Nuclear reactors use most surface water, not ground water.

Datacentres are no pickier. You can even cool a datacentre with saltwater, you just need a heat exchanger.

Also, closed loop does not evaporate. The loop is not closed if stuff escapes from it.

You're arguing with the actual terminology used in the nuclear industry. "Closed loop" or "closed cycle" designs have the water pumped in a cycle through cooling towers. The towers lose water to evaporation, taking heat with them, but the rest of the water is returned to be reheated again. "Open loop" or "open cycle" designs have no cooling towers. The water is heated and just discharged hot. They consume much more water (over an order of magnitude more), but most of that is returned. Closed loop are more common, but you see open loop in some older designs, and in seawater-cooled reactors.

"How often do you think I print?"

Seemingly not very.

I've printed many hundreds of kg on my P1S, thanks.

I do not consider having to write data out to a card and transport it back and forth between the printer and the computer to be the pinnacle of convenience. That's something that would be considered embarrassingly inconvenient for a 1980s printer, let alone a modern net-connected device. And it's designed to be inconvenient for non-cloud prints for a reason.

Also, anything sounds big when you put it in gallons. Doesn't sound so big when you mention that's 92 acre feet, the amount used by less than 20 acres / 8 hectares of alfalfa per year. Or when you mention that a typical *closed loop* 1GW nuclear reactor uses 6-20 billion gallons of cooling water per year (once-through uses 200-500 billion gallons, though most of that is returned, whereas closed loop evaporates it)

I don't think it has anything to do with that. As soon as I saw the headline, my mind went "cohort study". And sure enough, yeah, it's a cohort study. Remember that big thing about how wine improves your health, and then it turned out to just be that people who drink wine tend to be wealthier and thus have better health outcomes? And also, the "sick quitter" effect, where people who are in worse health would tend to stop drinking, so you ended up with extra sick people in the non-wine group? Same sort of thing. This study says they're controlling for a wide range of factors, but I'd put money on it just being the same sort of spurious correlations.

"I just put my models on a usb drive then plug said drive into the printer."

You must have a lot of spare time on your hands.

"It works great locally" - Um, no it doesn't?

They've made a nice easy-to-use ecosystem. For $400 you can get a P1S that supports adding an AMS, auto bed leveling, enclosed-chamber printing, high precision, high print speeds, and 300/100C nozzle/plate temps, and has an easy cloud print service and a robust ecosystem of models you can just download and print with no extra config straight from the app.

But yeah, their behavior is increasingly entering bad-actor territory. I wonder how long it'll be before they lock entry-level printers into their branded filament?

Someone funny in a dark way that this story is posted right above the FEC's attempt to control mass surveillance via hardware. This kind of thing makes it absolute clear that one of the core goals of these self-described "AI" systems it to finalize the capture of all PII on everyone and transmit it to centralized storehouses controlled by... who exactly?

"Way to go outing yourself as someone who lives locally to Cupertino. For anyone else who actually used it was fucking terrible."

Way to make assumptions. I lived in the US Midwest then; I have never lived in California much less the bay area.

Personally I haven't used a single mapping app, whether MapQuest, Garmin, Google Maps, Apple Maps, Open Street Map, or other that hasn't had some errors. There are how many mappable points and curves on the Earth? 1 trillion? 10 trillion? 100 trillion? No one has them all. And all the commercial services give bad directions from time to time; my spouse had to flag down a Forest Service ranger and send them after a couple that was blindly following Google Maps down a road they weren't going to make even in their big honkin pickup truck.

I never got the hate for Apple Maps, even in the first year or two after release. Apple clearly could not let themselves become captive to Google/Google Maps to a degree they would never be able to overcome, so they had to move forward with something. And even outside SoCal it was OK if not great in the US (I understand international maps took a long time to catch up, but that was true of Google Maps too). I think I used it 2/3 of the time after the first year of stabilization and it worked well enough.

Now one can criticize Apple for not using a tiny bit of their store of cash to speed up the process of expanding their own geomapping database, and I so criticized them at the time. But that didn't mean the product was some sort of failure because it wasn't.

Wow - that page reads like a step-by-step guide on how to violate the Robinson-Patman Act. Too bad we haven't had any real consumer protection enforcement in the US since the 1970s.

(And yeah, it was a fun degree. Just a BA :) Decided to go back to school for a second degree on a topic that had long been of interest to me)

DNN-based, like nearly all modern AI. Not Transformers, as far as I'm aware.

Explain how this doesn't count as reasoning. Or this. To name just a couple examples.

Yes, they work by fuzzy logical reasoning. That is literally how neural networks, including the FFNs in Transformers, work. Every neuron is a fuzzy classifier that divides a superposition of questions formed by its input field by a fuzzy hyperplane, "answering" the superposition with an answer ranging from yes to no to anything in-between. Since the answers to each layer form the inputs to the next layer, the effective questions form grow with increasing complexity as network depth grows. Transformers works by combining DNNs with latent states (works on processing concepts, not raw data, with each FFN detecting concepts in their input and encoding resultant concepts into their output) and an attention mechanism (the FFNs of a given layer can choose what information they "want to look at" in the next FFN).