History’s greatest grandmaster Garry Kasparov just wrote an in-depth review of Diego Rasskin-Gutman’s newly published “Chess Metaphors: Artificial Intelligence and the Human Mind” for the New York Review of Books. The article provides a spectacular summary of the history of computer chess programs and the AI engines that power them. What makes it a must-read, however, are Kasparov’s personal anecdotes about his own experiences playing computers, including, of course, his notorious loss to IBM’s Deep Blue in their 1997 rematch (Kasparov won the first match in 1996). This not only marked the first time a computer defeated a world chess champion, but was also seen around the world, for better or for worse, as the conclusive tipping point when raw machine intelligence overcame what we mere humans could handle.

Kasparov occupies an entirely unique post in the history of world chess champions – the tables turned during his guard. He started out in the mid-80s being able to beat the most sophisticated chess programs conclusively every single time; during the late-90s the exact reverse situation materialized. In fact, by the beginning of the new millennium $50 commercially available chess programs running on regular PCs could “crush most grandmasters” and when Kasparov last played two serious matches with such programs (in 2003) they both ended in a tie.

Of course Kasparov reminds as that just because computers can now decisively win at chess doesn’t mean that “chess is solved.” He writes:

“The number of legal chess positions is 10⁴⁰, the number of different possible games, 10¹²⁰. Authors have attempted various ways to convey this immensity, usually based on one of the few fields to regularly employ such exponents, astronomy…Diego Rasskin-Gutman points out that a player looking eight moves ahead is already presented with as many possible games as there are stars in the galaxy. Another staple…is to say there are more possible chess games than the number of atoms in the universe.”

Even if there were such a thing as “solving chess” – that is, both sides continuously playing the perfect game – these impressive numbers highlight how mathematically difficult, probably impossible it would be to actually accomplish this feat. And even if this were hypothetically possible, Kasparov notes the importance of not confusing narrow AI applications, such as playing chess, with general intelligence of the sort that allows humans to think, intuit, dream, walk up stairs, wash the dishes – to generally lead the kind of lives expected of (relatively) advanced sentient beings. Chess has been used for centuries as the ultimate metaphor for the mind, but Kasparov finally concludes that “perhaps chess is the wrong game for the times.”

Enter: Poker
Many players, including myself and several of my friends, started out playing poker directly from the chess world; yes the opportunity for profit is greater, but it is also because poker is in many ways more complex and challenging than chess. While chess is a 100% information game and entirely susceptible to computer calculation, poker is defined by hidden information and contains nuanced elements such as tells, bluffing, and emotional control which span distinct fields such as social psychology, risk-management, and game theory – all aspects that make it significantly more problematic both for players and AI programmers to master.

Something I have been advocating for several years, and it seems that Kasparov and others agree: Poker is where AI gaming researchers should be focused. Whereas chess programs can now consistently beat anyone in the world, no one has yet figured out how to beat advanced human players at poker. And it’s surely not for lack of effort – there are dozens of entities working on this problem every day. In fact, some claim that they have already built software that profitably beats online poker (for example: “How I Built a Working Poker Bot”). There are many others out there, and some make the claim that they are consistently winning. Of course it is difficult to assess the success of these programs – especially since many of them are likely not even public – yet I remain skeptical. It is probably possible to program something to beat the microlimits, but entering the realm of even mid-stakes amateur play is an entirely different ballgame.

I write this not to disregard people’s present efforts, but to encourage AI researchers to view the poker platform with the same enthusiasm and seriousness that I believe it deserves. The many disparate fields from which players must simultaneously draw information and the very social nature of the game make it perfectly suited for some truly interesting research of advanced gaming AI, and, dare I say it, even research dealing with problems of strong AI. At least it is surely more accurate than chess as a metaphor both of the mind and of the way humans interact with the world. A few brief reasons:

-Minds operate on heuristics, not algorithms.
-Our existence can be summed up as making choices in the face of limited information.
-We operate within a social context.
-Information that is presented to you may be false, and will surely be false >0% of the time.
-Individual instances of “chance” exist and do matter.
-There are limited resources and different entities have varying amounts of control over those resources.
-There are many variables from varying realms that go into making even the simplest decisions.

Let’s go beyond poker bots that can at best squeeze out profits playing for pennies, and begin thinking about a poker AI than can adapt to individual players, can make unwarranted bluffs only to establish a crazy image for uncertain future gains, can vary its playing style – in short, can outthink its opponents. Perhaps while venturing down this road of creating a social, competitive agent, one driven by the same impulses of survival and will towards progress that guide biological life, we will come one step closer to the birth of a true thinking machine. Just be careful: never trust a poker player.

[The Chess Master and the Computer, Garry Kasparov - New York Review of Books]
[Cylons Playing Poker via Anthony J. Cox]

Virtual worlds are a natural setting for experimenting and testing various AI applications and appear especially suited for “general” intelligence development because of their broadness, openness and theoretical lack of limitations and rules. Ben Goertzel and his company, Novamente, have been working on virtual pets and the results are pretty interesting. So while projects such as these are something to get excited about, it’s hard not to dismiss bigheaded goals as worthless hype without seeing the real work that has gone into specific planning and execution – at least something that does not recall the infamous Underpants Gnome plan of 1)Collect underpants 2)??? 3)Profit. So while I don’t think much will come out of this, I have to issue the small warning that the distinction between “virtual” and “real” is pretty nonexistent. All that data is comprised of real code stored on real servers with real physical locations managed by real people… And again while I do remain skeptical of anyone achieving anything close to sentient intelligence anytime soon, I am also a bit concerned about singular commercial entities working secretly on a closed AGI system. Not involving all stakeholders – that is, ALL OF US – raises some ethical quandaries that at least need to be debated.

I would put someone like Philip Rosedale (proven record, visionary thinker, deep pockets and connections) pretty high on the list of people that could make some serious headway in the field, especially in potential commercial applications; he certainly tackles the big issues. Let’s just hope Philip Rosedale doesn’t resemble Watchmen’s Ozymandias…

[New World Notes - Philip Rosedale Attempting to Create Sentient Artificial Intelligence That Thinks and Dreams in Second Life!]

The group of researchers…said about 100 million images can be read, adding that dreams as well as mental images are likely to be visualized in the future in the same manner.

The research will be published Thursday in the U.S. scientific journal Neuron.”

Wow.

Full article at Daily Yoimuri Online via Slashdot

The article notes:

“IBM will join five US universities in an ambitious effort to integrate what is known from real biological systems with the results of supercomputer simulations of neurons. The team will then aim to produce for the first time an electronic system that behaves as the simulations do. The longer-term goal is to create a system with the level of complexity of a cat’s brain. ”

Kurzweil must be psyched he’s taking all those supplements. We’re getting close, but still have a little ways to go to truly backwards program the brain.

I am weary about the ethical implications of achieving a feat like this, but I do believe it is important to continue with this sort of research as the medical and technological benefits are endless. I am excited to see the development of this bio-technology throughout my lifetime; nonetheless, we must be conscious of the ancillary effects.

Via The BBC (Thanks!)