YHK - yet another hacker news clone

Among my peers, some would want to interpret "it was tired" to mean that the road had too many vehicles on it.

barely have enough attention to read through the subtitle

I thought it was scaling

That would be pattern recognition

Rule #1 of reading CS papers - don't trust the anthropomorphized terms. Rule #2 - definitely don't extrapolate those terms to the real mind.

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...

This is the core insight, some in the original grad student author's own words:

https://www.quantamagazine.org/is-attention-the-essence-of-i...

"Sentences with pronouns such as “it” in them illustrate the challenge that attention overcomes. Consider: “The animal didn’t cross the street because it was too tired.” And: “The animal didn’t cross the street because it was too wide.” To translate these sentences correctly into French, an AI model must determine that, in the first one, “it” refers to the animal, which is a masculine word in French, so the translation should use that form of the pronoun, il. But in the second sentence, “it” refers to the street, a feminine word that requires a different pronoun, elle. How can the machine figure this out?"

"Bahdanau remembered doing translation exercises when he was learning English in school. To write each next word of his translated sentence, he’d have to glance all around the source text to figure out what should come next. “Literally you have your source sentence on the left and your writing on the right, and you’ll be, like, turning your head, left-right, left-right,” he said. “That process is what I expressed as a neural mechanism.”

That simplicity seems to me the real tell that this is the real insight. The author does a good job of walking the ready past a long list of distractors and conjuring a sense of a path to an insight, and then, there's the insight - a simple encoding of a simple task: "What is the next word? How do I figure that out? Left-right. Left-right"

An IMO under-appreciated aspect of attention is that it's an ingredient for building a Turing-complete evaluator, since (for example) it lets you implement mutable RAM by treating the attended data as a write log: https://yager.io/neural/turing/turing.html

The answer is clearly "no." The fact that neural networks can decipher patterns in our language without knowing what the sentence is about or even what the individual words mean makes it obvious that the entire A.I. "boom" is based on quicksand. These A.I. companies may sprinkle in some stuff that makes it look intelligent, but the current transformer/attention system will only get us so far.

As an NLP person it's very hard for me to endorse this explanation, because the Vaswani et al transformer (as it is described here) simply does not work until you add the most important ingredient of all: the positional encoding.

Most of the semantics of a sentence are indicated by the precise order the tokens appear. This is easiest to see in a language like English, since it is visible in two very prominent ways.

First, the morphology is fairly derivational, meaning you can keep adding suffixes to things and English speakers will generally get what you mean. For example, add some more suffixes to that word: derivational-ish-ly. You've probably never heard this specific variant, but you still probably have some idea of what it means anyway. The transformer can only capture these semantics if it has some representation of the fact that the suffixes inflect the meaning of the word's root (presumably something like `deriv`). English encodes this entirely by the position of the suffixes relative to the root. Without the positional information, there is no way to recover it, and sentences would lose much of their meaning, since English depends heavily on this construction.

Second, English has relatively few cases, and thus relies very heavily on the order of words to indicate what is the subject and what is the object. English specifically is Subject-Verb-Object, and the instinct of native English speakers to use SVO ordering is so strong, you will find them inserting nouns and pronouns into sentences that do not actually have subjects at all. "It's raining outside" is the most famous example. This sentence has no subject at all, and yet native English speakers feel a strong need to place "it's" at the beginning.

If the transformer did not have positional encodings it would be completely hopeless to recover what is the subject and what is the object from English sentences. There simply is no other way to reliably tell, other than inspecting the word positions.

It's worth noting we also do have the counterfactual. Generative language models that explicitly exclude positional information, like Latent Dirichlet Allocation, are only able to (essentially) cluster word co-occurrences. That is not as useless as it sounds—if I say "fire house burning ambulence" you kind of get the point even without a subject or an object—but it certainly does not generate the lucid, provoking sentences that modern LLMs do.

As a former neuroscientist of visual perception, I always feel compelled to point out that "attention" in the ML sense has very little to do with biological attention.

"Spreading activation" would be a better analogy.

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It's mostly harmless, though it occasionally results in head-scratchers from the biological POV. I once saw a ML paper talking about "retinal attention", which is funny, because the retina is one of the few places in the central nervous system where afawk, attention is impossible.

There's simply no efferent (brain->retina) non-motor nerves capable of biasing photoreceptor output; the retina is largely ballistic. (Eyeball motion is not attention.)

TLDR

The term "attention" confuses more than it reveals, trading well-known terminology for a weak analogy to how the human brain works. Kernel smoothing is all you need.

>> To be sure, the success of LLMs shows how much information about the world is encoded in the statistical patterns of language alone

How can we still have it so backwards. Language is just a medium to convey information on an underlying reality. LLM do not model the medium, they model the underlying reality (so far as the medium allows it).

Attention is not the key, if anything is the key it to intelligence it is scale. As long as all of the information is there for the model, with sufficient scale you will see intelligence emerge. This premise is core to the belief that the main blocker behind LLMs being "discovered" was not architecture, but in fact compute. Once sufficient compute is available eventually someone will be able to create an LLM that is intelligent.