891–900 of 1001 posts
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Q* is the optimal (ie, correct for the decision problem) function computing the total expected reward of taking an action from a given state in reinforcement learning.
That is just Q. The asterisk is new
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I agree that in and of itself it's not enough to be alarmed. Also i have to say i don't really know what grade school mathematics means here(multiplication? Proving triangles are congruent?). But I think the question is, whether the breakthrough is an algorithmic change in reasoning. If it is, then it could challenge all 4 of your limitations. Again this article is low on details so really we are arguing over our bes…
I think maybe I didn't make myself quite clear here. There are already algorithms which can solve advanced mathematical problems 100% reliably (prove theorems). There are even algorithms which can prove any correct theorem that can be stated in a certain logical language, given enough time. There are even systems in which these algorithms have actually been implemented. My point is that no technology which can solve…
Not perhaps in the sense of making mathematicians redundant but it seems like a breakthrough for ChatGPT type programs.
You've got to remember these things have gone from kind of rubbish a year or so ago to being able to beat most students at law exams now and by the sounds of it beat students at math tests shortly. At that rate or progress they'd be competing with the experts before very long.
So the bad guys won. OpenAI gets further from it’s non commercial origins and further from responsible research in this field.
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What I wonder, as a computer scientist: If you want to solve grade school math problems, why not use an 'add' instruction? It's been around since the 50s, runs a billion times faster than an LLM, every assembly-language programmer knows how to use it, every high-level language has a one-token equivalent, and doesn't hallucinate answers (other than integer overflow). We also know how to solve complex reasoning chains…
I think the answer is Money, Money, Money. Sure it is 1000000000x more expensive in compute power, and error prown on top as well, to let a LLM solve an easy Problem. But the Monopolies generate a lot of hype around it to get more money from investors. Same as the self driving car hype was. Or the real time raytracing insanity in computer graphics. If one hype dies they artificially generate a new one. For me, I just…
They have a pipeline of hypes ready to be deployed at a moment's notice. The next one is quantum, it's already gathering in the background. Give it a couple of years.
I feel very comfortable saying, as a mathematician, that the ability to solve grade school maths problems would not be at all a predictor of ability to solve real mathematical problems at a research level. The reason LLMs fail at solving mathematical problems is because: 1) they are terrible at arithmetic, 2) they are terrible at algebra, but most importantly, 3) they are terrible at complex reasoning (more specifica…
Math and Logic is just low-dimensional symbol manipulation that computers can easily do. You can throw data at them and they’ll show you theories that involve vectors of 42,000 variables while Isaac Newton had 4 and Einstein had 7 with Levi-Civita calculus. In short, what you consider “reasoning”, while beautiful in its simplicity, is nevertheless crude approximations to complex systems, such as linear regression or least squares.
3 days ago AI predicted fluid dynamics better than humans: https://www.sciencedaily.com/releases/2023/11/231120170956.h...
Google’s AI predicts weather now faster and better than Current systems built by humans: https://www.zdnet.com/google-amp/article/ai-is-outperforming...
AlphaZero based on MCTS years ago beat Rybka and all human-built systems in chess: https://www.quora.com/Did-AlphaZero-really-beat-Stockfish
And it can automate science and send it into overdrive: https://www.pbs.org/newshour/amp/science/analysis-how-ai-is-...
Seems like they have made progress in combining reinforcement learning and LLMs. Andrej Karpathy mentions it in his new talk (~38 minutes in) [1], and Ilya Sutskever talks about it in a lecture at MIT (~29 minutes in) [2]. It would be a huge breakthrough to find a proper reward function to train LLMs in a reinforcement learning setup, and to train a model to solve math problems in a similar fashion to how AlphaGo use…
Thanks for the links, very interesting. Wonder how a "self-play" equivalent would look like for LLMs, since they have no easy criterion to evaluate how well they are doing like in Go (as mentioned in the videos).
Of course in the real world, for a real intelligent system, reality is the feedback/reward system, but for an LLM limited to it's training set, with nothing to ground it, maybe this is the best you can do ...
The idea is essentially that you need to assume (but of course GI-GO) that most of the training data is factual/reasonable whether in terms of facts or logic, and therefore that anything you can deduce from the training data that is consistent with the majority of the training data should be held as similarly valid (and vice versa).
Of course this critically hinges on the quality of the training data in the first place. Maybe it would work best with differently tagged "tiers" of training data with different levels of presumed authority and reasonableness. Let the better data be used as a proxy for ground truth to "police" the lesser quality data.
> OpenAI defines AGI as autonomous systems that surpass humans in most economically valuable tasks. Are we really defining intelligence as economic value at this point? This is completely ridiculous. We have yet to decide exactly what human intelligence, how it manifests in the body, or how to reliably measure it. I get that people want to justify developing artificial intelligence before understanding intelligence i…
OpenAI is not setting the goal post that far, to say that they are aiming to develop a machine that is superior to humans at all tasks, including such tasks as raising a human child. That would be ridiculous.
Focusing on "economically valuable" tasks (the jobs that humans are employed and paid to do) sets the goal post more realistically.
I feel very comfortable saying, as a mathematician, that the ability to solve grade school maths problems would not be at all a predictor of ability to solve real mathematical problems at a research level. The reason LLMs fail at solving mathematical problems is because: 1) they are terrible at arithmetic, 2) they are terrible at algebra, but most importantly, 3) they are terrible at complex reasoning (more specifica…
For transformer models, they do learn from their mistakes but only during the training stage.
There’s no feedback loop during inference, and perhaps there needs to be something; like real-time fine-tuning.
Seems like they have made progress in combining reinforcement learning and LLMs. Andrej Karpathy mentions it in his new talk (~38 minutes in) [1], and Ilya Sutskever talks about it in a lecture at MIT (~29 minutes in) [2]. It would be a huge breakthrough to find a proper reward function to train LLMs in a reinforcement learning setup, and to train a model to solve math problems in a similar fashion to how AlphaGo use…
If this was actually done (and this is just wild baseless speculation), this would be a good reason to let Sam go.
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Actual reasoning shows the understanding and use of a model of the key features of the underlying problem/domain. As a simple example that you can replicate using chatgpt, ask it to solve some simple maths problem. Very frequently you will get a solution that looks like reasoning but is not, and reveals that it does not have an actual model of the underlying maths but is in fact doing text prediction based on a histo…
I generally agree with what you're saying and the first half of your answer makes perfect sense but I think the second is unfair (i.e. "[is it] easier to balance a barrel on a plank or a plank on a barrel"). It's a trick question and "it" tried to answer in good faith. If you were to ask the same question of a real person and they replied with the exact same answer you could not conclude that person was not capable o…