Machines like me

Ian McEwan´s latest novel, Machines like me does not disappoint if you are looking for a well-written and accurate work of fiction about Artificial Intelligence. The novel takes place in a slightly parallel universe, where Alan Turing did not kill himself and, instead, continued to make important contributions to computer science and to Artificial Intelligence throughout his life. In this world, similar to ours but different in some important respects, AI has evolved much faster and, in the 80s, it became possible to acquire, by a reasonable amount, humanoid robots that could be used as servants, friends or companions.

And, indeed, Adam, the robot, is all of these. From the three characters in the novel (the other two are Charlie and Miranda and yes, there is a sort of love triangle involved) Adam has, no doubt, the more fascinating personality. Without giving away too much, Adam, who starts as something like a sophisticated new laptop, which a 470-page “user manual”, becomes the hero of the story, raising in the mind of the reader many questions about machine intelligence, consciousness, and the rights of intelligent machines. His takes on the events that unfold are sometimes brilliant (e.g., “those who believe in the afterlife will never be disappointed“), other times unexpected,  but never off the mark.

Artificially intelligent or not, Adam is by far the most fascinating character of the lot, and we find ourselves empathizing with him (or it?), in a way that you may not expect

In the process of telling the story, Ian McEwan creates an alternative version of the history of computer science and Artificial Intelligence, which is accurate, thought-provoking, and, ultimately, quite plausible. I strongly recommend this book as an inspiring reading for the summer!

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Mastering Starcraft

The researchers at DeepMind keep advancing the state of the art on the utilization of deep learning to master ever more complex games. After recently reporting a system that learns how to play a number of different and very complex board games, including Go and Chess, the company announced a system that is able to beat the best players in the world at a complex strategy game, Startcraft.

AlphaStar, the system designed to learn to play Starcraft, one of the most challenging Real-Time Strategy (RTS) games, by playing against other versions of itself, represents a significant advance in the application of machine learning. In Starcraft, a significant amount of information is hidden from the players, and each player has to balance short term and long term objectives, just like in the real world. Players have to master fast-paced battle techniques and, at the same time, develop their own armies and economies.

This result is important because it shows that deep reinforcement learning, which has already shown remarkable results in all sorts of board games,  can scale up to complex environments with multiple time scales and hidden information. It opens the way to the application of machine learning to real-world problems, until now deemed to difficult to be tackled by machine learning.

Deepmind presents Artificial General Intelligence for board games

In a paper recently published in the journal Science, researchers from DeepMind describe Alpha Zero, a system that mastered three very complex games, Go, chess, and shogi, using only self-play and reinforcement learning. What is different in this system (a preliminary version was previously referred in this blog), when compared with previous ones, like AlphaGo Zero, is that the same learning architecture and hyperparameters were used to learn different games, without any specific customization for each different game.
Historically, the best programs for each game were heavily customized to use and exploit specific characteristics of that game. AlphaGo Zero, the most impressive previous result, used the spatial symmetries of Go and a number of other specific optimizations. Special purpose chess program like Stockfish took years to develop, use enormous amounts of field-specific knowledge and can, therefore, only play one specific game.
Alpha Zero is the closest thing to a general purpose board game player ever designed. Alpha Zero uses a deep neural network to estimate move probabilities and position values. It performs the search using a Monte Carlo tree search algorithm, which is general-purpose and not specifically tuned to any particular game. Overall, Alpha Zero gets as close as ever to the dream of artificial general intelligence, in this particular domain. As the authors say, in the conclusions, “These results bring us a step closer to fulfilling a longstanding ambition of Artificial Intelligence: a general game-playing system that can master any game.
While mastering these ancient games, AlphaZero also teaches us a few things we didn’t know about the games. For instance, that, in chess, white has a strong upper hand when playing the Ruy Lopez opening, or when playing against the French and Caro-Kann defenses. Sicilian defense, on the other hand, gives black much better chances. At least, that is what the function learned by the deep neural network obtains…
Actualization: The NY Times just published an interesting piece on this topic, with some additional information.

Kill the baby or the grandma?

What used to be an arcane problem in philosophy and ethics, The Trolley Problem, has been taking center stage in the discussions about the way autonomous vehicles should behave in the case of an accident. As reported previously in this blog, a website created by MIT researchers, The Moral Machine, gave everyone the opportunity to confront him or herself with the dilemmas that an autonomous car may have to face when deciding what action to take in the presence of an unavoidable accident.

The site became so popular that it was possible to gather more than 40 million decisions, from people in 233 countries and territories. The analysis of this massive amount of data was just published in an article in the journal Nature. In the site, you are faced with a simple choice. Drive forward, possibly killing some pedestrians or vehicle occupants, or swerve left, killing a different group of people. From the choices made by millions of persons, it is possible to derive some general rules of how ethics commands people to act, when faced with the difficult choice of who to kill and who to spare.

The results show some clear choices, but also that some decisions vary strongly with the culture of the person in charge. In general, people decide to protect babies, youngsters and pregnant women, as well as doctors (!). At the bottom of the preference scale are old people, animals and criminals. 

Images: from the original article in Nature.

The Second Machine Age

The Second Machine Age, by Erik Brynjolfsson and Andrew McAfee, two MIT professors and researchers, offers mostly an economist’s point of view on the consequences of the technological changes that are remaking civilisation.

Although a fair number of chapters is dedicated to the technological innovations that are shaping the first decades of the 21st century, the book is at its best when the economic issues are presented and discussed.

The book is particularly interesting in its treatment of the bounty vs. spread dilema: will economic growth be fast enough to lift everyone’s standard of living, or will increased concentration of wealth lead to such an increase in inequality that many will be left behind?

The chapter that provides evidence on the steady increase in inequality is specially appealing and convincing. While average income, in the US, has been increasing steadily in the last decades, median income (the income of those who are exactly in the middle of the pay scale) has stagnated for several decades, and may even be decreasing in the last few years. For the ones at the bottom at the scale, the situation is much worst now than decades ago.

Abundant evidence of this trend also comes from the analysis of the shares of GDP that are due to wages and to corporate profits. Although these two fractions of GDP have fluctuated somewhat in the last century, there is mounting evidence that the fraction due to corporate profits is now increasing, while the fraction due to wages is decreasing.

All this evidence, put together, leads to the inevitable conclusion that society has to explicitly address the challenges posed by the fourth industrial revolution.

The last chapters are, indeed, dedicated to this issue. The authors do not advocate a universal basic income, but come out in defence of a negative income tax for those whose earnings are below a given level. The mathematics of the proposal are somewhat unclear but, in the end, one thing remains certain: society will have to address the problem of mounting inequality brought in by technology and globalisation.

LIFE 3.0: Being Human in the Age of Artificial Intelligence

Max Tegmark’s latest book, LIFE 3.0: Being Human in the Age of Artificial Intelligence, is an enthralling journey into the future, when the developments in artificial intelligence create a new type of lifeform on Earth.

Tegmark proposes to classify life in three stages. Life 1.0, unintelligent life, is able to change its hardware and improve itself only through the very slow and blind process of natural evolution. Single cell organisms, plants and simple animals are in this category. Life 2.0 is also unable to change its hardware (excepto through evolution, as for Life 1.0) but can change its software, stored in the brains, by using previous experience to learn new behaviors. Higher animals and humans, in particular, belong here. Humans can now, up to a limited point, change their hardware (through prosthetics, cellphones, computers and other devices) so they could also be considered now Life 2.1.

Life 3.0 is the new generation of life, which can change both its software and its hardware. The ability to change the computational support (i.e., the physical basis of computation) results from technological advances, which will only accelerate with the advent of Artificial General Intelligence (AGI). The book is really about the future of a world where AGI enables humanity to create a whole range of new technologies, and expand new forms of life through the cosmos.

The riveting prelude, The Tale of the Omega Team, is the story of the group of people who “created” the first intelligence explosion on planet Earth makes this a “hard-to-put-down” book.  The rest of the book goes through the consequences of this intelligence explosion, a phenomenon the author believes will undoubtedly take place, sooner or later. Chapter 4 focus on the explosion proper, and on how it could happen. Chapter 5, appropriately titled “Aftermath: The Next 10,000 Years” is one of the most interesting ones, and describes a number of long term scenarios that could result from such an event. These scenarios range from a benevolent and enlightened dictatorship (by the AI) to the enslaved God situation, where humanity keeps the AI in chains and uses it as a slave to develop new technologies, inaccessible to unaided humanity’s simpler minds. Always present, in these scenarios, are the risks of a hostile takeover by a human-created AGI, a theme that this book also addresses in depth, following on the ideas proposed by Nick Bostrom, in his book Superintelligence.

Being a cosmologist, Tegmark could not leave out the question of how life can spread through the Cosmos, a topic covered in depth in chapter 6, in a highly speculative fashion. Tegmark’s view is, to say the least, grandiose, envisaging a future where AGI will make it possible to spread life through the reachable universe, climbing the three levels of the Kardashev scale. The final chapters address (in a necessarily more superficial manner) the complex topics of goal setting for AI systems and artificial (or natural) consciousness. These topics somehow felt less well developed and more complete and convincing treatments can be found elsewhere. The book ends with a description of the mission of the Future of Life Institute, and the Asilomar AI Principles.

A book like this cannot leave anyone indifferent, and you will be likely to take one of two opposite sides: the optimistis, with many famous representatives, including Elon Mush, Stuart Russel and Nick Bostrom, who believe AGI can be developed and used to make humanity prosper; or the pessimists , whose more visible member is probably Yuval Noah Harari, who has voiced very serious concerns about technology developments in his book Homo Deus and in this review of Life 3.0.

AlphaZero masters the game of Chess

DeepMind, a company that was acquired by Google, made headlines when the program AlphaGo Zero managed to become the best Go player in the world, without using any human knowledge, a feat reported in this blog less than two months ago.

Now, just a few weeks after that result, DeepMind reports, in an article posted in arXiv.org, that the program AlphaZero obtained a similar result for the game of chess.

Computer programs have been the world’s best players for a long time now, basically since Deep Blue defeated the reigning world champion, Garry Kasparov, in 1997. Deep Blue, as almost all the other top chess programs, was deeply specialized in chess, and played the game using handcrafted position evaluation functions (based on grand-master games) coupled with deep search methods. Deep Blue evaluated more than 200 million positions per second, using a very deep search (between 6 and 8 moves, sometimes more) to identify the best possible move.

Modern computer programs use a similar approach, and have attained super-human levels, with the best programs (Komodo and Stockfish) reaching a Elo Rating higher than 3300. The best human players have Elo Ratings between 2800 and 2900. This difference implies that they have less than a one in ten chance of beating the top chess programs, since a difference of 366 points in Elo Rating (anywhere in the scale) mean a probability of winning of 90%, for the most ranked player.

In contrast, AlphaZero learned the game without using any human generated knowledge, by simply playing against another copy of itself, the same approach used by AlphaGo Zero. As the authors describe, AlphaZero learned to play at super-human level, systematically beating the best existing chess program (Stockfish), and in the process rediscovering centuries of human-generated knowledge, such as common opening moves (Ruy Lopez, Sicilian, French and Reti, among others).

The flexibility of AlphaZero (which also learned to play Go and Shogi) provides convincing evidence that general purpose learners are within the reach of the technology. As a side note, the author of this blog, who was a fairly decent chess player in his youth, reached an Elo Rating of 2000. This means that he has less than a one in ten chance of beating someone with a rating of 2400 who has less than a one in ten chance of beating the world champion who has less than a one in ten chance of beating AlphaZero. Quite humbling…

Image by David Lapetina, available at Wikimedia Commons.