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The impactful innovations in AI these days aren't really from scaling models to be larger. It's more concrete to show higher benchmark scores, and this implies
by alyxya 11mo ago
The impactful innovations in AI these days aren't really from scaling models to be larger. It's more concrete to show higher benchmark scores, and this implies higher intelligence, but this higher intelligence doesn't necessarily translate to all users feeling like the model has significantly improved for their use case. Models sometimes still struggle with simple questions like counting letters in a word, and most people don't have a use case of a model needing phd level research ability.
Research now matters more than scaling when research can fix limitations that scaling alone can't. I'd also argue that we're in the age of product where the integration of product and models play a major role in what they can do combined.
- TheBlight 11mo ago"Scaling" is going to eventually apply to the ability to run more and higher fidelity simulations such that AI can run experiments and gather data about the world as fast and as accurately as possible. Pre-training is mostly dead. The corresponding compute spend will be orders of magnitude higher.
- alyxya 11mo agoThat's true, I expect more inference time scaling and hybrid inference/training time scaling when there's continual learning rather than scaling model size or pretraining compute.
- TheBlight 11mo agoSimulation scaling will be the most insane though. Simulating "everything" at the quantum level is impossible and the vast majority of new learning won't require anything near that. But answers to the hardest questions will require as close to it as possible so it will be tried. Millions upon millions of times. It's hard to imagine.
- emporas 11mo ago>Pre-training is mostly dead. I don't think so. Serious attempts for producing data specifically for training have not being achieved yet. High quality data I mean, produced by anarcho-capitalists, not corporations like Scale AI using workers, governed by laws of a nation etc etc. Don't underestimate the determination of 1 million young people to produce within 24 hours perfect data, to train a model to vacuum clean their house, if they don't have to do it themselves ever again, and maybe earn some little money on the side by creating the data. The other part of the comment I agree.
- pron 11mo ago> this implies higher intelligence Not necessarily. The problem is that we can't precisely define intelligence (or, at least, haven't so far), and we certainly can't (yet?) measure it directly. And so what we have are certain tests whose scores, we believe, are correlated with that vague thing we call intelligence in humans. Except these test scores can correlate with intelligence (whatever it is) in humans and at the same time correlate with something that's not intelligence in machines. So a high score may well imply high intellignce in humans but not in machines (e.g. perhaps because machine models may overfit more than a human brain does, and so an intelligence test designed for humans doesn't necessarily measure the same thing we think of when we say "intelligence" when applied to a machine). This is like the following situation: Imagine we have some type of signal, and the only process we know produces that type of signal is process A. Process A always produces signals that contain a maximal frequency of X Hz. We devise a test for classifying signals of that type that is based on sampling them at a frequency of 2X Hz. Then we discover some process B that produces a similar type of signal, and we apply the same test to classify its signals in a similar way. Only, process B can produce signals containing a maximal frequency of 10X Hz and so our test is not suitable for classifying the signals produced by process B (we'll need a different test that samples at 20X Hz).
- alyxya 11mo agoFair, I think it would be more appropriate to say higher capacity.
- pron 11mo agoOk, but the point of a test of this kind is to generalise its result. I.e. the whole point of an intelligence test is that we believe that a human getting a high score on such a test is more likely to do some useful things not on the test better than a human with a low score. But if the problem is that the test results - as you said - don't generalise as we expect them, then the tests are not very meaningful to begin with. If we don't know what to expect from a machine with a high test score when it comes to doing things not on the test, then the only "capacity" we're measuring is the capacity to do well on such tests, and that's not very useful.
- 11mo ago
- pessimizer 11mo ago> most people don't have a use case of a model needing phd level research ability. Models also struggle at not fabricating references or entire branches of science. edit: "needing phd level research ability [to create]"?
- nutjob2 11mo ago> this implies higher intelligence Models aren't intelligent, the intelligence is latent in the text (etc) that the model ingests. There is no concrete definition of intelligence, only that humans have it (in varying degrees). The best you can really state is that a model extracts/reveals/harnesses more intelligence from its training data.
- dragonwriter 11mo ago> There is no concrete definition of intelligence Note that if this is true (and it is!) all the other statements about intelligence and where it is and isn’t found in the post (and elsewhere) are meaningless.
- interstice 11mo agoI did notice that, the person you replied to made a categorical statement about intelligence followed immediately with negating that there is anything to make a concrete statement about.
- darkmighty 11mo agoThere is no concrete definition of a chair either.
- gafferongames 11mo agoAnd yet I'm sitting in one
- jfim 11mo agoCounting letters is tricky for LLMs because they operate on tokens, not letters. From the perspective of a LLM, if you ask it "this is a sentence, count the letters in it" it doesn't see a stream of characters like we do, it sees [851, 382, 261, 21872, 11, 3605, 290, 18151, 306, 480].
- tintor 11mo agoSo what? It knows number of letters in each token, and can sum them together.
- fzzzy 11mo agoHow does it know the letters in the token? It doesn't. There's literally no mapping anywhere of the letters in a token.
- danielscrubs 11mo agoIf it did frequency analysis then I would consider it having a PhD level intelligence, not just a PhD level of knowledge (like a dictionary).
- ACCount37 11mo agoThere is a mapping. An internal, fully learned mapping that's derived from seeing misspellings and words spelled out letter by letter. Some models make it an explicit part of the training with subword regularization, but many don't. It's hard to access that mapping though. A typical LLM can semi-reliably spell common words out letter by letter - but it can't say how many of each are in a single word immediately. But spelling the word out first and THEN counting the letters? That works just fine.