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What we currently observe is not a revival of AI research, but an emergent mutual exponentiation of large-scale data collection and classic statistical AI techn
by _pmf_ 10y ago
What we currently observe is not a revival of AI research, but an emergent mutual exponentiation of large-scale data collection and classic statistical AI techniques. They now have a nearly infinitely customizable source of training data and verification data that can be used to train bog standard neural nets.
- dpandey 10y agoAgree, except with the 'nearly infinite' qualifier. We have some large data sets now and we can draw a cat. We don't know if we can build anything interesting with those data sets yet.
- rspeer 10y agoThis is essentially true, and I'm sad that someone is so caught up in the hype that they'd downvote a simple description of the actual state of the field. What we have right now is a boom in AI engineering, with AI research fairly neglected because it doesn't produce the same kind of hype. Here's a Twitter thread [1] where NLP researchers describe people who work on machine translation as 'depressed', because they spend their time tweaking neural net parameters to get tiny improvements over the best techniques of last decade, with no opportunity to understand what causes an improvement. [1] https://twitter.com/haldaume3/status/805055952789573635 https://twitter.com/haldaume3/status/805055952789573635