The tells are in the cadence. And the not x but y. And the last line that basically says nothing, while using big words. It's like "In conclusion", but worded differently. Enough tells for me to click on their history. They have the exact same cadence on every comment. It's a bit more sophisticated than "chatgpt write a reply", but it's still 100% aigen. Check it out, you'll see it after a few messages in their history.
No, it doesn't. The "I'm an expert at AI detection" crowd likes to cite things like "It's not X, it's Y" and other expression patterns without stopping to think that perhaps LLMs regurgitate those patterns because they are frequently used in written speech.
I assign a <5% probability that GP comment was AI written. It's easy to tell, because AI writing has no soul.
The message is 100% AI written. And if you click on their username and check their comment history you'll see that ALL their comments are "identical". Just do it, you'll see it by the 5th message. No one talks like that. No one talks like that on every message.
Exactly, if a comment just feels a little off but you're unsure, do a quick scan of the profile, takes 15-30 seconds at most to get sufficient signal.
If it's actually AI, the pattern becomes extremely obvious reading them back-to-back. If no clear pattern, I'll happily give them the benefit of the doubt at that point. I don't particularly care if someone occasionally cleans up a post with an LLM as long as there is a real person driving it and it's not overused.
The other day on Reddit I saw a post in r/sysadmin that absolutely screamed karma farming AI and it was really depressing seeing a bunch of people defending them as the victim of an anti-AI mob without noticing the entire profile was variations of generic "Does anyone else dislike [Tool X], am I alone? [generic filler] What does everyone else think?" posts.
Looking at their profile I'm inclined to agree. But I think in isolation, this one post isn't setting off enough red flags for me. At the very least, they aren't just using default prompts.
I think at this point it's not easy to accurately detect whether or not something is AI written. A real person can definitely write like this. In fact, that's probably where the LLMs got their writing style from.
It's not totally novel, but it's very cool to see the continued simplification of protein folding models - AF2 -> AF3 was a reduction in model architecture complexity, and this is a another step in the direction of the bitter lesson.
I’m not sure AF3’s performance would hold up if it hadn’t been trained on data from AF2 which itself bakes in a lot of inductive bias like equivariance
Protein folding is in no way "solved". AlphaFold dramatically improved the state-of-the-art, and works very well for monomeric protein chains with structurally resolved nearest neighbors. It abjectly fails on the most interesting proteins - just go check out any of the industry's hottest undrugged targets (e.g. transcription factors)
> When it comes to very complicated things, physics tends to fall down and we need to try non-physics modeling, and/or come up with non-physics abstraction.
"When things are complicated, if I just dream that it is not complicated and solve another problem than the one I have, I find a great solution!"
Joking apart, models that can help target potentially very interesting sub phase space much smaller than the original one, are incredibly useful, but fundamental understanding of the underlying principles, allowing to make very educated guesses on what can and cannot be ignored, usually wins against throwing everything at the wall...
And as you are pointing out, when the complex reality comes knocking in it usually is much much messier...
I have your spherical cow standing on a frictionless surface right here, sir. If you act quickly, I can include the "spherical gaussian sphere" addon with it, at no extra cost.
As someone who loves SML/OCaml and has written primarily Rust over the past ~10 years, I totally agree - I use it as a modern and ergonomic ML with best-in-class tooling, libraries, and performance. Lifetimes are cool, and I use them when needed, but they aren't the reason I use Rust at all. I would use Rust with a GC instead of lifetimes too.
Either a lot of clones or a lot of reference counted pointers. Especially if your point of comparison is a GC language, this is much less of a crime than some people think
When I mean "use" them, I mean make heavy use of them, e.g. structs or functions annotated with multiple lifetimes, data flows designed to borrow data, e.g. You can often get by just with `clone` and lifetime elision, and if you don't need to eke out that last bit of performance, it's fine.
I looked through their torch implementation and noticed that they are applying RoPE to both query and key matrices in every layer of the transformer - is this standard? I thought positional encodings were usually just added once at the first layer
All the Llamas have done it (well, 2 and 3, and I believe 1, I don't know about 4). I think they have a citation for it, though it might just be the RoPE paper (https://arxiv.org/abs/2104.09864).
I'm not actually aware of any model that doesn't do positional embeddings on a per-layer basis (excepting BERT and the original transformer paper, and I haven't read the GPT2 paper in a while, so I'm not sure about that one either).
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