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Comment by dingo_bat | Hacker News Reader

I don't think there is a very sharp distinction between results oriented R&D and "basic research". In the article, IBM's deep blue is dismissed as a dead-end victory but apparently alphago is not? Why? They both seem identical to me in goals and research methodology.

On a side note, I cannot wait for general super intelligence. It cannot come soon enough. I'm tired of being poor and stuck in a fucking rut, and contemplating my death in a few short decades.

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Why? They both seem identical to me in goals and research methodology.

In theory taking the work done on AlphaGo (and more importantly AlphaGo Zero) and generalizing it to non-Go related problems should be a lot easier than taking the work done on deep blue and generalizing to non chess related problem.

Yes, AlpaGo Zero is mainly self taught. It means it learned to play through the game mechanics. There are no databases of moves or smart optimizations that are based on our understanding of go.

AlphaGo is an application of general tech, just like Deep Blue was an application of general tech.

I think the point was that Deep Blue was using brute force, where as Alpha Go had taught itself and Alpha Go Zero has gone the full distance to require zero outside help.

So Deep Blue wasn't such a big step in terms of General Artificial Intelligence because it was heavily dependent on the human optimisations and was just showing the power of number crunching rather than learning.

It's more of a continuum than that. Yes deep blue had more hand-tuning and custom-built structure. AlphaGo had less hand tuning and of course waaaay more brute Force on top.

If their goals were similar, Google would have stopped when AlphaGo beat top go players, and AlphaGo Zero wouldn't exist.

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