Tag Archives: Go

Unexpected Moves

When AlphaGo played and won against Sedol, it made innovative moves not only unexpected by human experts but also not easily understandable for humans. Apparently this shocked and scared some folks.

However, AI coming up with different concepts than humans is nothing new. Consider this article recounting the story of Eurisko, a genetic programming experiment in the late 1970s. This experiment, too, aimed at competing in a tournament; the game played, Traveller TCS, was apparently about designing fleets of ships and letting them fight against each other. Even this early, simple, and small-scale AI thing surprised human observers:

“To the humans in the tournament, the program’s solution to Traveller must have seemed bizarre. Most of the contestants squandered their trillion-credit budgets on fancy weaponry, designing agile fleets of about twenty lightly armored ships, each armed with one enormous gun and numerous beam weapons.”

(G. Johnson:
Eurisko, The Computer With A Mind Of Its Own)

Keep in mind there was nothing scary in the algorithm, it was really just simulated evolution in a rather small design space and the computer needed some help by its programmers to succeed.

The Eurisko “AI” even rediscovered the concept of outnumbering the enemy instead of overpowering him, a concept humans might associate with Lanchester’s models of predator-prey systems:

“Eurisko, however, had judged that defense was more important than offense, that many cheap, invulnerable ships would outlast fleets consisting of a few high-priced, sophisticated vessels. (…) In any single exchange of gunfire, Eurisko would lose more ships than it destroyed, but it had plenty to spare.”

(G. Johnson:
Eurisko, The Computer With A Mind Of Its Own)

Although Eurisko’s approach seemed “un-human”, it really was not. Eurisko only ignored all human biases and intuition, making decisions strictly by cold, hard data. This is a common theme in data mining, machine learning, and AI applications. Recommender systems, for example, create and use concepts unlike those a human would apply to the same situation; an article in IEEE Spectrum a couple of years ago (J. A. Konstan, J. Riedl: Deconstructing Recommender Systems) outlined a food recommender example and pointed out that concepts like “salty” would not appear in their models.

Transparency and auditability are surely problems if such technology is being used in critical applications. Whether we should be scared beyond this particular problem remains an open question.

 

(This is a slightly revised version of my G+ post, https://plus.google.com/+SvenT%C3%BCrpe/posts/5QE9KeFKKch)

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