Big science is historically non-optimal. But it keeps many scientists employed and perhaps one of these scientists may be the person making a future breakthrough. Would you rather the money be wasted elsewhere (as it usually is)?
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Replying to @IntuitMachine
Again, you have changed the subject. Most of this money is currently not being applied to general A.I. but to DL only.
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Replying to @Grady_Booch
I don't think you can make that conclusion that the money is being spent on DL only. I don't know yet where the Chinese, Canadians, French, Germans are placing bets. DoD have some ideas, which they call 'Third Wave AI', that goes beyond symbolists and connectionist approaches.
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Replying to @IntuitMachine
I am very familiar with DARPA’s program. Yes, it desires to push beyond DL, but DL is very much the baseline. As for China - and it is difficult to get uncensored information- they seem to have a maniacal DL focus.
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Replying to @Grady_Booch @IntuitMachine
But this conversation is distracting: my simple original point is that there is a huge pile of money at national levels being poured into some very specific A.I. approaches...and that is a little concerning to me, not unlike what happened in Japan.
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Replying to @Grady_Booch @IntuitMachine
You went off on a tangent suggesting I condemned general funding for research..that is not at all what I was saying. Don’t overthink the topic: my spidey sense is tingling, that is all.
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Replying to @Grady_Booch
If your concern is that there's going to be a lot of wasted money then I think that is misplaced. I guarantee that many of these nations will 'waste' a ton of money for minimal results. That's just the nature of basic research.
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Replying to @IntuitMachine
No, I am mostly concerned of the opportunity cost...as
@GaryMarcus has observed, a lot of these new and heavily funded efforts are proceeding with vigor but with hardly any understanding or appreciation for the history of AI2 replies 2 retweets 10 likes -
What great success of AI history are people forgetting? The high development cost and higher ownership cost of hand-coded knowledge bases?
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Google Search was built on classical AI and hand-coded knowledge bases. Not too shabby. So was Siri AFAIK, though embeddings surely play a role now. Weather-prediction is still classical models, not deep learning. Best driving systems still hybrids, as far as I know.
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Upon reflection I mispoke re Google Search; pre RankBrain there was lots of handcrafting, from what I understand, both in terms of rules and feature engineering, & lots of symbol-manipulation, but not that much AI and my use of “knowledge base” was sloppy.
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