> Type confusion in V8 in Google Chrome prior to 152.0.7977.82 allowed a remote attacker to execute arbitrary code inside the sandbox via a crafted HTML page.

So it was fixed in 152.0.7977.82 (before .83), if I read that right.

But we do have a hypothesis: that it is done by a large number of simple units with very high connectivity and in deep layers. This is what neural networks model.

Personally I was skeptical of this model of the brain, but they have achieved remarkable success in practice, as well as Nobel prizes. The neural networks people may have been onto something all along (I say that grudgingly).

That is, yes, ANNs are not brains. There are countless differences. But are there differences at the computational level? ANNs are meant to model brain computation, not brain biology.

(There is still a lot to debate there, I'm not saying "ANNs are perfect computational models for the brain")

This is asserted without evidence, and from a scientific standpoint, unjustified.

First, "detect patterns" makes it sound like a classification task, "is this a picture of a cat". But LLMs transform the input.

For example, an LLM can translate text between two languages while properly handling the names of the people described, no matter what those names are. That shows they are representing the text in a somewhat abstract way, that they can perform operations on that representation, and also convert it to useful output.

And, what I just described is the most general form of information processing algorithm. Science is not aware of any limitations in principle on such systems.

I am not saying LLMs have no limits, but "they only recognize patterns, and that is a true limit" is not a good argument.

> no way to associate the text vectors they manipulate with real-world phenomena.

Historically, that LLMs were text-only used to be a major argument for why they "lack access to meaning", see the Stochastic Parrot paper and the Octopus paper that it references. But even the authors of those papers have (grudgingly) conceded that the argument no longer holds due to multimodality.

I'm obviously not entirely serious here, but I think this is true to some extent: the number of memory safety bugs in a codebase is finite. Once you have a way to find them, you can drive that number down to zero.

C++ has become a far safer language thanks to LLMs - at least if you run the LLMs before you are attacked.

Here is how I'd put it: math has an enormous focus on discovery. It is why we have Godel's incompleteness theorems, the Cantor set, Zorn's lemma, and so forth. We name things after their discoverers.

It is possible that, going forward, no more things will be named after human discoverers. The last such naming (of something truly significant) may already have occurred.

That is a massive culture change, at the very least.

> We reuse WebAssembly linear memories under the covers to implement and sandbox the GC heap. A reference to a GC object is not a native pointer, it is a 32-bit index into the GC heap’s underlying linear memory [..] As far as being fast goes, it lets us use virtual-memory guard pages to elide explicit bounds checks, just like we do for linear memories

Array loads and stores still need an explicit bounds check, don't they? And struct loads and stores don't have one anyhow. Are there other bounds checks that Wasmtime is removing? I can't figure out what they mean here.

Yes, formal mathematics has such limits. We can't expect machines to be perfect and provably perfect. But the industry isn't assuming that. Why would it? Natural intelligence is not perfect or provably perfect, either.

Rather than certainty, measurement is often enough. We can't prove a program will always halt, but we can check it halts in a specific execution.

Approximation is also often all we need. Even if we can't prove that we can train a network with more than 50% success, if we can get multiple shots at that (using different data, or initial random weights, or training techniques, or something else), then we can reduce that danger exponentially. (I don't know that we have a guarantee of succeeding there, but this would be the hope, and I am not aware of anything showing it is impossible, unlike perfect provability.)

Finally, it is possible that perfect provability does work on the problems we care about. Godel and Turing etc.'s proofs rely on finding rare situations where we can't prove things - cleverly-constructed pathological cases - but perhaps human behavior does not fall into that set. Human behavior may not be a pathological case for proofs or learnability.

What is an "AI project"? The post doesn't define it.

Is it writing some software from scratch? Using an LLM chatbot by non-coders, either internally or externally? Or something else entirely?

Some examples would really help.

Or, rather, you can use a value after freeing it, but it will not be exploitable, because it will contain valid data of the right type. This is the same idea as Type-After-Type,

https://dl.acm.org/doi/10.1145/3274694.3274705

(Also similar to when you use indexes to an array in Rust and happen to read from a wrong but in-bounds index.)

Link: https://zig.guide/standard-library/allocators/

Text:

> The Zig standard library also has a general-purpose debug allocator. This is a safe allocator that can prevent double-free, use-after-free and can detect leaks.

For more detail, see:

https://github.com/ziglang/zig/issues/3180#issuecomment-5284...

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.