I have no doubt there's been analytical/semi-analytical models around for decades. I mean a program that can take an arbitrary geometry or class thereof with specific materials and simulate the high frequency vibrations and model interactions with the body with high fidelity (not through ad-hoc models) is probably still out of scope of real time simulation.

My point is really that there's often families of models that deal with one thing, from semi-analytical first coded in Fortran in the 80s that can run in milliseconds but is only valid in certain configurations with a low degree of accuracy, to "first principles" simulations that may well require a supercomputer to produce results to a useful degree of accuracy (and not in real time). So, just because you see someone claim they can "simulate X", and then another makes the same claim 40 years later, that doesn't mean they're doing the same thing.

For instance, aeronautics has XFOIL. It's a semi-analytical model first devised in the 80s that computes aeronautics coefficients for a certain class of airfoils (NACA). My understanding is it's a very clever, and industrially significant, piece of code, but ultimately it works in a narrow regime with some heavy simplifications. You can now get results from this in real time on a webpage. A proper CFD calculation to a NACA wing will take in the order of minutes to hours on a workstation (depending on requested precision and settings, e.g. speed of air), and while closer to first principles, it's still using physical simplifications (RANS). So yeah, although nominally people have been "simulating airfoils" for 40 years, the techniques have refined considerably, and will continue to do so (practical LES and, someday, DNS). It might be another century that people are still "simulating airfoils" in ever more accurate (nailing down within the constraints), high fidelity (lifting constraints) and generic ways.

Back to instruments, this is a difficult coupled problem, in fairly high frequencies (high frequencies = more expensive), with possible fluid-structure interactions, not to mention the geometries are fairly complex (to even get a workable mesh to begin with). My uneducated guess is we're still at either semi-analytical, or at the "considerably simplified first principles" stage for this type of problems. Just like DNS, I'm sure you could "just resolve the scales and run it through a simulation with a really tiny time step", and this is liable to be similarly expensive as DNS (million dollar single simulation). Additionally, they have to deal with the human ear, which is perhaps more unforgiving than an error plot on drag or lift. So I wouldn't dismiss news of instrument simulation as stale just because someone made something that produced similar artifacts in the past, as the methods will continue to evolve considerably.

I didn't know (but should have assumed) AI-generated podcasts existed. That's depressing.

I imagined if mankind had the ideal machine, that could automate anything, we would get rid of dull office work and back breaking physical labor, but not the things that are actually enjoyable: sharing with each other, entertaining each other, making art. I imagined a lively world of live performance and creation, since all subsistence work had been taken care of. Instead we might end up in the world of fifteen million merits.

It seems people don't mind letting their minds be hacked by machines that can create the form of what they find enjoyable, if not the substance. But I guess there's always been slop and the public for it. To imagine actual people wasting their limited time on Earth listening to these GPT logorrhea podcasts is truly depressing. The unchemical soma.

What are we even supposed to spend our days doing in this bright future of the AI champions'? Stop automating away the things that give people purpose, tackle real problems instead.

Besides that, one could easily imagine software created for similar purposes ("make me a file editor") by the same tool or handful thereof (claude and a very small "etc" for completeness) might share similar vulnerabilities, so this kind of broad net might be even cheaper to cast than one might imagine at first.

I recall my father (also a mathematician, incidentally) often repeating this to me.

So technically possible but also a tall ask (I didn’t know at the time of asking and my PI went with it).

I then came back and carried on without any immigration issues.

Meanwhile, median net salary in CH is 5'000-5'500 per month, double to triple its neighbors. So food is actually very affordable.

The food that costs more is the one someone cooked for you, which is logical considering the cook is likely paid more than your engineer (assuming that's your case) salary. But then again, minimum wage Italians are not eating out at the restaurant with any frequency. If you were an engineer in Switzerland instead, you could afford eating out there. The restaurants and terraces are never empty, anyways.

Now, if you want to enjoy a beer in the sun, you can get a 2CHF can at the supermarket and go fire up a barbecue at the lake of Zurich, I see people doing that all the time.

If that's the case, it affects earnings quite a bit. Say your investments beat inflation by 3 percentage points, you're effectively down to 2 percentage points after tax, so a 33% reduction in income.

Then there's everything to do with children (from direct subsidies to public schools or kindergarten slots), education (not everyone goes to university, for instance), and other subsidies that are income-dependent (two common ones in France are rent subsidy and a salary top-up for low-but-not-too-low incomes).

Plus, ultimately, 100% of what comes in goes out (modulo administrative costs) at the global scale, so you can't just average this or everywhere looks the same.

I did my PhD in France where we were legally employees like any other and did 100% research with like 100 hours training over the three years which could be 5min MOOCs counting for hours or classes the professors would sign us off on. We were hired by a specific researcher for a specific topic, unlike US students who join a broader program and explore their own directions more. My mentoring was drinking coffee with my advisor and colleagues and the odd e-mail exchange the day before turning in a paper.

I believe Germany and quite a few other European countries are similar. Any country that does 3 years PhDs is bound to cut on the student part of things.

So this article is really not saying anything controversial in the strictly ontological side of things, in fact it's already a relatively common stance to prefer supervising few (or, more rarely, none at all) students.

This researcher is saying "when I consider hiring someone as a workhorse, I might prefer AI instead"; what's the harm in that? Too many PhD students are used as disposable cheap labor, seeing little personal growth in their PhD journey and being generally neglected and abused.

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.

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.

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'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.

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.