EA - Can GPT-3 produce new ideas? Partially automating Robin Hanson and others by NunoSempere

Link to original articleWelcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Can GPT-3 produce new ideas? Partially automating Robin Hanson and others, published by NunoSempere on January 16, 2023 on The Effective Altruism Forum.Brief description of the experimentI asked a language model to replicate a few patterns of generating insight that humanity hasn't really exploited much yet, such as:Variations on "if you never miss a plane, you've been spending too much time at the airport".Variations on the Robin Hanson argument of "for common human behaviour X, its usual purported justification is Y, but it usually results in more Z than Y. If we cared about Y, we might do A instead".Variations on the genealogical argument: that the results of historical accidents are most likely not moral necessities or optimal systems.Motivation behind this experimentOne of reasons to be afraid of artificial intelligence might be because, if you think in the abstract about how a system might behave as it becomes extremely intelligent, you might conclude that it might be able to completely outmanoeuvre us because of its superior ability to grasp the true structure of the world.This possibility is scary in the same sense that a modern chemist is scary to a historical alchemist. Our current chemist can completely outmanoeuvre previous alchemists by using their superior understanding of natural laws to produce better explosions, more subtle poisons, or more addictive and mind-blowing drugs.I do buy this fear in the limit for a being of God-like intelligence. But it's not clear to me whether it also applies to current systems or whether it will apply to their close descendants. In particular language models seem like they are powerful remixers and predictors but perhaps limited to drawing from the conceptual toolkit which humans already have. On the other hand, because they have access to so much information, they might be able to be prompted so as to reveal new relationships, connections, and insights.Some conceptual insights which have been historically important are:Explaining natural phenomena not in terms of Greek or Roman anthropomorphic gods, but with reference to naturalistic, physical explanationsUnderstanding acceleration as distinct from motionScience as an experimental methodologyThe is/ought distinctionBayesian reasoningCeasing to accept the divine right of kings as a justification for monarchical governanceRandomized trials as a more robust way of generating generalizable knowledgeThe genealogical argument: understanding that systems (such as the details of the current prison system, our monetary system, the lack of color in men's clothes, or our attitudes towards gender and sex) are the result of historical accidents which could have gone differently. But often these systems are rationalized as being particularly adequate, or even morally necessary.But I don't think that language models are currently able to come up with original insights like the above from scratch (this would be very scary).Instead, I probe GPT-3's ability to come up with original variations of these three argumentative patterns:Variations on "if you never miss a plane, you've been spending too much time at the airport".Variations on the Robin Hanson argument of "for common human behaviour X, its usual purported justification is Y, but it usually results in more Z than Y. If we cared about Y, we might do A instead".Variations on the genealogical argument: that the results of historical accidents are most likely not moral necessities or optimal systems.The first pattern is known as an Umeshism. I associate the second pattern with Robin Hanson, who has had part of a fruitful career exploring some of its variations—though he is also known for other ideas, e.g., prediction markets and grabby aliens. I associate the third pattern with Nietzsche (who used it to criti...