Of course, In biology you have genetic mutations that survive or don’t (yes I’m oversimplifying). In economics new ideas start as new companies and survive or don’t. What’s fascinating is how closely machine learning (especially deep learning) mirrors that same process (2/n)
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In computing most search algorithms (take Djykstra’s as an example) are basically “rapid guess and check.” Machine learning and AI are essentially “guess and check” done incredibly quickly at a huge scale with beefed up computer hardware (3/n)
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So here’s my unifying theory of life: Systems are so complex that no one can predict anything, so basically we should try as many things as we possibly can and discard the losers as quickly as we can, knowing that learning takes so long we’ll all probably die first (4/n)
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Nope. Many cheap, fast to fail experiments > few large, expensive ones is a pretty universal rule
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Information is fiat because we fear uncertainty, and knowledge is the counterpart to entropy and lack of understanding in the world. I think this is an underrated analogy Austen. I think this is why education is all about extensibility, not individual subject matter
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Agreed on the overall conclusion, however ... That’s not how ML/AI algos work (e.g. backprop), BUT that is how developing techniques and implementing ML/AI works (the human parts). We’re all navigating different n-dim spaces with sparse resources. Tools are the same.
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Imagine a system that is not limited to Darwinian evolution but can use Lamarck's/Lysenko's version. That's what tech has. Well, ideally. (It requires an intelligent designer and, well, we're still unsure whether one of those exists.)
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Also Matt Ridley’s “The Evolution of Everything”
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David Deutsch’s “The Fabric of Reality” goes deeper on this
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