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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.

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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.

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.

 

Bell’s Theorem, or why the universe is even stranger than we might imagine

The Einstein-Podolsky-Rosen “paradox” was at first presented as an argument against some of the basic tenets of quantum mechanics.

One of these basic tenets is that there is genuine randomness in the characteristics of particles. For instance, when one measures the spin of an electron, it is only at the instant the measure is taken that the actual value of the spin is defined. Until then, its value was defined by a probability function, that collapses when the measurement is taken.

The EPR paradox uses the concept of entangled particles. Two particles are “entangled” if they were generated in such a way that they exhibit a totally correlated particular characteristic. For instance, two photons generated by a specific phenomenon (such as an electron-positron annihilation, under some circumstances) will have opposite polarizations. Once generated, these particles can travel vast distances, still entangled.

If some particular characteristic of one of these particles is measured (e.g., the polarization of a photon) in one location, this measurement will, probabilistically, result in a given value. That particular value will determine, instantaneously, the value of that same characteristic on the other particle, no matter how far the particles are. It is this “spooky action at a distance” that Einstein, Podolsky and Rosen believed to be impossible. It seems that the information about the state of one of the particles travels, faster than light, to the place where the other particle is.

Now, we can imagine that that particular characteristic of the particles was defined the very instant they were generated. Imagine you have one bag with one white ball and one black ball, and you separate the balls, without looking at them,  and put them into separate boxes. If one of the boxes is opened in Australia, say, and it is white, we will know instantaneously the color of the other ball. There is nothing magic or strange about this. Hidden inside the boxes, was all along the true color of the boxes, a hidden variable.

Maybe this is exactly what happens with the entangled photons. When they are generated, each one already carries with it the actual value of the polarization.

It is here that Bell’s Theorem comes to show that the universe is even stranger than we might conceive. Bell’s result, beautifully explained in this video, shows that the particles cannot carry with them any hidden variable that tells them what to do when they face a measurement. Each particle has to decide, probabilistically, at the time of the measurement, the value that should be reported. And, once this decision is made, the measurement for the other entangled particle is also defined, even if the other particle is on the other side of the universe. It seems that information travels faster than light.

The fact is that hidden variables cannot be used to explain this phenomenon. As Bell concluded “In a theory in which parameters are added to quantum mechanics to determine the results of individual measurements, without changing the statistical predictions, there must be a mechanism whereby the setting of one measuring device can influence the reading of another instrument, however remote. Moreover, the signal involved must propagate instantaneously, …

A very easy and practical demonstration of Bell’s theorem can be done with polarized filters, like the ones used in cameras or some 3D glasses. If you take two filters and put them at an angle, only a fraction of the photons that go through the first one make it through the second one. The actual fraction is given by the cosine squared of the angle between the filters(so, if the angle is 90º, no photons go through the two filters). So far, so good. Now, if you have the two filters at an angle (say 45º, so that half the photons that pass the first go through the second filter) and put an additional filter between them, at an angle of 22.5º, it happens that roughly 85% of the photons go through the (now) second filter. Of these, roughly 85% go through the third filter (which used to be the second). That means that, with the three filters in place, roughly 72% of the photons go through, way more than if you had just the two first filters, which were not changed in any way. This, obviously, cannot happen if the decision of the photons was determined from the start.

Do look at the video, and do the experience yourself.

New technique for high resolution imaging of brain connections

MIT researchers have proposed a new technique that leads to very high resolution images of the detailed connections of neurons in the human brain. Taeyun Ku, Justin Swaney and Jeong-Yoon Park were the lead researchers of the work published in a Nature Biotechnology article. They have developed a new technique for imaging brain tissue at multiple scales that leads to unprecedented high resolution images of significant regions of the brain, which allows them to detect the presence of proteins within cells and determine the long-range connections between neurons.

The technique actually blows up the size of the tissues under observation, increasing their dimension, while preserving nearly all of the proteins within the cells, which can be labeled with fluorescent molecules and imaged.

The technique floods the brain tissue with acrylamide polymers, which end up forming a dense gel. The proteins are attached to this gel and, after they are denatured, the gel can be expanded to four or five times its original size. This leads to the possibility of imaging the blown-up tissue with a resolution that is much higher than would be possible if the original tissue was used.

Techniques like create the conditions to advance with reverse engineering techniques that could lead to a better understanding of the way neurons connect with each other, creating the complex structures in the brain.

Image credit: MIT

 

AIs running wild at Facebook? Not yet, not even close!

Much was written about two Artificial Intelligence systems developing their own language. Headlines like “Facebook shuts down down AI after it invents its own creepy language” and “Facebook engineers panic, pull plug on AI after bots develop their own language” were all over the place, seeming to imply that we were just at the verge of a significant incident in AI research.

As it happens, nothing significant really happened, and these headlines are only due to the inordinate appetite of the media for catastrophic news. Most AI systems currently under development have narrow application domains, and do not have the capabilities to develop their own general strategies, languages, or motivations.

To be fair, many AI systems do develop their own language. Whenever a neural network is trained to perform pattern recognition, for instance, a specific internal representation is chosen by the network to internally encode specific features of the pattern under analysis. When everything goes smoothly, these internal representations correspond to important concepts in the patterns under analysis (a wheel of car, say, or an eye) and are combined by the neural network to provide the output of interest. In fact, creating these internal representations, which, in a way, correspond to concepts in a language, is exactly one of the most interesting features of neural networks, and of deep neural networks, in particular.

Therefore, systems creating their own languages are nothing new, really. What happened with the Facebook agents that made the news was that two systems were being trained using a specific algorithm, a generative adversarial network. When this training method is used, two systems are trained against each other. The idea is that system A tries to make the task of system B more difficult and vice-versa. In this way, both systems evolve towards becoming better at their respective tasks, whatever they are. As this post clearly describes, the two systems were being trained at a specific negotiation task, and they communicated using English words. As the systems evolved, the systems started to use non-conventional combinations of words to exchange their information, leading to the seemingly strange language exchanges that led to the scary headlines, such as this one:

Bob: I can i i everything else

Alice: balls have zero to me to me to me to me to me to me to me to me to

Bob: you i everything else

Alice: balls have a ball to me to me to me to me to me to me to me to me

Strange as this exchange may look, nothing out of the ordinary was really happening. The neural network training algorithms were simply finding concept representations which were used by the agents to communicate their intentions in this specific negotiation task (which involved exchanging balls and other items).

The experience was stopped not because Facebook was afraid that some runaway explosive intelligence process was underway, but because the objective was to have the agents use plain English, and not a made up language.

Image: Picture taken at the Institute for Systems and Robotics of Técnico Lisboa, courtesy of IST.

Stuart Russell and Sam Harris on The Dawn of Artificial Intelligence

In one of the latest episodes of his interesting podcast, Waking Up , Sam Harris discusses with Stuart Russell the future of Artificial Intelligence (AI).

Stuart Russel is one of the foremost world authorities on AI, and author of the most widely used textbook on the subject, Artificial Intelligence, a Modern Approach. Interestingly, most of the (very interesting) conversation focuses not so much on the potential of AI, but on the potential dangers of the technology.

Many AI researchers have dismissed offhand the worries many people have expressed over the possibility of runaway Artificial Intelligence. In fact, most active researchers know very well that most of the time is spent worrying about the convergence of algorithms, the lack of efficiency of training methods, or in difficult searches for the right architecture for some narrow problem. AI researchers spend no time at all worrying about the possibility that the systems they are developing will, suddenly, become too intelligent and a danger to humanity.

On the other hand, famous philosophers, scientists and entrepreneurs, such as Elon Musk, Richard Dawkins, Bill Gates, and Nick Bostrom have been very vocal about the possibility that man-made AI systems may one day run amok and become a danger to humanity.

From this duality one is led to believe that only people who are away from the field really worry about the possibility of dangerous super-intelligences. People inside the field pay little or no attention to that possibility and, in many cases, consider these worries baseless and misinformed.

That is why this podcast, with the participation of Stuart Russell, is interesting and well worth hearing. Russell cannot be accused of being an outsider to the field of AI, and yet his latest interests are focused on the problem of making sure that future AIs will have their objectives closely allied with those of the human race.