter a new industrial revolution. I think these technologies are going to have an enormous, transformative and positive impact on businesses, because of their efficiency, their impact on innovation, their impact on being able to make predictions and to find new solutions to problems, and in some case go beyond human cognitive capabilities”. Oren Etzioni quotes one of his colleagues, Eric Horvitz, to point out that the advances that AI will enable are badly needed, that is, the risk of not using AI is higher than the risk of moving forward: “He has a great quote when he responds to people who are worried about AI taking lives. He says that actually, it’s the absence of AI technology that is already killing people. The third-leading cause of death in American hospitals is physician error, and a lot of that could be prevented using AI. So, our failure to use AI is really what’s costing lives”.

Although I use the term “race” to reflect the intensity of the competition – and also as a link to Kai-Fu Lee’s book – most scientists do not see the international competition as a zero-sum game. On the contrary, there is room for collaboration as well. For instance, Andrew Ng says: “AI is an amazing capability, and I think every country should figure out what to do with this new capability, but I think that it is much less of a race than the popular press suggests”. Still other see the competition as a race, as pointed out by David Ferrucci: “To stay competitive as a nation you do have to invest in AI to give a broad portfolio. You don’t want to put all your eggs in one basket. You have to attract and maintain talent to stay competitive, so I think there’s no question that national boundaries create a certain competition because of how much it affects competitive economics and security”. This intense competition should receive the attention of public offices and some form of regulation is called for. For instance, Oren Etzioni says: “Yes, I think that regulation is both inevitable and appropriate when it comes to powerful technologies. I would focus on regulating the applications of AI—so AI cars, AI clothes, AI toys, and AI in nuclear power plants, rather than the field itself. Note that the boundary between AI and software is quite murky!”  We see once more the idea that AI is not an isolated technology, but a feature of advanced software systems. As told earlier, many scientists insist on the critical need for training to help people accommodate this new technology wave. To end on a positive note, Daniela Rus quotes the example of BitSource: “BitSource was launched a couple of years back in Kentucky, and this company is retraining coal miners into data miners and has been a huge success. This company has trained a lot of the miners who lost their jobs and who are now in a position to get much better, much safer and much more enjoyable jobs”.

3. Conclusion  

 This post is already quite long, due to the richness of the source and the complexity of the questions. I will simply end with a small summary of my key convictions (hence my key biases) about AI’s future in the years to come:

1. Nothing summarizes my way of thinking better than Pedro Domingo’s great quote that I used earlier: “ People worry that computers will get too smart and take over the world, but the real problem is that they’re too stupid and they’ve already taken over the world”. This is the first part of the introduction statement: the AI revolution – with all its current shortcomings - has already started.
2. “Unit techniques” – such as deep neural nets or Bayesian networks -  will continue to improve (because the domain is still young, and because there is a lot that we do not understand about our own successes with these techniques) but I expect to see more of the improvement coming from “system of systems” approach. Hence system engineering is critical part of AI science and technology, as the reading of Martin Ford’s book makes abundantly clear. However, the better understanding of deep learning is a fascinating quest (as shown in the book or in this great blog post from Google Brain’s team) that may bring revolutionary advances of its own.
3. It is very hard, not to say impossible, to forecast the next advances in AI methods. This makes predicting China’s future superiority, or Europe’s impediment, a risky proposition. Europe has a lot of strength in its science, academic and innovation network. At the same time (“en même temps”) extracting the value from what is currently at hand requires a level of investments, business drive, access to data and computing resources, and software engineering skills that Europe is comparatively lacking, as pointed out by Kai-Fu Lee. If you have a hard time to understand why massive computing power matters with AI, listen to Azeem Azhar conversation with Jack Clark in this great podcast.
4. I am a strong believer that biomimicry will continue to lead AI research towards new techniques and paradigms. For instance, it is safe to bet that progresses in perception will lead to progresses with reasoning. Because there is a continuum between narrow and general AI, or between weak and strong AI, progress will surprise us, even tough the “endgame ambition” of AGI is probably far away. The excessive hype about what AI can do today, or the heated debates about “superintelligence”, have created a reaction, especially from research scientists over 50, telling that “there is nothing new nor truly exciting about AI today”. I most definitely disagree. Though no one is able to foresee the next advances in AI science, I have a strong conviction that the previous accumulation of the past decade will create new amazing combinations and applications in the decade to come.