“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.

I’m going to simplify a little here, so don’t take this completely at face value.

Models are, quite literally, long series of numbers (called weights). A model might store the weights in 16 bit numbers (that is, 16 binary digits). The size of the model (and how much memory it needs) is determined by how many weights there are, and how many bits each weight takes. You can take a 16 bit model and rework it to use 8 bit, or even 4 bit numbers. The result intuitively behaves a lot like the same model, but with less precision to the weights. That makes the model take way less space in ram, but also makes it more likely for concepts (encoded in the weights) to overlap, which impacts model quality. Often the effect is that fine distinctions get lost.

In recent years, I’ve started feeling comfortable recommending it over Windows.

Sure, I’ll still tell friends and family to just get a Mac. But if you’re not going to do that, Windows is just a worse experience in most the important ways at this point, even for a non-techie user.

It is absolutely possible to run 100% locally, but in practice, at the high end, only quantized models. The full size top end models require hundreds of GB of video ram, and while you can buy that, it’s stupidly expensive. Quantized models can often perform nearly as well with a small fraction of the ram, but they do sacrifice a little in precision.

These models (well the good ones) ultimately all trace their origins to what you’d likely consider “stolen” data. Whether that’s ethical is debatable. If you’re in the “information should be free” camp, there may not be an issue here.

As for the power/environmental impact, for what they do LLMs are actually very low impact per-request. If you’re concerned about your personal AI power use, then I hope you never fly in an airplane, and minimize your driving because those are much bigger issues.

It’s the scale of use that makes AI an environmental problem, and that’s a question about corporate use of AI, not personal use of AI.

Honestly, this is nothing new.

Listening to ChatGPT is a solid improvement over listening to Tucker Carlson.

Don’t get me wrong, it’s not good! I don’t exactly trust the Sam Altmans of the world. But this is the same circus, different clowns.

Right?

The information is out there if you know how to sift through the nonsense.

Though trying to do this with nutrition really is hard mode. I don’t know of another topic that’s quite as flooded with nonsense.

this is all done with custom models by individuals.

This is really important for people to understand.

Powerful open weight models are already out there. Anyone can download them and run them. There is no possibility for government control of AI content without North Korean style internet controls.

Politicians and corporations will absolutely start trying to push for more controls. They will tell you to “think of the children” and they will use this to take power for themselves, knowing full-well that they can’t actually meaningfully prevent this from happening. We need people to understand that the push for control here has nothing to do with morality or protecting anyone.

The government doesn’t get to decide you’re guilty

I want to call out how important this is. This is one of the strongest checks against corruption in the countries that have it.

As a concrete example, if we didn’t have this in the US, I’m not sure there would be much to stop Trump’s justice department from bringing cases against Trump’s enemies in front of Trump appointed judges, and sending them to jail. Jack Smith and James Comey would likely be in jail right now if we didn’t have jury trials.

Curry is a global cuisine.

It may originate in India, but it has been incorporated into local cuisines all over the world. Most countries in Asia have their own versions of curry at this point. I’d argue that the UK does too, even if they call it “Indian”. Even the concept of curry as a category of food is largely a British invention, and many of the things we’ve been calling curry for more than a hundred years have little relation to Indian food.

Also, India is a large and diverse country. Many Indians don’t eat beef, but there are absolutely whole populations of Indians who eat beef curry regularly.