the vehicles start looking more like a wingless airplane than a car.

And what does the company's name mean? ;) (Hint: it's Greek)

It adds up to 40 miles per day, not 5. 5 would be a cloudy day.

I used to be anti-solar-panels-on-cars back when solar panels were expensive, and ones of reasonable efficiency were even more expensive - the argument was, "put it on your roof where it belongs". But that's just not the case any more. Adding solar is just not that much of a cost to the car. It adds some complexities, but mainly in the design / early manufacturing phase.

Also:

The average American drives 37 miles per day.

1) So if you're in a sunny climate, it covers all of said average-driver's driving. Otherwise, said average-driver has to plug in occasionally, but not nearly as often.

2) Most people drive less than the average (the average is skewed by a long-tail - small numbers of people who drive very far every year). What you actually should be meaning is the median US driver; the median drives 23 miles per day. Most Europeans, even less.

3) Even for said "average american", their daily average is skewed by long drives (e.g. road trips and similar). Wheren of course you're plugging in, you'd be plugging in even if the car was adding 80 miles a day. But when not on road trips, their daily average is lower.

4) Surely you can see the appeal of the tangential benefits, such as being unstrandable - where even if you run out in the middle of the desert 20 miles from the nearest town, you're still going to get there, just delayed (remember that EV ranges, if you drive very slowly, increase like 2x, so 40 miles a day becomes 80, so a 20 mile shortfall is only a ~4h delay on a sunny day).

5) Nobody is saying, "One car for everybody". Of course appeal varies by person and by location. Here in Iceland for example we have three problems. One, very little sun at all for a good chunk of the year. Two, even in the summer, when the days are long, the sun is mainly low and circles around you. Solar power just kinda sucks here in general. And three, the three-wheel config would mean that the centre wheel wouldn't align with tracks in the snow from other cars (although there is a slight advantage, in that it also wouldn't align with road ruts from studded tyres, which often fill with water in the rain and become hazardous).

But somewhere in the southern US, it's a great option.

No, it's more likely that girls and women learned to mask symptoms early for survival reasons.

If you look at hunter-gatherer societies, ADHD is paramount to survival because it avoids over-harvesting. Too little ADHD produces cultures prone to driving plants and/or animals extinct. ADHD is a critical stop-gap that prevents this. Studies show ADHD gatherers will not only collect more, they will do so in ways that cause far less damage, resulting in a far better second harvest and far better sustainability.

Into more modern times, ADHD was a valuable survival trait. Too little and you became vulnerable to diseases, pests, crop blight, etc. If you wanted a stable population, right the way through to the industrial era, you needed a high level of ADHD in the population.

It is only when neurotypical bosses decided that they had to micromanage everything and decided who lived and who died on the streets that you see ADHD symptoms suddenly and massively suppressed. But masking changes nothing. The levels will still be the same, they're just hidden.

It isn't. The under-diagnosis of girls is astronomical.

You are assuming ADHD is a new thing. No, it isn't. It is a survival thing. ADHD was critical in a very large percentage of the population for the bulk of the last 350,000 years. Without it, humanity would have gone extinct. It's merely not appropriate in a world in which neurotypicals make neurodiverse solutions a "bad thing".

They might drop by a little, but honestly not by much. Boys are mildly overdiagnosed for the reasons you give (but few places still medicate), girls are however massively underdiagnosed.

I never got myself into the whole "masculinity" obsession, even back in the 70s and 80s. The result of this was a discovery that computers launched me head-first through far fewer windows and pelted me with far fewer rocks. So, yeah, I was definitely seen as disposable though my childhood and teen years by pretty much everyone. (It's one reason anyone looking back at those as "golden years" is unlikely to win me over.)

Escapism is pretty much all I had at the time, and these days remains pretty much all I have - reality has grown far worse over the years and knowledge by the neurotypicals has not come with empathy but ammunition. There is guilt and shame in playing with creative writing or inventing, and the demons have got really bad on occasion, but you're right, it's not doing anyone any harm.

Both. It's best thought of as a positive feedback loop.

For those who didn't follow it, it's not that it's a contact binary that is so neat in and of itself, it's that when they modeled it, they determined that, the collision that formed it was less than 5 meters per second (less than 11 mph / 18 kph). Like a parking lot fender bender, but with the cars being ~750 billion tonnes.

The algorithm seems to have been failing to scale correctly, yes. On closer inspection, it looks like the billing software treated the bytes used as K used.

Did AWS use Grok to generate the billing sheets or the code for handling billing?

I'm serious. The errors reported look suspiciously like AI hallucinations or a signed integer being treated as unsigned.

Yeah, there's two main problems:

1) People entering the wrong fields. For example, medicine really needs workers, at all levels, but not enough people are going into it.

2) Certain manual labour fields, like field work and home construction, because... well, I think we all know why there's a shortage of workers in those fields.

Good question. Their POWER series of CPUs were not insignificant in capability, their chip designers were clearly technically sophisticated, and GPUs are just specialised vector processors with a few extra bells and whistles - stuff IBM is extremely familiar with.

It would not have been difficult to release a GPU or other LLM-specific processor to go along with the POWER11. They'd been working on the POWER11 for 4 years, they knew in 2020 that LLMs had a strong potential to be significant for Big Data processing - an area you use big iron for, they're not rank amateurs, they have plenty of reserve, they could have assembled an emergency team to build a vector processor that was custom-designed for just LLM work, and released an LLM processor card that could run circles around nVidia.

They didn't. Because, as has happened before, their management is simply too stupid and too slow.

Or let's put this another way. Show of hands - how many of you "spicy autocorrect" / "stochastic parrot" people had "AI will start mass-solving Erdos problems" on your forecast list a couple years back? Huh, none of you? Fascinating!

Take some time to reassess your priors. And while you do so, understand that, yes, they are doing logic / reasoning.

They weren't discovered by an LLM. They were known conjectures that were proven by an automated solving language that was linked to an LLM.

I'll take "Things That Didn't Happen For $200", Alex.

Only a handful of meaningful proofs have ever been done by automated formal theorem solvers (the Four Colour Theorem being the most noteworthy example - but its proof is so long that humans can't verify it). By contrast, AI tools have been solving Erdos problems en masse. The majority of them just bog-standard commercial models. In case you need help, the only ones on that list that were hybrid (AI / non-AI) in the actual solving phase are:

1) AlphaProof / DeepMind Prover Agent / AlphaProof Nexus
2) Aristotle (Harmonic)
3) Seed Prover / Seed Prover 1.5 (ByteDance)
4) AxiomProver (Axiom Math)

In each of the above, LLMs come up with the lemmas / strategies but then use Monte Carlo search ("brute force") or likewise to investigate what they came up with. These are a minority. In the "AI Standalone" category, these "hybrid" tools made up only ~20% of attempts and successful proofs. Hybrid tools actually made more of a contribution in the "AI Alongside Literature" (related literature found afterward) and even more of the "AI Building On Literature" (related literature known beforehand) categories, which is the opposite of what people like you expect.

And even with the hybrid tools, it's still the AI doing the heavy lifting when it comes to strategy. Non-AI theorem solvers, again, don't have a spectacular record for churning out novel proofs to unsolved problems. Tools like Lean are more about mathematical rigour - a passive environment that requires a driver (a human or AI) to feed it actual strategies, lemmas, and proof steps. And no, you cannot brute force "strategy" in the vast majority of cases, which is, again, why automated theorem solvers don't have much of a track record with unsolved mathematical problems.

Let's take a random example: the disproof of the unit distance conjecture. It was solved purely by a general purpose commercial GPT model, not custom-trained to mathematics, with no external tools. Read what the various mathematicians reviewing / commenting on it have to say (sections #3 and onward). Seriously, don't skip reading them, actually read them. This was one of Erdos's favourite problems. He mentioned it commonly in his lectures. Essentially every mathematician working in complex geometry has thought about this problem. The approach that the model came up with was highly novel approach, based on CM-fields and class field towers.

I know you don't want to accept this reality, but it is the reality, so you better improve your ability to accept it,. The field of mathematics is already doing so.

Dang, link didn't post.