What do you mean by this?

I haven't had much experience with ERNIE, but I've been using various different (Chinese) base models of late, and I'm excited to try it.

(Figures vary, see e.g. https://www.linkedin.com/posts/christham_4-weeks-on-claude-2... for $13,700/mo.)

If you hit your limits on two Max 20x plans, you could very well be exceeding $300k/yr in token use at current API rates.

The rest of your post is excessively emotional and off-target.

The same goes for our own time. A powerful AI can tell us things at the edge of what we can grasp -- or beyond what we can grasp -- and it would nevertheless be knowledge that we possess. We can work to verify it, we can have it explained to us as though we were schoolchildren, or we can simply accept it as a static fact of the natural world.

In truth, we're going to have to learn to coexist with minds that are more powerful than ours. At least in some respects, if not in all respects. The good scenario is that they share information with us, and we receive it however we can. "Head in the sand" and "mutual disdain" are bad outcomes.

> https://en.wikipedia.org/wiki/Hierarchy_of_the_sciences

First math and CS, where formal logic dominate, and AI can check/verify its own answers very quickly. Then physics, downstream of math. Then materials science, which in some cases is practically a branch of physics. Then chemistry more generally. Then biology. Then various social sciences.

It's amazing to me that language models are still such poor writers and communicators. The mathematics community wrestled with this recently, when many solutions dropped into their laps but were not explained very well...

Thing is, (1) generic LLMs will improve, and (2) their lack of ability in this area has a lot to do with the rhetorical and structural habits they pick up during RLHF/instruction-tuning, which trained them for general-purpose comms. Zero-training base models can presumably be trained specifically to communicate scientific concepts. Interesting times.

(Edited to add: With respect to (1), compare Opus 5 to 5.5. 5 was a terrible communicator, even of software/coding concepts, whereas 5.5 is excellent and leaves very little to be desired.)

Thing is, though, verification is knowledge itself. And if we can get to a point where the important questions have answers, we can surely have the verifiers explain those to us in ways we can understand them.

> https://www.whattoexpect.com/baby-names/list/top-baby-names-...

Liam and Mateo aside, the top 20 could have been pulled from 1840. Tradition still reigns, and Victorian-era names for boys are the rule rather than the exception.

Liam, the #1, is rather weird -- kind of modern and pan-ethnic -- and there are a lot of low-class-coded names like Ronin, Odin, Cash, Royal, etc. These tend to be confusing headscratchers more than they are aggressive, though.

Names that end with -er are in general quite rare. That said, it's true that Archer is in the top 100, and Gunner is somewhere around #500.

Given that LLMs are practically commodities and open models are a dime a dozen, this is pure insanity. There's no way there from here.

> https://bullfincher.io/companies/amazoncom/revenue-by-segmen...

Well over 60% of their revenue is B2C, strictly defined.

The scientific field doesn't have that many open problems -- whether P=NP is the greatest and most famous -- and they're set to fall like dominoes. A condition where CS is "solved" and has no major open problems is a very real near-term possibility, and probably a medium-term inevitability.

Though software engineering is a fundamentally different discipline, none of this implies that software development isn't in trouble. Human planners and software architects might remain, but very soon LLMs are going to write virtually all code.

The contrast with math and CS couldn't be starker, really. Everything that hinges on formal logic or logical relations is done for.

> https://en.wikipedia.org/wiki/List_of_unsolved_problems_in_c...

Then advances in algorithmic efficacy will be the province of machines, who will act in ways and at speeds that are beyond human.

What room, then, for CS students?

I wonder if biology and theology are picking up students. Those fields, I'd bet, won't be solved within our lifetimes.

Also, this would imply that people who work in glass and zeolite production/processing would be more at risk, which I don't believe is the case, so it's unlikely that this hypothesis holds up.

4-Carbon is, per benchmarks, said to be superior to Opus 5.5 at coding.

At general tasks -- search, medical, interfacing with maps and mail, planning, etc. -- the Google models are already superior.