bryan0

As others have said, buy whatever you want just never connect it to the internet.

Now many people are also claiming that TVs will surreptitiously connect to an open network, but as far as I can tell, there is no evidence of this actually happening [0]. Of course it’s technically possible, but until a researcher actually observes this behavior I would not lose sleep over this.

[0]: https://www.rtings.com/tv/learn/how-to-use-smart-tv-offline

they're referring to a screening kit like this: https://www.cologuard.com/

on iOS I still get badge notifications from unknown senders. I don't think there's a way to turn this off for some reason?

In other news, 30-year-old chess engine outperforms frontier llms in chess

> Using Starlink, a civilian commercial service, for offensive drone strikes to kill people has never been allowed, nor should it be. Starshield, a different system, is controlled by the US government. What they decide to do is up to them.

https://x.com/elonmusk/status/2114063986744275011

I guess I wasn’t clear, but using manim to make videos is not the hard part. The hard part is making them engaging and enlightening, and whatever else you want to attribute to 3b1b videos which make them a joy to watch and learn from.

the key word in OP's description is "good". If AI can create videos as engaging and enlightening as 3b1b, then we hit AGI.

> Why not pay taxes and let us, the people decide how to use it instead?

Maybe because the current administration has no interest in doing this and Anthropic doesn't want to wait for this cluster-f of a situation to sort itself out. Even if the current admin was for progressive taxation it might take them years to sort through all the bureaucracy to use this money effectively which might be much too late.

But in theory I think you're right.

I think here the question is what is the goal? If the goal of the code is to learn and teach, then understandability is critical. If the goal to efficiently produce useful software, then that is not necessarily the case, because the end-users could not care less if the code is understandable. They just want it to work. And maybe understandability and "working" are correlated, but not necessarily.

fair points but i disagree. LLMs are very good about explaining and summarizing complicated material. You can test this today with any modern LLM about any topic or paper you like. One thing that makes them so powerful is that they can customize their explanation to the specific querier.

Today, to explain and communicate these math proofs, it makes sense that a human working with an LLM would be the most effective at human explanation. However, given LLMs current abilities with explanation, it seems reasonable that LLMs will become equal to human experts in explanation. Again part of their power is that they can scale to 1-on-1 explanations for the particular audience which humans cannot do.

> "it's not X it's Y" construct.

I agree this is annoying, but this is not a fundamental feature of LLMs, this is "tick" of current models, like the em-dash. There have been countless of these "ticks" that have been refined and this process will continue.

OpenAI certainly does not really care about human understanding of these math problems. We've always said solving the hardest math problems would be a remarkable demonstration of AGI, and here is their demonstration. But it's not really about the math, this is about the power of LLMs and the path to AGI/ASI. Solving math problems is just a parlor trick for OpenAI.

I'm saying this as a tautology: the definition of human understanding requires a human.

Some mathematicians are not confident in the NS verification because it is non-trivial to tell if the Lean proof matches NS. I would argue this requires "human understability" to make this claim. The Lean proof is a very useful tool, but it does not replace human understanding.

This is so well written, deep, funny, and sad.

> Looking up at the stars in wonderment, or enjoying a complex puzzle game, or being inspired by an encouraging teacher, and dreaming that one day an unfeeling corporate robot[1] will burn a rainforest to compile a list of 700 highly obscure barely-readable facts that might be true that nobody can understand as part of a marketing gimmick to raise hype for an IPO[2].

But if I am going to critique it's main point it would be this part:

> in the pursuit of advancing humanity’s knowledge as quickly as possible, many academics will recoil in anger at the suggestion that it is worth spending any time making results understandable to humans. It is inefficient to explain or check a result when those resources can be spent generating another.

While obviously ridiculous, I think this does not go far enough. The problem isn't that people don't care and resources should not be spent to explain AI results to human minds, the problem is that AI will soon be better at explaining itself than humans. The puzzle then changes from can AI formally verify and explain a proof to: do human minds trust the proof. Do we believe that the proof is correct and not an elaborate AI hallucination using tricks that are beyond our understanding. In order to trust it a formal verification is not enough unless it is human understandable. In fact human understandability is the only thing that matters. That is something that AI can never do on its own.

I would have agreed completely with this 12 months ago. 6 months ago maybe. but not today. Of course anyone can have AI agents generate a big pile of shit, but that should no longer be the case for professionals. It actually does require a harness where agents are reviewing code with simplicity in mind, and running static analysis checks to reduce complexity.

> Why is it time to give it up?

I've been a software developer for 25 years, and coding software by hand is not a wise career choice right now.

This is interesting but sounds insane to me. At my startup the code has been 100% written by AI agents for the past 6 months. It’s like John Henry vs the Steam engine, time to give it up. The human job which is harder to replace is product manager. Understanding what is valuable to the customer and what the product needs to focus on.

Often times the constant (2 in this example) is a conjectured minimum, so anything below that is a noteworthy result. Think of it as breaking through some theoretical limit.

Does anyone else remember in the early 2000s the concerns around privacy with cell phone cameras? [0][1][2][3]. How long until people feel the same way about camera glasses?

[0] New Privacy Menace: Cell Phones? (2003): https://www.wired.com/2003/02/new-privacy-menace-cell-phones...

[1] More trouble for camera-phones (2003): https://arstechnica.com/uncategorized/2003/06/195-2/

[2] Camera Phones Raise Whole New Set of Privacy Issues (2004): https://www.edweek.org/policy-politics/camera-phones-raise-w...

[3] Cam Phones, Go Home (2005): https://time.com/archive/6671091/cam-phones-go-home/

This reminds me of during covid, when one of the biggest public proponents of vaccines in the UK was having an affair. The anti-vaxers then, instead of focusing on whether vaccines were helpful, focused on the affair. That allowed them to dismiss the pro-vaccine point of view without actually engaging on the merits of their argument.

some highlights:

> In September of 2016, arXiv received 9,869 submissions. In September of 2024, arXiv received 20,569 submissions. This September, arXiv received 40,363 submissions, which in turn generated almost 9,000 support tickets for arXiv staff and moderators.

> arXiv now limits submitters to up to two submissions per calendar month, with a limit of three total active submissions at any given time.