There's that pesky teleo[logy|nomy] again. Did we evolve *to* dodge ...? I don't think so. We (including plants and animals) did NOT evolve to do/be/function-as anything ... at all. It's an illusion - or a delusion. There is no purpose. Even the common trope that survival is the purpose is either way to oversimplified so as to be not even wrong or it's just not true ... at all.
So if this commitment to scientific materialism or mechanistic-ism (prohibiting the other 3 causes to lesser or greater extent) has been so successful, why abandon it? Every time we start programming in purposes, we end up with externalized/unintended costs that lead to catastrophe or dystopia. Haven't we learned that lesson yet? Of course, when the programmed in (formal, final, & efficient) causes are tightly, locally scoped ... very small - like building a better bridge or prosthetic device, the externalities are easily mitigated or absorbed by the ecosystem. Indeed, they offer "frozen" scaffolding for stigmergy et al. But when those other causes are very large (e.g. State Communism, eugenics, global fiat currency, PFAS, unitary and finite energy source, etc.), their externalities are not easily mitigated and can't be absorbed by the milieu. So when, not if, our artificial, engineered/fit to purpose, shell around the world collapses, those of us composed of accidentally, arbitrarily, slopped together garbage, like so many mutts scrambling through the streets for food of any kind will survive. The pure bred, fit to purpose, will all die out rather quickly. Death to the inbred. Long live the weeds and the mutts. On 6/20/25 11:30 AM, Pieter Steenekamp wrote:
Just one thought to toss into the mix: humans didn’t evolve to do astrophysics, drive Ferraris, or detect sarcasm on Twitter. We evolved to dodge predators, gather food, form social bonds, and pass on our genes — preferably in that order. The human brain is more like a rugged multitool than a precision instrument: built for “good enough, fast enough” responses in a chaotic and often hostile world. Now, if we set out to design a robot to function in today’s environments — say, hospitals, homes, or corporate boardrooms — we’re working with a very different set of goals. No need for snake-avoidance instincts or mushroom-edibility heuristics. No need for 30 trillion cells softly glowing in biophotonic harmony. No need for five trillion nerve impulses per second just to decide whether to scratch your nose. So even though a robot might never replicate the full sensory richness or biochemical subtlety of the human body, it may not need to. It could get away with a leaner, more focused design — one that does specific tasks better than humans, precisely because it’s not burdened with all our evolutionary baggage. Think of calculators: they’re completely clueless about context, but they’ll beat any of us in a mental arithmetic race, every time. I wouldn’t bet on a human-equivalent robot appearing next year — but ten years? Maybe. Especially if we stop trying to replicate every biological quirk and instead design for function. And when I say “function,” I mean not just doing what a human can do, but doing what the job needs — which is often a very different thing. Take Demis Hassabis’ current project: trying to simulate a single biological cell to improve drug discovery. Sounds simple — it’s just one cell — but it’s turning out to be a mammoth challenge. Meanwhile, a useful robot doesn’t need even one biological cell. It just needs actuators, sensors, and some reasonably clever code. This illustrates a broader point: biological systems are complex because evolution took the long road. Engineering can often take a shortcut. So yes, the human body is a marvel — a product of billions of years of trial and error. But that doesn’t mean it’s the most efficient solution for every task. It’s just the one that happened to work well enough to keep our ancestors from being eaten. After all, birds fly beautifully. But when we wanted to fly, we didn’t grow feathers. We built jets. On Fri, 20 Jun 2025 at 19:15, Prof David West <profw...@fastmail.fm <mailto:profw...@fastmail.fm>> wrote: Marcus made a comment recently about constructing an AI plus robotic body that provided the AI with sensory inputs comparable to a human being. It made me wonder about feasibility of such an idea. The average human body has about 100 billion nerve endings generating electrical impulses The average human (sex, weight, height sensitive) has about 30 trillion cells emitting ultra-weak biophotons; increasingly shown to play a role in inter-cellular communication It is extremely difficult to compare something like FLOPS for the brain, but best estimates suggest an average of 43 teraFLOPS, and up to 430 teraFLOPS for peak situations. Computers are capable of 1.1 exaFLOPS. But the brain uses 20 watts of power and the computer megawatts. Taking into account synaptic delay and refactory delay, each nerve ending could send a signal to the brain, or the brain could ‘process’ those signals at a rate between 10 Hz (cortex) to 1,000 Hz elsewhere. Also assume that the biophotons work mostly locally and maybe 1 percent actually end up triggering something akin to a nerve signal so, until we know more, it is unlikely that more than 30,000 to 300,000 additional signals reach the brain – less than noise, given what we know now. But that might change significantly in the future, especially as we learn more about quantum effects in the brain in general. The brain could receive 5 trillion discrete signals per second, but “pre-processing” reduces that to between 50 (average) and 500 million (peak) signals per second. .02-.03 percent of those signals are symbolic- originating in a phoneme, lexeme, word, number. Between .22 and 12.3 of the “non-symbolic” signals process by the brain have a mediating effect on symbolic processing, in the human brain. Some of this can be simulated by an AI. Take sarcasm as an example: humans use a lot of non-symbolic signals to detect sarcasm with a success rate of about 95%. AI’s must rely on context, on explicit labeling of training material, and, if available sound or images that can be analyzed. With a success rate of about 80%. Currently, an AI can simulate/emulate/equate to the roughly .02-.03 percent of the signal processing done by the human brain, i.e., that directly related to symbolic inputs. It can also deal with, roughly 80% (based on the sarcasm example) of the mediating non-symbolic signals (between .22 and 12.3 percent of signals processed by the brain. These numbers suggest, to me, that an AI is capable of simulating/emulating/equating-to about 1 to 15% of human brain signal processing. Of course, the human brain has all kinds of help elsewhere in the body, synthesizing, attenuating (reducing), and “pre-processing” signals. An AI has none of that help. So, it seems to me, that an AI must necessarily be a true idiot-savant for language manipulation and pattern recognition (image, sound). Only if we define human intelligence as nothing more than human abilities with language and visual/auditory pattern recognition can we say that artificial intelligence meets or exceeds (only in terms of speed) human intelligence. I used AI to generate all the numbers in the above.
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