https://github.com/trevorlinton/webkit.js/

Yes, we have a tendency to anthropomorphize, but (most) researchers are aware of this.

We see some signs of reasoning, but also we understand little about how they work.

The point of the paper, in fact, is that language models are getting "too big", and another approach is needed to make progress, so they were certainly predicting things about later models.

With that said, they talked about "pure" language models, so it is fair to say that they didn't talk about, say, LLMs that are multimodal or that have tool use, which are advances that happened after their paper.

It is true that LLMs often behave haphazardly, and do rely on statistics. But plenty of research has shown them behaving in methodical ways too. There are findings going both ways!

Granted, many of the strongest contradictory results appeared after the Stochastic Parrots paper, so it isn't like they were ignoring the literature at the time. But they did make a very strong claim, and in the half-decade since, a lot of evidence has come out against it.

Comments URL: https://news.ycombinator.com/item?id=48645276

Points: 2

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Note that wasm is still lean and fast - WASI is not part of core wasm, but layered on top.

That is, it is possible to implement wasm without WASI. That is also true for other wasm proposals like WasmGC. It is very possible that parts of the ecosystem will not implement certain proposals if they don't make sense there (e.g. parts of the embedded ecosystem may never add GC, etc.).

> I believe that artificial intelligence has three quarters to prove itself before the apocalypse comes, and when it does, it will be that much worse, savaging the revenues of the biggest companies in tech. Once usage drops, so will the remarkable amounts of revenue that have flowed into big tech, and so will acres of data centers sit unused, the cloud equivalent of the massive overhiring we saw in post-lockdown Silicon Valley.

We have seen 8 quarters since. Has any of that come to pass?

> Suppose one copies an LLM into AoE II and feeds into the AoE II-LLM ‘I feel lonely’ as an input. This AoE II-LLM replies: ‘I feel bad for you, maybe catch up with a friend? Closeness always helps in these situations’. One would be hard-pressed to make a convincing argument that, because of this response, an AoE II-LLM knows what helps in these situations

I don't see why one would be any more hard-pressed to make that conclusion about this system than a "normal" LLM.

That it is harder to "read" the data out is the only difference (the AoE II-LLM's output is encoded in game elements). But is ease of decoding an actual issue? If we can't understand a group of people that speak another language, does that say anything about them, or about us?

https://www.goodreads.com/en/book/show/217432753-the-ai-con

which describes LLMs as "souped-up autocomplete", complex statistics that cannot truly understand anything. A more recent example is this paper:

https://zenodo.org/records/20071869

which says,

> [LLMs], as turbo-charged statistical models (recall their formal relation to logistic regression) can only but provide correlations.

And, of course, the Stochastic Parrot paper is the classic example in this area. It is from 5 years ago, but "LLMs only do statistics / can't understand" is very much alive and active among academics, even if it is a minority position.

That Newton and Leibniz came up with similar ideas in parallel, independently, around the same time (what are the odds?), supports that.

https://en.wikipedia.org/wiki/Leibniz%E2%80%93Newton_calculu...

That is, rather than just translate activation to text, then text to activation, that final activation could then be applied to the neural network, and it would be allowed to continue running from there.

If it kept running in a similar way, that would show that the predicted activation is close enough to the original one. Which would add some confidence here.

But a lot better would be to then do experiments with altered text. That is, if the text said "this is true" and it was changed to "this is false", and that intervention led to the final output implying it was false, that would be very interesting.

This seems obvious but I don't see it mentioned as a future direction there, so maybe there is an obvious reason it can't work.

Vendors might also be running A/B testing or who knows what, even when you ask for a temperature of 0.

But, if you run a fixed model with temperature 0 on your local CPU, it will be deterministic (unless there are bugs).

So it would be more accurate to test the apps rather than the APIs, unless the goal is to warn people that just open chatgpt and ask there.