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AI bros still in shambles, news at 7.
A few weeks ago, Anthropic made a post about their new model, Mythos. As has been done by other members of the AI industry as far back as the release of GPT 2, the creators of it said it was too dangerous to release. The headline feature of Mythos, at least as described by Anthropic, was not code generation. Instead, they specifically hyped it as the most amazing thing ever for finding security vulnerabilities in code.
Several people, including here on this forum, shared the hype. As usual, I remained unconvinced. I've mentioned elsewhere that I don't think AIs are inherently incapable of finding security vulnerabilities in code, my main skepticism is that they will generate lots of false positives in the process that will make them a lot less useful than the companies selling them have advertised. And more importantly, I think they are currently incapable of designing and maintaining any significant projects that go beyond a basic bitch CRUD application or things of that sort. I'm also skeptical that there is all that much room for growth or improvement beyond their current capabilities, for a number of reasons that I won't get into right now.
But enough about my opinions, I'm just a retarded code monkey doing API integrations for boring tax software. Enter Daniel Stenberg, the creator and maintainer of curl. For those who don't know, if you have a program or library that makes HTTP requests, there is an extremely high likelihood that it is using curl under the hood. It's basically one of the foundational pieces of modern digital infrastructure, a "project some random person in Nebraska has been thanklessly maintaining since 2003", as XKCD might put it: https://xkcd.com/2347/
Stenberg/curl was one of the projects that was offered early access to Mythos. However despite being promised access initially, it took several weeks to get it. And even then he suddenly was no longer being offered direct access, but was offered to have someone else run Mythos against his codebase for him and to then share the results with him. This is a big red flag for me, because if Mythos does actually generate a lot of noise/false positives, it would make sense that Anthropic would want to hide that by running it themselves as many times as they could until it actually generated some real, actionable results.
In any case, the results that Stenberg got back were underwhelming. Mythos claimed to have identified 5 vulnerabilities. After investigating all of them, Stenberg and his team determined that only one of those was a vulnerability, and a low severity one at that. In Stenberg's own words: "curl is certainly getting better thanks to this report, but counted by the volume of issues found, all the previous AI tools we have used have resulted in larger bugfix amounts."
Most damning from Stenberg is this: "My personal conclusion can however not end up with anything else than that the big hype around this model so far was primarily marketing. I see no evidence that this setup finds issues to any particular higher or more advanced degree than the other tools have done before Mythos. Maybe this model is a little bit better, but even if it is, it is not better to a degree that seems to make a significant dent in code analyzing."
So I'm asking @self_made_human and others who seem more on-board with the AI hype train: does this report from a knowledgeable and experienced developer change your opinions on the future trajectory of AI at all?
Full article by Stenberg can be found here:
https://daniel.haxx.se/blog/2026/05/11/mythos-finds-a-curl-vulnerability/
I thought all the talk about software vulnerabilities would peter out for now, but I don't think that marketing is the only explanation.
Materialists are making the logically consistent assumption that if humans are computers, then AI is guaranteed to surpass our capabilities in every respect. So they predict a future which may not be real if materialism isn't real, and are hallucinating that such a future has arrived out of a cycle of fear and a desire to get ahead of it.
Strictly speaking, just because one hyped-up thing failed to hit the mark, it doesn't mean that it isn't coming, especially given the pace of developments. But Charlie Kirk said it right: AI is destined to throw our assumptions into chaos one way or another, and I, for one, am curious to see exactly what gets discredited as our knowledge and actual experience is forced to increase. Though it would be nice if we had a better understanding of things before we're forced to learn it inadvertently.
Neuroscience still has a lot of ground to cover, but we already know the brain isn't a binary computer. It seems to me that one very easily could be a materialist and think that the brain is not a computer and I've always been a bit puzzled by the consistent tendency to equivocate them.
The claim isn't that the brain is a "binary computer", it's that it's that however the brain works, it does not have computational capabilities that go beyond what is expressible by a Turing machine. So far we haven't been able to come up with a physical system of whatever sort that everyone agrees is able to come up with results that something like a digital computer can not even in principle. Roger Penrose does think that the human brain is one of those, and some mathematical insights humans can have are literally examples of super-Turing computation, but most everyone else thinks he's being a crank about this.
Your link says
It then goes on to explain that, arguably, "everything is computer."
Perhaps the human mind is a computer in the sense that everything is, but there doesn't seem to be good evidence that it is a computer in the sense that the metaphor is helpful to understanding the human mind. The human brain does not create representations of stimuli, store them, manipulate them, and retrieve them later upon demand according to a series of algorithmic rules.
Perhaps the human mind can't perform any mathematical calculations that cannot be performed by a Turing machine, but that doesn't mean that saying it is a computer is a helpful analogy. A digital tape recorder can record any song that a record can, but it's not helpful to call a record player a computer either - the mode of operation is different.
While I am sure that "not everyone agrees" my understanding is that it seems pretty clear that the universe, itself, is not simulable.
That's the thing. People didn't decide a priori that "everything is a computer". People just went looking for things that can't be mapped into computers all over nature and never found one.
This is pretty much what the debate comes down to though, remember the original argument was about whether we should expect AIs to surpass humans in everything humans can do. People keep trying to claim that humans have some magical domains of competence that will remain out of reach of AIs. For this to be an useful argument against claims of AI doom, it needs to cash out as the human mind doing some sort of work that shows up as output in the world, like a symphony or a beautiful masterpiece on a canvas. The theory of computation is very different from actual computer engineering, and the Aeon magazine writer seems to not understand this. It doesn't say anything about bytes, files, subroutines, operating systems, databases, images or buffers, just that there is some finite-length (but probably very long) lawful process that generates the speech or movement that shows that the thinking happened, and that the process could be translated to be run by a Turing machine.
I'm not a theoretical physicist but I'm pretty willing to bet that a physics paper that appeals to Gödel's incompleteness theorem for wide-ranging claims about the ultimate nature of reality will not end up receiving wide scientific agreement. The Gödel argument is basically the same thing Roger Penrose goes on about, and it goes back to John Lucas in 1959. It's had plenty of time to convince people and as far as I understand it by and large hasn't done that.
Apparently a previous reply was eaten, my sincere apologies if this ends up a double-post.
The fact that "people" latch on to an easy metaphor does not necessarily indicate that the metaphor is good. The fact that the people most familiar with computers latch on to this metaphor also does not necessarily indicate that the metaphor is good.
This wasn't my claim, though.
The Aeon author did tackle the idea that the mind is an algorithm, which is, as I understand it, part of the theory of computation. We have good reasons to think the brain does not run on an algorithm; as the author of the piece I linked to points out, memory is extremely inexact, which is the opposite of what we would expect if the brain operated in an algorithmic manner.
But to take a step back, even if we wish to draw a distinction between "computer as hardware" and "computer as information processing device" the linguistic overlap invites us to confuse the two. And I don't think this is good; the analogy breaks down quickly in practice and invites us to forget the massive differences between the brain and electronic computers; it's true the brain uses electrical impulses but it also uses chemicals and is much slower than a computer. This metaphor, turned loose into the wild, has led to the popularization of what should be obviously implausible ideas, such as "mind uploading" or even that a computer could have emotions that we know in humans are substantially influenced by hormones.
In short, the idea that the mind is a computer is a sloppy one even if the motte is more defensible than the bailey by far precisely because the word "computer" makes it inherently a metaphor that yields a motte-and-bailey, even subconsciously.
I am not a theoretical physicist, or a mathematician, or a neurologist, but I am pretty sure you are wrong.
As I understand it, it works something like this. Gödel's incompleteness theorem says you can't algorithmically "solve" math (in the sense that there's not a super-algorithm that can do all mathematics). Penrose said "aha but humans can so we're BETTER THAN TURING MACHINES." The skepticism of Penrose isn't that Gödel is wrong, it's about whether or not humans can do that. If Gödel's incompleteness theorems suggest that our universe isn't a simulation, that's a different line of argument.
Yep, this is a much less prone to confusion way of saying it than "the mind is a computer".
And this is utterly confused. Douglas Hofstadter's cartoon illustrated the error pithily way back in Gödel, Escher, Bach. The algorithm is exact (the small, correct sums in the Hofstadter cartoon), but it's also too precise and constrained to do mind-like stuff directly in the small. Instead, the mind runs on a sort of virtual machine (big numbers built from the small sums in the cartoon) built up by the algorithm that can do complex pattern recognition and creative solutions, but is also constantly getting things wrong. As we see from AIs, virtual machines like this can be implemented on silicon just fine and they exhibit the same behavior of being able to do difficult useful stuff but also constantly getting details wrong on their own.
I sorta agree here. It's basically an accident of history that "computers", things with hard drives, keyboards, operating systems, files, RAM and CPUs, and "computation", the evaluation of primitive recursive mathematical functions which matches what a Turing machine (which, again, isn't a "machine" that you build from wires and bolts, but a mathematical construct), ended up using the same terminology up to "computer" being right there in the name "computer science". This is why the cognitive science school is called "computationalism" instead of "computerism" and the practitioners optimistically thought that given a name like that, obviously people would think Turing machines, not quad core Mac Pros.
The problem with Penrose's argument is that humans are doing math pretty much as you'd expect if constrained by Gödel. By stumbling into theorems, working hard trying to prove them, and sometimes finding themselves stuck and unable to show something as either true or untrue. The crackpot smell with the physics paper is that Gödel's theorem is ultimately pretty limited. It says that any formal system powerful enough to do any sort of interesting math in allows stating the equivalent of the liar's paradox, which cannot logically resolve to be either true or false, therefore you can't have a mechanism for determining the truth of any proposition because you have liar's paradox propositions floating around. The equivalent impossibility theorem for computer science is the halting problem, you can't write a program that looks at the source code of any program and tells whether the program will terminate. For simulations, this would be saying something like that you need to actually run the simulation to see what kind of state it ultimately ends up in (and whether it stops at a steady state or goes on forever), and can't just look at the simulation's source code and figure it out. But it doesn't prohibit running the simulation and looking at what happens in it while it's running.
Even assuming the article is correct, I'm not sure it'll tell us anything useful about human capabilities versus silicon. Halting problem style arguments do claim that we can't build a literal machine-god that can figure out the exact trajectory of our universe ahead of time just by thinking hard. But that's not necessary to have machines that are better at doing everything humans value doing.
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