Yes, and a lot of cope. No young person will read this, and "yeaah I should go to college to study for a CS degree." Job security is going to shit in most university specialties.
Unis are eating themselves alive; you have to be brain-dead to go there (maybe only to pursue a military officer career, as the world war ramps up) when the trades are so hot right now.
The target audience of this post is boomers and, I guess, some millenials not-yet-disu
illusioned with, who look fondly back on their education.
I think you underestimate the difference between white collar and blue collar work. The trades are not for everyone, and it's not a simple substitution to just pick another career track and be fine with it.
To me, it is not at all obvious that the "level" of the training set is an upper limit to the capabilities of an LLM.
Sure, the LLM hasn't been exposed to material more advanced than the most capable human domain experts have produced. However, it has seen and learned from a vast amount of information that these domain experts are completely unaware of. Why shouldn't the LLM be able to use that information to produce output that's beyond the capability of a domain expert?
Yup, the e2e proof of FLT (estimated effort of 5 years & 1M$ by the best human in the field) and a counter example for a millennium prize (similarly valued at 1m$.
I hadn't seen anything three years ago produced by an LLM that looked better than the most mediocre humans. The argument here isn't about today's output, it's about the potential of LLMs to outperform humans. And it's far from clear that the architecture is bound this way, or that it only repeats stuff it's already heard.
They don't have to worry about people blocking DC construction in the US either. All new AI datacenters are designated "dual-use" so the federal government is already letting local councils know to fuck off.
they can also put them in better places, vs trying to arbitrage expensive electricity costs and various US corruption thats built more around paying people off than getting things done
Yeah, but that brings up another problem that China doesn't have. Their federal government is always on the same page. The US federal government changes every two years and it doesn't seem like one side is going to be allowing building of AI data centers anymore and in fact might just ban AI in general.
It's hard enough to build them in the US that multiple companies are unironically spending 10s of millions of dollars to try to build them in space
Which cynics could say is marketing hype, but I tend to believe it's extremely difficult to build anything land/energy intensive in the modern United States
There are places that aren’t in the US that also aren’t in space. I still don’t get why it makes any sense to put this stuff in space, but if it does, it’s not the political difficulty of building stuff on earth. So it’s hard in the US. You’re telling me Mexico wouldn’t allow it? Argentina? Mongolia? Build a data center on a French nuclear test site in the Algerian desert and it’s still easier than space.
Not really sure if it's sarcasm, but let me make a side remark.
It's really stunning how much more effective the "Standard ML" notation (embraced by Haskell, Lean etc.) is compared to writing proofs in classical logic.
This "UX problem" is, I think, the reason why is mathematical community embracing automated provers maybe 50 years later than they could have. Automated people wanted the better language, but the mathematicians largely resisted.
So seeing this, it would be preposterous for me to think that any language, natural or not, has the last say in this. We're gonna be stuck with learning new languages and formalisms for a long time.
The boy finally gets government regulation he's been begging for, in the form of total government control, becomes subservient to the corrupt, evil interests at the helm of that government, and ends up loving exactly none of it?
Hard disagree. I use 3.8 flash in Antigravity a lot, and thoroughly prefer it to most Pro-class models. It's really fast, and I've had it make crazy progress on compiler-like problems that previous models including Opus simply failed at. On ultra plan you can have it going for hours, and make incremental progress with good prompting for review interrupts. It solved a problem I couldn't solve for weeks in under 6 hours. 10k LOC total. The harness and test suite is key.
