[api]

We can build less accurate computers and analog computers. Neither of these even begin to approach what brains can do. A self-driving car's computer takes hundreds of watts to run, uses reduced precision and custom silicon wherever possible, and does not begin to approach the navigational ability of a mouse or bird whose brain consumes less than one watt of power.

The human brain didn't evolve to perform consciously explicit and exact calculations on huge numbers, but our navigational and positional awareness abilities do far more impressive things with far more data much faster than this. A monstrous amount of effective but subconscious number crunching is involved in being aware of where your body is in space using nothing more than vision and sensorimotor feedback, taking apart auditory input (including FFT-like transforms), etc.

I really think CS people suffer from Dunning-Kreuger when they hand wave around the impressiveness of biological systems. Study some actual biology and neuroscience. What biological systems do as a normal part of metabolism and cognition is as awesome and mind-blowing as the vast energies, times, and distances found in astronomy. Computers are specialized devices that perform impressive feats of specialized computation but they do not even approach what biological systems do in terms of total data throughput per unit energy, learning ability, or associative and versatile memory to name just a few.

Edit: computers seem so impressive to us because we built them specifically to do the things we didn't evolve to do very well, but I have little doubt that if there were some kind of evolutionary forcing function selecting us for conscious explicit number crunching ability we would not need computers and wouldn't have built them.

[tehsauce]

> A self-driving car's computer takes hundreds of watts to run, uses reduced precision and custom silicon wherever possible, and does not begin to approach the navigational ability of a mouse or bird whose brain consumes less than one watt of power.

I would not trust the brain of a mouse or a bird to drive me in a car. Also the self-driving car computers which take hundreds of watts to run do not take advantage of custom silicon to the greatest possible extent, because the relevant algorithms are evolving rapidly. There is probably at least an order of magnitude or two of power efficiency that can be gained with current systems if the algorithms were truly baked into the chips.

[api]

I wasn't comparing performance at a specific task but performance at tasks of equal or greater difficulty.

Mouse and bird brains have evolved to operate mouse and bird bodies, not cars, and their learning ability isn't as powerful as a primate or a human so I doubt they could learn to drive a car as well as us or our specialized self-drive computers.

But... what they do manage in terms of controlling mouse and bird bodies is vastly more sophisticated and impressive than driving a car. A mouse runs around on four independently controlled legs and can tackle a vast array of terrains while dodging or chasing moving objects. Birds can navigate in 3d space while flying with articulated flapping wings with complex control surfaces operated by dozens of muscles.

Driving a car is ridiculously easy compared to anything like that. If mouse and bird brains had evolved to control cars I'd absolutely trust them to drive me around at least as much if not more than I trust a Tesla's autopilot. Driving is a simpler problem than operating a mouse body.

Don't get me wrong: our self-drive AIs are amazing engineering achievements. I'm just pointing out the impressive performance of tiny brains using fractions of a watt of power at much more difficult tasks.

The thing that blows my mind and makes me hypothesize quantum computing or even P=NP is the power requirements of those brains. It's "impossible." I'm not suggesting that we can't figure it out, just that we haven't yet and that it's probably going to take more or different approaches than we think it will take.

Immune systems were once considered so "impossible" that it led several researchers to abandon science in frustration, but we eventually got a good understanding of what was going on (and it's impressive!). Understanding immune systems had to wait for molecular genetics and modern evolutionary learning theory among other things. I suspect that really replicating brain-like performance will have to wait for something as far beyond our current state of the art as those were in the 1920s.

[234rwe4]

Parent was making the point that we have no computer with a similar architecture as the human brain (billions of tiny compute elements). Artificial neural networks try to simulate that, but the simulate billions of parameters on thousands of core (CPU/GPU).

Of course it's highly inefficient, just like for the brain is highly inneficient to exactly multiply two numbers.

So you also suffer of Dunning-Kreuger, you imagine that all that computers can be are von-neumann machines.