Inference is free and instant
fume.substack.com
Inference is free and instant
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Re: Inference is free and instant
#2“In my humble opinion, these companies would not allocate a second of compute to lightweight models if they thought there was a straightforward way to achieve the next leap in reasoning capabilities.”
The rumour/reasoning I’ve heard is that most advances are being made on synthetic data experiments happening after post-training. It’s a lot easier and faster to iterate on these with smaller models.
Eventually a lot of these learnings/setups/synthetic data generation pipelines will be applied to larger models but it’s very unwieldy to experiment with the best approach using the largest model you could possibly train. You just get way fewer experiments per day done.
The models bigger labs are playing with seem to be converging to about what is small enough for a researcher to run an experiment overnight.
Re: Inference is free and instant
#3I think the discussion around "exponentials" with top-end LLM (think 3.5 sonnet, gpt-4 not the smaller models) scaling is really pointless. The heuristic we have for what to expect from performance is just scaling, which has worked pretty well. These benchmarks are imperfect in lots of ways, aren't necessarily sensitive to showing exponential progress and it is difficult to predict step changes in capability in advance.
If you zoom out on the first graphic from December 2023 back to 2020, the capabilities of models released at that time on these benchmarks would be much much lower. The best lens for future performance of large models is uncertainty.
Re: Inference is free and instant
#4“In my humble opinion, these companies would not allocate a second of compute to lightweight models if they thought there was a straightforward way to achieve the next leap in reasoning capabilities.” The rumour/reasoning I’ve heard is that most advances are being made on synthetic data experiments happening after post-training. It’s a lot easier and faster to iterate on these with smaller models. Eventually a lot of…
> You just get way fewer experiments per day done.
Smaller/simpler/weird/different models can be an incredible advantage due to iteration speed. I think this is the biggest meta problem in AI development. If you can try a large range of hyper parameters, fitness function implementations, etc. in a few hours, you will eventually wipe the floor with the parties forced to wait days, weeks and months for their results each time.
The bitter lesson certainly applies and favors those with a lot of compute and data, but if your algorithms fundamentally suck or are approaching a dead end, none of that compute or information will matter.
Re: Inference is free and instant
#5> No matter how fast Searle is, he won't be able to come up with a beautiful and original Chinese poem that has the creative spark special to humans
Why not?
> Of course, at some level of complexity, it will be stuck in a local maximum of work quality simply because the book has no guide on how to solve the problem at hand.
I find this a pretty un-optimistic view, especially from someone building a coding autopilot. Having myself used LLMs for a bunch of software development in the last year, it seems its 'local maximum' is no different from a developer's _if_ you split the process up appropriately. The author alludes to this when they mention 'workflow'.
Everyone is trying to use LLMs in a 'single inference pass', assuming that's as good as it gets, but that's like trying to get find human creativity in a single cascading activation of neurons. A brain doesn't fit on an axon. So, I kinda think the author should be less shy about their optimism. Inference is soon ~free, as they say, so to me, naive as I might be, the future of AI coding agents is not limited to grunt tasks, it is as creative and exploratory as any human coder.
Ps. Fume looks cool. I'd suggest people take a look at aider.chat and claude-engineer too (on github).
Re: Inference is free and instant
#6> As clearly seen
lol no. Are we looking at the same graph? You can't just slap a trend line on 9 data points from 3 different companies and call it "clear".
Re: Inference is free and instant
#7we've like barely trained these things?
the entirety of common crawl is 424 terabytes. that's merely 6 days of 8K raw video.
Re: Inference is free and instant
#8> Fume isn't built on the premise that LLMs are going to get substantially smarter in the coming months. Instead, we're certain they're going to get much cheaper and faster.
I'm so tired of the assumption that AI tools are going to get increasingly more capable until they can take effectively take over any task that humans currently excel at. They are already useful, but they don't seem likely to take over everything. This is especially true when it comes to making critical decisions.
This take about cost, however, seems well-grounded. I appreciate clear statements like this that can act as guiding principles for what kinds of things to build, and how to anticipate changes in the coming months and years.
Re: Inference is free and instant
#9Nice ad for Fume
Re: Inference is free and instant
#10Why is Searle's Room the seemingly de facto thought experiment for this stuff? You always have to take an extra couple of steps to make it work, and it never really sells it to me. Its true he was distinctly concerned with AI, but he was not at all speaking to the same context, i.e., before "the bitter lesson."
I humbly suggest looking into guys more like Quine. His problem of "radical translation" is much more easily mapped to LLMs. (Thinking specifically here of the model as the "translator"). Its maybe a little harder to grasp for non-domain experts, but at least there is no need for hyperstitional armchair interpretations of old problems in order to make it relevant.
People jump straight into cognitive science/philosophy with this stuff, I just want to be like "whoa, slow down! So much to establish before that.."