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How would your Python script differentiate misleading questions from genuine questions? Like the following: Q: How many parts will the Andromeda galaxy brea

How would your Python script differentiate misleading questions from genuine questions? Like the following: Q: How many parts will the Andromeda galaxy break into if a grain of salt is dropped on it? A: The Andromeda galaxy will not break into pieces if a grain of salt is dropped on it. Q: What percentage of Earth surface is covered with water? A: Water covers 71% of the Earth's surface. Q: Why would people ask AI questions that are illogical? A: There is no one answer to this question. Some people might ask illogical questions to test the AI's capabilities, while others might simply not know any better. > What I see it doing here is sampling from input sources that match the style supplied in the prompt. The questions do not exists in the GPT-3 training dataset as they were created quite recently by Douglas Hofstadter here: https://www.economist.com/by-invitation/2022/06/09/artificial-neural-networks-today-are-not-conscious-according-to-douglas-hofstadter https://www.economist.com/by-invitation/2022/06/09/artificia... > Language models aren’t built to perform reasoning. Have you heard about Minerva? > In “Solving Quantitative Reasoning Problems With Language Models”, we present Minerva, a language model capable of solving mathematical and scientific questions using step-by-step reasoning. Source: https://ai.googleblog.com/2022/06/minerva-solving-quantitative-reasoning.html https://ai.googleblog.com/2022/06/minerva-solving-quantitati... Example problems: https://minerva-demo.github.io https://minerva-demo.github.io You should also take a look here: https://www.metaculus.com/questions/4903/if-tested-would-gpt-3-demonstrate-text-based-intelligence-parity-with-human-4th-graders/#comment-99128 https://www.metaculus.com/questions/4903/if-tested-would-gpt... Again, the questions were made after GPT-3 was trained. > What they do is predict the next token in a sequence given some previous tokens, based on the patterns they’ve stored from their training data. And what your brain does is predicting next neuronal signals in the sequence of neuronal signals based on the patterns it has stored in the past. That's what brains do. So what? The results matter.

throwaway1851 4y ago
1. I wasn't suggesting that those questions were in the training data. That’s not my contention at all. My contention is that GPT-3 is sampling from patterns it has observed in text to generate more text - and only that. Do you imagine that it has some reasoning process in which it evaluated the effect of a grain of salt on the Andromeda galaxy, before it decided to spit out a formulaic denial of the question’s premise? 2. Thanks for the link to Minerva. Haven’t had a chance to read that, and it’s interesting. It does seem to be a project specifically aimed at getting quantitative reasoning, meaning that there are a lot of architectural priors going into that objective. 3. Your last point is quite reductive and strange. When I engage in a conversation, I don’t just vomit up patterns I’ve seen before. I critically evaluate information I’m taking in, I consider my past experiences, I apply imagination and curiosity, and I decide if I have something to say in response. If I do, I search for the language patterns that seem capable of expressing what I have to say. This process is nothing like a generative model regurgitating plausible but empty blather. I think humans only do that for specific reasons (performatively, to fill page counts, to write placeholder copy, etc.).

throwaway1851 4y ago

1. I wasn't suggesting that those questions were in the training data. That’s not my contention at all. My contention is that GPT-3 is sampling from patterns it has observed in text to generate more text - and only that. Do you imagine that it has some reasoning process in which it evaluated the effect of a grain of salt on the Andromeda galaxy, before it decided to spit out a formulaic denial of the question’s premise? 2. Thanks for the link to Minerva. Haven’t had a chance to read that, and it’s interesting. It does seem to be a project specifically aimed at getting quantitative reasoning, meaning that there are a lot of architectural priors going into that objective. 3. Your last point is quite reductive and strange. When I engage in a conversation, I don’t just vomit up patterns I’ve seen before. I critically evaluate information I’m taking in, I consider my past experiences, I apply imagination and curiosity, and I decide if I have something to say in response. If I do, I search for the language patterns that seem capable of expressing what I have to say. This process is nothing like a generative model regurgitating plausible but empty blather. I think humans only do that for specific reasons (performatively, to fill page counts, to write placeholder copy, etc.).