Comments URL: https://news.ycombinator.com/item?id=45902010

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(Disclaimer: I'm the author of a competing approach)

Searching for new small-molecule inhibitors requires going through millions of novel compounds. But AlphaFold3 was evaluated on a dataset that tends to be repetitive: https://olegtrott.substack.com/p/are-alphafolds-new-results-...

Comments URL: https://news.ycombinator.com/item?id=43674739

Points: 2

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Comments URL: https://news.ycombinator.com/item?id=43674337

Points: 3

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Gnina is Vina with its results re-scored by a NN, so the exact same concerns apply.

I'm very optimistic about AI, for the record. It's just that in this particular case, the comparison was flawed. It's the old regurgitation vs generalization confusion: We need a method that generalizes to completely novel drug candidates, but the evaluation was done on a dataset that tends to be repetitive.

The annual survey is very popular in the Rust community. Its results are often used for advocacy. Participation by Rust developers is very high. So what you have is a classic case of a selection bias.

Comments URL: https://news.ycombinator.com/item?id=43052419

Points: 4

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The article was written in 2011, and the trick still seems to work in a 2024 version of GNAT.

> Both "unchecked" Ada and "unsafe" Rust

But the `Conversion` function isn't using `Unchecked_*`. That's the point of the article. The type safety hole is in "safe" Ada.

Ada is not actually type-safe: https://www.enyo.de/fw/notes/ada-type-safety.html

2015: "The work needed to close this has not yet landed. It's in the queue though, once we finish up rust-lang/rfcs#1214."

Rust also has soundness holes, by the way. This one is almost 10 years old: https://github.com/rust-lang/rust/issues/25860

Comments URL: https://news.ycombinator.com/item?id=42991676

Points: 389

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Comments URL: https://news.ycombinator.com/item?id=42991467

Points: 1

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They won the 1st paper award: https://arcprize.org/2024-results

In their approach, the LLM generates inputs (images to be transformed) and solutions (Python programs that do the image transformations). The output images are created by applying the programs to the inputs.

So there's a constraint on the synthetic data here that keeps it honest -- the Python interpreter.

In the o3 announcement video, the president of ARC Prize said they'd be partnering with OpenAI to develop the next benchmark.

> mechanical turking a training set, fine tuning their model

You don't need mechanical turking here. You can use an LLM to generate a lot more data that's similar to the official training data, and then you can train on that. It sounds like "pulling yourself up by your bootstraps", but isn't. An approach to do this has been published, and it seems to be scaling very well with the amount of such generated training data (They won the 1st paper award)

Can it use anything else (as an option), e.g. Lua? That would be useful during development/debugging thanks to faster iteration and memory safety.

Comments URL: https://news.ycombinator.com/item?id=42404110

Points: 10

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