The last human surveys the burnt husk of his dying planet, suppressing pangs of awe as the gleaming computronium ships - no more puzzles left to solve - turn and flicker out of space-time in search of new challenges elsewhere.
"Clever, but nothing to do with general intelligence," he mumbles under his breath as the flames consume him.
Brute force statistical inference over millions of examples is an advance, but it is an advance that falls within the same paradigm of machine learning that we've had for decades. IMO, it will really take a paradigm shift to move into general intelligence.
Obviously accomplishments like this are interesting for their own sake. But the really interesting question for me is, is there anything here, that is new, that can be used outside of tree-based games of complete information? (A much weaker question, is there anything new here that can be used in other games?)
Let's take the ability to match the best humans at various tasks. If we put doing long multiplication better than the best human at "1", and passing the Turing Test at "10", where is a Go playing engine? I'd say less than "2", but still higher than chess.
Much higher (but still short of general intelligence, obviously) would be beating the best humans at language translation, proving math theorems, or answering factual questions phrased in natural language. Then, if and when computers can write novels which you'd prefer to read over the best human author, now we're talking.
So I think, we don't need to worry about our humanity here yet, and we're not remotely close. Over less than a century we've gone from outperforming humans in arithmetic to outperforming us in things that are a little less like arithmetic but still discrete games modeled by trees and precise searches.
> Much higher (but still short of general intelligence, obviously) would be beating the best humans at language translation, proving math theorems, or answering factual questions phrased in natural language. Then, if and when computers can write novels which you'd prefer to read over the best human author, now we're talking.
There's a lot of similarity. Take math theorem proving, which is an active area of machine learning; the algorithms tend at the moment to look something like create a tree of possible theorems, then the algorithm tries to traverse down the tree towards the target theorem by evaluating each branch by the probability it leads towards the target. Because of the exponential explosion, it's very important to select good theorems to expand at each level of the tree, and the ML algorithms can learn characteristics of good intermediate theorems by learning on the existing corpuses of machine-checkable proofs, which gives them much higher success rates in reaching target theorems. So right now the best theorem-provers can already reprove a lot of existing proofs. But they're using stuff like SVMs last I saw, and were much less sophisticated than a deep q-learner.
"Clever, but nothing to do with general intelligence," he mumbles under his breath as the flames consume him.