It's an interesting paradox: the more we made computing accessible, the less we got out of it.

When a PC was expected to boot to an OS and not much else, we had all the freedom - by necessity - to tinker and learn. Hardware was barely enough for most day-to-day usage, so we upgraded relatively frequently and got to know the physical innards as well.

This is all so streamlined today that even computers can be smartphones with "apps", or even just a browser that gets you to google slides and everything else (or the MS equivalents). It was probably a necessity that, as computers became infrastructure, they would become simplified, so 90% of the population can indeed file their tax return online (and the remaining 10% have their younger family members do it).

This also means that people nowadays simply don't know that they can walk into any second hand store and get a $200 PC with a warranty that'll be much more productive than any smartphone if they have the knowledge to use it properly. But was there really a loss? These are, for the most part, people that would not have been able to hop on the internet wagon if it'd relied on maintaining a linux distro at all. That's regarding adults; children now do indeed grow up with walled systems for the most part, and that might be a loss.

The build quality and usability on mac laptops is something else, I've yet to see even 2k€+ laptops that people typically get for their jobs that aren't a pain to use without a mouse and monitor. Whereas I'm sitting here in front of my macbook and not touching the mouse next to it most of the time.

That's definitely valuable, but not for a child in my opinion, it's the type of luxury equivalent to a Mercedes over a Renault. Perfectly defensible but, just like a Mercedes is hardly a starter car, I don't think an MBP is that fit for a starter PC. It's also mostly useless if you're not traveling for work regularly.

That said, does any of that even matter any more? People were learning Blender, programming and whatever else 15 years ago on low to mid range machines already. The equivalently priced - or dirt cheap second hand - machines of today are multiple times more capable at everything. Stick Linux and a $5 mouse in it and you're 90% of the way to a macbook pro in terms of user experience.

That's to say, I agree with the core of the article: kids will make the most out of the least. But I disagree that this particular laptop is a necessity or a boon for that. If anything, it's a hindrance for being a mac.

In an organization, the number of sequential steps doesn't really scale with number of participants, does it? Rather with dependent steps of the tackled process; say, devise building permit request, await approval, purchase materials, move materials to site, hire workforce, etc.

Theoretically, each of those steps is parallelizable to some extent. Amdahl's law equivalent here would be that some delays are outside the reach of an organization to improve. For instance, a building permit will take the time it takes to be examined based on an external public administration.

I'm probably missing most of your point, but wouldn't the fact that we have inverse problems being applied in real-world situations somewhat contradict your qualms? In those cases too, we have to deal with noisy real-world information.

I'll admit I'm not very familiar with that type of work - I'm in the forward solve business - but if assumptions are made on the sensor noise distribution, couldn't those be inferred by more generic models? I realize I'm talking about adding a loop on top of an inverse problem loop, which is two steps away (just stuffing a forward solve in a loop is already not very common due to cost and engineering difficulty).

Or better yet, one could probably "primal-adjoint" this and just solve at once for physical parameters and noise model, too. They're but two differentiable things in the way of a loss function.

> You could in principle create a simulation with the same mathematical properties as the physical world but no one has ever done that. I'm not sure if we even know how.

What do you mean by that? Simulating physics is a rich field, which incidentally was one of the main drivers of parallel/super computing before AI came along.