Describing it as "overproduction of academics" is kind of begging the question, though: is it not at least as much "deprioritization of basic research and education"?
It's not like the current demand for scientists is somehow a completely natural value, arrived at objectively and with no human biases involved.
And the private sector is heavily to blame for that. In ways that you even describe, as well as others (as another commenter noted, regulatory capture is one).
The problem is that the Department of Justice is part of the Executive Branch, and due to the burgeoning of the Imperial Presidency over the past several decades, that means that as soon as a new President is voted in, he can order the DoJ to change all their priorities to match his.
Our system doesn't have to be this way, even with the federal/state split; it doesn't even have to be this way with the designation of the DoJ as being within the Executive Branch. It's taken a lot of erosion of norms and flagrant breaking of laws to get to the point the US is at now.
There’s a Civilization-game style “tech tree” for cultural and social development. Some societies are further along in that development than others.
Pakistan faces the same cultural problem as Afghanistan and parts of the middle east: in large parts of the country, extended kinship groups dominate society, precluding the development of civic institutions and functioning government. That’s not true for the whole country. Parts of Pakistan are culturally like India or Bangladesh: it has a long history of governance by central institutions, even if that governance is dysfunctional. Imagine if 50% of the U.S. population was Appalachians. The U.S. would be a much less successful country also.
The opposite is true! You’ll get farther in life when you realize that how groups of people are socialized to behave matters a lot—and that’s true whether you’re talking about corporate culture or a country’s culture.
People whose brains are as soft as their hearts sell false equality, but its harmful. It’s like telling the obese person they’re great and that their problems are due to “bad genetics” or factors outside their control. It’s a polite lie and it is damaging.
Understanding that culture is just a type of technology is how you get miracles like Lee Kuan Yew’s Singapore: https://www.jstor.org/stable/20045923. He thought culture was destiny, and he harnessed that realization to make his culture rich.
> And you'll get a lot farther in life if you stop thinking of real people and their development and culture as video game abstractions.
Oh, it’s far too late for that. As the kids say, he’s cooked. He’ll be complaining about hypothetical Appalachians invading New England or New York or the United States (all actual examples, see below) in the nursing home.
I don’t understand. Do you (1) think Appalachia is great, or (2) you agree that Appalachia lags the rest of the U.S., but think that has nothing to do with how Appalachian parents socialize their children to behave what they teach their kids to value?
Pakistan spent quite a bit on education in East Pakistan up until 1971. and I've even pointed you to the article in Prothom Alo where Bangladeshi experts admitted that but you do you. It's not like Ibn Khaldun didn't hit on similar points with asabiyya but saying we have A/B testing here is wild.
No*, but the nuclear waste problem is a problem for 50, 100, 1000 years from now.
Climate change is a problem for 50 years ago. And now. Very, very much now.
Having to, in the worst case, designate some small areas that we choose as uninhabitable "nuclear waste zones" in a few decades is vastly preferable to having to designate entire regions of the world as uninhabitable "too hot to live" zones around the same time. And that's if we don't find some better way to handle the nuclear waste.
* Not in the sense of "a permanent and comprehensive solution". However, the actual spent nuclear fuel can now be reprocessed and reused in newer reactor designs, down to a tiny fraction of what we would have considered "nuclear waste" with the earliest designs in the mid-20th century.
They do the things they think they can do very well.
Why would they try to build electric batteries, wireless modems, electric cars, solar cells, or quantum computers, if their R&D hadn't already determined that they would likely be able to do so Very Well?
It's not like any of those are really in their primary lines of business anyway.
In Upstate New York (from which I have just moved), February is the depths of winter. The temperature there can plunge to -10°F (for the highs) for a week straight. It's not until early April that you're really guaranteed to see things thawing for good. (March can be a crapshoot; sometimes it's looking like spring, with warm breezes and birds returning, and other times you get 4 feet of snow dumped on you. In the same week.)
The maritime climate of the British Isles makes an enormous difference to the climate they experience—certainly as compared to the continental US, and to a lesser degree as compared to continental Europe. It's actually kind of fascinating teasing apart which of our cultural truisms about seasons originated on this side of the Atlantic Ocean, vs which ones were developed once we had colonized the New World.
But it's more than just the temperature, or the day length.
There's a big difference between 40-50°F in November, when the trees are brown and barren, and you're looking ahead to winter, and you swear there's a hint of frost in the air...
...and 40-50°F in April, when the leafbuds are coming out, and the geese are flying back north, and is that a crocus coming up over there?
This seems like another instance of a problem I see so, so often in regard to LLMs: people observe the fact that LLMs are fundamentally nondeterministic, in ways that are not possible to truly predict or learn in any long-term way...and they equate that, mistakenly, to the fact that humans, other software, what have you sometimes make mistakes. In ways that are generally understandable, predictable, and remediable.
Just because I don't know what's in every piece of software I'm running doesn't mean it's all equally unreliable, nor that it's unreliable in the same way that LLM output is.
That's like saying just because the weather forecast sometimes gets it wrong, meteorologists are complete bullshit and there's no use in looking at the forecast at all.
>That's like saying just because the weather forecast sometimes gets it wrong, meteorologists are complete bullshit and there's no use in looking at the forecast at all.
Are you really not seeing that GP is saying exactly this about LLMs?
What you want for this to be practical is verification and low enough error rate. Same as in any human-driven development process.
Yes, they are; through the lens the person above offered that is.
In practice, all we ever get to deal with is empirical/statistical, and the person above was making an argument where they singled out LLMs for being statistical. You may reject me taking an issue with this on principled grounds, because regular programs are just structured logic, but they cease to be just that once you actually run them. Real hardware runs them. Even fully verified, machine-checked, correctly designed/specified software, only interacting with other such software, can enter into an inconsistent state through no fault of its own. Theory stops being theory once you put it in practice. And the utmost majority of programs fail the aforementioned criteria to begin with.
> people observe the fact that LLMs are fundamentally nondeterministic
LLMs are not "non-deterministic", let alone fundamentally so. If I launch a model locally, pin the seed, and ask the exact same question 10x, I'll get the same answer every single time down to the very byte. Provided you select your hardware and inference engine correctly, the output remains reproducible even across different machines. They're not even stateful! You literally send along the entire state (context window) every single time.
Now obviously, you might instead mean a more "practical" version of this, their general semantic unpredictability. But even then, every now and then I do ask the "same" question to LLMs, and they keep giving essentially the "same" response. They're pretty darn semantically stable.
> In ways that are generally understandable, predictable, and remediable.
You could say the same thing about the issue in the OP. You have a very easy to understand issue that behaves super predictably, and will be (imo) remediable just fine by the various service providers.
Now think of all the hard to impossible to reproduce bugs people just end up working around. The never ending list of vulnerabilities and vulnerability categories. The inexplicable errors that arise due to real world hardware issues. Yes, LLMs are statistical in nature, not artisanally hardwired. But in the end, they're operated in the same empirical way, along the same lines of concerns, and with surprisingly similar outcomes and consequences at times.
You're not going to understand the millions (or really, tens or hundreds of millions) of lines of code running on a typical machine. You'll never be able to exhaustively predict their behavior (especially how they interact with terabytes of data or more over time) and the defects contained within. You'll never remediate those defects fully. Hell, even for classes of problems where such a thing would be possible to achieve structurally, people are resisting the change.
If they want to take an issue with LLMs, a plain gesturing at their statistical nature is just not particularly convincing. Not in a categorical, drop the mic way at least, that's for sure.
Small site operators like us know very well that the utility they can get by scraping us is marginal at best. Based on their patterns of behavior, though, my best guess is that they've simply configured their bots to scrape absolutely everything, all the time, forever, as aggressively as possible, and treat any attempt to indicate "hey, this data isn't useful to you" as an adversarial signal that the site operator is trying to hide things from them that are their God-given right.
But it is not necessary to see the results that are being described.
If sites like my tiny little browser game, with roughly 120 weekly unique users, are getting absolutely hammered by the scraper-bots (it was, last year, until I put the Wiki behind a login wall; now I still get a significant amount of bot traffic, it's just no longer enough to actually crash the game), then sites that people actually know and consider important like acme.com are very likely to be getting massive deluges of traffic purely from first-order hits.
Yes; I get a lot of requests for a mostly a small set of paths on my site that look like they're attempts at finding exploitable surfaces. Things like /auth/bind-session, /auth/check?jwt=, etc. (And those are just the ones that are coming up in the obvious error reports; when I go looking at the logs there are more.)
It's not like the current demand for scientists is somehow a completely natural value, arrived at objectively and with no human biases involved.
And the private sector is heavily to blame for that. In ways that you even describe, as well as others (as another commenter noted, regulatory capture is one).
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