51–60 of 139 posts
I don’t want to say too much too soon, but I am pretty excited about this.
That having been said, many LLMs are being run on SIMD GPUs, in warps, basically they are just doing a lot of vector multiplications, activation functions and kv self attention (the expendive step).
The issue is we want the LLMs to be one-way through the layers, whereas turing-complete programming languages support loops and no well-defined stopping time. You can stick a simple computer into an LLM, but it won’t be able to do long loops.
However, for these specific workloads, the need to attend only to the latest state is indeed a huge optimization! Gone is the need for n^2 complexity that dominates the cost, now it is (log n)^2 attention which is far smaller.
This shows the downside of using AI to write up your project. I see the eloquent sentences, but don't get the message. > This works, but the actual execution happened outside the model. The model specified the computation, then waited for an external system to carry it out. > Our transformer also emits a program, but instead of pausing for an external tool, it executes that program itself, step by step, within the sa…
What are the AI tells? The only one I found is redundancy, but it makes sense because this is trying to be approachable to laymen. Like, you have a great point (the benefit of this approach isn't explained), but that's a mistake humans frequently make.
This shows the downside of using AI to write up your project. I see the eloquent sentences, but don't get the message. > This works, but the actual execution happened outside the model. The model specified the computation, then waited for an external system to carry it out. > Our transformer also emits a program, but instead of pausing for an external tool, it executes that program itself, step by step, within the sa…
> Is it speed? > Is it that you can backprop through this computation? Do you do so? With respect, I feel that you may not have read the article. > Because the execution trace is part of the forward pass, the whole process remains differentiable: we can even propagate gradients through the computation itself. That makes this fundamentally different from an external tool. It becomes a trainable computational substrate…
This shows the downside of using AI to write up your project. I see the eloquent sentences, but don't get the message. > This works, but the actual execution happened outside the model. The model specified the computation, then waited for an external system to carry it out. > Our transformer also emits a program, but instead of pausing for an external tool, it executes that program itself, step by step, within the sa…
>This shows the downside of using AI to write up your project. I see the eloquent sentences, but don't get the message. Not really sure what this obsession with calling things you don't like AI generated is but it's poor form. If you have something to say about the text then say it. Otherwise leave baseless accusations out of it. >What's the benefit? Is it speed? Where are the benchmarks? Is it that you can backprop…
Admonishing someone for correctly identifying AI-written or AI-edited blog posts is poor form, friend.
It is without a doubt written by an LLM. All of the telltale signs are there. I work with these tools 8-20 hours a day and after a while the verbiage and grammatical structures stick out like a sore thumb.
Get off the high horse. I too think this is a very interesting read. I was fascinated with the subject, but the presentation was nauseatingly distracting and immediately sets off yellow flags about how Percepta operates, and what kind of quality they're willing to settle with. It tells me they are more interested in appearances and superficiality.
The numbers that are there categorically cannot be trusted, because hallucinating those details is quite common for models. There is simply no indication that a human adequately proof-read this and therefore any of its claims must be taken with a grain of salt. Don't forget the recent Cloudflare+Matrix debacle: https://news.ycombinator.com/item?id=46781516
I share the same concerns as OP; this post lacks metrics and feels like someone did something cool and raced to get an AI to post about it, instead of giving it a proper treatment.
they created a new training dataset which also has computation solving step by step (multiplying two numbers or playing sudoku) and then trained a transformer on it- as a result, the model performs the computation(multiplying two numbers) "inside" itself instead of calling calculator (or python)?
++ And they also figured out how to make attention faster?
This shows the downside of using AI to write up your project. I see the eloquent sentences, but don't get the message. > This works, but the actual execution happened outside the model. The model specified the computation, then waited for an external system to carry it out. > Our transformer also emits a program, but instead of pausing for an external tool, it executes that program itself, step by step, within the sa…
Very light on details that article is.