So I guess it comes down to a neurology question. How much algorithmic complexity have we found in the brain?
As far as I’m aware, we’ve found a few islands of neurons that work together in an obvious way, to track location on a grid for example, and hormone cycles that form a nice negative feedback loop, to keep you at an acceptable blood-sugar level for example. Most of it is still a mystery glob of neurons and other cells, albeit with a fixed pattern of layers and folds.
If we had measured massive algorithmic complexity in the brain, I’d agree with you. As it is, though, it seems unclear how much of the structure we see is conventional algorithm, and how much is the equivalent of an ANN architecture, that ultimately does the same job as no structure but learns more efficiently, or even is just a biological spandrel.
A combination of unique, varied parts is a complex algorithm.
A bunch of the same part repeated is a complex model.
Model complexity is not in any way similar to algorithmic complexity. They’re only described using the same word because language is abstract.
So I guess it comes down to a neurology question. How much algorithmic complexity have we found in the brain?
As far as I’m aware, we’ve found a few islands of neurons that work together in an obvious way, to track location on a grid for example, and hormone cycles that form a nice negative feedback loop, to keep you at an acceptable blood-sugar level for example. Most of it is still a mystery glob of neurons and other cells, albeit with a fixed pattern of layers and folds.
If we had measured massive algorithmic complexity in the brain, I’d agree with you. As it is, though, it seems unclear how much of the structure we see is conventional algorithm, and how much is the equivalent of an ANN architecture, that ultimately does the same job as no structure but learns more efficiently, or even is just a biological spandrel.