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Apparently GPT-3 has learned a large number of personality types and their fine grained biases, well enough to simulate a poll with good accuracy. This means yo

Apparently GPT-3 has learned a large number of personality types and their fine grained biases, well enough to simulate a poll with good accuracy. This means you can poll it any time to check how a population of choice would have reacted had something happened. The language model does not carry the just biases of its builders, it learns all biases equally, you just need to specify the desired bias (personality type) when you call it. The responsibility falls with the user, builders are not to blame this time unless they didn't cover every bias equally well. > GPT3 can simulate real people, Jack Clark https://jack-clark.net/2022/10/11/import-ai-305-gpt3-can-simulate-real-people-ai-discovers-better-matrix-multiplication-microsoft-worries-about-next-gen-deepfakes/ https://jack-clark.net/2022/10/11/import-ai-305-gpt3-can-sim...

fuzzc0re 4y ago
Ok so GPT-3 can model biases well. This still doesn't solve problems such as the optimal aggregation of citizens' preferences, which is the actual optimization problem of policy making. Just to give an idea of how complex a field this is, there is a subdomain of economic theory and information theory called social choice https://en.wikipedia.org/wiki/Social_choice_theory https://en.wikipedia.org/wiki/Social_choice_theory that works with these issues and itself does not have many theories about policy formation, mostly choice between already formed policies. If a neural net finds the way to write policies that are Nash equilibrium collective decisions in all possible democracy systems then we will be close to solving the problem.