I meet lots of people who tell me fatalistically (& often despondently) that it's near impossible to do important work on neural nets today, unless you have huge compute and huge data sets.
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In particle physics, Freeman Dyson found that, contrary to conventional wisdom, only a small fraction of the most important progress comes from building bigger accelerators. Much of it comes from much harder-to-control improvements in detectors and the like.pic.twitter.com/J2ejGimM0t
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Why is big science so seductive? In part, because it seems guaranteed: you can plan, you can see success from the start. That's much less nerve-inducing (and _seems_ less uncertain) than needing to have clever ideas along the way.
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Back to neural nets: a danger in scaling up your computational power is that you start to focus _only_ on questions that require that computational power. You hire specialists who thrive in that environment, but who aren't so good at playing with basic, fundamental questions...
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... and your culture starts to tilt that way, driving out people who do like to play with basic, fundamental questions.
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Take all this with a grain of salt. Neural nets are a side interest, not my main interest. Maybe I'm wrong. But I don't think so. This dynamic has played out in genome sequencing, in particle physics, & in many other areas. Big science is attractive, but often small science wins
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Does this mean computational power or big data is useless? No, of course not. There are important questions that can likely only be addressed that way. But if you want to work on AI, it seems to me a mistake to be too focused on the need for lots of data and lots of compute.
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End of conversation
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#Creativity loves constraint.#genius ...remember,#Einstein did so much with just a#thought#experiment! http://www.businessinsider.com/5-of-albert-einsteins-thought-experiments-that-revolutionized-science-2016-7 …#science#truth#research#grind#inspirepic.twitter.com/7RZPgH3y1C
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