Of course, evolution is statistical. But as I wrote in AlphaGo critique: blurring distinction between evolution & learning blurs causal mechanisms. May as well call it all change and lump rock formation with classical conditioning; key question is whether you need strong priors.https://twitter.com/plinz/status/968490295968063488 …
We already observe that physics is changing. The universe expands etc. When we observe change we can of course always construct an underlying regularity. We don’t need to discard any possibility.
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In practice we do discard possibilities in order to actually get stuff done. Then, after the fact, if we find what we've built is sufficiently incompatible with observation, we discard and start rebuilding.
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It's the 'getting stuff done' metric that regularizes the hypothesis space in this case compared to e.g. updating priors based on evidence. Hypotheses that are non-actionable or impossible to distinguish between can be merged or discarded without loss of performance.
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