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> This embodies the very fundamental difference between science and engineering. Not really though. In engineering, you have heuristics, even if you don't know
by ummonk 3y ago
> This embodies the very fundamental difference between science and engineering.
Not really though. In engineering, you have heuristics, even if you don't know why they work. In the case of deep learning / AI, there seems to be very little in the way of built up heuristic knowledge - it's just "try stuff and see what works for every problem".
- epistasis 3y agoI think that's also the difference between science and engineering: has the tool/technology been around enough to learn heuristics, or is everything still in the "fuck around and find out" phase?
- liuxiansheng 3y agoI think if this was truly the case then wouldn't ML algorithm development be a solved problem with AutoML? I don't think AutoML is close to ubiquitous which means there must still be value in heuristics and a deeper understanding of our tools.
- robotresearcher 3y agoAll the model topologies that have names are heuristics. The idea of a 'layer' is a heuristic. And so on. You don't really just try stuff. You choose very few things to try from the space of models. And you choose craftily. We have quite a lot of domain craftiness now, if you think about it that way.