[FRIAM] AI
Pieter Steenekamp
pieters at randcontrols.co.za
Fri Jun 20 14:30:46 EDT 2025
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 <profwest at 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.
>
> davew
>
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