Charles Rosenba..., handle truncated

@bzogramm... (Charles Rosenbau...) — 10h Physics tells you how to compute optimally. If you want to maximize raw compute/watt, you want to move particles as slowly as possible. Kinetic energy scales with the square of speed, so 1/10th the velocity means 1/100th the energy. Maybe you only get 1/10th of the work done, but the efficiency boost is far bigger. Now if you're using something light and fast like an electron, you're going to have a bad time because just about anything can knock that electron onto a different path. Fighting noise gets hard, so if you're minimizing speed, more mass helps. Computers move electrons around at GHz speeds. The brain moves ions around at <1 kHz, and is thousands of times more efficient. @mech2code (Matt) — 11h Please consult the charts [Embedded charts: left chart "Power Density (W/cm²) vs Clock Frequency (Hz)" scatter of processor generations (4004, 8086, 80286, 80386, 80486, Pentium, Pentium II/III/Pro, AMD K5/K6/K7/K8, POWER2/3/4/5, Itanium2, Ivy Bridge, Cell, etc.) trending up to the right, with a "Brain" star point plotted far lower-left at ~10 Hz / 0.01 W/cm² (with cartoon face doodles added). Right chart: "40 Years of Microprocessor Trend Data" 1980–2020, plotting Transistors (thousands), Single-Thread Performance (SpecInt), Frequency (MHz), Typical Power (Watts), Number of Logical Cores, with "Moore's Law" trend line labeled in red, cut off on the right edge]

Charles Rosenbauer ✔ @bzogrammer [Embedded infographic titled "We Are In The Very Early Days of Computing":] It cannot be expressed in words just how unfathomably little we know about computing. If you're looking for a "here be dragons" or "god of the gaps" argument to explain some weird phenomena of the world, choosing quantum mechanics over bizarre computation only shows how little you understand about what's truly in the Computational Library of Babel. There are more possible programs that can be fit into a mere 32 bytes of data than there are atoms in the observable universe. We know NOTHING about what computers can truly do, and we could innovate in software until the last stars burn out and would still know ABSOLUTELY NOTHING. [Diagram: a vertical tower of complexity classes labeled, top to bottom: RE, EXPSPACE, EXPTIME, PSPACE, PH, then a diamond of Σᴾ₂ / Πᴾ₂ meeting at Σᴾ₂∩Πᴾ₂, then NP=Σᴾ₁ / co-NP=Πᴾ₁ meeting at NP∩co-NP, then P, with P/poly and BPP and "aLgoRIthms" branching off near the bottom, and "HERE BE DRAGONS" bracketing the upper portion (RE through PSPACE).] Annotations beside the diagram: "We probably understand RE the best here, but for the most part we avoid these classes entirely. We know almost nothing about what's out here." "Even then, by 'understand', I mostly mean that we've spent time understanding the properties of the weirdest stuff in RE. As for what kinds of useful things you can do with it, I guess we have interpreters, computers themselves, and a few other things, but this is mostly unexplored. We've largely scared ourselves off from exploring this seriously." "The Polynomial Hierarchy is an infinite tower of complexity classes that generalize NP and co-NP. We know there's a ton of weird stuff here, but it's almost entirely unexplored because programmers are deathly afraid of nondeterministic algorithms and anything that runs slower than quasi-linear time." "To say that we know more about space or the bottom of the ocean than we know about anything here is a vast understatement." "WEIRD stuff starts happening here. If you read old schizo Cybernetics stuff where Wiener or McCulloch start applying information theory and differential equations to understanding biology, sociology, or theology, a lot of the stuff they're doing lands here in NP. Even then, it's generally in only the most primitive corners of NP and Cybernetics was largely dead by the time we actually started understanding how weird NP really is." "Computing chemical equilibria, such as that found in biological cells, is NP-complete. The 'complete' part means that it can emulate anything in this entire complexity class, as well anything below." "Programmers will occasionally venture here, but generally are deathly afraid of it." ">99% of human-written code is in a tiny subset of this. If you have a rigid model of what 'algorithms' are and the kinds of properties they have, it's entirely because you're constrained to this tiny, well-behaved complexity class. Even then, we generally stick to the tiniest, easiest parts of it." 3:59 PM · Feb 21, 2026 · 10.4K Views