Live data from Hacker News

S1: A $6 R1 competitor?

timkellogg.me

91–100 of 430 posts

Re: S1: A $6 R1 competitor?

#91
If an LLM output is like a sculpture, then we have to sculpt it. I never did sculpting, but I do know they first get the clay spinning on a plate.

Whatever you want to call this “reasoning” step, ultimately it really is just throwing the model into a game loop. We want to interact with it on each tick (spin the clay), and sculpt every second until it looks right.

You will need to loop against an LLM to do just about anything and everything, forever - this is the default workflow.

Those who think we will quell our thirst for compute have another thing coming, we’re going to be insatiable with how much LLM brute force looping we will do.

Re: S1: A $6 R1 competitor?

#92
post #59

Earlier quoted context omitted.

> They think it’s the end all be all for everything. Is (human-based) general intelligence not one of the fundamental enabling elements of literally every human activity throughout history, regardless of how many layers of automation and technology one has to peel back to get to it? Can you maybe imagine how the ability to create arbitrary amounts of general intelligence, completely divorced from the normal lengthy b…

>> Real AI, artificial intelligence, is a fever dream. This is machine learning except the machines are bigger than ever before. There is no intellect. > Do you have any evidence to support this conclusion? And does it even matter? If "fake intellect" can replace a human, that human still has to deal with the very real issue or not having a job anymore. If "fake intellect" is used to conduct mass surveillance, and di…

[deleted]

Re: S1: A $6 R1 competitor?

#93
post #67

Earlier quoted context omitted.

When you're only used to ollama, how do I go about using this model?

I think we need to wait for someone to convert it into a GGUF file format. However, once that happens, you can run it (and any GGUF model) from Hugging Face![0] [0] https://huggingface.co/docs/hub/en/ollama

So this?

https://huggingface.co/brittlewis12/s1-32B-GGUF

Re: S1: A $6 R1 competitor?

#95
post #53

If chain of thought acts as a scratch buffer by providing the model more temporary "layers" to process the text, I wonder if making this buffer a separate context with its own separate FNN and attention would make sense; in essence, there's a macroprocess of "reasoning" that takes unbounded time to complete, and then there's a microprocess of describing this incomprehensible stream of embedding vectors in natural lan…

I had this exact same thought yesterday. I’d go so far as to add one more layer to monitor this one and stop adding layers. My thinking is that this meta awareness is all you need. No data to back my hypothesis up. So take it for what it’s worth.

My thought on the same guess being - all tokens live in same latent space or in many spaces and each logical units train separate of each other…?

Re: S1: A $6 R1 competitor?

#96

This thing that people are calling “reasoning” is more like rendering to me really, or multi pass rendering. We’re just refining the render, there’s no reasoning involved.

"...there’s no reasoning involved...wait, could I just be succumbing to my heuristic intuitions of what is (seems to be) true....let's reconsider using System 2 thinking..."

Or there is no objective reality (well there isn’t, check out the study), and reality is just a rendering of the few state variables that keep track of your simple life.

A little context about you:

- person

- has hands, reads HN

These few state variables are enough to generate a believable enough frame in your rendering.

If the rendering doesn’t look believable to you, you modify state variables to make the render more believable, eg:

Context:

- person

- with hands

- incredulous demeanor

- reading HN

Now I can render you more accurately based on your “reasoning”, but truly I never needed all that data to see you.

Reasoning as we know it could just be a mechanism to fill in gaps in obviously sparse data (we absolutely do not have all the data to render reality accurately, you are seeing an illusion). Go reason about it all you want.

Re: S1: A $6 R1 competitor?

#97

> having 10,000 H100s just means that you can do 625 times more experiments than s1 did I think the ball is very much in their court to demonstrate they actually are using their massive compute in such a productive fashion. My BigTech experience would tend to suggest that frugality went out the window the day the valuation took off, and they are in fact just burning compute for little gain, because why not...

This claim is mathematically nonsensical. It implies a more-or-less linear relationship, that more is always better. But there's no reason to limit that to H100s. Conventional servers are, if anything, rather more established in their ability to generate value, by which I mean, however much potential AI servers may have to be more important than conventional servers that they may manifest in the future, we know how to use conventional servers to generate value now.

And thus, by this logic, every company in the world should just be buying as many servers as they can get their hands on, because More Servers = More Value.

Obviously, this is not happening. It doesn't take much analysis to start listing the many and manifold reasons why. Many of those reasons will apply to GPUs as well. Just as if everything in AWS got 10x faster, overnight, this would not create a situation where everyone suddenly starts grabbing more servers in AWS. Obviously everyone would start trimming down, even if perhaps in a few years time they'd find some way to use this burst of power such that they can use more later. This can't happen overnight, though. It would take time, and not "weeks" or "months" but "years" at scale.

Incorporating the important variable of time in the analysis, if AIs become literally hundreds of times cheaper to run, today, then it is perfectly logical that the near-term demand for the hardware to run them is also going to go way, way down. However much potential AI may have, it is fairly clear looking out at the AI landscape right now that there isn't really anyone out there unlocking vast amounts of value and sitting there wringing their hands because they just can't get more GPU compute. The GPU rush has been from fear that someone will figure out how to "really" unlock AI and then they'll be stuck without the hardware to compete.

It may be the case that vastly cheaper AI will in fact be part of unlocking that value, and that as the AI industry grows it will grow faster as a result... but that's still going to be on a multi-year time frame, not a tomorrow time frame. And all those GPUs and all those valuations are still broadly based on them being valuable real soon now, not in a few years, and all those GPU purchases are on the assumption they need them now, or on a timeframe where we can't be waiting around, rather than waiting for some rounds of exponential doublings to bring price down. The hardware curve in 5 years may be higher but the curve in the next year would be lower, and by a lot.

And, you know, who's to say we're done? I doubt there's another 100x in there, but is someone going to eke out another 2x improvement? Or a 10x improvement? Making it easier to run lots of experiments makes it much more likely for that to happen. I'm skeptical of another 10x general improvement but 10x improvements for specific, important use cases I can't rule out.

Edit: I should also point out this is an extremely common pattern in technology in general. Often the very hardest part is producing a thing that does a particular task at all. Once we have it in hand, once we can use it and learn how it operates and what its characteristic operating modes are, once we can try modifications to it in the real world and see what happens, optimizing it becomes much easier, sometimes explosively so by comparison. Taking any first iteration of a tech that is practical and then trying to straight-line demand based on it is silly, in all sorts of ways and all directions. The internal combustion engine, for example, has had a myriad of impacts on the world and certainly after various improvements many, many millions if not billions of them have been made... but any company that reacted to the first couple of cars and just went ballistic buying those first-generation internal combustion engines would have lost everything, and rather quickly.

Re: S1: A $6 R1 competitor?

#98

Earlier quoted context omitted.

What is even the possible usage of AI for national security? Generating pictures of kittens riding nuclear weapons to the very end like in Dr Strangelove?

Lol: Where I live (Memphis) both “one” and “two” are considered two syllable words. Seriously. Our kids were taught this in the best public elementary school.

How does that work, one-uh, two-uh?

Re: S1: A $6 R1 competitor?

#99
post #53

If chain of thought acts as a scratch buffer by providing the model more temporary "layers" to process the text, I wonder if making this buffer a separate context with its own separate FNN and attention would make sense; in essence, there's a macroprocess of "reasoning" that takes unbounded time to complete, and then there's a microprocess of describing this incomprehensible stream of embedding vectors in natural lan…

> this incomprehensible stream of embedding vectors as natural language explanation, in a way returning to encoder/decoder architecture

this is just standard decoding, the stream of vectors is called the k/v cache

Re: S1: A $6 R1 competitor?

#100

Earlier quoted context omitted.

"...there’s no reasoning involved...wait, could I just be succumbing to my heuristic intuitions of what is (seems to be) true....let's reconsider using System 2 thinking..."

Or there is no objective reality (well there isn’t, check out the study), and reality is just a rendering of the few state variables that keep track of your simple life. A little context about you: - person - has hands, reads HN These few state variables are enough to generate a believable enough frame in your rendering. If the rendering doesn’t look believable to you, you modify state variables to make the render mo…

Is this a clever rhetorical trick to make it appear that your prior claim was correct?

If not: what am I intended to take away from this? What is its relevance to my comment?

Post reply on HN