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.

The Great Filter: are we rare, are we first, or are we doomed?

Fermi’s Paradox (the fact that we never detected any sign of aliens even though, conceptually, life could be relatively common in the universe) has already been discussed in this blog, as new results come in about the rarity of life bearing planets, the discovery of new Earth-like planets, or even the detection of possible signs of aliens.

There are a number of possible explanations for Fermi’s Paradox and one of them is exactly that sufficiently advanced civilizations could retreat into their own planets, or star systems, exploring the vastness of the nano-world, becoming digital minds.

A very interesting concept related with Fermi’s Paradox is the Great Filter theory, which states, basically, that if intelligent civilizations do not exist in the galaxy we, as a civilization, are either rare, first, or doomed. As this post very clearly describes, one of these three explanations has to be true, if no other civilizations exist.

The Great Filter theory is based on Robin Hanson’s argument that the failure to find any extraterrestrial civilizations in the observable universe has to be explained by the fact that somewhere, in the sequence of steps that leads from planet formation to the creation of technological civilizations, there has to be an extremely unlikely event, which he called the Great Filter.

This Great Filter may be behind us, in the process that led from inorganic compounds to humans. That means that we, intelligent beings, are rare in the universe. Maybe the conditions that lead to life are extremely rare, either due to the instability of planetary systems, or to the low probability that life gets started in the first place, or to some other phenomenon that we were lucky enough to overcome.

It can also happen that conditions that make possible the existence of life are relatively recent in the universe. That would mean that conditions for life only became common in the universe (or the galaxy) in the last few billions years. In that case, we may not be rare, but we would be the first, or among the first, planets to develop intelligent life.

The final explanation is that the Great Filter is not behind us, but ahead of us. That would mean that many technological civilizations develop but, in the end, they all collapse, due to unknown factors (some of them we can guess). In this case, we are doomed, like all other civilizations that, presumably, existed.

There is, of course, another group of explanations, which states that advanced civilizations do exist in the galaxy, but we are simply too dumb to contact or to observe them. Actually, many people believe that we should not even be trying to contact them, by broadcasting radio-signals into space, advertising that we are here. It may, simply, be too dangerous.


Image by the Bureau of Land Management, available at Wikimedia Commons

The wealth of humans: work and its absence in the twenty-first century

The Wealth of Humans, by Ryan Avent, a senior editor at The Economist, addresses the economic and social challenges imposed on societies by the rapid development of digital technologies.  Although the book includes an analysis of the mechanisms, technologies, and effects that may lead to massive unemployment, brought by the emergence of digital technologies, intelligent systems, and smart robots, the focus is on the economic and social effects of those technologies.

The main point Avent makes is that market mechanisms may be relied upon to create growth and wealth for society, and to improve the average condition of humans, but cannot be relied upon to ensure adequate redistribution of the generated wealth. Left to themselves, the markets will tend to concentrate wealth. This happened in the industrial revolution, but society adapted (unions, welfare, education) to ensure that adequate redistribution mechanisms were put in place.

To Avent, this tendency towards increased income asymmetry, between the top earners and the rest, which is already so clear, will only be made worst by the inevitable glut of labor that will be created by digital technologies and artificial intelligence.

There are many possible redistribution mechanisms, from universal basic income to minimum wage requirements but, as the author points out, none is guaranteed to work well in a society where a large majority of people may become unable to find work. The largest and most important asymmetry that remains is, probably, the asymmetry that exists between developed countries and underdeveloped ones. Although this asymmetry was somewhat reduced by the recent economic development of the BRIC countries, Avent believes that was a one time event that will not reoccur.

Avent points out that the strength of the developed economies is not a direct consequence of the factors that are most commonly thought to be decisive: more capital, adequate infrastructures, and better education. These factors do indeed play a role but what makes the decisive difference is “social capital”, the set of rules shared by members of developed societies that makes them more effective at creating value for themselves and for society. Social capital, the unwritten set of rules that make it possible to create value, in a society, in a country or in a company, cannot be easily copied, sold, or exported.

This social capital (which, interestingly, closely matches the idea of shared beliefs Yuval Harari describes in Sapiens) can be assimilated, by immigrants or new hires, who can learn how to contribute to the creation of wealth, and benefit from it. However, as countries and societies became adverse at receiving immigrants, and companies reduce workforces, social capital becomes more and more concentrated.

In the end, Avent concludes that no public policies, no known economic theories, are guaranteed to fix the problem of inequality, mass unemployment, and lack of redistribution. It comes down to society, as whole, i.e., to each one of us, to decide to be generous and altruistic, in order to make sure that the wealth created by the hidden hand of the market benefits all of mankind.

A must-read if you care about the effects of asymmetries in income distribution on societies.

IEEE Spectrum special issue on whether we can duplicate a brain

Maybe you have read The Digital Mind or The Singularity is Near, by Ray Kurzweil, or other similar books, thought it all a bit farfetched, and wondered whether the authors are bonkers or just dreamers.

Wonder no more. The latest issue of the flagship publication of the Institute for Electrical and Electronic Engineers, IEEE Spectrum , is dedicated to the interesting and timely question of whether we can copy the brain, and use it as blueprint for intelligent systems.  This issue, which you can access here, includes many interesting articles, definitely worth reading.

I cannot even begin to describe here, even briefly, the many interesting articles in this special issue, but it is worthwhile reading the introduction, on the perspective of near future intelligent personal assistants or the piece on how we could build an artificial brain right now, by Jennifer Hasler.

Other articles address the question on how expensive, computationally, is the simulation of a brain at the right level of abstraction. Karlheinz Meier’s article on this topic explains very clearly why present day simulations are so slow:

“The big gap between the brain and today’s computers is perhaps best underscored by looking at large-scale simulations of the brain. There have been several such efforts over the years, but they have all been severely limited by two factors: energy and simulation time. As an example, consider a simulation that Markus Diesmann and his colleagues conducted several years ago using nearly 83,000 processors on the K supercomputer in Japan. Simulating 1.73 billion neurons consumed 10 billion times as much energy as an equivalent size portion of the brain, even though it used very simplified models and did not perform any learning. And these simulations generally ran at less than a thousandth of the speed of biological real time.

Why so slow? The reason is that simulating the brain on a conventional computer requires billions of differential equations coupled together to describe the dynamics of cells and networks: analog processes like the movement of charges across a cell membrane. Computers that use Boolean logic—which trades energy for precision—and that separate memory and computing, appear to be very inefficient at truly emulating a brain.”

Another interesting article, by Eliza Strickland, describes some of the efforts that are taking place to use  reverse engineer animal intelligence in order to build true artificial intelligence , including a part about the work by David Cox, whose team trains rats to perform specific tasks and then analyses the brains by slicing and imaging them:

“Then the brain nugget comes back to the Harvard lab of Jeff Lichtman, a professor of molecular and cellular biology and a leading expert on the brain’s connectome. ­Lichtman’s team takes that 1 mm3 of brain and uses the machine that resembles a deli slicer to carve 33,000 slices, each only 30 nanometers thick. These gossamer sheets are automatically collected on strips of tape and arranged on silicon wafers. Next the researchers deploy one of the world’s fastest scanning electron microscopes, which slings 61 beams of electrons at each brain sample and measures how the electrons scatter. The refrigerator-size machine runs around the clock, producing images of each slice with 4-nm resolution.”

Other approaches are even more ambitious. George Church, a well-known researcher in biology and bioinformatics, uses sequencing technologies to efficiently obtain large-scale, detailed information about brain structure:

“Church’s method isn’t affected by the length of axons or the size of the brain chunk under investigation. He uses genetically engineered mice and a technique called DNA bar coding, which tags each neuron with a unique genetic identifier that can be read out from the fringy tips of its dendrites to the terminus of its long axon. “It doesn’t matter if you have some gargantuan long axon,” he says. “With bar coding you find the two ends, and it doesn’t matter how much confusion there is along the way.” His team uses slices of brain tissue that are thicker than those used by Cox’s team—20 μm instead of 30 nm—because they don’t have to worry about losing the path of an axon from one slice to the next. DNA sequencing machines record all the bar codes present in a given slice of brain tissue, and then a program sorts through the genetic information to make a map showing which neurons connect to one another.”

There is also a piece on the issue of AI and consciousness, where Christoph Koch and Giulio Tononi describe their (more than dubious, in my humble opinion) theory on the application of Integrated Information Theory to the question of: can we quantify machine consciousness?

The issue also includes interesting quotes and predictions by famous visionairies, such as Ray Kurzweil, Carver Mead, Nick Bostrom, Rodney Brooks, among others.

Images from the special issue of IEEE Spectrum.

To Be a Machine: Adventures Among Cyborgs, Utopians, and the Futurists Solving the Modest Problem of Death

Mark O’Connell witty, insightful and sometimes deeply moving account of his research on the topic of transhumanism deserves a place in the bookshelf of anyone interested in the future of humanity. Reading To Be a Machine is a delightful trip through the ideals, technologies, places and characters involved in transhumanism, the idea that science and technology will one day transform human into immortal computer based lifeforms.

For reasons that are not totally clear to me, transhumanism remains mostly a fringe culture, limited to a few futurists, off-the-mainstream scientists and technology nuts. As shared fictions go (to use Yuval Harari’s notation), I would imagine transhumanism is one idea whose time has come. However, it remains mostly unknown by the general public. While humanists believe that the human person, with his/her desires, choices, and fears, should be the most important value to be preserved by a society (check my review of Homo Deus), transhumanists believe that biological based intelligence is imperfect, exists purely because of historical reasons (evolution, that is) and will go away soon as we move intelligence into other computational supports, more robust than our frail bodies.

O’Connell, himself a hard-core humanist, as becomes clear from reading between the lines of this book, pursued a deep, almost forensic, investigation on what transhumanists are up to. In this process, he talks with many unusual individuals involved in the transhumanist saga, from Max More, who runs Alcor, a company that, in exchange for a couple hundred dollars, will preserve your body for the future in liquid nitrogen (or 80k for just the head) to Aubrey de Grey, a reputed scientist working in life extension technologies, who argues that we should all be working on this problem. In de Grey’s words, cited by O’Connell “aging is a human disaster on an unimaginably vast scale, a massacre, a methodical and comprehensive annihilation of every single person that ever lived“. These are just two of the dozens of fascinating characters in the book interviewed in place by O’Connell.

The narrative is gripping, hilarious at times, but moving and compelling, not the least because O’Connell himself provides deep insights about the issues the book discusses. The characters in the book are, at once, alien and deeply human, as they are only trying to overcome the limits of our bodies. Deservedly, the book has been getting excellent reviews, from many sources.

In the end, one gets the idea that transhumanists are crazy, maybe, but not nearly as crazy as all other believers in immortality, be it by divine intervention, by reincarnation, or by any other mechanisms so ingrained in mainstream culture.

Homo Deus: A Brief History of Tomorrow

Homo Deus, the sequel to the wildly successful hit Sapiens, by Yuval Harari, aims to chronicle the history of tomorrow and to provide us with a unique and dispassionate view of the future of humanity. In Homo Deus, Harari develops further the strongest idea in Sapiens, the idea that religions (or shared fictions) are the reason why humanity came to dominate the world.

Many things are classified by Harari as religions, from the traditional ones like Christianism, Islamism or Hinduism, to other shared fictions that we tend not to view as religions, such as countries, money, capitalism, or humanism. The ability to share fictions, such as these, created in Homo sapiens the ability to coordinate enormous numbers of individuals in order to create vast common projects: cities, empires and, ultimately, modern technology. This is the idea, proposed in Sapiens, that Harari develops further in this book.

Harari thinks that, with the development of modern technology, humans will doggedly pursue an agenda consisting of three main goals: immortality, happiness and divinity. Humanity will try to become immortal, to live in constant happiness and to be god-like in its power to control nature.

The most interesting part of the book is in middle, where Harari analyses, in depth, the progressive but effective replacement of ancient religions by the dominant modern religion, humanism. Humanism, the relatively recent idea that there is a unique spark in humans, that makes human life sacred and every individual unique. Humanism therefore believes that meaning should be sought in the individual choices, views, and feelings, of humans, replaced almost completely traditional religions (some of them with millennia), which believed that meaning was to be found in ancient scriptures or “divine” sayings.

True, many people still believe in traditional religions, but with the exception of a few extremist sects and states, these religions plays a relatively minor role in conducting the business of modern societies. Traditional religions have almost nothing to say about the key ideas that are central to modern societies, the uniqueness of the individual and the importance of the freedom of choice, ideas that led to our current view of democracies and ever-growing market-oriented economies. Being religious, in the traditional sense, is viewed as a personal choice, a choice that must exist because of the essential humanist value of freedom of choice.

Harari’s description of the humanism schism, into the three flavors of liberal humanism, socialist humanism, and evolutionary humanism (Nazism and other similar systems), is interesting and entertaining. Liberal humanism, based on the ideals of free choice, capitalism, and democracy, has been gaining the upper hand in the twentieth century, with occasional relapses, over socialism or enlightened dictatorships.

The last part of the book, where one expects Harari to give us a hint of what may come after humanism, once technology creates systems and machines that make humanist creeds obsolete, is rather disappointing. Instead of presenting us with the promises and threats of transhumanism, he clings to common clichés and rather mundane worries.

Harari firmly believes that there are two types of intelligent systems: biological ones, which are conscious and have, possibly, some other special properties, and the artificial ones, created by technology, which are not conscious, even though they may come to outperform humans in almost every task. According to him, artificial systems may supersede humans in many jobs and activities, and possibly even replace humans as the intelligent species on Earth, but they will never have that unique spark of consciousness that we, humans, have.

This belief leads to two rather short-sighted final chapters, which are little more than a rant against the likes of Facebook, Google, and Amazon. Harari is (and justifiably so) particularly aghast with the new fad, so common these days, of believing that every single human experience should go online, to make shareable and give it meaning. The downsize is that this fad provides data to the all-powerful algorithms that are learning all there is to know about us. I agree with him that this is a worrying trend, but viewing it as the major threat of future technologies is a mistake. There are much much more important issues to deal with.

It is not that these chapters are pessimistic, even though they are. It is that, unlike in the rest of Homo Deus (and in Sapiens), in these last chapters Harari’s views seem to be locked inside a narrow and traditionalist view of intelligence, society, and, ultimately, humanity.

Other books, like SuperintelligenceWhat Technology Wants or The Digital Mind provide, in my opinion, much more interesting views on what a transhumanist society may come to be.