They made a claim about language models in general, not just ones that had been released so far.
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.
My main criticism of the paper is that it says LLMs work "haphazardly", using probabilistic information. That is a hypothesis, but it is stated as a known fact, a fundamental limitation.
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.
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?
> [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.
I had the same question. I think that could be answered by using the predicted activation, but I don't see that in the paper.
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.
A carb counting app might use API calls to these frontier models and then do some kind of analysis. It could see if different models agree or not, or multiple calls, and with how much variance.
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.
Another way to put it: if training a model cost 72,000 tons of carbon, and it then gets used by 100 million people (typical of major models), the cost per person is 0.00072 tons.
Per the article, the average human uses over 5 tons per year (Americans: 18). Adding 0.00072 to 5 is not really noticeable.
It is academically interesting what pure neural networks can do, of course. But when someone goes to Claude and tries to do something, they don't care if it solves the problem using a neural network or a call out to Python. So long as the result is right.
More generally, the ability to use tools is a form of intelligence, just like when humans and crows do it. Being able to craft the right Python script and use the result is non-trivial.
An "elaborate harness" that can break down a problem into sub-tasks, write Python scripts for the ones it can't solve itself, and then combine the results, seems able to solve a wide range of cognitive tasks?