Could have. Didn't want to. They were more interested in low-end CPUs with specialized hardware to accelerate gaming, targeting game console manufacturers and set-top box manufacturers as their primary customers..

Pretty much, yes. Apple left AIM because nobody could build an efficient laptop chip. Apple left Intel because they did, but only briefly, and then sat on their laurels while the iPhone CPU basically caught up.

2017? Three years to build M1? Seems optimistic. I'm pretty sure Apple was building Mac OS X on Intel internally even back before it was called Mac OS X as a hedge against PowerPC not keeping up, and it remained as a skunkworks project until they needed it, at which point they polished it and shipped it. It seems almost certain that Apple also had builds of macOS running on ARM (likely iPads) long before 2017. There were rumors that Apple was testing ARM-based Mac hardware back in 2014 (though this *might* have been confusion because of the Touch Bar using an ARM CPU), and rumors that ARM-based Macs would happen go as far back as 2011.

It was never really a question of whether Apple would switch the Mac to ARM. Once Apple started designing chips, the writing was on the wall for their future with Intel. The only question was the timing. Did Skylake increase the pressure to build a desktop-grade chip sooner? Maybe. Was it the reason Apple switched? No. That was always going to happen, for the same reason Apple switched from VxWorks on the Airport Base Stations to Darwin, and switched from Pixo OS to Darwin on the iPod. They like to be in control over their destiny.

The spec it came up with includes: which specific material is used for which specific component, additional components to handle cases where there's chemically incompatible or thermally incompatible materials in proximity, what temperature regulation is needed where (and how), placement of sensors, pressure restrictions, details of computer network security, the design of the computers, network protocols, network topology, design modifications needed to pre-existing designs - it's impressively detailed.

I've actually uploaded what it's produced to GitHub, so if the most glorious piece of what is likely engineering fiction intrigued you, I would be happy to provide a link.

I've mentioned this before, but I had Gemini, ChatGPT, and Claude jointly design me an aircraft, along with its engines. The sheer intricacy and complexity of the problem is such that it can take engineers years to get to what all three AIs agree is a good design. Grok took a look at as much as it could, before running out of space, and agreed it was sound.

Basically, I gave an initial starting point (a historic aircraft) and had each in turn fix issues with the previous version, until all three agreed on correctness.

This makes it a perfectly reasonable sanity check. If an engineer who knows what they're doing looks at the design and spots a problem, then AI has and intrinsic problem with complex problems, even when the complexity was iteratively produced by the AI itself.

Monolith?

Natural NNs appear to use recursive methods.

What you "see" is not what your eyes observe, but rather a reconstruction assembled entirely from memories that are triggered by what your eyes observe, which is why the reconstructions often have blind spots.

Time seeming to slow down (even though experiments show that it doesn't alter response times), daydreaming, remembering, predicting, etc, the brain's searching for continuity, the episodic rather than snapshot nature of these processes, and the lack of any gap during sleep, is suggestive of some sort of recursion, where the output is used as some sort of component of the next input and where continuity is key.

We know something of the manner of reconstruction - there are some excellent, if rather old, documentary series, one by James Burke and another by David Eagleman, that give elementary introductions to how these reconstructions operate and the physics that make such reconstructions necessary.

It's very safe to assume that neuroscientists would not regard these as anything better than introductions, but they are useful for looking for traits we know the brain exhibits (and why) that are wholly absent from AI.

They're not replicating our capabilities, nor could they. The architecture is completely wrong, as is the design philosophy. Brains are not classifiers, the way neural network software is, they are abstraction engines and dynamic compositors.

You will find that books written by the infinite monkeys approach are less useful than books written by conscious thought, and that even those books are less useful than books written and then repeatedly fact-checked and edited by independent conscious thought.

It is not, in fact, the book that taught you things, but the level of error correction.

You are correct.

When it comes to basic facts, if multiple AIs that have independent internal structure and independent training sets state the same claim as a fact, then that's good evidence that it's probably not a hallucination but something actively learned, but it's not remotely close to evidence of it being fact.

Because AIs have no understanding of semantics, only association, that's about as good as AI gets.

Thatâ(TM)s why the OS has protections to limit each appâ(TM)s access to data from other apps. Allow side-loading, but require multiple warnings to install any app that doesnâ(TM)t participate in sandboxing. Make it scary enough that nobody installs non-sandboxed apps unless they are backup apps, and even then, only after carefully vetting the source.

Insects tend not to stay exclusively in natural preserves, unless you have a different definition of what a preserve is that I'm not aware of.

Insects migrate. They move around to feed on food sources outside the immediate area. Some, like the monarch butterfly or the dragonfly, migrate thousands of miles, even across entire oceans. The notion that a preserve would protect insects from pesticides is based on an assumption of localized effect that doesn't always line up with the real world.

When a large percentage of insects that leave the preserve don't come back because they fed on crops sprayed with pesticides, that diminishes the population of the preserve, both directly by their loss and indirectly by the loss of offspring that would have come back in a future generation.

And if we reach a point when the only place where the insects are surviving is in the preserve, the loss of insects that leave the preserve would also reduce the genetic diversity of the preserve and accelerate the collapse of the species.

5,000 Indians controlling 100,000 robots, though, would represent a huge cost savings in this case.

First, you wouldn't have to hire in the local market, so the labor would be cheaper. The median salary in India is about $350 per month. California's minimum wage (assuming four 40-hour workweeks) is $2,640 per month. So that's almost an order-of-magnitude reduction in cost by itself.

Second, at least half of a delivery person's time is spent in a vehicle going from place to place, and in low-density areas, that increases to maybe more like 80%. Assuming the vehicles are autonomous, the workers controlling the bots won't have to do anything with a bot while it is in transit (and presumably the autonomous vehicles themselves will require only occasional human intervention), so one person will be able to control a large number of bots, taking control of a bot only when one arrives at a destination.

Third, if the bot is autonomous except in exceptional circumstances, the percentage of time that a human will be in charge will be reduced even further.

So if you assume that the cost per hour is lower by a factor of 7.5, that 75% of time is spent driving (no interaction), and that 90% of deliveries are done fully autonomously without human intervention, and that intervention takes only a quarter of the time that it would take to deliver a package entirely by hand, that would mean that a remote delivery driver would cost 0.75% as much per delivery, and that would only improve over time as the reliability of automation improves and the number of interventions decreases.

The end of for-pay non-artistic manual labor is near.

They're now a multiply convicted monopolist, which means everything they do from now on is going to be under much more careful scrutiny, by the courts, by other companies' lawyers, and by the DOJ. As a result, they're likely to get a lot more cases and *lose* a lot more cases. And at the rate things are going, fines are the least of their worries. They should be worrying that the DOJ might demand them to spin off the entire App Store ecosystem into a separate company and completely lose control. A little bit of short term profit doesn't really balance that out.

Sick of politicians who use their power for their own benefit instead of the general welfare... which to some extent is almost all politicians, but not remotely equally.

You misspelled Steve Jobs and Phil Schiller. The mistakes happened back when Tim Cook was still VP of operations. My exact words when those iOS App Store rules were first announced were "This is an antitrust lawsuit waiting to happen." It was an obviously bad idea seventeen years ago, and the antitrust landscape has only gotten worse for Apple since then.

These big tech companies need to hire legal counsel with a more paranoid opinion of the law. They'd get in a lot less trouble if they paid some of their lawyers to tell them whether they should do something, rather than paying them to find ways to justify doing what they've already decided to do and look for legal loopholes to help them get away with doing it.