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by GregarianChild
1945 days ago
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It's hard to predict the future. Alan Turing invented neural nets in a little-known paper entitled Intelligent Machinery (see [1]), in 1948. Since, the use of NNs has moved away decisively from inspiration by nature. I reckon, nature's last big win in AI were convolutional NNs: Kunihiko Fukushima's neocognitron was published in 1980, and inspired by 1950s work of Hubel and Wiesel [2]. Modern deep learning is largely an exercise in distributed systems: how can you feed stacks and stacks of tensor-cores and TPUs with floating point numbers, while minimising data movement (the real bottleneck of all computing)? Not unlike, I think, how airplanes were originally inspired by birds, but nowadays the two have mostly parted ways, for solid technical reasons. [1] http://www.alanturing.net/turing_archive/pages/Reference%20A... [2] https://en.wikipedia.org/wiki/Neocognitron |
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I think this comment really sums up very well what is at the core of our discussion: the future of mathematics and science.
My strong belief is that thousands of years from now, Archimedes and Gauss will still be remembered, and everything we think is great now will be forgotten while they are not. That tells me that they were much farther ahead of their times than us, even though they didn't have modern computers.
Mathematicians and computer scientists both have it totally backwards imo. On the one hand, mathematicians think they have something to teach us about computer science, but they refuse to use technology properly. On the other hand, when we write code, it's all governed by mathematical laws and there are many questions (but maybe not you know, coding standards or the philosophy of writing good code) we could really use the guiding hand of mathematicians with, and they need to catch up with the times and we programmers need to accept they have something valuable to offer and to teach us.