3 ms·
True, of course. But at some indeterminate point theories start seeming awfully ad-hoc even if they still retain other properties. Let's say I propose that no
by arjie 10y ago
True, of course. But at some indeterminate point theories start seeming awfully ad-hoc even if they still retain other properties.
Let's say I propose that no existing solar panel designs respond to artificial light. Easily falsifiable. Say you perform the experiment and find that panels from First Solar do respond to artificial light. I refine my hypothesis to "no existing solar panel designs respond to artificial light except for First Solar". You find that SunEdison panels do, by testing at your lab in SF. I refine my hypothesis to "no existing solar panel designs respond to artificial light when tested outside San Francisco except for First Solar". You repeat the experiment in San Jose, and I refine my hypothesis again.
We're revising the model, keeping it falsifiable, and keeping it consistent with existing evidence. It would not be without merit to claim that this is something of a toy hypothesis, however, and that our time would be better served otherwise.
- narrator 10y agoThis sounds like the scientific equivalent of "overfitting" in machine learning. The point of a theory is not to predict the training set well, but to generalize well to new cases. This is what string theory fails at.
- zamalek 10y agoNNs are modeled after the human brain. It's not an immensely crazy to point out that we could be overfitting ourselves by continuously exposing ourselves to a single theory.
- wolfgke 10y agoThe successful backpropagation algorithm works completely different from anything that has been observed in the human brain up to now.