You can't know that. Currently, 8 billion humans generate a few scientific breakthroughs per year. You'd have to run several billion ChatGPTs for a year with zero breakthroughs to have any confidence in such a claim.
With billions of GPT output streams, how do you actually discover and rank what’s significant? Screen them through some even more powerful models? I imagine it’s like a volcano eruption of text where some are absolutely brilliant and most is worthless and finding the jewels is even more demanding than generating it all.
Some theories are easily testable. For instance, ask it to write some code to efficiently solve traveling salesman problems, and then test the code on some sample problems. You can score the quality of solutions and time taken, and manually inspect the best ones.
At this point there is no framework that suggests GPT understands the underlying data. It can’t assign meaning as a human would. It can’t consume hundreds of math textbooks and learn the principles of math and then apply them more broadly to science textbooks and research papers. It can’t even reliably add two numbers.
Yes, brute forcing with hard AI can produce many thoughts. But the AI wouldn’t know they are correct. It couldn’t explain why. Any discovery would only be attributable to randomness. It wouldn’t be learning from itself and its priors.
> At this point there is no framework that suggests GPT understands the underlying data. It can’t assign meaning as a human would.
Actually there are many indications that GPT understands the data, because its output mostly makes sense. The reason it can't assign meaning the way a human would is because a human can correlate words with other sensory data that GPT doesn't have access to. That's where GPT creates nonsense.
Think carefully about what "understanding" means in a mechanistic sense. It's a form of compression, and a few billion parameters encoding the contents of a large part of the internet seems like pretty good compression to me.
GPT doesn't display understanding of purely abstract systems, so I doubt it's an issue of lacking sensory information. It can't consistently do arithmetic, for example - and I think it would be presumptuous to insist that sensory information is a prerequisite for mathematics, even though that's how humans arrived at it.
It's not yet clear why it struggles with arithmetic. It could be data-related, could be model-related, although scaling both seems to improve the situation.
In any case, GPT could still understand non-abstract things just fine. People with low IQ also struggle with abstract reasoning, and IQ tests place GPT-3 at around 83.
