Modern civilization and the trappings of technology has lead to the decline of our own intuition. Many of us have become unaware of its value or even its very existence. Intuition as a basis of complex computation is easily dismissed as an approach outside of the conventional. This lack of conventionality leads many researchers to ignore its potential.
The intuitive mind is a sacred gift and the rational mind is a faithful servant. We have created a society that honors the servant and has forgotten the gift. — Albert Einstein
The research that I do in Artificial Intelligence (AI) revolves around the idea that advanced cognitive machines will use intuition as the substrate of its intelligence (see: “artificial intuition”). Our own human minds provide ample evidence for general intelligence. Humans are fundamentally intuition machines and our rational (and conscious) self are just a simulation layered on top of intuition-based machinery (see: “cognitive stack”). This is in stark contrast to Descartes famous saying “I think. therefore I am” (Cogito ergo sum), which implies that our rational thinking is what separates us from all of biology. We thus have a cognitive bias to demand technologies and methodologies that are driven by logical machinery. This is indeed the reason for multi-decade failure of Good Old Fashioned AI (GOFAI) which attempted to solve the problem of intelligence from formal logic as its starting point.
One of the counter-intuitive predictions of intuition based machines is “how can logical thought arise from intuition machines?” Since 2012, we have seen the incredible advances of Deep Learning technology. Deep Learning networks are intuition machines. These systems learn to perform inference (or make predictions) by using induction. Deep Learning systems have been able to perform tasks that are usually reserved for biological brains. Tasks that have known to be difficult for conventional computing, such as facial and speech recognition, can be performed at super human levels by these machines.
Deep Learning networks however are incapable of performing logical tasks such as long division. One should not expect to be able to teach an animal (i.e. your dog) to perform multiplication much less addition or subtraction. However, human brains are able to perform all sorts of logical problems. We have to ask though, can a caveman be able to do multiplication? Are we innately capable of advanced logical cognition or is this capability something we learned as a consequence of our advanced civilization?
The big chasm that needs to be crossed to achieve more general artificial intelligence is what is known as the “semantic gap”. How do we fuse the capabilities of Deep Learning (sub-symbolic) system with logical (symbolic) systems?
Human minds are capable of performing great feats of logical reasoning. How are our minds able to do this if our machinery is all intuition based? I am going to make the assumption here that we don’t have any innate logical machinery. It is unlikely that Homo sapiens have evolved this cognitive machinery in the short time we’ve existed in this planet. Therefore, to bridge the semantic gap, we need to bridge it using intuition only mechanisms. What this means is that we don’t need to perform a fusion of logical components with intuition components. All we ever need is intuition components.
Therefore we need to show ample evidence that complex logical thinking can be performed by an intuition machine.
This is where AlphaZero makes its revolutionary revelation. AlphaZero is the latest evolution of DeepMinds’s Go play program. I have written previously about AlphaGo Zero (different from AlphaZero) and how it was able to learn to master the game of Go from scratch (without human knowledge). 99% of Westerners have never played the game of Go and simply don’t understand it at all. So the relevance of DeepMind’s AlphaGo Zero achievement has been muted. We don’t understand the enormity of the achievement. Go however has been known to be a game of intuition. So it’s somewhat (ignorantly) unsurprising that an intuition machine (one based on Deep Learning) is able to master the game.
However, what DeepMind’s new incarnation (AlphaZero) is able to do is play the game of chess. This of course may not be surprising to many since the game of chess has been ‘solved’ by computer ever since IBM’s DeepBlue bested Kasparov in 1996. It may not be remarkable for the uninitiated that it took AlphaZero a few hours to master the game of chess from scratch. It may not be remarkable that AlphaZero was able to destroy the best chess playing program (Stockfish) in 100 games.
What is truly remarkable is how AlphaZero played in dismantling its more logical opponent. To give you an idea, I will quote some impressions from the chess playing community.
It approaches the ‘Type B,’ human-like approach to machine chess dreamt of by Claude Shannon and Alan Turing instead of brute force. — Gary Kasparov.
I always wondered how it would be if a superior species landed on earth and showed us how they play chess. I feel now I know. — Peter Heine Nielsen
“It doesn’t play like a human, and it doesn’t play like a program. It plays in a third, almost alien, way.” — Demis Hassabis (who also plays chess)
For those who understand chess play, it’s probably best to watch the actual game play of AlphaZero versus Stockfish. What you will see is how an intuition based system dismantles an opponent that is based on logic (that is, one that can’t refuse a gambit). Below are games with expert commentary:
AlphaZero plays a very different game of chess. It is willing to sacrifice pieces in order to gain a positional advantage over its opponent. It is playing a kind of chess judo where it uses an opponents eagerness in achieving an immediate gain against itself. It sets up its opponent into what is known in chess as “zugzwang”, where every move that one makes leads to a worse outcome. It seems to have a more holistic sense of the game of chess where all its pieces move in a highly coordinated manner. AlphaZero plays a game that maximizes its creativeness against a logical opponent that is unable to see beyond short term gains. It plays a game of chess that is not only unimaginable, but would in the past been placed in a pedestal for all to marvel.
The paper about AlphaZero was presented in the recently concluded NIPS 2017 conference. It is an extremely short paper, the main body is only 7 pages long. It provides an interesting detail about how extensively it evaluates the board position to decide on its move.
AlphaZero searches just 80 thousand positions per second in chess, compared to 70 million for Stockfish.
The intuition machine is using 1,000 times less evaluations than the logical opponent.
What you are witnessing here with AlphaZero is validation of my original thesis about intuition machines and their ability to perform logical reasoning. This is the semantic gap being bridged. This is an extremely difficult AGI milestone being surmounted at a record pace. I doubt anyone in the AI community expected this kind of progress to be achieved so quickly. Yet is has happened and the landscape has been changed forever.