Make more what? Dragon capsules? Currently SpaceX has no plans to make any more dragons beyond the five they currently have. I believe the fifth capsule had its inaugural flight this year with the private Axiom-4 excursion. These dragon capsules are currently rated for just five flights each, but SpaceX and NASA are working to extend their certifications to 15 flights each. Currently the contract with SpaceX does not include additional missions that would have been flown by Boeing. While it's possible NASA could try to buy some more flights, I don't think they will. SpaceX has the falcon 9 pretty well booked, and their other resources are fixed on Starship. I'm not saying NASA "needs" the Starliner, though. Just that it's not a simple thing to substitute Dragon for Starliner.

In other news, the catholic church is suing OpenAI because they had the idea to simply make suicide illegal a thousand years ago and have been using it ever since.

Not sure if it's a trade secret or a copyright case, the news often don't mention the fine details.

That's a good point. Here on /. I can assume people know what open world games are. Out in the real world movies are probably the better analogy.

> It used to be my go-to site for all things computer related.

Me too.

They were slightly cheaper than Amazon for the same product, then I did a big project which got slightly downsized and I wound up with $400 in "restocking fees" for a couple of pieces of factory-hologram-tape sealed network gear, after I paid $100 in return shipping.

Learned my lesson real fast.

I think this view gives the crypto bros too much credit. They might now be thinking about taking advantage of the opportunity to wrestle the system away from the Wall Street bros, but there was no such plan.

To intercept them you still have to identify them, which you can't do until after they perform the sabotage. Given that, what's the benefit in sinking them rather than seizing them? Sinking them gains you nothing, seizing them gains you the sabotage vessel. It probably won't be worth much, but more than nothing. I guess sinking them saves the cost of imprisoning the crew, but I'd rather imprison them for a few years than murder them.

Well it's a problem or the US too. The last bastion of American industry is heavy equipment including construction and agricultural machines. Interestingly this is also a prime industry for Europe too. A lot of stuff is still made locally. But China is now making their own versions and just beginning to exporting them. And it's a double whammy. They can make them cheaper, but choose to just barely undercut western equivalent products (say by 20%), making a huge profit.

The Trump administration seems to think so. Increased government regulation through tariffs will fix it, along with bigger government. Ironic how the GOP has done a complete 180 over the years isn't it.

Except that in the case of Canada, there was a great deal of trade in both directions, in terms of commodities and also finished goods. It was a mutually beneficial trade arrangement too. It promoted the US' interests without ham-fisted authoritarian threats. This sort of trade made the US the powerhouse it was. In other words this was a sharp shift in US government attitude from one of friendship to one of an adversary (which is really how all relationships and business deals have ever been done in Trump's life). Sadly there's no going back. The damage is done and the US will never ever recover the good will and trade benefits it once had with its closest allies and trading partners, no matter what a future Democrat does to try to undo the damage, now that Trump's attitude has become the attitude of the GOP.

With China, though, there was no abrupt shift. China's goals have always been clear. The only thing they want from the west in terms of trade is raw commodities and foreign currencies. Whereas the west demands cheap goods, full stop. So China's domination of European industry and economies has been ongoing for years, and it's benefited by European policy and attitudes. China is happy to build high quality items and sell them for a premium, but there's no very little demand from the west. If there was demand, we wouldn't have seen local manufacturing capability disappear in the first place. Tariffs are not going to change these fact.

The movie analogy is old and outdated.

I'd compare it to a computer game. In any open world game, it seems that there are people living a life - going to work, doing chores, going home, etc. - but it's a carefully crafted illusion. "Carefully crafted" in so far as the developers having put exactly that into the game that is needed to suspend your disbelief and let you think, at least while playing, that there are real people. But behind the facade, they are not. They just disappear when entering their homes, they have no actual desires just a few numbers and conditional statements to switch between different pre-programmed behaviour patterns.

If done well, it can be a very, very convincing illusion. I'm sure that someone who hasn't seen a computer game before might think that they are actual people, but anyone with a bit of background knowledge knows they are not.

For AI, most of the people simply don't (yet?) have that bit of background knowledge.

Very much unlike an intern (the doing-over part).

You ignored his core point, which is that "rocks don't think" is useless for extrapolating unless you can define some procedure or model for evaluating whether X can think, a procedure that you can apply both to a rock and to a human and get the expected answers, and then apply also to ChatGPT.

And yet, when asked if the world is flat, they correctly say that it's not.

Despite hundreds of flat-earthers who are quite active online.

And it doesn't even budge on the point if you argue with it. So for whatever it's worth, it has learned more from scraping the Internet than at least some humans.

Heh. It says a lot about the pace of AI research and discussion that a paper from last year is "old".

This is a common thread I notice in AI criticism, at least the criticism of the "AI isn't really thinking" or "AI can't really do much" sorts... it all references the state of the art from a year or two ago. In most fields that's entirely reasonable. I can read and reference physics or math or biology or computer science papers from last year and be pretty confident that I'm reading the current thinking. If I'm going to depend on it I should probably double-check, but that's just due diligence, I don't actually expect it to have been superseded. But in the AI field, right now, a year old is old. Three years old is ancient history, of historical interest only.

Even the criticism I see that doesn't make the mistake of looking at last year's state of the (public) art tends to make another mistake, which is to assume that you can predict what AI will be able to do a few years from now by looking at what it does now. Actually, most such criticism pretty much ignores the possibility that what AI will do in a few years will even be different from what it can do now. People seem to implicitly assume that the incredibly-rapid rate of change we've seen over the last five years will suddenly stop, right now.

For example, I recently attended the industry advisory board meeting for my local university's computer science department. The professors there, trying desperately to figure out what to teach CS students today, put together a very well thought-out plan for how to use AI as a teaching tool for freshmen, gradually ramping up to using it as a coding assistant/partner for seniors. The plan was detailed and showed great insight and a tremendous amount of thought.

I pointed out that however great a piece of work it was, it was based on the tools that exist today. If it had been presented as recently as 12 months ago, much of it wouldn't have made sense because agentic coding assistants didn't really exist in the same form and with the same capabilities as they do now. What are the odds that the tools won't change as much in the next 12 months as they have in the last 12 months? Much less the next four years, during the course of study of a newly-entering freshman.

The professors who did this work are smart, thoughtful people, of course, and they immediately agreed with my point and said that they had considered it while doing their work... but had done what they had anyway because prediction is futile and they couldn't do any better than making a plan for today, based on the tools of today, fully expecting to revise their plan or even throw it out.

What they didn't say, and I think were shying away from even thinking about, is that their whole course of study could soon become irrelevant. Or it might not. No one knows.

We didn't. The media and the PR departments did. In the tech and academia worlds that seriously work with it, the terms are LLMs, machine learning, etc. - the actual terms describing what the thing does. "AI" is the marketing term used by marketing people. You know, the people who professionally lie about everything in order to sell things.