The fact that you're unwilling to pay the incredibly low price of attaching even a pseudonym to your statement speaks volumes. Not even by mentioning it in your AC post.

You should read the actual words you quoted. I answered the question asked... then you responded saying "it's obvious they were asking a different questions". What a dipshit.

Has she? Which mission? I looked and can't find anything on the rocket she (or her company) launched, but maybe I'm just missing something.

Because it's almost always better to spend someone else's money than your own. Failing that, it's almost always better to spend future money rather than current money. The "almosts" are there because the terms have to be scrutinized, but if the terms are good... you hang onto the cash and revenues you have. Having cash gives you flexibility and enables you to take better advantage of opportunities. And if you end up not needing the cash for anything, you can always invest it. This may not be the first rule of business, but it's got to be in the top ten.

It's honestly a good rule for personal finances, as well. If someone offers you a million dollar loan at 2% interest, your immediate answer should be "Can I get more"? And for more quotidian stuff, it's better to front-load income and defer expenses whenever and wherever possible. Pay bills when they're due, not when they arrive. Use a credit card and pay the balance off every month, letting you keep your money in your account for longer, interest-free. These strategies allow you to make some money by investing it, of course, but mostly it gives you flexibility if you need it. Which if you have decent savings you never should... but it's still better to have it and not need it than need it and not have it.

There is an increasing body of evidence that this is also how human reasoning normally works. Usually, we make decisions first and only afterwards (if necessary) engage our reasoning ability to generate an explanation of our decision. Experiments show that we're actually just as good at explaining decisions that were the opposite of the ones we made, assuming we can first be tricked into believing that they were our decisions.

Of course there are cases where we engage in lengthy, logic-based sequences of reasoning to derive a conclusion, especially if we've spent many years in higher education that trains us to think that way. This is particularly true when we reason in groups, exchanging ideas and critiquing others' reasoning and conclusions. It appears that for most of human history this is probably the primary way we employed reason, though in such social reasoning the goal is less to ensure accurate results than it is for the most skilled and smartest debaters to get their way (thereby improving their genes' probability of survival)... though it's still the case that making better arguments is the best way to "win" and that better arguments are more likely to be at least directionally correct. I'm sure you've noticed that your own reasoning gets better when you attempt to order your thoughts to potentially present them to a critical listener. This even works quite well when the "listener" isn't able to be critical; hence the utility of "rubber duck debugging". Of course, discussing with an intelligent and listener who is engaged in trying to critique your reasoning and offers their own counterpoints for your critique works even better.

It shouldn't surprise us that AI exhibits similar characteristics. Their core structure is at least notionally modeled on the neural networks of our own brains, and they're trained on large bodies of our text. Their evolution over the last few years also models our thought processes. The first hugely-successful LLMs just produced output without any sort of "thought process". That quickly ran into limitations, then someone came up with the idea of feeding their output back in and enabling them to engage in "self-talk" during which they could evaluate, criticize and improve their conclusions. This "reasoning overlay" looks at least superficially similar to the stream-of-thought self-talk that we engage in when thinking our way logically through a problem. And again, much like us, when LLMs do engage in a longer chain of thought, they actually can explain their conclusions by looking at the sequence of steps they went through.

There are also ways in which human and AI cognition differs substantially, of course, and in many ways AI thinking is far inferior to human cognition. Some of this is based on their different approach to context. An AI with a 1M token context window can keep at its "fingertips" far more precise context then we can -- we struggle to remember more than about 7 numbers for more than a few seconds -- but we're much better at maintaining a "big picture", which we achieve not by remembering everything but by being better at selectively extracting the most salient bits (I expect that improving AI ability to do this will be one of the next large steps forward in capability).

Any way, the point is that AI "thinking" is far more similar to ours than you credit it to be.

No one can really define what "real AI" is. As for whether it will try to kill us... it is utterly impossible for us to say what AI might or might not do as it continues to improve. Your point about its reward system is potentially valid, but with a little thought it's pretty easy to come up with ways in which a superintelligence might destroy us while trying to serve us. Even more to the point, we don't know what the "derived" motivation system inside the systems is. We can't look inside. Observing the self-talk stream-of-consciousness gives us some insight, insight that we actually can't have into humans' thought processes, but it's not definitive.

Do some reading about "instrumental" vs "final" goals. I highly recommend Nick Bostrom's book, "Superintelligence". It's pretty old and quite dated in a lot of ways, but it gives you the tools to think usefully about these questions.

It's a way of reframing sloppy practices as a signal to your investors that your software has become powerful. Most of the horror stories about AI showing signs of becoming hyperintelligent are coming from the companies themselves, which should tell us something.

While the energy source of wind and solar are free, building and maintaining the plants is not free.

If all they were building were a single 100MW data center, sure: renewables would be cheaper to build. But they evidently are planning a major campus that will consume most of the plant's 615 MW output over the next 25 years.

A solar installation, in a favorable location, capable of supplying 600MW around the clock (with battery backup) would have to be roughly 1800MW in capacity. It would be among the largest inthe world, on the order of ten thousand acres in size at a build cost of maybe 2 billion. The battery backup system to ensure 99.9% uptime would be 9x the size of the largest li-ion grid storage system ever built, and set you back on the order of 4-5 billion dollars.

While it's probably cheaper to go renewable than build a *new* nuclear plant, if you can reactivate and one for just two billion that looks like a bargain, if you have a use for all that power. Inability to save money by load following is the financial Achilles' heel of this generation of reactors, but if you have a guaranteed customer for most of your output, years in advance, that's as close to an ideal economic case for them as anything could be.

Obviously, if you approximate a complex system containing 1e30 quantum particles using a couple of differential equations and a few boundary conditions, you're not going catch every detail.

This is just too fucking hilarious. If I ask Google for "European search engines" the very first link [after the "AI overview"] is literally that page.

If any Europeans are saying they have to use Google because they don't know about the European search engines, it's because they have never looked for European search engines. The very words are the key to the solution. Google it, Europeans, and then you can be free of Google! Google just one final time.

ToS is the strongest argument, but the distillers are not parties to the ToS. They get their data from data brokers. It is possible that data brokers are violating the ToS, but it would be hard to write ToS that precluded running queries for third parties without creating problems for consultancies and other businesses. Even presuming the ToS could be written to preclude the data brokers doing that, it doesn't affect the resulting model.

But the general shape of the argument brings us right back to unclean hands: we worked hard on this model and it's not fair for you to profit off our work in a way that doesn't have our permission.

I think the fair-minded position is to see what people think when the documentary comes out. You're essentially arguing a negative here -- that there *can't* be anything of value that hasn't been said yet.

Also, I don't think saying something *new* is necessarily critical. Sometimes saying something obvious but in an interesting way is worthwhile.

In this case, Holmes gave access to the filmmaker. Obviously she has an agenda. If the filmmaker is smart, he has an agenda that's different from hers. If he knows his business he'll find something interesting to show you from that conflict.

Now looking at the trailer -- holy shit that woman gives off batshit crazy vibes. That's new to me. I expected her to be slick, persuasive, like Saruman in Lord of the Rings: someone. you'd need real strength of mind to resist being persuaded by. If I'd just handed someone like that my business card, I'd get it back on some pretext then head as fast as I could for the door.

It doesn't make it *right*, but it does make claiming it is *wrong* inconsistent with their own behavior. This could prevent the US companies from suing the Chinese companies seeking an injunction (due to the "unclean hands" doctrine), and probably blocks them from seeking monetary damages in most US jurisdictions.

And suing may undermine the US companies own intellectual property claims by exposing their shaky foundations. An AI model isn't *expression*, so it can't be copyrighted. Insofar as the service allows the underlying model to be deduced through regular usage, trade secret protections don't apply because that's *reverse engineering*, which trade secrets don't prevent.

This leaves violations of terms of service. But setting aside the dubious enforceability of anti-reverse engineering provisions, the Chinese companies may not even be parties to the ToS agreement if they are obtaining the model data through a network of contractors and shell companies.With only moderate paranoia, they can effectively shield themselves from some kind of US tort claim.

Which leaves them with the model they've created from the US company's model, but which the US company has no IP claim upon. If I download a DeepSeek model that's been (hypothetically speaking) trained on Claude, Anthropic might not like that, but what can they do about it?

It's a strange situation. US investors are spending cumulatively on over half a trillion dollars a year in the hope of owning a breakthrough frontier model that is the end of the economic world as we know it. But the essence of the model might not be intellectual property at all.

Relying on proprietary USA software or services is a national security risk for all non-USA countries. So good for Switzerland.

It's just as big of a problem for USA users too. Everyone should be minimizing their proprietary dependencies.

I've never used TiVo, but I'm still using MythTV after more than 20 years. (Anybody else here using it? ...Crickets.)

I ditched cable 15 years ago, but it works fine with an antenna. (Or, at least it will until they screw us all over with ATSC 3.0.)

They can't remove the auto-ad-skip on free open-source software, but I never used it anyway. It's not 100% reliable at detecting the ad boundaries, and it occasionally skips over part of the actual show. Instead, I just FF at 60X through the ads.

Think of the categories as fixed, and the people moving *through* them. Over 65s are exiting the category at a lower rate.