Plus, 24/7 access isn't necessarily the best for patients. Crisis hotlines exist for good reason, but for most other issues it can become a crutch if patients are able to seek constant reassurance vs building skills of resiliency, learning to push through discomfort, etc. Ideally patients are "let loose" between sessions and return to the provider with updates on how they fared on their own.
I agree with your point except for scientific papers. Let's push ourselves to use precise, non-shorthand or hand waving in technical papers and publications, yes? If not there, of all places, then where?
"Know" doesn't have any rigorous precisely-defined senses to be used! Asking for it not to be used colloquially is the same as asking for it never to be used at all.
I mean - people have been saying stuff like "grep knows whether it's writing to stdout" for decades. In the context of talking about computer programs, that usage for "know" is the established/only usage, so it's hard to imagine any typical HN reader seeing TFA's title and interpreting it as an epistemological claim. Rather, it seems to me that the people suggesting "know" mustn't be used about LLMs because epistemology are the ones departing from standard usage.
colloquial use of "know" implies anthropomorphisation. Arguing that usign "knowing" in the title and "awarness" and "superhuman" in the abstract is just colloquial for "matching" is splitting hairs to an absurd degree.
You missed the substance of my comment. Certainly the title is anthropomorphism - and anthropomorphism is a rhetorical device, not a scientific claim. The reader can understand that TFA means it non-rigorously, because there is no rigorous thing for it to mean.
As such, to me the complaint behind this thread falls into the category of "I know exactly what TFA meant but I want to argue about how it was phrased", which is definitely not my favorite part of the HN comment taxonomy.
I see. Thanks for clarifying. I did want to argue about how it was phrased and what is alluding to. Implying increased risk from "knowing" the eval regime is roughly as weak as the definition of "knowing". It can be equaly a measure of general detection capability, as it can about evaluation incapability - i.e. unlikely news worthy, unless it reached top HN because of the "know" in the title.
Thanks for replying - I kind of follow you but I only skimmed the paper. To be clear I was more responding to the replies about cognition, than to what you said about the eval regime.
Incidentally I think you might be misreading the paper's use of "superhuman"? I assume it's being used to mean "at a higher rate than the human control group", not (ironically) in the colloquial "amazing!" sense.
I really do agree with your point overall, but in a technical paper I do think even word choice can be implicitly a claim. Scientists present what they know or are claiming and thus word it carefully.
My background is neuroscience, where anthropomorphising is particularly discouraged, because it assumes knowledge or certainty of an unknowable internal state, so the language is carefully constructed e.g. when explaining animal behavior, and it's for good reason.
I think the same is true here for a model "knowing" somethig, both in isolation within this paper, and come on, consider the broader context of AI and AGI as a whole. Thus it's the responsibility of the authors to write accordingly. If it were a blog I wouldn't care, but it's not. I hold technical papers to a higher standard.
If we simply disagree that's fine, but we do disagree.
This is something I think many people don't appreciate. A perfect example in practice is the Journal of Personality and Social Psychology. It's one of the leading and highest impact journals in psychology. A quick search for that name will show it as the source for endless 'news' articles from sites like the NYTimes [1]. And that journal has a 23% replication success rate [2] meaning there's about an 80% chance that anything you read in the journal, and consequently from the numerous sites that love to quote it, is wrong.
The purpose of peer review is to check for methodological errors, not to replicate the experiment. With a few exceptions, it can't catch many categories of serious errors.
> higher retraction/unverified
Scientific consensus doesn't advance because a single new ground-breaking claim is made in a prestigious journal. It advances when enough other scientists have built on top of that work.
The current state of science is not 'bleeding edge stuff published in a journal last week'. That bleeding edge stuff might become part of scientific consensus in a month, or year or three, or five - when enough other people build on that work.
Anybody who actually does science understands this.
Unfortunately, people with poor media literacy who only read the headlines don't understand this, and assume that the whole process is all a crock.
I disagree. Your data doesnt make the grandparent's assertion false. Cost of living != per capita or median income. Factoring in sensible retirement, expensive housing, inflation, etc, I think the $120k figure may not be perfect, but is close enough to reality.
Since when "minimum wage" means "sensible retirement" ?
More like it means ending up with government-provided bare minimum handouts to not have you starve (assuming you somehow manage to stay on minimum wage all your life).
We agree, minimum wage doesnt mean that. And in a large metro area, that's why $120k is closer to min wage than a good standard of lliving and building retirement.
Adjusted for inflation? Without (crippling) debt accrual and adequate emergency fund, retirement, etc? Did you have children or childcare expenses? These all knock on that total compensation quickly these days, which is the main argument in this particular thread of replies.
Correct, I mean in the sense of "living a standard of life that my parents and friends parents (all of very, very modest means) had 20 years ago when I was a teenager."
I mean a real wage associated with standards of living that one took for granted as "normal" when I was young.
It's simpler than that. "Prestigious" universities emphasize research prestige over all else on faculty. Faculty optimize for it and some even delight in being "hard" (bad) teachers because they see it as beneath them.
Less "prestigious" universities apply less of that pressure.
It can also be different within the same university, by department. I graduated from a university with a highly ranked and research oriented engineering department. I started in computer engineering which was in the college of engineering but ended up switching to computer science which was in the college of arts and sciences. The difference in the teachers and classroom experience was remarkable. It definitely seemed like the professors in the CS department actually wanted to teach and actually enjoyed teaching as compared to the engineering professors who treated it like it was wasting their time and expected you to learn everything from the book and their half-assed bullet point one way lectures. Unfortunately or fortunately, depending on your view, it also meant having to take more traditional liberal arts type electives in order to graduate.
> AI is always being touted as the tool to replace the other guy's job. But in reality it only appears to do a good job because you don't understand the other guy's job.
This is a well considered point that not enough of us admit. Yes many jobs are rote or repetitive, but many more jobs, of all flavors, done well have subtleties that will be lost when things are automated. And no I do not think that some "80% done by AI is good enough" because errors propagate through a system (even if that system is a company or society), AND the people evaluating that "good enough" are not necessarily going to be those experienced in that same domain.
