Meet Duplex, your new assistant, courtesy of Google

Advances in natural language processing have enabled systems such as Siri, Alexa, Google Assistant or Cortana to be at the service of anyone owning a smartphone or a computer. Still, so far, none of these systems managed to cross the thin dividing line that would make us take them for humans. When we ask Alexa to play music or Siri do dial a telephone number, we know very well that we are talking with a computer and the replies of the systems would remind us, were we to forget that.

It was to be expected that, with the evolution of the technology, this type of interactions would become more and more natural, possibly reaching a point where a computer could impersonate a real human, taking us closer to the vision of Alan Turing, a situation where you cannot tell a human apart from a computer by simply talking to both.

In an event widely reported in the media, at the I/O 2018 conference, Google made a demonstration of Duplex, a system that is able to process and execute requests in specific areas, interacting in a very human way with human operators. While Google states that the system is still under development, and only able to handle very specific situations, you get a feeling that, soon enough, digital assistants will be able to interact with humans without disclosing their artificial nature.  You can read the Google AI blog post here, or just listen to a couple of examples, where Duplex is scheduling a haircut or making a restaurant reservation. Both the speech recognition system and the speech synthesis system, as well as the underlying knowledge base and natural language processing engines, operate flawlessly in these cases, anticipating a widely held premonition that AI systems will soon be replacing humans in many specific tasks.

Photo by Kevin Bhagat on Unsplash

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European Commission releases communication on Artificial Intelligence

Today, April 25th, 2018, the European Commission released a communication entitled Artificial Intelligence for Europe, and a related press release, addressing what could become the European strategy for Artificial Intelligence.

The document states that “Like the steam engine or electricity in the past, AI is transforming our world, our society and our industry. Growth in computing power, availability of data and progress in algorithms have turned AI into one of the most strategic technologies of the 21st century.

The communication argues that “The EU as a whole (public and private sectors combined) should aim to increase this investment [in Artificial Intelligence] to at least EUR 20 billion by the end of 2020. It should then aim for more than EUR 20 billion per year over the following decade.” These values should be compared with the current value of 4-5 billion, spent in AI.

The communication also addresses some questions raised by the increased ability of AI systems to replace human jobs: “The first challenge is to prepare the society as a whole. This means helping all Europeans to develop basic digital skills, as well as skills which are complementary to and cannot be replaced by any machine such as critical thinking, creativity or management. Secondly, the EU needs to focus efforts to help workers in jobs which are likely to be the most transformed or to disappear due to automation, robotics and AI. This is also about ensuring access for all citizens, including workers and the self-employed, to social protection, in line with the European Pillar of Social Rights. Finally, the EU needs to train more specialists in AI, building on its long tradition of academic excellence, create the right environment for them to work in the EU and attract more talent from abroad.”

This initiative, which has already received significant press coverage, may become Europe’s answer to the strong investments China and the United States are making in Artificial Intelligence technologies. There is also a fact sheet about the communication.

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.

Portuguese Edition of The Digital Mind

IST Press, the publisher of Instituto Superior Técnico, just published the Portuguese edition of The Digital Mind, originally published by MIT Press.

The Portuguese edition, translated by Jorge Pereirinha Pires, follow the same organization and has been reviewed by a number of sources. The back-cover reviews are by Pedro Domingos, Srinivas Devadas, Pedro Guedes de Oliveira and Francisco Veloso.

A pre-publication was made by the Público newspaper, under the title Até que mundos digitais nos levará o efeito da Rainha Vermelha, making the first chapter of the book publicly available.

There are also some publicly available reviews and pieces about this edition, including an episode of a podcast and a review in the radio.

The last invention of humanity

Irving John Good was a British mathematician who worked with Alan Turing in the famous Hut 8 of Bletchley Park, contributing to the war effort by decrypting the messages coded by the German enigma machines. After that, he became a professor at Virginia Tech and, later in life, he was a consultant for the cult movie 2001: A Space Odyssey, by Stanley Kubrick.

Irving John Good (born Isadore Jacob Gudak to a Polish jewish family) is credited with coining the term intelligence explosion, to refer to the possibility that a super-intelligent system may, one day, be able to design an even more intelligent successor. In his own words:

Let an ultraintelligent machine be defined as a machine that can far surpass all the intellectual activities of any man however clever. Since the design of machines is one of these intellectual activities, an ultraintelligent machine could design even better machines; there would then unquestionably be an ‘intelligence explosion,’ and the intelligence of man would be left far behind. Thus the first ultraintelligent machine is the last invention that man need ever make, provided that the machine is docile enough to tell us how to keep it under control.

We are still very far from being able to design an artificially intelligent (AI)  system that is smart enough to design and code even better AI systems. Our current efforts address very narrow fields, and obtain systems that do not have the general intelligence required to create the phenomenon I. J. Good was referring to. However, in some very restrict domains, we can see at work mechanisms that resemble the that very same phenomenon.

Go is a board game, very difficult to master because of the huge number of possible games and high number of possible moves at each position. Given the complexity of the game, branch and bound approaches could not be used, until recently, to derive good playing strategies. Until only a few years ago, it was believed that it would take decades to create a program that would master the game of Go, at a level comparable with the best human players.

In January 2016, DeepMind, an AI startup (which was at that time acquired by Google by a sum reported to exceed 500M dollars), reported in an article in Nature that they had managed to master the complex game of Go by using deep neural networks and a tree search engine. The system, called AlphaGo, was trained on databases of human games and eventually managed to soundly beat the best human players, becoming the best player in the world, as reported in this blog.

A couple of weeks ago, in October of 2017, DeepMind reported, in a second article in Nature, that they programmed a system, which became even more proficient at the game, that mastered the game without using any human knowledge. AlphaGo Zero did not use any human games to acquire knowledge about the game. Instead, it played millions of games (close to 30 millions, in fact, played over a period of 40 days) against another version of itself, eventually acquiring knowledge about tactics and strategies that have been slowly created by the human race for more than two millennia. By simply playing against itself, the system went from a child level (random moves) to a novice level to a world champion level. AlphaGo Zero steamrolled the original AlphaGo by 100 to 0,  showing that it is possible to obtain super-human strength without using any human generated knowledge.

In a way, the computer improved itself, by simply playing against itself until it reached perfection. Irving John Good, who died in 2009, would have liked to see this invention of mankind. Which will not be the last, yet…

Picture credits: Go board, picture taken by Hoge Rielen, available at Wikimedia Commons.