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> It remains unclear whether continuing to throw vast quantities of silicon and ever-bigger corpuses at the current generation of models will lead to human-equi
by drob518 7mo ago
> It remains unclear whether continuing to throw vast quantities of silicon and ever-bigger corpuses at the current generation of models will lead to human-equivalent capabilities. Massive increases in training costs and parameter count seem to be yielding diminishing returns. Or maybe this effect is illusory. Mysteries!
I’m not even sure whether this is possible. The current corpus used for training includes virtually all known material. If we make it illegal for these companies to use copyrighted content without remuneration, either the task gets very expensive, indeed, or the corpus shrinks. We can certainly make the models larger, with more and more parameters, subject only to silicon’s ability to give us more transistors for RAM density and GPU parallelism. But it honestly feels like, without another “Attention is All You Need” level breakthrough, we’re starting to see the end of the runway.
- embedding-shape 7mo ago> I’m not even sure whether this is possible. Based on what's happened so far, maybe. At least that's exactly how we got to the current iteration back in 2022/2023, quite literally "lets see what happens when we throw an enormous amount data at them while training" worked out up until one point, then post-training seems to have taken over where labs currently differ.
- drob518 7mo agoRight, but we played the scaling card and it worked but is now reaching limits. What is the next card? You can surely argue that we can find a new one at any time. That’s the definition of a breakthrough. I just don’t see one at the moment.
- embedding-shape 7mo ago> I just don’t see one at the moment. Did you see the one before the current one was even found? Things tend to look easy in hindsight, and borderline impossible trying to look forward. Otherwise it sounds like you're in the same spot as before :)
- drob518 7mo agoThat’s what I’m said. Breakthroughs happen. No doubt about it, and they are unpredictable. Hence a breakthrough. But right now we’re using up runway with nothing yet identified to take us to the next level. And while sometimes breakthroughs happen, sometimes they don’t.
- deleted 7mo ago[deleted]
- functional_dev 7mo agobetter tooling and integration
- davebren 7mo agoIt works as far as consolidating existing human knowledge, but general intelligence doesn't suddenly emerge from it. They're out of data now but if there was 10x more (assuming it's not rehashed from existing data) and they had a 10x larger model then it would be better but that's only because there's more that it can copy from.
- htrp 7mo agoWe pay people to create more high quality tokens (mercor, turing) which are then fed into data generating processes (synthetic data) to create even more tokens to train on
- drob518 7mo agoBut does that really help, or do you get distortion? The frequency distribution of human generated content moves slowly over time as new subjects are discussed. What frequency distribution do those “data generating processes” use? And at root, aren’t those “data generating processes” basically just another LLM (I.e., generating tokens according to a probability distribution)? Thus, aren’t we just sort of feeding AI slop into the next training run and humoring ourselves by renaming the slop as “synthetic data?” Not trying to be argumentative. I’m far from being an AI expert, so maybe I’m missing it. Feel free to explain why I’m wrong.
- htrp 7mo agoThat's the problem in a nutshell. There is an art to how you generate the synthdata so that you don't get crappy trained models (especially when mistakes cost XX million dollars). It's also theoretically why facebook paid 14bn for alex wang and scale ai
- xmprt 7mo agoI see a lot of researchers working on newer ideas so I wouldn't be surprised if we get a breakthrough in 5-10 years. After all, the gap between AlexNet and Attention is All You Need was only 6 years. And then Scaling Laws was about 3-4 years after that. It might seem like not much progress is being made but I think that's in part because AI labs are extremely secretive now when ideas are worth billions (and in the right hands, potentially more). Of course 5-10 years is a long time to bang our heads against the wall with untenable costs but I don't know if we can solve our way out of that problem.
- supliminal 7mo agoThe echoes of A.I. winter.
- marcosdumay 7mo agoYep, last time we got "a lot of researchers working on newer ideas", it took them 20 years to get into a working idea, and other 20 years to get it mature enough to make an AI boom.
- ghywertelling 7mo agoI think we will see models becoming small reasoning core which don't remember tonnes of facts but can reason with data fed to it or they can search.
- krainboltgreene 7mo ago> The current corpus used for training includes virtually all known material. This is just totally incorrect. It's one of those things everyone just assumes, but there's an immense amount of known material that isn't even digitized, much less in the hands of tech companies.
- drob518 7mo agoWhat large caches of undigitized content exists? Surely, not everything has been digitized, but I can’t think it’s much in percentage terms.
- cgh 7mo agoThe Vatican Library contains roughly 1.1 million printed books and around 75,000 codices, only a small percentage of which have been digitised.
- drob518 7mo agoWhich is what percent of the world’s content? 0.000000001% or something similar. It’s nothing in the scheme of things. To put it another way, if we were to digitize that continent and train on it, our AIs would not get noticeably better in any way. It doesn’t move the needle.
- fwip 7mo ago1.1 million being 0.000000001% implies a total count of 1e17 books in the world - the real number is closer to 1e8.
- drob518 7mo agoYou’re missing the point. And we’re not just talking about books, whatever that might mean. We’re talking about all documents ever made. Every magazine article, every blog and web page, every Word doc, etc. I’m pretty sure that whatever is in the Vatican archives is tiny by comparison. Given the age of the Vatican archives, I can also guarantee that many of those “books” are nothing more than page fragments. Very few will be full codices or long scrolls. Many will date before the printing press when document production was slow and laborious.
- deleted 7mo ago[deleted]
- Sol- 7mo agoI think in domains like Math and Software Engineering, they are less constrained by training data anyway. They can synthetically generate and validate programs. To what extent that scales into novel insights is a different matter, but I think they dream of the AlphaGo Zero moment at least in verifiable domains.
- davebren 7mo agoHow can it ever play against itself on novel software tasks? First it has to come up with the task. Then it can write tests but then it needs to verify that the tests are correct, mixture of experts can come to wrong conclusions, etc...
- 0rbiter 7mo ago[dead]