Maybe hardware will morph into a more heterogenous mix than we have now, with many single-package cpu-gpu nodes working in parallel, instead of a few cpus orchestrating a gigantic sea of GPUs, but maybe not.

I'm more convinced that this is just one stage of the cycle. GPUs in consumer hardware weren't really a thing before the 90s. From the 90s to like 2013 graphics software (and it's ability to load hardware) improved rapidly. Since then it's kind of stagnated and a lot more of the focus has shifted to doing the same amount of rendering with less power/less heat/less space. Even if we do see a shift toward SoCs/APUs/whatever in consumer hardware over the next few years, I'd bet on some sort of paradigm shift to come along (fully raytraced rendering?) and swing the pendulum pack toward big discrete GPUs at some point again.

In the US at least, being raise by a single parent is a massive statistical indicator for basically every negative social outcome. Poverty, criminality, future generational single parenthood, etc.

The problem of course is that the effector/sensor is usually still a physical object that has a cost to replicate, even though the control circuitry in the FPGA is free to replicate. As is the case with ultrasonic transducers.

The SR71 was famously painted black as a mitigation for surface heating issues because the black paint conducted heat away from the areas the were really susceptible to ram-heating.

Perhaps reflective surfaces reflect some percentage of the incoming energy away, but thermally conductive surfaces conduct a larger percentage of that energy away and are able to safely sink it into some thermal mass.

If you simply eat at a calorie deficit and aren’t taking specific steps to preserve muscle (resistance training and high protein), a lot of the weight you lose will be muscle mass.

This is one reason why people struggle so much with dieting. They lose lot's of weight at first, but then plateau when the body adapts. When they get discouraged and fall off the diet, they put the weight back on quickly because they now are burning less calories than when they started.

The most effective way of losing weight long term is likely some combination of a slight caloric deficit paired with resistance training (to discourage the body from shedding metabolically expensive tissue) and short bouts of reverse dieting . (ie. slight deficit for 3 weeks, then 1 week of a slight surplus, etc.)

The commenter above is talking about how we often ignore the adaptability of the body, and that if you suddenly cut caloric intake by say 300 a day, the body will surprisingly quickly adapt by lowering metabolic rate to compensate. This can be done through the simple modulation of hormones and may also involve the shedding of metabolically expensive tissue, like muscle.

The type of work I do is highly niche. I’ve recently been working on a specific problem for which there are probably only a hundred at most implementations running on production systems, all of them highly proprietary. I would be surprised if there were any implementations in GPTs training set. With that said, this problem is not actually that complicated. A rudimentary implementation can be done in ~100 lines of code.

I asked GPT-4 to write me an implementation. It knew a decent amount about the problem (probably from Wikipedia). If it was actually capable of something close to reasoning it should have been able to write an implementation, but when it actually started writing code it was reluctant to write more than a skeleton. When I pushed it to implement specific details it completely fell apart and started hallucinating. When I gave it specific information about what it was doing wrong it acknowledged that it made a mistake and simply gave me a new equally wrong hallucination.

The experience calmed my existential fears about my job being taken by AI.

This one was considerably lower. But IIRC Alaska is a common station location for f-22s so may have just been coincidence.

Aside from that, there's the fact that if you can actually afford it, then it's likely better to finance than to pay cash because you can invest the full purchase price of the car for a higher return than the interest you would pay.

Since they are just general digital logic machines they can be obviously implement a cpu and are great are parallel computation, but in most cases cpus and gpus are much better at general computation because they’re specifically designed for that task.