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I don't understand the comments here. AI means we can literally brute-force any scientific mystery by trying out millions of algorithms until we find one that

I don't understand the comments here. AI means we can literally brute-force any scientific mystery by trying out millions of algorithms until we find one that fits (like Wolfram's New Kind of Science). General relativity was a brilliant theory, but we don't have billions of Einsteins to keep coming up with new theories. Soon, we will. And obviously, brute forcing is only a last resort, AIs will be smarter than that for most problem-sets. Who cares that it's a black box? Humans still need to verify a new model or theory (the AI will help with that too) but it'll shave decades off of discoveries. Why would humans lose domain knowledge? (There's plenty of non-economists writing economics papers already, and they seem human!) AI might dramatically increase the rate of scientific discoveries, and make a lot more people want to become scientists. It'll also help reclaim science from big institutions and academia, and help small independent groups make significant discoveries again without gatekeepers. Patent clerks might be able to make scientific discoveries again! And why would AI necessarily be purely imitative (i.e. only be capable of building off past knowledge, rather than generating new knowledge or at least using abstract reasoning)? See this tweet from a DeepMind researcher, plenty of people are working on this: https://twitter.com/Inoryy/status/1522621712382234624 https://twitter.com/Inoryy/status/1522621712382234624

mdp2021 4y ago
> Who cares that it's a black box To solve a specific problem, you just need a "mysterious solver". To increase human knowledge, you instead need a formally expressed insight. In the article I read, «In 2020, Google’s AI team DeepMind announced that its algorithm, AlphaFold, had solved the protein-folding problem». Now, I understand that AlphaFold "solves protein-folding problem_s_", while "the protein-folding problem" would be a theoretical problem which calls for a theory to be solved. This is the difference. They are two different matters, having things done and having us more knowledgeable.
randcraw 4y ago
"AI means we can literally brute-force any scientific mystery by trying out millions of algorithms..." This is exactly wrong. If true, then AI could readily solve any human-level problem we could throw at it. But AI still can't master any of the basic skills inherent in human children, much less in adults working in construction crews, or health care aides, or janitors, or even just driving a car. AI's ability to build robots capable of even basic tasks like giving a haircut or climbing over broken objects littering a stair -- they're nonexistent, and even after a decade of deep learning, they're not getting appreciably better. Deep learning has shown us two things: 1) deep NNs can learn how to solve approximation problems astonishingly efficiently and capably. That's AlphaGo and AlphaFold. And 2) a lot of what humans assumed to be signs of intelligence, like playing go and chess, understanding voice and speaking, recognizing objects in images -- these are just "monkey see, monkey do" skills based in probabilistic pattern matching, which deep NNs can do very, very well. But cognitive skills that require reasoning -- deduction, induction, and the recognition and rejection of contradiction -- these must identify relevant domain knowledge (semantics) and the ability to reason about it, not just the efficient matching of a pattern in a matrix using a GPU. Alas, the effective and scaleable means of solving these problems still remain a total mystery. So if you can generate a zillion clueless hypotheses per second using the very best deep NN, it will still drive over a cop dressed like a clown who's waving his arms the path of your smartcar, nor will it ever invent general relativity.
- naveen99 4y ago
  I think this is about to change. Robotics will be solved.

mdp2021 4y ago

> Who cares that it's a black box To solve a specific problem, you just need a "mysterious solver". To increase human knowledge, you instead need a formally expressed insight. In the article I read, «In 2020, Google’s AI team DeepMind announced that its algorithm, AlphaFold, had solved the protein-folding problem». Now, I understand that AlphaFold "solves protein-folding problem_s_", while "the protein-folding problem" would be a theoretical problem which calls for a theory to be solved. This is the difference. They are two different matters, having things done and having us more knowledgeable.

randcraw 4y ago

"AI means we can literally brute-force any scientific mystery by trying out millions of algorithms..." This is exactly wrong. If true, then AI could readily solve any human-level problem we could throw at it. But AI still can't master any of the basic skills inherent in human children, much less in adults working in construction crews, or health care aides, or janitors, or even just driving a car. AI's ability to build robots capable of even basic tasks like giving a haircut or climbing over broken objects littering a stair -- they're nonexistent, and even after a decade of deep learning, they're not getting appreciably better. Deep learning has shown us two things: 1) deep NNs can learn how to solve approximation problems astonishingly efficiently and capably. That's AlphaGo and AlphaFold. And 2) a lot of what humans assumed to be signs of intelligence, like playing go and chess, understanding voice and speaking, recognizing objects in images -- these are just "monkey see, monkey do" skills based in probabilistic pattern matching, which deep NNs can do very, very well. But cognitive skills that require reasoning -- deduction, induction, and the recognition and rejection of contradiction -- these must identify relevant domain knowledge (semantics) and the ability to reason about it, not just the efficient matching of a pattern in a matrix using a GPU. Alas, the effective and scaleable means of solving these problems still remain a total mystery. So if you can generate a zillion clueless hypotheses per second using the very best deep NN, it will still drive over a cop dressed like a clown who's waving his arms the path of your smartcar, nor will it ever invent general relativity.

naveen99 4y ago
I think this is about to change. Robotics will be solved.