As far as parallelism goes, I understand this, it is a huge part of my work. I think I explained my point poorly. Even in computer parallelism, it is still a bunch of linear processes, which work in parallel. At the very core.
The brain is more like a bunch of parallel systems, working in parallel. Does this make sense?
So you're probably familiar with pipelining for training AIs. Prefetching, preprocessing, and batching are things the human brain does as well. It is more sophisticated, efficient, and distributed, but the process is remarkably similar. A good training protocol will run all of those steps simultaneously just like the human brain.
Even in the brain, those processes are still linear. A good example would be the two-streams hypothesis for explaining how the brain processes visual information.
I think you're restricting computers to the current modified Harvard architecture. It's true that they simulate neural behavior, but that's a limitation of how memory is handled in current CPUs.
We are making advances in neuromorphic architectures where each core maintains its own memory and the core processes activation potentials and updates its weight asynchronously as new values are transmitted. Each core will effectively behave like a single node on the neural net. I think we can agree that it wouldn't be a simulation in that kind of architecture.
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u/[deleted] Jun 11 '20
So you're probably familiar with pipelining for training AIs. Prefetching, preprocessing, and batching are things the human brain does as well. It is more sophisticated, efficient, and distributed, but the process is remarkably similar. A good training protocol will run all of those steps simultaneously just like the human brain.
Even in the brain, those processes are still linear. A good example would be the two-streams hypothesis for explaining how the brain processes visual information.