Comments by "John Smith" (@JohnSmith-op7ls) on "Sabine Hossenfelder" channel.

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  18. Saying this mimics biological neural networks is just nonsense. First off, we don’t even understand how biological NNs work beyond the surface level. There’s over 3000 types of cells in the brain and we don’t understand how almost all of them fully work much less how they all work together. It was only recently that we learned the brain has a lymphatic system. Only more recently we learned the glial cells, previously thought to have nothing to do with cognitive function, is actually very important to it. All systems like this do is speed up digital NNs, their processing and maybe their training. Digital NNs are so primitive compared to biological ones that the man who coined the term neural network layered regretted it as it implied so much hype and misunderstanding. Digital NNs are nothing more than a bunch of simple functions which take an input value, do very simple math to it against some fixed internal constants set during training, then output the value to the next layer of functions. Essentially, they’re just a way of encoding an algorithm which can handle a lot of variability in the input quality. You could get the same effect by hand coding out countless logic branches if you wanted to. These are called expert systems. So, yeah, how our neurons and synapses and the supporting cells operate are far, far more complex not only in terms of processing information but in the fact they can dynamically change in response to thousands of chemical compounds, not to mention physically changing their structure. Even if you used something like a FPGA processor to dynamically change the functional pathways of the processor, this is a very crude approximation of what biological NNs and you still wouldn’t know how to emulate their function because we don’t know how they function. And don’t even get stared on how nonsense it is to claim a brain has the equivalent to X FLOPS. That just shows a complete misunderstanding of the topic. You can’t make a comparison to transistors when you don’t even know what the brain is actually processing at any given time. Simply taking an average of neurons and how often they fire or number of neurons and synapses and claiming that represents a workload capability which translates to transistors is comical.
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