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So tell me something.... Have AI techniques actually changed in the last 20 years, or is there just more data, better networking, better sensors, and faster co
by cujo 7y ago
So tell me something....
Have AI techniques actually changed in the last 20 years, or is there just more data, better networking, better sensors, and faster compute resources now.
By my survey of the land, there haven't been any leaps in the AI approach. It's just that it's easier to tie real world data together and operate on it.
For a university, what changes when you teach? This sounds like researchers feeling like they can't churn out papers that are more like industry reports vs advances in ideas.
- buboard 7y agoIndeed. The academic theory for NNs has been there since before the 90s, and is solidly grounded in a mathematical framework. Whatever new techniques arose after 2010 are a) empirical results obtained by semi-trial-and-error and b) unexplainable mathematically.
- currymj 7y agoThere hasn't been any kind of paradigm shift but there have been a bunch of real although incremental improvements. Improved optimization algorithms, interesting and novel neural network layers. Some of this is even motivated by mathematical theory, even if you can't prove anything in the setting of large, complex models on real-world data. The quote from Hinton is something like, neural networks needed a 1000x improvement from the 90s, and the hardware got 100x better while the algorithms and models got 10x better.
- cujo 7y agoSo I guess this kind of leads to my original point. If you're a school, nothing has really changed that would require you to invest gobs of money to teach AI. It only matters if your idea of research is trying to create something you can go to market with. So basically every graduate chemistry program.