Tanenbaum is an academic. The ivory tower got (and gets) the microkernel debate wrong. Microkernels are a "better architecture" and that, to them, justifies per

Tanenbaum is an academic. The ivory tower got (and gets) the microkernel debate wrong. Microkernels are a "better architecture" and that, to them, justifies performance levels that are abysmal. It took them decades to get it working in the first place, and again this is not seen as a problem. This is how academia works. For a long time: micro kernels were universally perceived as better despite never having seen a working one despite the few attempts at a working kernel could not run basic programs, never mind a decent interface despite every successful kernel ever being monolithic (exception, to some extent, is QNX) and of course these messages were posted from machines that ran ... monolithic kernels And of course : they admitted to using their power to force people "the right way" in this debate. Both ast and tanenbaum point out that they'd deduct points/fail students who took the wrong stance. (presumably, of course, they'd also fire any phd students who do the same) Please keep this in mind next time you hear that academics widely support idea X. Think of academia like a hell of a lot of climate debates, except with the protagonists' positions usually not very well supported, or outright contradicted by the real world. Or even worse : the position is a choice, with no real argument one way or the other, with a strict hierarchy between people, and the higher ups very willing to abuse their power. It took me a long time to get out of academia, and out of this madness. I wrote papers, dissertations, and here's how that plays out "Good boy ! +1 (not in pay of course). Publish. Copy to the library. Now put it in the trash and work on something else" (I had failed papers too, of course). Let's just say that this is far from the only debate that works this way. There's many other things, like in programming languages, (extreme) static typing, (pure) funcional programming, dependent typing, garbage collection, rpc interfaces, object databases, machine learning nonsense, ... Barring other information, I have realized, a good bet is this : if >70% of the academic researchers are on the same side for any particular claim, bet against that claim, just for that reason alone. And get out of academia, of course. Google, facebook, yahoo, microsoft, ... all are building large machine learning teams with actual resources, by far the best researchers in the world, people who demand actual results on some problems (yes, that's a plus), and continuity. there is an extreme obsession with learning algorithms in academia. The whole thing is a worthless waste of time, for one simple reason : feature engineering can make k-means beat the crap out of the most advanced algorithm anyone's ever come up with. But new learning algorithms are much easier to write, much smaller to prove things about, and thus much, much easier to get a phd dissertation through a committee with. In practice "deep learning" is by far the most successful algorithm (which is another way to say running a 1963 algorithm at scale)