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I read it again, all I got is that you want to redefine intelligence specifically to not include current AI That feels like goalpost moving. Having it be inter

I read it again, all I got is that you want to redefine intelligence specifically to not include current AI That feels like goalpost moving. Having it be interpretable is a useful quality but it’s not the same as intelligence

crabbone 3y ago
Well... no, I don't want to redefine "intelligence". I don't know how you got this idea. What I'm saying is that "intelligence" didn't mean "guessing the best answer by averaging many known, externally generated answers" before neural network approach became popular. Intelligence was always about finding good solutions to problems. In rare cases doing nothing is a good solution to a problem, in less rare cases, statistically guessing is a good solution, but it still doesn't answer the question of how to find a good solution. No amount of improving the accuracy of statistical guess will answer this question because what we need is a proof that the claim is correct, but the guess is the opposite of a proof -- it's asking you to take the claim on faith. In practical terms, compare these two scenarios: 1. A neural net AI is diagnosing breast cancer in a patient. Suppose, it was a male patient (a very rare occurrence, that's likely to fail statistically guessing AI). AI fails to find cancer and the patient dies. 2. A radiologist diagnosing breast cancer in a patient. Same circumstances as before. The radiologist fails to identify cancer, the patient subsequently dies. In the first case, we don't even expect the AI to be correct. Nobody's taking the blame, because, well... we've constructed this AI to be guessing statistically. It couldn't have enough data to guess reliably because in order to do so it needed to see more breast cancer in male patients than we have data for. In the case of a radiologist, we expect the radiologist to understand that they are treating an exceptional case. We will blame them, and probably sue for malpractice because we expect them to be able to reason, to be able to explain why what they see "makes sense" and why their response to what they saw "made sense". We will hold them responsible for failing this patient. In common understanding of the word "intelligence", the radiologist is intelligent (or, at least, we expect them to be), while neural net isn't.

crabbone 3y ago

Well... no, I don't want to redefine "intelligence". I don't know how you got this idea. What I'm saying is that "intelligence" didn't mean "guessing the best answer by averaging many known, externally generated answers" before neural network approach became popular. Intelligence was always about finding good solutions to problems. In rare cases doing nothing is a good solution to a problem, in less rare cases, statistically guessing is a good solution, but it still doesn't answer the question of how to find a good solution. No amount of improving the accuracy of statistical guess will answer this question because what we need is a proof that the claim is correct, but the guess is the opposite of a proof -- it's asking you to take the claim on faith. In practical terms, compare these two scenarios: 1. A neural net AI is diagnosing breast cancer in a patient. Suppose, it was a male patient (a very rare occurrence, that's likely to fail statistically guessing AI). AI fails to find cancer and the patient dies. 2. A radiologist diagnosing breast cancer in a patient. Same circumstances as before. The radiologist fails to identify cancer, the patient subsequently dies. In the first case, we don't even expect the AI to be correct. Nobody's taking the blame, because, well... we've constructed this AI to be guessing statistically. It couldn't have enough data to guess reliably because in order to do so it needed to see more breast cancer in male patients than we have data for. In the case of a radiologist, we expect the radiologist to understand that they are treating an exceptional case. We will blame them, and probably sue for malpractice because we expect them to be able to reason, to be able to explain why what they see "makes sense" and why their response to what they saw "made sense". We will hold them responsible for failing this patient. In common understanding of the word "intelligence", the radiologist is intelligent (or, at least, we expect them to be), while neural net isn't.