The government seems like it's in a rough spot. If they let Mythos out, they seem worried people could use it to mass-hack the internet. China seems to not care so much about this and they're right behind. I don't really know what the answer is.
It's also "out-brute forcing them." It just never gets tired. If a mathematician picks a research direction and spends a whole week on it and it doesn't pan out, they will likely be annoyed, need a break for a while, etc. This thing just does not ever get tired or discouraged or care; it's just onto the next thing until something ends up working.
It seems like you're arguing with this guy for the sake of argument. If anesthetics "yielded some improvement" in Alzheimer's temporarily then any surgery would get the same effect and we would already know about it. And what is the second point you are trying to make here - that zero surgeries are ever performed annually on Alzheimer's patients for any reason? And of the ones that are, the family wouldn't notice the dramatic cognitive improvement shown in this article, but they noticed it here?
It is true. I don't care about having infinite frontier-level intelligence, and I don't care if Fable can one-shot frobnicate a klaxelzorp with a benchmark performance of 97%. I doubt most people do, in fact. I just want something that meets the baseline level of intelligence needed to be a really, really good pair programming agent. It shouldn't have any silly dealbreaker issues involving laziness or hallucinations, it should be smart enough to bounce ideas off of, and it should automate doing tedious boilerplate. And - most of all - I want to be able to afford using it as much as I want. That's what has happened here.
I think the fear is that the people in charge may be too gung-ho about AI and go do something like this, rather than saying that LLMs will spontaneously develop some kind of data analytics power.
Surprising they didn't clean that from the data before training. It's easy to identify, a simple search->replace gets most of it, and a cheap LLM can identify the edge cases (e.g. avoiding "Claude Shannon" -> "Kimi Shannon" or something).
The guy is genuinely worried for his future. I see nothing shameful or narcissistic about that, and I don't think it makes sense for you to wish him any harm.
Can you imagine ChatGPT terminating a conversation because it thought your question was "low effort?" The behavior wouldn't be viewed as helpful or aligned, and nobody would use it.
StackOverflow was, all too often, not helpful or aligned. It died because the staff were unable to get the moderators to be helpful.
That's because the purpose of this article is not to have an objective debate over their abilities at all. Most interesting research in this field isn't. Instead, it's to present a new technique to improve LLM performance, which is much more interesting than (once again) rehashing the philosophy of LLM personhood.
The reason I think this is a bad idea is that it lulls you into a false sense of security. The article makes recommendations that seem thorough and sensible - keyword "seem" - but, as mentioned elsewhere here, there are other potential hidden sources of telemetry (in CarPlay and Android Auto), and who knows what else.
For this kind of thing to succeed as a general lifestyle, you would need to invest an enormous amount of time making potentially irreversible modifications to all kinds of electronic equipment - only to be virtually guaranteed to miss something.
Do this kind of thing if you want, but don't be fooled into thinking you're actually solving the problem for real.
That makes sense. If you could magically just get the top d PCs in quadratic kernel space without having to compute the whole kernel matrix, and then just do top-d quadratic PCs -> ridge, would that be better than doing the PCA -> top-d -> quadratic kernel ridge as you are now?
Absolutely. Who cares if the LLM automates some of the grunt work? Mathematicians are artists, and they paint with ideas. The goal is map out more of the beautiful structure of how things work. The enjoyment in it derives entirely from the payoff of seeing the larger view of how things fit together. If part of their process involves bouncing things off of other people, or even LLMs, I don't think it matters much, nor does it take away from the enjoyment in getting things figured out.