The 2014 Intern Trick That Made ChatGPT Possible
Is 'Attention' the Essence of Intelligence?
In 2014, Dzmitry Bahdanau, a 23-year-old intern at the University of Montreal, invented a mathematical operation called attention to help neural networks translate long sentences. It let a network focus on the right words anywhere in a sentence, solving the pronoun problem that had stumped machine translation. Three years later, Google's transformer made attention the core of AI. Now researchers ask if attention is the essence of intelligence.
That's quite a leap, isn't it?
- antics
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 p […]
- jvanderbot
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...
- scarmig
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.
- andy_ppp
The original paper title is catchy but it’s more like “positional encoding and attention and parallelism across attention heads are all you need…”
And it turns out you probably need massive compute, about 1/3 RLHF, all the worlds data and millions of people connecting their private data to the AI to learn from amongst other things :-/
- 0xWTF
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 […]