Live data from Hacker News

Visualizing Attention, a Transformer's Heart [video]

3blue1brown.com

21–30 of 181 posts

Re: Visualizing Attention, a Transformer's Heart [video]

#21
post #16

Earlier quoted context omitted.

Link? Sounds fun and reminds me of this tweet [0] [0] https://x.com/jaschasd/status/1756930242965606582

Nah someone down voted it. And yes, it looks like that + 20 others that are animated.

Downvotes == empty boats. If "Empty Boat parable" doesn't ring a bell, Google it...

Re: Visualizing Attention, a Transformer's Heart [video]

#23
Hold on, every predicted token is only a function of the previous token? I must have something wrong. This would mean that within the embedding of "was", which is of length 12,228 in this example. Is it really possible that this space is so rich as to have a single point in it encapsulate a whole novel?

Re: Visualizing Attention, a Transformer's Heart [video]

#24

Earlier quoted context omitted.

Nah someone down voted it. And yes, it looks like that + 20 others that are animated.

Downvotes == empty boats. If "Empty Boat parable" doesn't ring a bell, Google it...

unless an algorithm decides to block or devalue the content, but yeah i looked it up, very interesting parable, thanks for sharing.

Re: Visualizing Attention, a Transformer's Heart [video]

#25
post #8
post #6

I finally understand this! Why did every other video make it so confusing!

Because: 1. good communication requires an intelligence that most people sadly lack 2. because the type of people who are smart enough to invent transformers have zero incentive to make them easily understandable. most documents are written by authors subconsciously desperate to mentally flex on their peers.

Pedagogy requires empathy, to know what it's like to not know something. They'll often draw on experiences the listener is already familiar with, and then bridge the gap. This skill is orthogonal to the mastery of the subject itself, which I think is the reason most descriptions sound confusing, inadequate, and/or incomprehensible.

Often, the disseminating medium is a one-sided, like a video or a blog post, which doesn't help, either. A conversational interaction would help the expert sense why someone outside the domain find the subject confusing ("ah, I see what you mean"...), discuss common pitfalls ("you might think it's like this... but no, it's more like this...") etc.

Re: Visualizing Attention, a Transformer's Heart [video]

#26

Hold on, every predicted token is only a function of the previous token? I must have something wrong. This would mean that within the embedding of "was", which is of length 12,228 in this example. Is it really possible that this space is so rich as to have a single point in it encapsulate a whole novel?

it's not about a single point encapsulating a novel, but how sequences of such embeddings can represent complex ideas when processed by the model's layers.

each prediction is based on a weighted context of all previous tokens, not just the immediately preceding one.

Re: Visualizing Attention, a Transformer's Heart [video]

#27

Hold on, every predicted token is only a function of the previous token? I must have something wrong. This would mean that within the embedding of "was", which is of length 12,228 in this example. Is it really possible that this space is so rich as to have a single point in it encapsulate a whole novel?

at that point what it has is not a representation of the input, its a representation of what the next output could be. ie. its a lossy process and you can't extract what came in the past, only the details relevant to next word prediction

(is my understanding)

Re: Visualizing Attention, a Transformer's Heart [video]

#28
post #6

I finally understand this! Why did every other video make it so confusing!

> Why did every other video make it so confusing!

In my experience, with very few notable exceptions (e.g. Feynmann), researchers are the worst when it comes to clearly explaining to others what they're doing.

I'm at the point where I'm starting believe that pedagogy and research generally are mutually exclusive skills.

Re: Visualizing Attention, a Transformer's Heart [video]

#29
Awesome video. This helps to show how the Q*K matrix multiplication is a bottleneck, because if you have sequence (context window) length S, then you need to store an SxS size matrix (the result of all queries times all keys) in memory.

One great way to improve on this bottleneck is a new-ish idea called Ring Attention. This is a good article explaining it:

https://learnandburn.ai/p/how-to-build-a-10m-token-context

(I edited that article.)

Post reply on HN