That was my first thought too. (Assuming the article is correct - all kinds of stuff gets published that doesn't hold up in the longer run.)

Remember that time is also big. Suppose that there have been 1000 interstellar civilizations in our galaxy during the past 5 billion years, and that each one lasted a million years before collapsing. How many would be active right now?

Frankly, I don't consider it a paradox at all. We've explored approximately 0% of our galaxy and found nothing. That doesn't tell us much.

Doesn't he know he's supposed to play the fiddle while he destroys his country?

Its like releasing a troop of monkeys in an office and hoping they'll somehow manage to coordinate your next product release.

Do we work at the same company?

In principle, it should be possible to develop a vaccine for certain forms of mental illness. Regrettably, no such thing currently exists and antivaxers are probably too far gone.

You enjoy your horse dewormer, I'll take the mRNA gene-editing.

I'm skeptical about that second part.

As for trolls, I wonder if some anti-vax propaganda is being pushed by hostile foreign powers as a way of hurting us.

It's just a story about Meta and AI.

Oh, nevermind.

For extremists of all flavors, violence is always preferable to the voting booth.

Surely they're not going to cut off the data centers just so ordinary people can have electricity?

Well, yes. But the politicians say the scientists are wrong.

Who are you going to believe? Geeky nerd types, or the people who actually run the real world?

grift.
an established government project that was slashed.
a limited usage project feeding from government funding.
this project has an elliptical office scat stain smell.

Yeah, it's going to be hard to establish a plausible positive ROI for interstellar travel. I think even public funding for probes will be problematic, since it will take years just to get flyby photos. Probably the first flyby will be funded by some rich guy.

Adoption is a function of existing adoption and existing availability of resources.

Extremely good OS' and truly amazing computers have failed, not because they were too expensive or had issues, but because they were perceived to be unpopular OR simply lacked enough software that could run on them.

Extremely bad OS' and truly awful computers have succeeded for the opposite reason.

Linux has an extremely good architecture, at least for machines that are mostly operating with CPUs. When most of the work is done by the GPU, it's less obvious that Linux provides the necessary mechanisms to manage resources and abstract away the hardware specifics. That doesn't mean I expect Linux to start fading, but I don't think a centralised OS is necessarily where things will go. However, the reason it hasn't done better so far is simply because potential users and potential software houses had a negative view of it (even though actual users were almost invariably loving the system).

Windows is crashing now not because it is a slow, bug-ridden piece of spyware (which it is, but users have never cared about that in the past), but because it is perceived in increasingly negative light.

They can get vertical samples from space and the ground. What they can't get from space or the ground is how the internals of a system evolve.

The problem with "models" in the abstract is that there are a lot of them and some of the time the thing you want is to know the same point relative to the system and not to the mudball.

Lots of people have gut feelings about AI, positive or negative.

Most of the AI research I can see people doing is focusing on single-element problems (such as finding counter-examples to a mathematics hypothesis) or solving very simple engineering systems (at most a dozen or so components). Most of the benchmarks are even simpler (write a short story at the level generally asked in English classes of primary school kids).

As a result, I think that people have developed either a very cynical outlook or a very optimistic look. Neither of these is entirely realistic, for the simple reason that no actual useful problem fits any of those descriptions.

Digging much deeper, I'm finding that when problems get into the hundreds or thousands of elements, even ChatGPT Sol 5.6 and Claude Fable 5 struggle badly to keep track of accurate relationships, let alone any level of detail.

If you want to solve a relatively simple coupled system (whether to use solar panels or direct solar heating, and which side of the house to place them), I'm sure AI is fine. If you want AI to write a five hundred word story detailing a conversation between Kim Possible, Rufus, and a smoked herring, I'm sure it could cope. Just.

If you want it to do anything hard enough that a human could do with an assistant, the tests I've been using (meaningful rather than synthetic problems) suggest you've developed a fantastically expensive blue smoke generator. We are going to need to see the inference engines developed for previous generations of AI, and the reasoners developed for the semantic web, and perhaps tools that haven't begun to be imagined yet, before AI can do anything non-trivial.