Visualization of possible chess move sequences (try it here)

Artificial Intelligence has always held a special affinity for games. Chess, in particular, was long considered a realm reserved for exquisite human intelligence: the greatest chess players are called Grandmasters; a large percentage of them are eccentric Russian introverts. Gary Kasparov's defeat, by IBM's specialized supercomputer Deep Blue in 1997, was heralded as a major milestone (he contends the match was unfair). But while the dominance of chess-playing software is culturally significant, does it matter for AI?

Chess, like Checkers, Connect-4, and Go, is a game of perfect information. That is, everything useful for choosing your next move is right there on the board (it would be nice to know what your opponent will do next, but you can assume that your opponent is just trying to make the best possible move too). If you had a computer powerful enough, it could consider every possible next move, every possible response, and so on, and finally deduce, absolutely, how to guarantee a particular outcome. To do this is to solve chess, to answer the question: is it possible for white to force a win? Checkers is solved (both players can force a draw). Connect-4 is solved (the first player can force a win). Chess has too many possible board positions to be solved anytime soon.

Deep Blue can compete with human players by searching many moves ahead, testing all possible combinations, and choosing the next move that leaves its opponent with the worst best option. This approach is called minimax search. Since the computer can't search through to all possible checkmates, it searches to a given depth and scores the resulting board position by the pieces each player still has (roughly speaking, a pawn is 1 point, knights and bishops are 3 points each, a rook is 5 points, and the queen is 8 points). Using this rubric, or heuristic, and searching 10-15 moves into the future, makes for an extremely formidable opponent.

Minimax theory was established by John von Neumann in 1928 and the algorithm was improved in the 1950s and 60s to run more efficiently. Deep Blue contains no general innovation that improves significantly on these now classic techniques. The heuristic for evaluating boards has been refined, and the program has a huge database of well-known openings and end-game sequences-when 5 or fewer pieces are left on the board. Thus, Deep Blue is less a marvel of Artificial Intelligence than of engineering: its success is a direct product of the number of positions it can consider in a second (200 million). This is the Brute Force method of problem solving at its finest.

Most real world problems are not like chess. Political maneuvering, for example, is a game of imperfect information, where each player must guess at underlying motives and resources from superficial clues. The language of political, and in particular war-time gamesmanship, has shifted markedly away from chess… towards poker. Obama tipped his hand, Chavez is bluffing, Ahmedinejad is all in.

And Artificial Intelligence for poker is still far behind humans. The University of Alberta's Polaris system earned a narrow victory at the 2^nd man-machine poker match last July, but the competition involved heads-up limit poker: one-on-one games where the only possible bets are $10 or $20. Compared with the main event at the World Series of Poker, which has no betting limit, and about 10 players at one table, this is something of a "toy" problem. Recent research focuses on how to model opponents-that is, automatically refining the software's understanding of the meaning of each players' bets as information is gathered about how those players play.

Over the next decade, I would guess that poker research, perhaps backed by military funding, will expand significantly. And unlike Deep Blue, poker software that can dominate a table full of professional players, will be the product of significant advances in the field of Artificial Intelligence.

I was a biologist once, before I got into television, so I find these times particularly trying when I see schoolteachers and otherwise intelligent people calling evolution into question. That's part of the reason that I jumped at the chance to co-produce a story about bio-inspiration (the other reason being that I LOVE geckos…which will make more sense if you watch our QUEST Bio-inspiration segment).

Bio-inspired design borrows its creative inspiration from models and systems in nature, that is, plant and animal parts that have been slowly tweaked for over 3.8 billion years. But that doesn't mean that nature's designs are perfect. In fact, that's what makes the process of engineering things based on natural models so difficult. You have to figure out how to pull the aces from the evolutionary discard pile. As professor Bob Full at U.C. Berkeley explained in our first phone conversation, that's also why scientists now use the term "bio-inspiration" rather than the more commonly known term "biomimicry." Biologists and engineers are not looking to simply mimic nature, because there are all kinds of dead ends and redundancies in natural systems that would be pointless to recreate in an optimized, man-made piece of technology. One of the examples he gave me is a kind of grasshopper that if you were to copy it, you would copy neurons that go to nothing, they don't connect to any muscles, and that's because during evolution the adults lost their ability to fly. The neurons going to the muscles are still there, but the muscles aren't there anymore. No need to copy that, right?

So what a biomimeticist does is look to nature to find plants & animals with remarkable performance abilities, and studies their adaptations for inspiration to design something new. For example, if you want to make a tiny robot that can fly, then look at the best fliers. If you want to design a blade that moves quickly through fluids, or an Olympic swimsuit that minimizes drag, then look to the most efficient swimmers. Now that's what I call "intelligent design!"

Watch the Bio-Inspiration: Nature as Muse television story report online.

The term "artificial intelligence", was coined in the summer of 1956, on the bucolic grounds of Dartmouth College in Hanover, New Hampshire. There, John McCarthy (who would later go on to teach at Stanford), Marvin Minsky, Claude Shannon, Nathan Rochester and six other conference participants came together to lay out the framework for this exciting new field which would "…find how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves." (McCarthy et al., 1955)

Though it was McCarthy who persuaded his nine other colleagues at the conference to adopt the term "artificial intelligence" to describe the nascent field, the seeds of artificial intelligence were planted earlier. Alan Turing, who was instrumental in breaking the German's Enigma code during WWII, published a paper in 1950 that laid out what came to be known as the "Turing Test:" if a machine could carry out a conversation with a human in such a sophisticated manner as to trick the human into thinking that he or she was conversing with another human, then the machine would have displayed true "intelligence."

But nearly 60 years later, the world still awaits a machine capable of exhibiting "general A.I.", instead of the "narrow A.I." demonstrated by IBM's chess-playing Deep Blue or Stanford University's Stanley, an autonomous robotic vehicle, or other impressive albeit limited applications of A.I. For example, Deep Blue may be able to beat Gary Kasparov at chess but can it beat a 10 year-old at a game of checkers? The lack of a general A.I. is made even more stark when juxtaposed with Moore's Law, a maxim that goes back to 1965 when Intel founder Gordon Moore postulated that the number of transistors on a computer chip would double roughly every 18 months.

There's even a term – "Singularity" – that is being used to describe the moment when technological progress will leapfrog and herald the creation of computers that not only achieve human-like intelligence, but also give rise to a progeny of computers who will be smarter then their digital forbears. Though he didn't coin the term (sci-fi writer Vernor Vinge did), the most famous exponent of this belief is inventor Ray Kurzweil. He places the Singularity as occurring sometime before 2050 and believes that with the advent of this unheralded technological progress, mankind may solve some of our society's most pressing ills, such as global warming, and even conquer death, by uploading one's consciousness into a virtual medium.

Though this seems a far stretch from engineering a domestic robot like Stanford's Artificial Intelligence Robot, top A.I. researchers like Stanford's Andrew Ng and Daphne Koller do believe that computing systems will some day be as smart or smarter than humans. When I spoke with Dharmendra Modha about his work into cognitive computing at IBM, he talked effusively about creating an "i-Brain," a digital accessory that people could carry around, making decisions and processing information like its human cousin. But if you're like me, and lament those moments when you've misplaced your keys or other instances of poor neural performance, you can't help but think that such a device can't arrive soon enough. On second thought, I'll wait until v2.0 hits the shelves.

Watch the Artificial Intelligence: Thinking Big television story report online.

And don't miss our Web Extra: A Dose of A.I. In this QUEST web exclusive, Stanford University computer science professor and artificial intelligence (A.I.) researcher Daphne Koller provides an elegant explanation of how A.I. can be employed in the examining room to diagnose a patient's illness more accurately than a human clinician. Find out more and learn how medical diagnosis is just the tip of the iceberg when it comes to tasks that rely on making sense of a sea of data to arrive at an informed conclusion.