undefined | Better HN

0 pointsmjburgess2y ago0 comments

No issue with transformers -- the entire field of statistical learning, decision trees to NNs, do the same thing... there's no mystery here. No person with any formal training in mathematical finance, applied statistics, hard experimental sciences on complex domains... etc. would be taken in here.

I'm trying my best to inform people who are interested in being informed, against an entire media ecosystem being played like a puppet-on-a-string by ad companies. The strategy of these companies is to exploit how easy is it to strap anthropomorphic interfaces over models of word frequencies and have everyone lose their minds.

Present the same models as a statistical dashboard, and few would be so adamant that their sci-fi fantasy is the reality.

0 comments

8 comments · 5 top-level

fellendrone2y ago· 2 in thread

> models of word frequencies

Ironically, your best effort to inform people seems to be misinformed.

You're talking about a Markov model, not a language model with trained attention mechanisms. For a start, transformers can consider the entire context (which could be millions of tokens) rather than simple state to state probabilities.

No wonder you believe people are being 'taken in' and 'played by the ad companies'; your own understanding seems to be fundamentally misplaced.

saeranv2y ago

I think they are accounting for the entire context, they specifically write out:

>> P(next_word|previous_words)

So the "next_word" is conditioned on "previous_words" (plural), which I took to mean the joint distribution of all previous words.

But, I think even that's too reductive. The transformer is specifically not a function acting as some incredibly high-dimensional lookup table of token conditional probabilities. It's learning a (relatively) small amount of parameters to compress those learned conditional probabilities into a radically lower-dimensional embedding.

Maybe you could describe this as a discriminative model of conditional probability, but at some point, we start describing that kind of information compression as semantic understanding, right?

nerdponx2y ago

It's reductive because it obscures just how complicated that `P(next_word|previous_words)` is, and it obscures the fact that "previous_words" is itself a carefully-constructed (tokenized & vectorized) representation of a huge amount of text. One individual "state" in this Markov-esque chain is on the order of an entire book, in the bigger models.

1 more reply

divan2y ago· 1 in thread

Do you have blog or anything to follow?

mjburgessOP2y ago

I may start publishing academic papers in XAI as part of a PhD; if I do, I'll share somehow. The problem is the asymmetry of bullshit: the size of paper necessary for academics to feel that claims have been evidenced is book-length for critique but 2pg for "novel contributions".

jameshart2y ago

“There’s no mystery here”

Nobody’s claiming there’s ‘mystery’. Transformers are a well known, publicly documented architecture. This thread is about a video explaining exactly how they work - that they are a highly parallelizable approach that lends itself to scaling back propagation training.

“No person with … formal training … would be taken in here”

All of a sudden you’re accusing someone of perpetuating a fraud - I’m not sure who though. “Ad companies”?

Are you seriously claiming that there hasn’t been a qualitative improvement in the results of language generation tasks as a result of applying transformers in the large language model approach? Word frequencies turn out to be a powerful thing to model!

It’s ALL just hype, none of the work being done in the field has produced any value, and everyone should… use ‘statistical dashboards’ (whatever those are)?

eutectic2y ago

Different models have different inductive biases. There is no way you could build GPT4 with decision trees.

andoando2y ago

I don't think thats true, modeling the data as vectorizations and having paths between them is entirely different from say, building a decision tree. The attention head is also something entirely different.

And the model is capable of doing much more than just making simple predictions, it can build generalized abstractions and form coherent outputs on text/logic its never seen.

j / k navigate · click thread line to collapse