CAR-T is not a cure in any sense of the word, in part because "cure" is just not a scientifically valid concept in oncology. I would know, CAR-T saved my life, but this was at great cost and is not even remotely close to a guarantee, and the side-effects can be beyond devastating https://news.ycombinator.com/item?id=49827669. At best, CAR-T is more like a tradeoff: often just a coin-flip's chance to live, for long-term—maybe even permanent—life-shattering consequences.
Correct. The people here saying CAR-T is a cure have no clue at all what they are talking about, and I say this as someone that is only alive right now because of CAR-T https://news.ycombinator.com/item?id=49827669
There is no world in which CAR-T is a "cure", especially since this isn't even a scientific term in oncology. We generally say if there is no relapse after 5 years post-treatment, then any cancer is a "new" cancer, so 5 years of remission is the closest thing to a "cure", but this isn't a scientific term.
Also, we barely have more than 3 years data for CAR-T for most cancers. And even still, the survival rates aren't great, in many cases 50% compared to e.g. ~20% for previous chemo-immunotherapies plus marrow transplants. And this ignores how massively immunocompromised (or so permanently brain-damaged you are effectively senile) CAR-T can leave you. You can be severely immunocompromised (literally identical to or worse than AIDS / late-stage HIV) for at least a year in close to half of cases, but maybe even permanently, in perhaps as high as 10% of cases (at least for lymphomas).
I say this as a person that is only alive because of CAR-T treatment 1.5 years ago. CAR-T is amazing, and a far better treatment than previous treatments, but calling it a "cure" is deeply misleading and mostly clueless. Currently, it is simply a much better last-ditch effort than the previous ones.
HN is now heavily astroturfed or overrun by bots (or, alternately, low-quality posters now indistinguishable from the previous), and this is especially so in AI-related threads.
One tell is that most comments barely exceed one or two sentences (because otherwise AI detection gets easier and much more reliable), when this was not as much the case many years ago. The drive-by comments are also low / zero quality, mostly expressing a feeling or agreement/disagreement, and primarily driven by ideology or pre-existing beliefs and commitments.
Look at non-AI-related threads and you'll notice a large distribution shift relative to AI-related ones.
EDIT: Basically HN is orange Plebbit now. If you doubt this, compare HN discussions to those on e.g. lobste.rs, LessWrong, The Motte, DSL, ACX, or other old obscure forums. You'll notice those places have their own very serious biases and problems, but it is obvious the vast majority of posters are nevertheless human and making some minimal efforts.
Now compare Reddit and 2026 HN to the above, and see if you can confidently say the same.
IMO this would track, applied mathematics (even e.g. data science, though perhaps calling that applied math is a bit generous / insulting to more serious applied math) is in some ways more exciting now because it is far easier to surface complex / appropriate methods for the task at hand, and you can more confidently explore these methods because the AI sort of "has your back" in catching some of the more obvious beginner errors you make during these explorations. Plus, applied math feels roughly more results- than process-focused, compared to pure math.
IMO the divide here between pure vs. applied math feels a lot like the divide between those who enjoyed coding for the understanding it led to, i.e. the writing itself was the joy, vs. those that primarily coded for the results. I enjoy the creative part of coding, the thought of software jobs just devolving into writing specifications and doing code review very much kills it for me.
Writing is pretty hard for a lot of people, maybe especially so if they are more non-verbal thinkers, and then doubly so again if one must write not in one's native language.
Depends on the writing. Writing a blog post or something is challenging because you want to have an engaging style. But I see people at work using LLMs to fill out tickets, which is the easiest thing in the world. It's just a plain description of things, it requires no skill at writing at all. It baffles me that people are using LLMs for something like that, which should take less than a minute of your time to fill out and will be more pleasant for the recipient than the slop the model produces.
Here I would maybe argue that the simplicity makes these kinds of tasks tedious (like doing taxes), so it also makes a lot of sense for people to want to just throw AI at it. Tedious, easy rote tasks can be much more unpleasant than engaging ones.
I think people massively over-rely on AI and I really worry about the consequences of this. But there is nothing baffling at all about the basic appeal, IMO.
AI is very bad at properly handling statements that make heavy use of vague quantifiers (e.g. "some", "most") and also commits a lot of pretty serious logical fallacies. It is also bad at handling subtle logical negation, generally.
One of the most egregious negation issues I run into a lot is when I (or someone) makes a statement of the form: "not X" or "X is thus not true", and the AI then proceeds to interpret or summarize this as 'whatever is the opposite of X is the case'". This will cause it to go down a useless path investigating or disputing the opposite of X, which generally has no relevance or bearing on anything.
It also often very harmfully will replace your carefully chosen words with weirdly specific academic operationalizations or formalisms, then again waste huge amounts of text refuting / showing "problems" that result from that formalism, all of which again have no bearing or relevance on the original statement. An example would be you saying something like "intelligence, generally, must surely explain some of the differences in X", and then it will go "actually IQ does not correlate with X", unless you specifically tell it not to conflate psychometric IQ with intelligence generally.
Sometimes this is helpful, but the more specific / technical the domain, the more often you specifically have to prevent it from going down stupid paths that should be obvious given the expert context and wording, because it can seem almost hungry to try to catch you in some kind of insipid 'gotcha'. Much of these issues often clearly arise immediately from the first-pass "reword what the user said" part, given the reasoning traces.
And also to think in different directions than you would have gone in isolation. Regardless, this is a great heuristic / rule that I will be sharing and keeping in mind.
This feels very related to the issues re: the presence or absence of world models in LLMs. Insofar as they have world models (or "intuitions"), these would seem to have to be primarily verbal-linguistic (or symbolic, when using math). LLM world models are not likely (currently) very spatial, in contrast to e.g. V-JEPA-2 models, which likely do have some basic spatial models (and perhaps "intuitions").
There's an old joke about funding, goes something like:
"Why you are always demanding more funding? Why can't you be more like the mathematicians, all they need is a desk, some paper, and a pencil, and a garbage can, and they just do fine. Or how about philosophy, for that matter? They don't even need the garbage can"
I mean, obviously with modern computational mathematics, this doesn't hold so simply, but there is this confound about math research also not getting much funding also because much of it isn't that expensive, relatively speaking.
Last I knew, at least in America, universities that want their math prof's to do research also expect those prof's to bring in plenty of outside funding. You could argue about the costs of that desk, paper, pencil, and such - but modern "research" universities have evolved into extremely high-overhead operations, and The Beast Must Be Fed.
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