131–140 of 150 posts
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IMO: If the comment is a quality comment, I don't care if the poster used AI, if its a low-quality or inappropriate comment (and a summary of the existing comments counts), then downvote or, for egregious cases, flag it.
The issue (temporarily) is that many consider a long comment = high effort = probably high quality. Now we have to change our heuristics.
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Even 65B models approach a level of being almost usable, but still fall short in my personal experience.
This is why I'm not understanding the excitement around open source models, they pale in comparison to GPT-4 quality, so I have no use for them until we have something comparable.
I think it likely depends on the use case, but many llama models can be returned and there are quite literally thousands of free versions available.
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Is there a place where you guys discuss... things? I'm layman interested in this topic akin to pop-physics/maths, but have no chance to just read papers and "get it". On the other hand, immediately available resources focus more on how-to part of it rather than on what's up overall. Also, do you have something like 3b1b/pbs/nph for it? Content that you can watch and say "well, yep, good job".
I don't have any great recommendations and unfortunately my advice may be not what you want to hear. What I tell my students is "You don't need to know math to build good models, but you need to know math to know why your models are wrong." But this is even a contentious statement within the community. (Personally I'm more interested in exploring what we can build and understand rather than focusing on throwing more…
All valuable AI research is secret now, they just churn out papers to waste time
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I don't have any great recommendations and unfortunately my advice may be not what you want to hear. What I tell my students is "You don't need to know math to build good models, but you need to know math to know why your models are wrong." But this is even a contentious statement within the community. (Personally I'm more interested in exploring what we can build and understand rather than focusing on throwing more…
Absolutely fantastic advice. Thank you!
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They sure as hell have no incentives to make Neural Network faster and more accessible, for starters.. (Considering they right now make more money and have more control, the less accessible and the more computation-hungry AI models are) To be fair, this approach (claims to) only speed up inference, not training, so all the GPUs are needed anyway.
I wouldn't be so quick to conspiracy. I'm the author of a work and a famous blog post that trains a particular common architecture much faster (don't want to dox myself too much) and with far fewer parameters, but it has been rejected several times and is now arxiv only. Our most common complaint was "who would use this? Why not just take a large model and tune it?" That question alone held us back a year (had over a…
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What is the sum of odd numbers in this set: 4, 7, 12, 1, 3 The sum of odd numbers in the given set is 4 + 7 + 1 = 12. Therefore, the answer is 12.
You're using an LLM as if you don't understand how or why it works. Math is not what it's for. Here, look at what ChatGPT 4 does (for a case that hasn't been fed to it by a hundred other users yet): What is the sum of odd numbers in this set: 12345654321, 123456543212, 123456543213, 12345654324? Output only the sum, no code. The sum of the odd numbers in the set {12345654321, 123456543212, 123456543213, 12345654324}…
>>> (-1 + 2 + 9 * 5) =
... (-1 + 2 + 9 * 5) = 43
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…ETH Zurich is an illustrious research university that often cooperates with Deepmind and other hyped groups, they're right there at the frontier too, and have been for a very long time. They don't have massive training runs on their own but pound for pound I'd say they have better papers.
ETH Zurich is one of the top labs in the world. Disney Research also works with them a lot. Another "sleeper" is University of Amsterdam that has rockstars like Max Welling and his students Kingma, Salimans,van den Berg, and Hoogeboom. It's easy to get hyped up on the big tech labs because they have the most compute, but the best papers come from smaller labs and unfortunately more lately face larger challenges in ge…
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You're using an LLM as if you don't understand how or why it works. Math is not what it's for. Here, look at what ChatGPT 4 does (for a case that hasn't been fed to it by a hundred other users yet): What is the sum of odd numbers in this set: 12345654321, 123456543212, 123456543213, 12345654324? Output only the sum, no code. The sum of the odd numbers in the set {12345654321, 123456543212, 123456543213, 12345654324}…
Not sure what you mean by 'using'. The prompt is from one of the benchmarks used in scoring LLM models - don't recall which. Here is another from BBH [1]: >>> (-1 + 2 + 9 * 5) = ... (-1 + 2 + 9 * 5) = 43 [1] https://github.com/suzgunmirac/BIG-Bench-Hard/tree/main
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Not sure what you mean by 'using'. The prompt is from one of the benchmarks used in scoring LLM models - don't recall which. Here is another from BBH [1]: >>> (-1 + 2 + 9 * 5) = ... (-1 + 2 + 9 * 5) = 43 [1] https://github.com/suzgunmirac/BIG-Bench-Hard/tree/main
This is even worse than the other, just straight up use a calculator.