If it holds up, you could monitor kids in class, dementia patients.. wild. Start your startup engines.
Extrapolating, this also suggests neurolink should work and you can probably do it with less invasive tech.
From the paper:
”However, whether these models encode, retrieve and pay attention to information that specifically relates to behavior in general, and to comprehension in particular remains controversial”
edit: Mapping from a statistical model directly to one specific individual’s brain may turn out to be intractable for things like brain implants.
But, the chances are pretty good that there will be strides made in unexpected areas.
>Manuscripts are not assessed based on their perceived importance, significance or impact https://www.nature.com/srep/guide-to-referees#criteria
It's a good thing for science that not all journals are impact chasers. Scientists are by definition not perfectly reliable evaluators of impact, because science is about exploring the unknown. Publishing work that's only passed a technically focused peer review allows for unexpected impact.
>It's a good thing for science
Maybe, but we should treat them like arXiv.org type e-Print archive. People are posting them to HN and thinking that because it's Nature.com site it's solid science.
Was this cross checked against arbitrary Inputs to GPT-2? I gather, with 1.5 Billion parameters, you can find a representative linear combination for everything.
The Bible Code comes to mind (https://en.wikipedia.org/wiki/Bible_code).
If something serious was on the line, with this type of analysis, you'd be fired.
Reading this it feels like we might as well give up on there being any science any more, tbh. For this to appear in Nature -- it feels like the rubicon has been crossed.
How can we expect the public not to be "anti-vax" (etc.), or otherwise scientifically competent in the basic tennets of modern science (experiment, refutation, peer review) -- if Nature isnt?
To quote the authors: ”We propose that deep neural networks encode a variety of features…”
Run GPT on other inputs, run fMRIs on other inputs: call that dataset, (G, F).
Now consider all possible subsets, (g_i, f_i) in (G, F) ...
How many show correlation in their choice of NN property, and their choice of brain property (ie., blood flow)?
My guess: *many*.
This is trivial to refute if you have any sense of the scientific method. Construct the converse hypothesis and test it. They didnt.
What's the point of Nature?
This article is the pinnacle of modern pseudoscience.
It's run by the same publishers as the journal Nature, but is a significantly lower impact journal.
edit: https://mitpress.mit.edu/9780262680530/parallel-distributed-...
Note that this is exactly the wrong way to form and attempt to refute a scientific hypothesis. The authors don't start with some new observations that require explanation, they start with a hypothesis already fully-formed ("...these models encode information that relates to human comprehension..."), and then go out and collect observations to confirm this hypothesis.
I'm sure that if asked, the authors would say that they are simply trying to answer a scientific question, but it's obvious that they already have the answer they want and they're just trying to find data to support it. The problem of course is that if one is already convinced of the answer, one can always find evidence to "prove" it. It's a kind of confirmation bias.
From the paper: ”These advances raise a major question: do these algorithms process language like the human brain? Recent studies suggest that they partially do: the hidden representations of various deep neural networks have shown to linearly predict single-sample fMRI, MEG, and intracranial responses to spoken and written texts.”
