Please, please call it MAGAputer.

What's wrong with running a secondary coolant loop at hundreds of degrees? Assuming we have all this energy to play with (run a compressor).

The economics, on the other hand, yeah you wouldn't think so. But you would think Bezos would pay somebody to run some numbers before backing the idea in public.

In the old days I would have said the DoD (now hosted at http://war.gov/ - impressive!?) would surely pioneer this. But these days big tech is so much richer than even the military-industrial complex.

The data transfer part is basically just starlink.

You aren't going to remotely host your gaming PC this way. But for deep neural nets, which have a massive computation-to-IO ratio, that's a different ballgame. E.g. for an LLM the input might be a prompt of a couple dozen bytes, and the output might be a paragraph of a couple hundred bytes. But a lot of computation is needed to get from the input to the output.

(The AI would also need an onboard mirror of any part of the internet it can search for answers and citations).

So is it your prediction that Waymo is not scalable, and will level off in number of trips per week at under, say, 10 million?

My prediction is that self-driving won't level off before handling a large share of all trips for several reasons: the cars are already somewhat adaptable and will become moreso; that increased trips will provide ever-increasing data to train on; that temporary roadway changes e.g. for construction will become handled more systematically to accommodate automation e.g. waze, google maps, waymo / tesla cars; and that the world's network of roads is actually pretty finite for most purposes.

Don't get me wrong, the average age of a car on the road is 12.6 years, so any kind of transition takes time. And self-driving has a lot of room left for improvement. But it is not facing any insurmountable roadblocks to a whole lot more growth, and running out of training data certainly isn't one.

Are they dieing? Are they really? "Publishing" is going away, because there are so many ways to write, edit, and market books yourself. Did this study look into the number of e-books that are being sold, and shared? I agree that reading literature is slowing. But I don't think it's going away.

Sure, it's right before the Antifa chapter meeting at the bingo hall.

and now, thanks to the collapse of the educational system and AI, we're boldly entering the age of vibe-reasoning.

One side says the border isn't a problem when it observably is and that inflation is under control when it is not.

The other side tells you that American cities are a "war zone" and that immigrants are eating dogs and cats off the street.

Both of these are the same. /eyeroll

This is fair, to an extent, but the "one side is far worse than the other" is an acknowledgment that many people stop far short of.

No, we're at the "prove it" stage.

Sure, it's fair and accurate to characterize the response to those issues by "the left" as such, but I was talking about "the press" which did no such thing. If your point was to dispel the myth of the "liberal media", you did a pretty tidy job of it.

People Who Don't Explain Acronyms

What's RTO?

"no you"!

Remember how waymo bootstrapped their self-driving cars - with sensor-instrumented human drivers. They can still do that as needed. In fact they are doing that in Tokyo as we speak:

https://ancillary-proxy.atarimworker.io?url=https%3A%2F%2Fwww.reuters.com%2Fbusine...

Of course waymo never did rely on opportunistically scraping crowdsourced data in the first place, so "running out" was never going to be an issue.

AI's model weights (like a person's brain) don't need to store everything - the weights (the bare llm) need to store enough to read instructions, seach databases and documents, and recognize things. Which they already do. Once the AI is "out in the world," it will gather its own subsequent data.

For example, how does a self-driving car 'run out of training data'? They are gathering vast amounts every day from their already-deployed cars. Probably more than they can handle.

Same with call center AI. Where could they get more speech data? Well obviously it gets reams more data every day in the course of doing its job, to get better over time.