Seems like not-enough-people is a relatively straightforward problem to solve, if your country isn't completely awful. You just invite some immigrants in, and presto, you've got more people. There are plenty of people around the world looking for stable, decent places to live, so sourcing shouldn't be a problem.

Too-many-people is a much trickier problem, since nobody wants to be voted off the island.

Soon it will be just like over-the-air TV, except you're paying Amazon $180/year for the privilege of watching their ads.

I think they are going for "reduced waiting time" and "more consistent high quality preparation", with an option for "cheaper prices" at some point (if they feel they must). It's not clear that AI will actually provide any of those things, but that's the goal.

The song will advise you to "share and enjoy", and the beverage will be almost (but not quite) entirely unlike the one you wanted.

Depends on how clearly written the instructions are. Even when the instructions are correct and unambiguous, language that is well-defined to someone with experience is often inscrutable to the newbie. (e.g. have you ever been following a recipe that tells you to "fold in" an ingredient and had to figure out exactly what "folding in" is supposed to consist of in that context?)

Traditionally your newbie barista would ask their co-worker at that point, so I'm not sure that having an AI on-hand provides much benefit unless it's faster and/or more accurate than getting a co-worker's attention would be. If it turns out not to be helpful, it will go away quickly enough.

There never was a proper CUDA "moat".

Sure it is a convinient way to do some things with a lot less work than other approaches, but most sane people doing anything on large scale will eventually want to optimize the actual running of things. As long as the development was fast enough in algoritms and hardware that any incremental gains from optimiation were less important such was obvioisl low priority. But once the number of users for a service grows huge, even say a 5% imprevement is suddenly huge numers in real terms and thus worth some effort.

... and for good reason. Difficult projects are risky and expensive, and if they don't work on the first try, the voters blame the politician and then very soon afterwards he isn't a politician anymore (or at least, not an employed one). Even if they do succeed, the politician will get blamed if they turn out to be more expensive than predicted (which they always do, because that's the nature of difficult projects).

The "safe" way to do it is leave the old system in place and running, and install the new system next to it. Let them both run simultaneously for an extended period of time, with the old system still in charge and the new system running and computing results, but its results aren't actually controlling anything; they are only recorded to verify that its behavior is always the same as the old system given the same inputs.

Once you've thoroughly tested and debugged the behavior of the new system that way, you flip the switch so that now the new system is in control and the old system is merely having its results recorded. Let the system run that way for a period of time; if anything goes wrong you can always flip the switch back again. If nothing goes wrong, you can either leave the old system in place as an emergency backup (for as long as it lasts), or decommission it.

This logic made a lot of sense on the original Mac 9" screen. It makes less sense on a modern Mac with multiple large monitors, where the distance between your window's content and the menu bar can be significant, and your mouse may move up past the the menu bar and into the screen "above" if you aren't careful.

But it doesn't. For example, the lawyers that keep submitting court filings with fake references evidently have wrong assumptions about what AI is and is not (for now)

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

250 is less than 1/3 of 500?

A more typical use case for now would be using AI to generate some code, and then testing/fixing the code. Not running the AI every time to solve an instance of the problem.

Side note, I wonder if this paper compared AI performance to human performance. You think people can do towers of hanoi consistently?

Yes, excess energy that the grid can't accept should be stored for later use when possible, or perhaps used to generate hydrogen (or synthesize gasoline or whatever fuel is most appropriate) for resale.

At the easiest level it is to think of it as linked web pages running locally.

Basically you did stacks of cards that could contain things like information, buttons and such and you could then navigate them like you can a web site today.

But on a more complex level it could actually also have actual scripts and such in addition.

As to what to use it for:
-We did a adventure game based on exploring a crazy mages whacky dungeon on it and shared it with others.
-But more seriously it was used for things like training materials and presentations extensively. Less for actual more complex programs.

Well, it depends on your loaclity.

Basically where I live we have a "expectation of privacy" rule. That limits scopes of laws.

Like if you are naked in a fenced in area with no expected visibility outside he area you cannot be prosecuted for public indicency, but if you do that in your open front yard you will be..

Same applies to many other things too where a place with expectation of privacy has special protections for things like photographing, where you need permission in such, but not in places without and many more activities.

That is the difference.

Being able to code without having to look away from the screen whenever you need to press a key seems like a big win for efficiency to me. Every time you look away from the screen, you have to relocate your text-line of interest again when you look back; that may take only half a second, but if you're doing it 10 times a minute, those half-seconds add up.