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> We benchmark humans with these tests – why would we not do that for AIs?

Because the correlation between the thing of interest and what the tests measure may be radically different for systems that are very much unlike humans in their architecture than they are for humans.

There’s an entire field about this in testing for humans (psychometry), and approximately zero on it for AIs. Blindly using human tests – which are proxy measures of harder-to-directly-assess figures of merit requiring significant calibration on humans to be valid for them – for anything else without appropriate calibration is good for generating headlines, but not for measuring anything that matters. (Except, I guess, the impact of human use of them for cheating on the human tests, which is not insignificant, but not generally what people trumpeting these measures focus on.)



There is also a lot of work in benchmarking for AI as well. This is where things like Resnet come from.

But the point of using these tests for AI is precisely the reason we use for giving them to humans -- we think we know what it measures. AI is not intended to be a computation engine or a number crunching machine. It is intended to do things that historically required "human intelligence".

If there are better tests of human intelligence, I think that the AI community would be very interested in learning about them.

See: https://github.com/openai/evals




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