failures on deep learning now don’t mean there won’t be improvements later. 20 years and billions of $ later though #deeplearning is still stuck in the same place.
might that be a clue?
response to Yoshua Bengio on the future of AI @Mediumhttps://link.medium.com/v974FzRCoR
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your "billions of $ later" comment is misleading: most DL researchers weren't working on language or compositionality in language (admittedly hard problems). there have been lots of success stories in DL (e.g., speech recognition), where the money has been well spent
so you think none of that was relevant to language after all? certainly not portrayed that way
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of course it's "relevant": that's a low bar. The goal of the earlier work in DL was *not* compositionality in language. Now that this is a goal of current research, let's see where they get. My point was that you are misrepresenting history to overstate an idea.
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In layman's terms, Google & Microsoft have probably spent over $1bn in the last few years alone. And the goal should be human-like accuracy, not ML benchmarks that show incremental improvement on leaderboards, whilst not emulating human-like accuracy, as said by
@sir_deenicus
End of conversation
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