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This is a great comment and what holds a lot of today's AI back is indeed complexity. The only question I would add is whether it's still science (hypothesize,
by smoyer 5y ago
This is a great comment and what holds a lot of today's AI back is indeed complexity. The only question I would add is whether it's still science (hypothesize, predict and test as stated) if it's not a human performing the expected steps - e.g. is training a neutral network through feedback iterating through these steps (with short cycle times?)
- musingsole 5y agoProbably depends on how you define science. If you put science on a spectrum with it on one end and magic on the other (so the spectrum is more of explainable knowledge versus useful but inexplicable rituals), AI definitely moves towards the magic end of the spectrum and maybe into a field commonly called "engineering" for the fact that it bastardizes knowledge with utility.
- smoyer 5y agoMy question above would definitely not extend to the word engineering (and by association I probably have to withdraw the idea that AIs could do science.)
- netcan 5y ago>> The only question I would add is whether it's still science (hypothesize, predict and test as stated) if it's not a human performing the expected steps This is possibly just a semantic question. If so, I have no problem calling it something else.
- PaulDavisThe1st 5y ago> If it's not a human performing the expected steps Nothing is performing those steps in a NN.
- marcus_holmes 5y agothis is my question with a lot of this "science" - how are we ensuring that we're not incorporating researcher biases when setting up and training a new model? To take a reasonably provocative example: if a new climate model predicted extremely low rates of change, would it be tuned so it showed change in the accepted range? If so, then how useful is this model to science?