Pretty sure I’ve seen this particular case discussed here previously, and the conclusion was that actually they had published something related already, and fed it to the “co-scientist” AI. So it was synthesising/interpolating from information it had been given, rather than generating fully novel ideas.
However, the team did publish a paper in 2023 – which was fed to the system – about how this family of mobile genetic elements “steals bacteriophage tails to spread in nature”. At the time, the researchers thought the elements were limited to acquiring tails from phages infecting the same cell. Only later did they discover the elements can pick up tails floating around outside cells, too.
So one explanation for how the AI co-scientist came up with the right answer is that it missed the apparent limitation that stopped the humans getting it.
What is clear is that it was fed everything it needed to find the answer, rather than coming up with an entirely new idea. “Everything was already published, but in different bits,” says Penadés. “The system was able to put everything together.”
That was concerning the main hypothesis that agreed with their work. Unknown whether the same is also true for its additional hypotheses. But I’m sceptical by default of the claim that it couldn’t possibly have come from the training data, or that they definitely didn’t inadvertently hint at things with data they provided.
I’m not sure what the concept of and “entirely new” or “fully novel” idea means in practice. How many such things actually exist and how often should we expect any mind however intelligent to find one? Ideas can be more or less novel, and we can have thresholds for measuring that, but where should we place the bar?
If you place it at “generate a correct or useful hypothesis you don’t actually have enough data to locate in idea-space” then that seems like a mistake.
I’d put it more near “generate and idea good enough to lead to a publishable scientific paper or grantable patent.” This still seems pretty close to that? Sometimes “obvious” implications to scientific papers go unacknowledged or unexplored for a very long time.
If we make the criteria too strict, then maybe I never had a single Original Thought™ in my life. Everything is just a remix.
I suspect that in practice, “original thought” means a combination that was never made (popular) before, if it seems to work or passes some other criteria (e.g. artistic), i.e. not just a random text.
I agree, but when people want to use the presence or absence of Original Thought™ as a criterion for judging the capabilities of AI, then drawing that line somewhere matters, and the judge should write it down, even if it is approximate.
Pretty sure I’ve seen this particular case discussed here previously, and the conclusion was that actually they had published something related already, and fed it to the “co-scientist” AI. So it was synthesising/interpolating from information it had been given, rather than generating fully novel ideas.
Per NewScientist https://www.newscientist.com/article/2469072-can-googles-new-research-assistant-ai-give-scientists-superpowers/
That was concerning the main hypothesis that agreed with their work. Unknown whether the same is also true for its additional hypotheses. But I’m sceptical by default of the claim that it couldn’t possibly have come from the training data, or that they definitely didn’t inadvertently hint at things with data they provided.
I’m not sure what the concept of and “entirely new” or “fully novel” idea means in practice. How many such things actually exist and how often should we expect any mind however intelligent to find one? Ideas can be more or less novel, and we can have thresholds for measuring that, but where should we place the bar?
If you place it at “generate a correct or useful hypothesis you don’t actually have enough data to locate in idea-space” then that seems like a mistake.
I’d put it more near “generate and idea good enough to lead to a publishable scientific paper or grantable patent.” This still seems pretty close to that? Sometimes “obvious” implications to scientific papers go unacknowledged or unexplored for a very long time.
If we make the criteria too strict, then maybe I never had a single Original Thought™ in my life. Everything is just a remix.
I suspect that in practice, “original thought” means a combination that was never made (popular) before, if it seems to work or passes some other criteria (e.g. artistic), i.e. not just a random text.
I agree, but when people want to use the presence or absence of Original Thought™ as a criterion for judging the capabilities of AI, then drawing that line somewhere matters, and the judge should write it down, even if it is approximate.