It definitely is relevant to a discussion about linters. And as I said on my comment - it's just an example, I mentioned other types of discussions as well.
Oh, absolutely. I started writing Python professionally in 2010. Whenever a new group of people started working on a Python project, one of the first things they had to decide was tabs versus spaces. Everyone would begin by saying that the question was unimportant and silly, but then immediately add a “but” followed by a detailed justification of their personal preference. That would lead to people spending two full days exchanging arguments and links to blog posts.
Worse, sometimes you would start working on a project without having that discussion first, only to discover a few days later that the code was failing because different people had different indentation settings in their IDEs, both regarding tabs versus spaces and the number of spaces. You would then have to pause the work, go back, have the discussion, and decide who was going to fix all the code committed so far.
This only stopped when linters became popular. They may be a little like vaccines: when they work, you do not see them working, so it is easy to forget what things were like before. But believe me, when they appeared, they were a breath of fresh air. We could finally focus on discussing the work rather than the conventions.
And tabs versus spaces is only one example. There used to be PR discussions about countless style issues throughout the entire lifetime of a project. That was not entirely unreasonable, because a consistent style genuinely makes a project easier to work on. But when every individual convention has to be negotiated by humans, it takes enormous amounts of energy and becomes tedious very quickly.
You both seem to be using a different definition of "singularity" from the one I'm familiar with. I've always understood it to mean a rapid feedback loop in which AI creates successive, increasingly capable generations of AI outside human control, rather than simply a level of technological advancement that would be incomprehensible to someone
> And this thread is seemingly full of people claiming AI can read it while simultaneously sharing that AI could not read the actual message, only the decoy as demonstrated in TFA.
That’s 100% on the authors for failing to make the default main “hidden” text and the decoy easily distinguishable. The way this is set up is incredibly confusing.
I would be very willing to pay more! The choice between “you may get a correct answer, or you may get lied to, without a clear way to distinguish between the two” and “you may get a correct answer, or a clear indication that the answer was not found” is pretty clear. One is a much more useful tool than the other. I don’t see any real incentives for companies making LLMs to keep their AI factually unreliable. (Full disclosure: I work for one, but I’m definitely not in the rooms where such decisions would be made.)
Maybe that’s because I work with agentic AI in my day job, but this seems utterly obvious to me: no reasonable person would ever claim that LLMs are better at keeping secrets or enforcing rules than human employees.
This notice is not about comparing humans and LLMs. It seems that the system was designed in the only reasonable way: with a deterministic permissions layer separate from the agent. But that layer failed to work properly.
So the notice is comparing the difference between how the system was supposed to work and how it actually worked in reality. Normal post-mortem stuff.
I like to dunk on Meta as much as the next guy, but I think this makes sense: deterministic verification like this is not, and should never be, the LLM’s job. The tools it has access to should enforce the permissions layer, ensuring that the LLM can never perform actions the user themselves should not be allowed to perform. In this case, the tool failed to do that.
Is it? Both supervised learning and reinforcement learning are ways of training the model, and the difference between them is not that big. I would say that innate means "in the weights", while non-innate means things the model learned during inference, during its "lifetime".
I think this is exactly it, but let me ask another question (which is not rhetorical, I really don't know). Does the fact that one can describe what consciousness is and where it came from in humans help them to detect it in non-human and/or non-biological entities?
It’s obviously not a new model capability. But using this well-known, existing capability to solve this particular issue is only obvious after the fact.
It’s a useful trick to have in one’s toolbox, and I’m grateful to the author for sharing it.
The top comment categorized scraping as abuse ("abuse such as [...] scraping") - that's precisely why some accuse its author of lack of self awareness.
As a childless OMSCS graduate, I also can’t imagine doing it while having kids, because it took basically all of my free time. That said, I met quite a few people in the program who were in situations similar to yours. I have no idea how they managed it, but they somehow did.