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But he talks a lot about analysing data in context (which it seems is shorthand for "prior information that is relevant to the current input") and it's not unfa

by muglug 10y ago

But he talks a lot about analysing data in context (which it seems is shorthand for "prior information that is relevant to the current input") and it's not unfair to say that we're still a long way from having machines that can make the sorts of connections between different texts that the author describes.

nl 10y ago
It's true that we (as in everyone working on these problems - not just IBM) don't have this working. Two points, though: 1) This is probably the most active area of research in machine learning/AI at the moment. The same type of progress that was made in image understanding 2 years ago using deep learning techniques is now being seen in NLP. See for example[1]. 2) It isn't at all clear that "making connections" of the type he talks about is needed to generate the types on insights that he claims. For example, the work StitchFix is doing combining analogy reasoning on words along with other non-textual data[2] indicates to me that the current state-of-the-art absolutely could "understand" that Bob Dylan wrote about themes that were associated with anti-war sentiment. [1] "Teaching Machines to Read and Comprehend" http://arxiv.org/abs/1506.03340 http://arxiv.org/abs/1506.03340 [2] http://multithreaded.stitchfix.com/blog/2015/03/11/word-is-worth-a-thousand-vectors/ http://multithreaded.stitchfix.com/blog/2015/03/11/word-is-w..., https://github.com/stitchfix/context2vec https://github.com/stitchfix/context2vec,
- Bartweiss 10y ago
  I agree that this isn't a particularly special accusation, and that existing algorithms probably could get "anti-war" out of Dylan lyrics (especially with some meta content; most people learn about Dylan from sources stretching beyond his lyrics). I still think there's a legitimate complaint here, though: IBM isn't doing unusually badly, but they're promising an exceptional amount. Even if Watson is a good product, and symbolic understanding isn't needed for good AI, they're still making claims they can't deliver on.

by muglug 10y ago

nl 10y ago

It's true that we (as in everyone working on these problems - not just IBM) don't have this working. Two points, though: 1) This is probably the most active area of research in machine learning/AI at the moment. The same type of progress that was made in image understanding 2 years ago using deep learning techniques is now being seen in NLP. See for example[1]. 2) It isn't at all clear that "making connections" of the type he talks about is needed to generate the types on insights that he claims. For example, the work StitchFix is doing combining analogy reasoning on words along with other non-textual data[2] indicates to me that the current state-of-the-art absolutely could "understand" that Bob Dylan wrote about themes that were associated with anti-war sentiment. [1] "Teaching Machines to Read and Comprehend" http://arxiv.org/abs/1506.03340 http://arxiv.org/abs/1506.03340 [2] http://multithreaded.stitchfix.com/blog/2015/03/11/word-is-worth-a-thousand-vectors/ http://multithreaded.stitchfix.com/blog/2015/03/11/word-is-w..., https://github.com/stitchfix/context2vec https://github.com/stitchfix/context2vec,

Bartweiss 10y ago
I agree that this isn't a particularly special accusation, and that existing algorithms probably could get "anti-war" out of Dylan lyrics (especially with some meta content; most people learn about Dylan from sources stretching beyond his lyrics). I still think there's a legitimate complaint here, though: IBM isn't doing unusually badly, but they're promising an exceptional amount. Even if Watson is a good product, and symbolic understanding isn't needed for good AI, they're still making claims they can't deliver on.