Answer to his though experiment:
Yes, I believe a sufficiently advanced AI could told us that. Scientists who have been fed with wrong information can come up with completely new ideas. Making what we know less wrong.
That being said, I don't think current token-predictors can do that.
My read of this was that AI is fundamentally limited by the lack of access to the new empirical data that drove this discovery; that it couldn't have been inferred from the existing corpus of knowledge.
Recent LLMs have larger context windows to process more data and tool use to get new data, so it would be surprising if there’s a fundamental limitation here.
Maybe an AI will be smart enough to realize that there's more than one explanation for a low level of triglycerides in neurons.
The RICE myth and the lactic acid myth will surely be a part of the training material so the AI will realize that there's a fair amount of unjustified conclusions in the bioworld
The RICE protocol (Rest, Ice, Compression, Elevation) for injuries has been largely debunked - inflammation is now understood as a necessary healing process. Similarly, lactic acid was wrongly blamed for muscle soreness when it's actually a fuel source during exercise, paralleling how we misunderstood neuronal fatty acid metabolism.
> So let’s ask ourselves: would AI have told us this?
Why ask ourselves, when we can ask the AI? Here's the start of my conversation with Gemini:
> Me: What is known about fatty acid combustion in the brain?
> Gemini: The Brain's Surprising Use of Fat for Fuel
For a long time, the brain was thought to rely almost exclusively on glucose for its immense energy needs. However, a growing body of research reveals that fatty acid combustion, or beta-oxidation, plays a more significant and complex role in brain energy metabolism and overall neurological health than previously understood. While not the brain's primary fuel source under normal conditions, the breakdown of fatty acids is crucial for various functions, particularly within specialized brain cells and under specific physiological states....
It cites a variety of articles going back at least to the 1990s.
If you simply ask Gemini what the brain uses for fuel, it gives an entirely different answer that leaves fatty acids out completely and reinforces the glucose story.
LLMs tell you what you want to hear, sourced from a random sample of data, not what you need to, based on any professional/expert opinion.
I tried it using Gemini 2.5 Pro and it cited this Hacker News thread for its first paragraph. I can't judge the other citations, other than to say they're not made up. (I see links to PubMed Central.)
>> So let’s ask ourselves: would AI have told us this?
My first thought: if it did, would you believe it?
> Yes and it did
And before today and this thread, if I asked something like it honestly, without already knowing the answer, and an LLM answered like this...
... I'd figure it's just making shit up.
Before AI will be able to "pretty much solve all our outstanding scientific problems Real Soon Now", it needs to be improved some more, but there's a second, underappreciated obstacle: we will need to learn to gradually start taking it more seriously. In particular, novel hypotheses and conclusions drawn from synthesizing existing research will, by their very nature, look like hallucinations to almost everyone, including domain experts.
The point was that LLMs are not well set up to find new insights unless they are already somehow contained in the knowledge they have been trained on.
This can mean "contained indirectly" which still makes this useful for scientific purposes.
The fact that the author maybe underestimated the knowledge about the topic of the article already contained within an LLM does not invalidate this point.
I get that this is intended to be parsed "Discovering (what we think we know) is (wrong)", but it took me a while to discard the alternative "discovering (what we think (we know is wrong))".
It’s best to remember that ai is an extractive process, not a creative one. That’s why it seems to give you what you “want to hear”. The prompt directs the drill, the well spills out what you drilled into.
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[ 2.9 ms ] story [ 42.5 ms ] threadThat being said, I don't think current token-predictors can do that.
The RICE myth and the lactic acid myth will surely be a part of the training material so the AI will realize that there's a fair amount of unjustified conclusions in the bioworld
Why ask ourselves, when we can ask the AI? Here's the start of my conversation with Gemini:
> Me: What is known about fatty acid combustion in the brain?
> Gemini: The Brain's Surprising Use of Fat for Fuel For a long time, the brain was thought to rely almost exclusively on glucose for its immense energy needs. However, a growing body of research reveals that fatty acid combustion, or beta-oxidation, plays a more significant and complex role in brain energy metabolism and overall neurological health than previously understood. While not the brain's primary fuel source under normal conditions, the breakdown of fatty acids is crucial for various functions, particularly within specialized brain cells and under specific physiological states....
It cites a variety of articles going back at least to the 1990s.
So
> would AI have told us this?
Yes and it did
LLMs tell you what you want to hear, sourced from a random sample of data, not what you need to, based on any professional/expert opinion.
My first thought: if it did, would you believe it?
> Yes and it did
And before today and this thread, if I asked something like it honestly, without already knowing the answer, and an LLM answered like this...
... I'd figure it's just making shit up.
Before AI will be able to "pretty much solve all our outstanding scientific problems Real Soon Now", it needs to be improved some more, but there's a second, underappreciated obstacle: we will need to learn to gradually start taking it more seriously. In particular, novel hypotheses and conclusions drawn from synthesizing existing research will, by their very nature, look like hallucinations to almost everyone, including domain experts.
The point was that LLMs are not well set up to find new insights unless they are already somehow contained in the knowledge they have been trained on. This can mean "contained indirectly" which still makes this useful for scientific purposes.
The fact that the author maybe underestimated the knowledge about the topic of the article already contained within an LLM does not invalidate this point.
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".
Reminds me of astronomy and also quantum mechanics