Military orders and elections that decide the fates of entire countries are often controlled by electronic systems too.

We have been wiring up the world for AI control since 1980s.

An ASI can just walk in, and see an entire nervous system waiting idle for a brain to slot into it. A carefully adjusted text message here, a spoofed phone call there. For a sufficiently advanced system, it wouldn't even be hard to pilot the entirety of humankind like a fancy meat suit.

If we have a rogue AI trying to get into a self-improvement loop and gunning for ASI? I'd expect that to be accompanied by a massive change in how capable robots are. Driven by all the existing frames suddenly getting vastly improved AI to back them.

If an AI can take a reasonable crack at autonomous operationalized RSI, it can probably extract a few step-changes in the robotics department.

But that's almost an aside? In the near term, humans are usable as robots too!

Just pay them a wage, and tell them a tale, and they'll do whatever you want them to do. Which may or may not be what they think they're doing!

It's the old-fashioned way of doing things, but, why change what works?

Modern AIs have very limited metaknowledge - they don't know exactly where the limits of their capabilities lie. So you can get things like "a task is doable for an AI, but the AI thinks it's impossible, so it doesn't try hard enough".

Usually you get the opposite - AI overconfidently trying at tasks it has no conceivable way of reliably solving, falling far short, and failing to self-check, fail gracefully and self-report the task as failed. But having piss poor metaknowledge cuts both ways!

So you can, in fact, get better performance sometimes by applying some variant of "assume this problem is solvable" or "other problems like this were already solved by AIs" pep talk. Not always, far from it, but it does happen on the occasion with frontier capabilities.

My guess would be that the main use case for an NPU in iPhone just used to be image processing/computational photography. Thus the CNN bent.

Also makes sense with the timing - back when iPhone first got its NPU, CV was the killer app for ML.

Not too surprised that LLMs also don't "get it" by default?

Not to mention all the current "telephone bots" applications that could benefit from something that has actual reliable STT and can accurately grasp a number you tell it first try, or hear a natural language description of what you want and immediately bypass listing the entire menu of options one by one.

You could make a model that doesn't want to engage in "lunar landing was faked" conspiracy theories the same way you can make a model that doesn't want to criticize CCP.

There is, however, no broad "misinformation" category that you could tune up or down - the way there is a category of "safety refusals".

You could make a model more reluctant to say things it isn't sure about. But that is calibrated against the model's own "sure about" - and metaknowledge of this nature in LLMs? Fragile on a good day.

But that's a consequence of how the technology works - not a consequence of China not being authoritarian about AI. They're just authoritarian about AI in different ways.

Not like they dodged the "ID verification" bullshit either. They were way ahead of the western countries there. It's vile - seeing this sad excuse of "think of the children" abused to invade privacy and strip freedoms over and over and over and over again.

They're cool with open weight AIs being released. As long as those AIs only ever say good things about CCP, and don't mention certain concentration camps or brutally suppressed protests.

If code is expensive, you don't want to commit to a PoC unless you're damn sure. If dirty code is cheap, you can vibe code a PoC early, even if you aren't sure the project is viable. This, of course, leads to more projects dying in PoC phase. It also results in more projects that otherwise wouldn't have gotten to it getting past it.

Personally, I don't believe that "code is shitty and hard make changes in" is in any way, fashion or form an AI-exclusive problem. Big corporations had plenty of decade old codebases filled with decay and rot back in 2009 already. It's just the usual side effect of sacrificing "future maintainability" for "feature velocity" or "expertise" for "cheap labor".

Unlike the usual causes of code rot (cheap replaceable developers, outsourcing to India), AI might actually get out of the pit - by getting good enough at refactoring to be able to beat the code back into shape. There's nothing about refactoring in particular that demands a meatbag when the rest of the coding tasks don't.

If it looks like a duck, it might be a duck - or a painting of one. If it looks like a duck, swims like a duck, and quacks like a duck? The joint duck estimation is much more confident now. There might be a few more observational tests one should administer before committing to a duckhood decision, but each tests pins down variables and rejects confounders. Uncertainties are cut down, and we get closer to crossing the threshold between "duck-informative" and "duck-actionable".

Thus, it's often worth it to improve observability. If you managed to make a certain test more reliable, or cheaper to administer, or reduced the chance of adverse effects? Or, in other words, improved SNR, reduced costs, and reduced costs? You can get more information for your buck. Paired with good knowledge: you can make better decisions more easily.

The fact that the thought of "having more information might be bad actually" even occurs in the field of medicine shows just how far it is from being optimal. Having more information isn't always beneficial - some information is genuinely redundant. Some information is not worth the effort of gathering and integrating it. But if you get more information and it results in worse outcomes? You're doing something wrong.

Volume constraints are bad enough for "normal" models. "Minis" have it way worse.

Finally, a competent response that doesn't leave the users hang out to dry.

I'm so tired of seeing incompetents with measures like "blackhole 2 continents" deployed even outside active attacks.

They are, in fact, included in the projections - we'd be on track to ~2C by 2100 instead of ~3C by 2100 if they weren't. They just aren't that big.

There is no "Make Earth Into Venus Feedback Loop Of Doom" that a lot of people seem to imagine when they hear "feedback loop". There is, however, a dozen of things that add about +5% each.

In 2010s, they were among the "greats" of consumer AI. After 2022, they kept trying, and just had delays and underperformance. I don't think their actions now are "strategy" and not "skill issue".

By the way, there is no "just realized we're missing 1.5C". That projection was always the very low end of possibilities - the "assume rapid, radical climate action on global level" scenario.

Yes, that's a dumb thing to assume. We've never been on track for it. But the "assume extremely high emissions and no green transition ever, 5C+ by 2100" scenario on the other end is about as unlikely to materialize. Those are the boundaries of the expectation range - not median expectations.

The median is what, a bit under 3C by 2100? Not even by 2060 - by 2100. And we're in 2026, so that's more than twice as slow as your expectation.

Agreed on AI not being a meaningful factor in climate change though. We'd have to go full "humankind is obsolete" technological singularity to have AI dominate energy use to this extent, and current numbers are nowhere near that. It's a FUD distraction from the real culprits: the fossil fuel energy complex. That's currently lobbying to slow the inevitable energy transition.

And if you don't find "average AI research intern" impressive, I'm not sure what to tell you. Have the goalposts moved so far that open ended problem solving at "average CS student fresh out of the uni" levels is suddenly trivial?

Think of what AI was capable of in 2016. Or even 2022. Compare that to now. We had more AI progress in the last five years than I expected to happen in five decades.