FYI: The author of this blog post also wrote "How to do research at the MIT AI Research Lab". While it's fair to suspect he may not be fully up to scratch with how things are done, he should have a pretty good overview
My criticism is towards this paper, not necessarily - the author. Surely, he knows something about AI (otherwise it would be impossible write anything gaining such publicity) and philosophy (AFAIK it is his field).
Though, even if someone is accomplished scientist in a given field, it does not mean they are incapable of making (to put it mildly) questionable statements (Noam Chomsky on data-driven NLP, Judea Pearl on Deep Learning, Roger Penrose on quantum measurement and consciousness; from historical - Albert Einstein on quantum physics).
Yet, there are many errors which won't be noticed by newcomers, but are demonstrably false for researchers and practitioners. It is dangerous as novices may be prone to "appeal to authority" and mistake witty style for knowledge.
Don't take me wrong - I am all for sharing ideas, even half-baked. But I think that it works well better when there isn't artificially boosted confidence.
If you can excuse the slightly combative tone, data-driven (i.e. statisical) NLP is a big potato and Chomsky was dead on the money: you can model text, with enough examples of text, but you can't model language. Because text is not language.
Which is why we have excellent dependency parsers that are useless outside the Brown corpus (if memory serves; might be the WSJ) and very successful sentiment classifiers for very specific corpora (IMDB), etc, but there is no system that can generate coherent language that makes sense in a given conversational context and even the most advanced models can't model meaning to save their butts. And don't let me get started on machine translation.
Like I say - apologies for the combative tone, but in terms of overpromising, modern, statistical NLP takes the biscuit. A whole field has been persisting with a complete fantasy -that it's possible to learn language from examples of text- for several decades now, oblivious to all the evidence to the contrary. A perfect example of blindly pursuing performance on arbitrary benchmarks, rather than looking for something that really works.
On the one hand you make this sound extremely bad, while at the same time you describe it as just "making questionable statements".
Also, maybe I misunderstood the analogy, but I think you're being very unfair putting Albert Einstein who was wrong on quantum physics in the same basket as Roger Penrose with his view on consciousness, which may be questionable, but hasn't been disproved.
stared|7 years ago
Though, even if someone is accomplished scientist in a given field, it does not mean they are incapable of making (to put it mildly) questionable statements (Noam Chomsky on data-driven NLP, Judea Pearl on Deep Learning, Roger Penrose on quantum measurement and consciousness; from historical - Albert Einstein on quantum physics).
Yet, there are many errors which won't be noticed by newcomers, but are demonstrably false for researchers and practitioners. It is dangerous as novices may be prone to "appeal to authority" and mistake witty style for knowledge.
Don't take me wrong - I am all for sharing ideas, even half-baked. But I think that it works well better when there isn't artificially boosted confidence.
YeGoblynQueenne|7 years ago
If you can excuse the slightly combative tone, data-driven (i.e. statisical) NLP is a big potato and Chomsky was dead on the money: you can model text, with enough examples of text, but you can't model language. Because text is not language.
Which is why we have excellent dependency parsers that are useless outside the Brown corpus (if memory serves; might be the WSJ) and very successful sentiment classifiers for very specific corpora (IMDB), etc, but there is no system that can generate coherent language that makes sense in a given conversational context and even the most advanced models can't model meaning to save their butts. And don't let me get started on machine translation.
Like I say - apologies for the combative tone, but in terms of overpromising, modern, statistical NLP takes the biscuit. A whole field has been persisting with a complete fantasy -that it's possible to learn language from examples of text- for several decades now, oblivious to all the evidence to the contrary. A perfect example of blindly pursuing performance on arbitrary benchmarks, rather than looking for something that really works.
Aqua|7 years ago
Also, maybe I misunderstood the analogy, but I think you're being very unfair putting Albert Einstein who was wrong on quantum physics in the same basket as Roger Penrose with his view on consciousness, which may be questionable, but hasn't been disproved.