> The answer to this behavior isn't being mad at or boycotting particular airlines, it is regulation.
Or, you know…accept that a cat is a cat, and stop trying to regulate scratching simply because you don’t like it.
(I get that you probably understand this, but the “regulation is the solution” is kind of a leap. People whining about efficient market pricing have always struck me as just fundamentally misunderstanding what capitalism is.)
You didn’t know, because it started with Gloria Steinem in the 70s.
Wikipedia tries to make it sound like it’s some old tradition, but it’s actually fairly new, with the prior historical references being obscure until it was resurrected. It’s right there in the article:
> “Ms. was another acceptable, but rarely used, abbreviation for Mistress in England in the 17th and 18th centuries.[12] During the 19th century, however, Mrs. and Miss came to be associated almost exclusively with marital status,[13] and Ms. was popularized as an alternative in the late 20th century“
Cant read it, but the title literally says “1986, ‘Ms’ joins the Times vocabulary”, so I’m guessing that, you know…1986 is when it became the Times’ standard. This likely corresponds to the campaign of Geraldine Ferraro, which is what I personally remember as the point where the term became widespread.
I don’t understand this need to rewrite history. Nobody in this thread is objecting to the use of the term. Even the wikipedia article says the term was resurrected in the 80s.
Hate to break it to you, but you’re arguing with me about something I never said. I said it was revived in the 70s.
The term is recent in a linguistic sense. Even if you personally think it’s ancient history, “my dude”, there are plenty of us alive where it was invented in our lifetime.
A teacher explained this to me in elementary school as some new fad.
> Skeletor will return next week with more disturbing reminders about passage of time...
It's recent both linguistically and culturally. It was a thing in my lifetime, to the point where I remember explaining it to other people in high school (which was itself about a decade after the term became "popular").
I realize that the average age on HN is quite young, but we're not talking about ancient history here.
> For what it's worth, the median human is 31.1 years old. I wouldn't call something older than the median human "very recently, culturally."
That’s cute. Now try searching for the median age in developed countries, where people care enough to make up revisionist history about minor culture debates? (nevermind, I did it for you; it’s around 50)
More to the point, what’s the human lifespan in first-world countries? (average lifespan in the US is almost 80)
(All this is a tongue-in-cheek way of saying that perhaps disparaging people for being older than you is not such a bright idea. I know it’s hard to believe, but we’re not going anywhere for a while.)
The person you're responding to isn't disparaging you. They're simply stating their opinion. That's all a judgment about whether something is recent can be.
Fine, that’s fair. It’s not explicitly disparaging, it’s just implying that facts older than you, personally, can remember are irrelevant. I don’t know what to call this, but none of the words that come to mind are positive.
Someone on a parallel thread explicitly called the 1980s “ancient”, so whatever.
I said the word is recent, linguistically and culturally. I said it was "fairly new". I also said when it was popularized, in terms of actual decades (70s-80s).
You've chosen to die on this particular hill of whether or not something is "recent". I don't know why.
What? You had a whole thing about rewriting history because the parent said the NYT adopted 40 years ago, then you made a big deal about how it was in 1986 and resurrected in the 80s and talked about rewriting history.
What was that all about if you both perfectly agree on the timeline?
> Classy edit, btw.
Edit? I did not edit my post. I think you may not understand what that means. It is neither classy nor unclassy, it is a type of meme.
> What? You had a whole thing about rewriting history because the parent said the NYT adopted 40 years ago, then you made a big deal about how it was in 1986 and resurrected in the 80s and talked about rewriting history.
It's simply a fact that the term was revived in the 70s and popularized in the 80s. The person I replied to was trying to imply that was incorrect, by suggesting that the NY Times was some long-time holdout.
> So your argument is that anyone with a memory longer than yours, yet well within the median human lifespan, is “ancient”.
Deftly convincing my rhetorical opponent of my youthful vigor by misunderstanding what they wrote and getting noticeably worked up about being called ‘ancient’
Thus far, these agent vulnerabilities are all demonstrating million-monkey intelligence, rather than "super" intelligence, in the sense of some swarm of robo-Neo super hackers.
That's consistent with my experience using them for coding, where even the best models mostly work by being persistently midwit (with infinite knowledge) more than individually brilliant.
One corollary of this is probably that any project that has had an army of actual monkeys pounding on it for years is likely more resistant to LLM attack than one that has not. Everyone is worrying about individual robot geniuses finding obscure vulnerabilities in Linux, PHP, Python, etc., but they should probably be more worried about the permutations of less-known software floating around their systems.
Also, (setting aside Standard Oil’s business practices), this is a common meme in oil exploration historically, and even today. Early prospectors were honestly pretty stupid, and their principal differentiation was risk tolerance.
There a lot of parallels with AI…it doesn’t take a genius to see the long-term potential, but the risk is in implementation, so it’s a mad rush to the finish line by a wide variety of less scrupulous early players. Rockefeller got his big start by sweeping up failing producers who were all capital weak, and I’m sure something similar will happen here during the inevitable bust.
I can do the hard, risky work of drilling wells / deploying AI, or I can sell you a vision of that future, today. The most dangerous technologies in terms of grifting are the ones that everyone can understand.
It’s become a reddit meme that this is the WeWork IPO redux, but that’s fairly obviously ridiculous.
WeWork was renting cubicles and calling it technology.
Whatever you think about this market, the LLM technology is real, obviously important, and Anthropic is the market leader. The only similarity is that they’re both famous and well-funded.
Anthropic is renting GPUs and calling it technology.
Sure, they have some flashy models, harnesses, and tooling around it, but their competition isn't that far behind - and some of that competition is 100% free. Their moat is closer to a puddle, to the point that there are entire companies built around the concept of dynamically routing requests either to them or their competitors.
Anthropic is the market leader in a rapidly-evolving market, where customers can and will jump ship the second a competitor's model looks shinier or cheaper.
> Anthropic is renting GPUs and calling it technology.
While you can imitate the style of my response by ignoring all of the technical details, you've still ignored all of the details.
WeWork was not a technology company. Anthropic is. You even admit it, but you want to hide your actual argument ("the technology is a commodity") behind an irrelevant rhetorical flourish.
It's fine to argue that Anthropic's business is bad. You don't need to make a ridiculous, unsuitable metaphor on top of it.
I didn't claim the valuation is reasonable. I said that the comparison with WeWork is ridiculous.
> The problem with WeWork's IPO wasn't that they aren't a tech company. It's that they didn't have a good enough business model.
No, they had a fine business model - there are many other companies using it today (including WeWork). It just wasn't worth a technology company multiple.
(Let's also not forget the mountain of crazy things - such as the founder leasing his own properties to the company at sweetheart rates - in the WeWork S1, that actually torpedoed the offering. Anthropic's listing is nothing like WeWork. Nothing. Not even remotely.)
Right. But The Atlantic, for instance, is mostly opinion and commentary --- in fact, on any given day, that's all you'll see them running on the front page --- but they've funded some pretty intense investigative reporting (Caitlin Dickerson's pieces, in particular the most recent Darien Gap thing, great illustrations of that). Salon never did, so far as I know.
And for a reason. It's expensive. Only the tiny handful of winners of the digital transition (including The Atlantic) can now afford it.
The Atlantic and the NYT are now quite flush. IMO people should be subscribing to local newspapers instead, to fund local reporting, which is in a dire state.
They had paid cultural news staff, and actually they were the very best those times. Charles Taylor, Andrew O'Hehir, Stephanie Zacharek for film eg. Similar for music and literature.
> I'm implying they may be biased in what they chose to report on and how they frame it, yes. Deciding which figures to turn into a chart is an editorial decision.
I think the Apollo thing is a fairly dumb hot take based on a near-term wiggle in a graph, but it's hilarious that you are calling them biased.
Pot, kettle, black, etc.
Substantively, I don't think it's surprising at all to see those lines go up -- it would be shocking if they didn't go up, given the intensity of the hype. The slope is what matters. In particular, I wouldn't argue that your graph shows any kind of acceleration in adoption over time.
I’m surprised it’s so low, actually. AI is defined so broadly that it includes voice recognition and machine learning of any sort, and in any business function. It’s hard to draw conclusions from that. I wish they had kept it narrowed to LLMs specifically.
It’s too bad they changed the definition midstream, too. It would have been nice to see the two lines side by side.
> It also had more arginine, an amino acid that children need to grow.
My kingdom for even a tiny bit of scientific literacy amongst people who are reporting on science.
Not only is this absurd ("children" need all amino acids to grow...as does anyone else), arginine isn’t even one of the essential amino acids. Your body makes it unless something is seriously wrong.
Hopefully it's just my lazy reading/thinking, but this part of the article feels like scientism to me. Picking out some random micronutrients this strain has more of, praising that it has less gluten. It's just using "science" to justify whatever they already wanted to do.
Not my area of expertise, but my understanding is that this is an extremely limited claim (i.e. unborn babies / neonatal supplementation, or children with genetic defects), and not something you can even remotely generalize to "children" as a whole. The way we teach this in biochem 101 is "arginine is conditionally essential", with the "conditional" being "periods of stress or sickness".
In any case, I seriously doubt the author is talking about this, given the imprecision of the language. Also, eating this wheat specifically for arginine supplementation is goofy gym-science. Arginine is trivial to supplement.
More like: we all need arginine, but our body makes it, so we don't need to supplement it. Wikipedia writes this:
Preterm infants are unable to synthesize arginine internally, making the amino acid nutritionally essential for them. Most healthy people do not need to supplement with arginine because it is a component of all protein-containing foods and can be synthesized in the body from glutamine via citrulline.
Arginine is a "conditionally essential" amino-acid, i.e. there are circumstances when the internal production is insufficient, so it must exist in food.
The need for arginine may exist during some illnesses and not for children in general, but for very young babies, especially for those born prematurely.
Don't worry too much. If you follow basic food recommendations, even those given out 60 years ago, your child can grow up fine. Even if you only follow it just half the time. Basically, all those articles have to be taken with a grain of salt. What kills you is: smoking, drinking, tanning, long periods of only junk food. And those seem to affect you mainly after age 25. Children are remarkably sturdy.
When you first read a popular science article that garbles a subject you've developed expertise in, you begin to wonder how much of what you believe to be scientific truth is bullshit (because you learned it from a popular science article.)
Well, you sort of expect the pop-science press to be crap. It's when you read papers in Science and Nature and come to the same conclusion that you start to go galaxy brain.
That's all true, but they don't mention the relative costs of those different tasks, or how AI helps them.
Optimizing molecular properties of a lead is important, but ultimately not a huge source of costs. In any case, LLMs probably have as much chance of predicting outcomes as anyone else (i.e. very small).
Manufacturing is somewhat expensive, but most of the cost sits in actually creating a production process, which is not a thing LLMs can do.
Clinical trials are where most of the costs lie, but unless you can create a magical classifier that can tell me if my drug will make it past phase 3, you're not significantly bending the cost curve.
My reading of the hypothesis is this, interested in your thoughts:
> unless you can create a magical classifier
A generator could concentrate on pockets while remaining unable to judge most drugs. And LLM RLVR a) works well, and b) is black box optimisation and so is suited since it doesn't need system identification (which you can't do for complex biological systems). So the hypothesis is that, yes making a classifier is very very hard, but you could through some (lots?) initial interactive data, end up with our generator outputting with high probability drugs-that-end-up-passing-trials-with-probability-p. Whether these drugs thus generated are drugs we care about (i.e, whether `p` is good enough) and whether the whole pocket it generates in is large enough, and how jagged this behaviour is (i.e distribution of `p` across taxonomies of drugs), are the few questions we want answered.
Well first off: molecular screening (which is basically what you're describing) is inadequate for drug discovery. There are too many other factors that matter: water positions, molecular flexibility, short and long-range electrostatics, entropy loss, coordinating ions, and more. You need not only the "pocket", but an accurate model of the system's conformational change over time, to have any hope of understanding binding energetics. It's a bigger problem for small molecule drug discovery than biologics, but it still matters.
That said, we have extremely well-refined force fields that attempt to model the underlying physics, and still fail to do this well. There's absolutely no reason to believe that a generic LLM will bring anything to the table that improves the situation. Even with the hand-wavy assertion that we (somehow) get "lots and lots of data", and somehow train a purpose-built model to do the same, it's kind of like saying that we could turn a minivan into a formula one car with enough effort. Different tools for different jobs.
I actually do believe that informatics approaches will be helpful given lots of system-specific data, but when I say that, I mean "a bespoke neural energetic model that is trained specifically for some molecular system down to customized force-field parameters", not "Claude".
So sure, maybe Anthropic will invest lots of time and money and energy into making these kinds of molecular simulation models, which they can then use for high-throughput screening experiments. But even if they do all of that, there's still no guarantee that they'll bring anything to the party that hasn't been done before...because this is the lowest-value step of drug discovery, and a model like that won't tell you if your drug is going to be toxic, or have off-target effects, or have metabolic problems, or...a million other things, which are what kill drug programs in phase 1 and beyond.
I appreciate you diving into the details, that's helpful to me. I've reread their post. They do say Claude many times so yes it's clear they are framing their work this way.
We have seen how these large models learn high order levels of understanding that is unexpected, e.g. physics in video generation. That makes me not want to dismiss the work in this area particularly when paired with orders of magnitude more computation being applied than us normies have access to.
This almost seems like catch-up to Google DeepMind stuff where it is research for research sake. So in that sense I can see the skepticism where one can look at this as taking Claude and pouring it over a problem like sweet LLM syrup that will make it all taste great.
Hmm.. I think it's my terminology issue. I just meant pocket as in informal word for "subspace", that is, generators can learn to generate good candidates within some subspace without needing to be good classifiers across the whole space.
Calling these "machines" LLMs seems to sell the process short. Using the same style of attention in a different domain is very valuable. An LLM wasn't what created a video of a panda on the moon. I am not remotely an expert, just a software person with a lot of interest over the years.
Or, you know…accept that a cat is a cat, and stop trying to regulate scratching simply because you don’t like it.
(I get that you probably understand this, but the “regulation is the solution” is kind of a leap. People whining about efficient market pricing have always struck me as just fundamentally misunderstanding what capitalism is.)
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