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]

Many people I meet, especially those outside the tech industry, are frequently unsure of what to make of virtual worlds/Second Life. Either they can’t disconnect the concept from video games, or they simply “don’t get the point;” regardless, what is missing for these people is a logical application, a use, a function.

That is why virtual conferences are usually the first scenario I mention; every working soul can relate to them, and immediately grasp the obvious benefits a virtual conference would have over a real one. Who can argue against spending less money, cutting your environmental impact, and skipping the other stresses associated with forced business travel?

Linden Lab recently published a case study detailing how IBM has used Second Life, titled: “How Meeting In Second Life Transformed IBM’s Technology Elite Into Virtual World Believers.” It provides a pretty good review, mixing attendees’ reactions with stories of individuals and departments within the organization that were behind it all. I encourage readers who are skeptical about virtual worlds and particularly their use in business to look over this short report – maybe IBM’s experience will inspire you.

[PDF - How Meeting In Second Life Transformed IBM’s Technology Elite Into Virtual World Believers]

For a demo of Olive 2.2, check out this video:

Yesterday, Market Watch posted a press release titled “Virtual Worlds – Better Experience and Less Costly Than Conference Calls” explaining that “With the challenging financial times most enterprises are curtailing travel and rethinking how their organizations hold events, training sessions, conduct periodic meetings, or improve their collaboration processes.”

Welcome Enterprise Virtual Worlds.

This past October, Forrester Research released a report titled ” Will Unified Communications Make Virtual Worlds Relevant To Business” By TJ Keitt and Henry Dewing. The report states: “As the workplace has changed and as decentralized information workers demand tools to allow them to work together, companies have to rethink their collaboration, training, and knowledge management schemes. According to a recent survey of North American and European enterprise IT decision-makers, 49% view implementing an enterprise collaboration strategy as a priority in the next 12 months. However, current collaboration and knowledge management tools are still developing and often jumble efforts to bring together employees rather than streamlining their interactions. Consolidating information worker tools in a holistic platform is a trend that Forrester has labeled as the Information Workplace. Virtual worlds, with their rich graphical interfaces, seem — at least on the surface — to be great for simulating a gamut of experiences to these distributed information workers.”

Furthermore, it is a new tool of efficiency at the corporate level. The misconception that virtual environments and social media are at the hands of immature youth needs to be put to the side and enterprise environments need to be embraced by the corporate main stream.

With business’s having to cutback, the holistic platform of Virtual Worlds offers a new model of efficiency and cost savings. Something that should be welcomed during these economic times.