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Alphago didn't learn from zero either. It has a pre-processor that identifies sets of patterns with known features, and also:

"AlphaGo was initially trained to mimic human play by attempting to match the moves of expert players from recorded historical games, using a database of around 30 million moves".

AlphaGo != AlphaGo Zero

That's for an earlier system (which also used less compute).

AlphaGo was followed by AlphaGo Zero (which is the topic of this article) which did not use the process that you describe, it used only the rules of the game and the winning condition.

Oops, my mistake. Thanks for the correction.

Yes! So perhaps one way to make the machine more efficient, is by one of pre-programmed “general” models, that can be attuned to a particular problem in a much shorter time?

And long collaborative study sessions.