The hilarious part is, yes, I don’t care how fast my launcher is.

But… also, Steam is by far the slowest, least responsive program on my computer.

“Faster than Steam” feels like a joke.

I don’t use a lot of non-open source applications (and not a lot of desktop applications generally). Have they gotten so bad that the slowness of Steam doesn’t feel bad to people anymore?

the models only need to be right some of the time and they still constitute a threat.

Right!?!

And with how cheap and fast they are to run, they don’t have to be right that often as long as a separate run can verify/validate claims. In a lot of ways what the LLMs do should probably be thought of as just a very sophisticated sort of brute force approach. You’re effectively just searching the space of combinations of known vulnerabilities. It doesn’t require particular cleverness, just an encyclopedic knowledge, a reasonable way to prune the search space, and the ability to put in the compute time.

I must be missing something, because you’re getting a bunch of upvotes. I don’t disagree with anything you said, but it feels like a total non-sequitur to what I was saying.

Yes - proper air-gapping is a good idea. But asking the big AI labs to air gap is kind of pointless. Liability already gives them legal incentives to avoid incidents. If that didn’t stop them, a letter signed by a bunch of academics sure won’t. More importantly, open-weight models capable of doing all the things that we’re concerned about are already in the hands of bad actors around the world.

This letter is asking us to lock the front door of the house when the back wall is missing.

The ones in Russia definitely are. They don’t have to be local models - you run them in the cloud like anyone else.

The idea that the frontier labs have LLMs with special capabilities that make them dramatically better than the top open-weight models is largely marketing.

The reality is that most systems are poorly secured, and LLMs make exploiting vulnerabilities fast, cheap, and easy. What would have taken a trained security researcher weeks before can be done by a much less skilled attacker with an LLM in a few hours.

This is stupid. Open-weight models that can largely do what the OpenAI/Anthropic models did are already out in the wild.

Do these people not understand that you can’t put the cat back in the bag?

I think people are right that the solution here isn’t to try a different project.

Instead of being confrontational about it, though, I’d recommend a different aproach.

evelyn: .lol 😜

you: I know! I know. The branding is pretty silly. But this really is the right tool for the task. Unesco and Wikimedia use it. And we can do our own branding for $50/month. 🤷

A lot can be fixed by just acknowledging people. They’re trying to evaluate something they don’t understand, and having a reaction. It’s not even unreasonable. The right thing to do is just acknowledge and move on.

Usually that’s all it takes. Don’t let yourself be embarrassed when you’re being reasonable. If they won’t move on, that’s the point where I start thinking about job applications somewhere else.

Doesn’t mean they’re wrong!

I’ll just upvote the both of you. We all need to be more aware of our echo chambers.

I think most the serious investors know. But, in a bubble, stock value can keep going up long past the point that makes sense. If you think that’s going to happen, it can make sense to invest even if you think it’s a losing play in the long run.

This should really make the timing more urgent for Anthropic. I was legitimately surprised when they decided to delay the IPO.

Also there’s still a real chance that Anthropic has some real value proposition that I’m not understanding, though I’ve yet to find someone who can make a coherent argument for what that looks like.

Right? This is going to be an interesting story to follow.

On the one hand, they don’t want to IPO right now, probably because they don’t think investors are keen to buy at the price they’re asking.

On the other hand, I don’t really see things getting better for them as long as cheap open-weight models that are almost as good as Anthropic’s exist. As more people figure this out, I think it’s going to get worse not better. So I don’t think they can afford to wait long.

I think it’s important to call out that this isn’t an issue with AI being brilliant at hacking. It’s that these systems are basically totally unsecured, and the main protection is that no one with the technical skill is bothering to try to get access.

This isn’t to say this isn’t a serious problem. We’re moving from a world where you need a trained security expert to spend non-trivial time to get access to these systems to one where anyone with access to an un-restricted agentic LLM can do this trivially and quickly.

As a civilization, we need to start taking security seriously.

“The outputs of this opt-in vulnerability scanner will be fully model-generated, without human review or triage,” Anthropic explained. “This will enable faster and more frequent scanning, but means that it is possible reports will be incorrect or invalid.”

When I saw the headline, I was wondering about this specifically. This may make this service not super useful.

My experience with AI security reviews is that they’re fantastic at finding faults, but they always find a list of things to complain about. If there are no real/serious faults they’ll start finding things that kind of have the same shape as a security issue, but really aren’t if you dig into them. I’ve regularly had an LLM generate a list of 10-15 issues ranging in severity from “nits” to “critical” where none of them were actual issues.

Periodic reviews seem like they could get annoying really quickly, becoming more of a maintenance burden than a help.

I don’t?

No one does anything well when they don’t want to do it.

The real solution here isn’t to try to figure out how to force yourself to do something you don’t want to, it’s to figure out how to want to study. The people who can study a broad range of things effectively are the ones who think of themselves as good at learning, and want to understand as much possible. For most of them, part of their self story is that they’re good at learning, and understand things. That can be part of your self story too if you choose it.

I definitely don’t want to pretend that’s an easy change to make. Motivation is one of the harder things. I would like to be able to tell you how to be motivated, but I can’t. But if you’re looking for the difference between you and the ones who seem to study effortlessly, that’s it. They’re not smarter or more capable on any fundamental level. At least not at a level that matters for most things. They just actually want to do it.

Retractions do happen regularly. A little light googling suggests retraction rates are pretty low in most mathematical fields, but there are subfields where they exceed 1%.

I don’t know if the <0.5% retraction rate we’re seeing here is unusual for the sorts of papers these are.

And that’s kind of my point. I read the article and I still have no idea. I doubt the author of the article knows either. Which makes me think that they had an agenda going into the whole affair.

Domain pricing doesn’t really vary that much over most of the sites. The price is usually based on the specific domain. .com domains are usually pretty pricey. Some TLDs are very cheap.

There are definitely issues with what OpenAI is doing. I’m the last to defend them.

That said, a small correction rate after uploading a bunch of preprints just doesn’t seem like news. An article that headlines with that, but doesn’t even bother to tell us what the normal rate of corrections and withdrawals is for humans really feels like propaganda to me.

I would really like to live in a world where we don’t reward this sort of “journalism” with attention.

I really love the simplicity and security focus of Porkbun. It feels targetted at engineers, not wannabe startup CEOs.

Ohh. Yeah. It’s a concern.

People said the same things about TV. I don’t know that they were wrong.

But it is here to stay, and I think it will do more good than harm on balance. The sheer access to information LLMs can provide to someone who knows how to sort fact from hallucination is staggering.

But, yes. I do share your concerns. My view is that, at this point, people who don’t make an effort to exercise their minds are already a lost cause. I’m not sure how much worse LLMs can make it.

I think LLMs are going to cause massive change, not all of it good.

Ultimately, I’m an optimist. I think in the long run, LLMs are going to enable us to do amazing things that we wouldn’t have been able to do without them. In the short term… I dunno. It’s going to be a rough transition.

I do think the people who think LLMs are going to keep getting better, leading to either some sort of techno-utopia, or kill us all off maybe don’t really understand how they work and what the implications are.

I also think the people who are categorically against AI are making a mistake out of fear, but that’s pretty understandable.

I think of Mistral as primarily making models for API use that you can use in all sorts of places. I didn’t even know they had an app. I think the app is probably mostly intended as an easy way to try out the models, not their main business.

I do see what you’re saying. You can account for all of these factors and it still turns out that, largely, individual LLM use just doesn’t use that much power compared to most things people do day to day. Inference is so cheap that even dozens of requests don’t amount to much. I could look up and give you a bunch of numbers, but I don’t think that’s likely to convince anyone who doesn’t do the research themselves. It’s so easy to come up with sources that say what you want. I’d encourage you to actually look into this yourself.

Training costs are higher, but you train once and use repeatedly. Right now, total training costs are stupidly high, but that’s because we’ve got an arms race between the frontier labs to spend as much money and compute as they can for truly marginal gains in quality. The solution to that problem isn’t for individuals to stop using AI, it’s to stop those assholes from wasting so much power.

Individual LLM use is so cheap, that it really isn’t worth wasting people’s energies thinking about limiting that. Instead of being distracted by attempts to make this an issue of personal responsibility, we should be focusing on what will actually make a difference. We should be focused on supporting policies that lead to systemic change. A carbon tax would change corporate behavior right quick, and not just for AI companies.