Of course, attention is almost certainly a heuristic readers use, but to even bring it up as key to intelligence begs so many questions about LLMs and the mind...
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A computer can't read a file, it is just sending electric signal, sensing the voltage, probing the magnetic field, etc.
"read" a file is anthropomorphized terms for what the machine do when processing a file.
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Maybe we should just stop worrying and embrace the new meaning of those words?
As a former perceptual neuroscientist, I can assure you that ML "attention" has very little to do with biological attention.
E.g., a high-level auditory neuron might primarily fire in response to high-pitched voices, but if you hear someone say your name in a high-pitched voice at a party, you can bias that neuron to try more selectively fire in response to just their voice, "tuning out" similarly-pitched voices.
When you think about it that way, you can bridge the two types of cognition more usefully. The how of intelligence, what attention consists of, can remain completely different, while the why can feel so universal that humans can easily forget they're talking to a machine.
How attention affects biological neurons has little in common with the ML mechanism of attention.
Salience explains the "why", but I'm talking about the "how".
People are doing what people do: they're desperately moving the goal post to keep pretending humanity is particularly special and that intelligence is particularly special. Unique snowflakes and all that, we're the center of the universe. It's nothing more than elaborate human weakness & fragility on display.
We're in the first inning of this, intelligence is not going to end up being special, it's going to be trivial and low cost. See: Chinese models ten years from now.
Astra, Opus, Fable, Argon - they're all radically beyond the top 1% of humanity intellectually already, and it's going to get a lot worse quickly. The sole aspect the top 1% beat them on, and increasingly rarely, is at hyper specialization, where a person is elite at what they do. For $20-$100-$200 per month I can get access to an omnimath genius intellect far beyond any human that has ever existed, and that AI will work for me like a dog. Humanity should be scared, and the reason HN hates AI so much, is because it will wipe out very large numbers of their lucrative jobs that they imagined would form long-term careers (six figures, stock options, path to wealth, elitism re competition).
And suddenly we're looking at people who read ticker tape or by following with their finger for insights into neuroscience?
We survived comparisons to hydraulics, clockwork, steam, circuits, information processing, and networks. I'm positive we'll survive comparisons to attention and models too.
If it's an argument from pedantism, let us remind ourselves to be language descriptivist than language prescriptivists. Prescriptivists never win.
So instead of being a useful, if imperfect, analogy from an outside domain, trying to use ML attention to understand biological attention just results in confusion (like this article).
Neuroscientists aren't going to tell ML researchers they can't use "attention", but nor are they going to pay attention to them (pun intended). Neural networks are almost always too divergent from biology to shed light on real neurons.
1. https://www.thoughtco.com/polysemy-words-and-meanings-169164...