pkoiralap

True that. It is however fair to assume that the architecture we currently have can still improve. Human brain can see information once and remember it pretty well. I wouldn't bet against AI models being able to do the same in the near future.

All technology and infrastructure needs maintenance. Sure there will be drones. However, there will also be companies and people that specialize in creating and maintaining the said drones. Additionally, the shock and immediate disruption that we all are expecting out of this is flawed assumption I think.

I do however hate to admit that it might be possible that the world simply wouldn't require as many humans to function (birth rates are already declining in developed countries), however, I am too sober to have that discussion right now :D.

My two cents on this follow two major discussion topics.

1. Can AI models self improve? 2. Will it replace humans?

IMO, answer to both is "yes", with a big nuance. For 1, imagine millions of AI agents coming up with different ways to improve the model architecture. It will end up happening just because of sheer quantity/statistics at that point, let alone quality. Furthermore, even if it's just a 1% improvement, that's just one iteration. You can basically do the same thing again with the improved model, and get an even better one.

For 2, its also a yes unfortunately. It has been ages since I have had to write tests for my code. I read it, understand it, see if it does what it is supposed to do, and rarely add things that are missing. The rarity of me doing the said intervention has increased significantly over the course of time.

The two big nuances here are a.) Economy b.) Human element [The author touches upon this in the article in a nice way I think, so only talking about economy]. Global cognitive workforce is a $50T market (mostly middle/upper middle class, 55% to 75% of all global labor income). If the said workforce is made to quit their job, then it's just corporations paying each other. Especially given the fact that the same workforce also happens to be their biggest customer. Taking away your customer's purchasing power does not sound beneficial. Thus capitalistic forces will somehow balance it out and hopefully it will settle down. Also, for 1, running millions of AI agents is an expensive task, you have to collect funds from the same said customer base to be able to create a self improving all knowing god of a model, and I would like to believe that we are quite a bit away from that. Although, I could be wrong here and that would just suck ass.

That being said, AI companies will try to get as much as they can from that share and that will cause a serious shift in labor market. The ones to stay will be those who not just use AI, but also understand the work. At the end of the day, if your AI agent decides to run a "rm -rf /" on the production server, it is still you that would have enabled it.

Huge kudos to the security researchers for 1, finding an exploit, and 2, unlike copyfail, excluding a zero-day ready-to-use LPE script that anyone could have used.

I tried using this for LPE on a Rocky9 for a couple of hours and thankfully couldn't get it to work. So that means unless you have quite some free time on your hand, or are extremely good at doing what you do, you can't actually use this to get LPE on enterprise distros.

This is fascinating research. I feel this is a significant leap in interpretability research. Since we know J-Space exists and is bi-directional, we can train models on the same and come up with meta cognition abilities.

I also fear that the big corporations might use the same to run targeted ads, capitalistic shenanigans. Which they might already be doing through system prompts.

Justice to Karen

> We would like to thank:

>

> Karen Oyelaran, who found the issue on Day 1 and is currently appealing her GitHub rate limit via a web form that is also AI-triaged

It's one thing to report a vulnerability, another entirely to make a crazy exploit available for any tom, dick, and harry to take and use. It was irresponsible of whoever came up with it to release it in the world without first giving major distros a head's up.

I was coming up with the same intuition. However, it's like a whack-a-mole. What about cronjobs and slurmjobs and other services? Is there a way to do this directly on systemd so that all other processes inherit it rather than doing it on each one?

Does anyone have a workaround for it? Edit: I don't understand why the comment would be downvoted.

While it is true that guides and business owners are always looking for opportunities to earn extra cash, the reporting is a tiny bit off here.

Start of AMS like symptoms can easily be mistaken for walking fatigue and dehydration. It is easier to identify if you are at rest, but during the trek that is seldom the case. So when you actually start realizing something is wrong, you already are at an elevated risk. The only thing that works in these cases is to descend and as fast as possible at that.

Considering the fact that AMS will absolutely and a 100% kill you if you play around with it, guides presenting trekkers with an option of helicopter rescue is not that bad, at least if you look at the worst that can happen.

That would indeed be the case if one has never learned the stuff. And I am all in for not using AI/LLM for homework/assignments. I don't know about others, but when I was in school, they didn't let us use calculators in exams.

Today, I know very well how to multiply 98123948 and 109823593 by hand. That doesn't mean I will do it by hand if I have a calculator handy.

Also, ancient scholars, most notably Socrates via Plato, opposed writing because they believed it would weaken human memory, create false wisdom, and stifle interactive dialogue. But hey, turns out you learn better if you write and practice.

I think the 'better than googling' part is less about the final code and more about the friction.

For example, consider this game: The game creates a target that's randomly generated on the screen and have a player at the middle of the screen that needs to hit the target. When a key is pressed, the player swings a rope attached to a metal ball in circles above it's head, at a certain rotational velocity. Upon key release, the player has to let go of the rope and the ball travels tangentially from the point of release. Each time you hit the target you score.

Now, I’m trying to calculate the tangential velocity of a projectile from a circular path, I could find the trig formulas on Stack Overflow. But with an LLM, I can describe the 'vibe' of the game mechanic and get the math scaffolded in seconds.

It's that shift from searching for syntax to architecting the logic that feels like the real win.