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Seeing the dramatic differences in scores just going from high to xhigh is just another demonstration of the bitter lesson: Just keep scaling search and learnin
by 10xDev 2mo ago
Seeing the dramatic differences in scores just going from high to xhigh is just another demonstration of the bitter lesson: Just keep scaling search and learning.
We are probably going to need a lot more GPUs.
- energy123 2mo ago> Dramatic difference Isn't this just the difference between getting 0 right and getting 1 right?
- vatsachak 2mo agoI mean, theoretically you can solve every finitary problem with a brute force solution... Richard Sutton specifically states that the search has to be smart. We know that the brain uses recurrent connections and is shallow. I think a lot more money has to go into architecture. Feed Forward transformers can only scale so far
- deleted 2mo ago[deleted]
- altcognito 2mo agoWhile I think this is true, remember as we get more efficient we just decide to scale even bigger. So more GPUs, and more efficient. I agree with the sibling comment, effiency is probably the more important component at this point. We are hitting not just a practical engineering roadblock for scaling with current technology, I think we have definitely hit a financial and logistical roadblock for up scaling with the number of GPUs (on an immediate basis)
- Razengan 2mo ago> We are probably going to need a lot more GPUs. Or a breakthrough in algorithms etc. The human brain, heck all bio brains, are proof that you don't need a lot of power or size for intelligence.
- altcognito 2mo ago20 watts for inference AND training!
- aeyes 2mo agoFor intelligence, I expect the next breakthrough to be colocation of memory and compute in the same chip. And we'll need much more of this memory, probably a few petabytes.
- ryandvm 2mo agoThe human brain has 80 billion neurons and a 100 trillion synapses. I think you're underselling the processing power of that warm chunk of meat. The real message of the last 15 years has actually been the opposite: if you throw enough processing power at it, intelligence emerges.
- dbspin 2mo agoMoreover we've known for quite a while now that glial cells also participate in cognition and moderate learning (e.g.: [1]). When you take those connections into account the numbers get really staggering. 85 billion glial cells with trillions of protein channels facilitating communication between the glial syncytium [2]. [1] https://www.sciencedirect.com/science/article/pii/S1934590913000076 https://www.sciencedirect.com/science/article/pii/S193459091... [2] https://pmc.ncbi.nlm.nih.gov/articles/PMC5063692/ https://pmc.ncbi.nlm.nih.gov/articles/PMC5063692/
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- orbital-decay 2mo agoThe real question is not how many "weights" the human brain has (neurons+synapses may or may not translate into "weights", and brain might be also inefficient for what it is), but rather how much evolutionary and social "compute" was necessary to pack everything into that capacity.
- thunky 2mo agoI think you're helping GPs point: there is a lot of efficiency gains to be made to match the processing power of the brain, given it's size and power draw.
- emp17344 2mo agoThis isn’t really how it works anymore. Agents rely heavily on tool use and the agentic harness to perform tasks. Pre-training is no longer very effective.
- HDThoreaun 2mo agoI thought models werent allowed tools on arc-agi?
- bevekspldnw 2mo agoThese aren’t raw base models they are the result of a ton of RLHF and various adjustments. Bitter lesson wildly overstated in this context.
- theptip 2mo agoNah, the last few generations have more RLVR in the data mix. Which is more CPU intensive and very much amenable to the bitter lesson as you can reduce the loss by doing more rollouts in your tool environment.
- froh 2mo agorlhf = reinforcement learning from human feedback (had to look it up)
- visarga 2mo agoI think it's more RLVR (reinforcement learning from verified rewards). The RLHF is just to align models to human preferences, meaning to behave nice.
- redanddead 2mo agoWhat makes you say that
- versteegen 2mo agoMore accurate to say RLHF aligns models to human preferences, most significantly to be helpful.
- fastball 2mo agoMore RLHF is in fact scaling.
- bevekspldnw 2mo agoYes, but not in the “dump another chunk of all written language in the bucket and stir”-sense which is what bitter lesson became synonymous with. That may not be the intent of the original article, but over the past few years that’s what the phrase turned into.
- Salgat 2mo agoKind of refreshing though that the "throw more processing at it" scaling we saw in the 90s has returned in a different way. For a while we were really bottlenecked in our advances by relatively low levels of parallelism (most software used by your average user doesn't scale cleanly with more than a few threads).
- dyauspitr 2mo agoAnd dozens of data centers in every state so tokens are dirt cheap.
- navigate8310 2mo agoThere goes my plan to buy a PC for the next decade
- echelon 2mo agoThe whole of knowledge work is being automated. We've barely begun to see the GPU build out. This is just the start. I'd imagine they're going to 10x this, maybe 100x this.
- y1n0 2mo agoyeah, spacex's planned million satellite datacenter-in-space constellation doesn't seem as absurd.
- Schlagbohrer 2mo agoSam Altman was saying we'll eventually need to build a dyson sphere. We'll see how far human society can withstand this kind of escalation. I'm guessing one more year before there are major fractures in the basic economic/social/political relationship that get so bad it actually prevents more buildout.
- davidpapermill 2mo agoI actually think we're in a strange situation with AI compute. Right now, we have models that are statistical models of language, with a world model and reasoning "falling out" of a lot of effort. It's like we've made something that's a little bit intelligent, and now we're trying to amplify that trick to create something that's quite intelligent. And - don't get me wrong - it works. But it's also super, super inefficient. We're having machines "think out loud" to compensate for the quality of their thought processes. We elongate the path to make up for the progress made on a given step. I tink there's probably a much smarter way of doing things that will require qualitative architectural (and quite possibly hardware) innovations. Right now we're on the path to a Dyson sphere: that's probably not going to be necessary once we figure out a smarter way to think.
- orbital-decay 2mo agoOr a lot better efficiency.
- dmitriy_ko 2mo agoAnd a lot more electricity to power them.
- XCSme 2mo agoNot always, in some cases, changing to a higher reasoning makes the AI doubt itself too much, and skip over the correct answer by overcomplicating the problem and polluting the context. It would be nice to see on which categories of problems the extra thinking makes it better and on which it makes it worse.
- andai 2mo agoI think I have this problem but with my human brain.
- Schlagbohrer 2mo agoThis shows up in OpenAI's graphs on their announcement page. There is a peak performance datapoint in the graphs past which (to the right on the graph indicating more resources spent) peformance declines. And it's on every graph on that page!
- XCSme 2mo agoAnd in my tests, that point of "overthinking" depends on the problem's complexity, so it's not necessarily that using "xhigh" is always bad or good.
- hyperbovine 2mo agoOr a new model. The human brain does far more with far less.
- ld4nt3 2mo agoYes an no current models can read and output much more faster with differing quality tho.
- andai 2mo agoI said a few months ago, "man, Opus is great, but sometimes when talking with it I have the feeling like, this thing should be about 10 times bigger." When Mythos was announced after that, I was pleasantly surprised to hear about it. But when it turned out to be only two times bigger, I was a little disappointed! (I am even more disappointed with the safety filters, but that's kind of a separate discussion... "Fortunately" I find that I can usually edit my prompt by single character and get through...)
- Schlagbohrer 2mo agoWhat do you mean bigger? Bigger functional context window?
- nbardy 2mo agoWe’re definitely going to need a lot of Gpu’s