How sure are we about this statement, and why? I have been hearing this a lot, and it might be true, but I would like to read some research into this area.
You write as if this is your conclusion but it's really your premise.
> if it was only trained on papers supporting the dogma.
It's not, it's also trained on all the papers the authors of the current study read to make them think they should spend money researching fatty acid combustion in the brain.
> so that information is probably already in the training data.
Run an offline copy of Gemma with training cutoff before this study came out and it will also tell you about fatty acid combustion in the brain, with studies going back to the 60s and taking off around the 2000s or 2010s.
> So let’s ask ourselves: would AI have told us this? Remember, when people say AI they are about 95% saying “machine learning”, so would it really have told us about this after having been trained on years and years of the medical literature telling it that neurons are obligate glucose users and don’t really have a role for triglycerides? Of course not. And this is why I keep saying (and I’m sure not the only one) that we simply don’t know enough to teach the machine learning algorithms yet. Not to mention that some of what we’d be teaching them is just wrong to start with.
But yeah, if Gemini cites papers from the 90s, then either the shift in thinking on this topic was further back than Lowe makes it seem here (and the "new findings" are already established for decades) or the model interpreted the old papers differently than the scientists did back then.
I think what Lowe meant was that an LLM could not have come up with this "on its own", if it was only trained on papers supporting the dogma.
So it cannot produce novel insights which would be a requirement if LLMs should "solve science".