just a concept -- do you think it is possible to reduce the brain to an algorithm?
the only way i can think of this happening is if you were to map out the brain and then simulate the whole thing. this would be very innefficient. any better ways, or speculation of weather this is possible?
Thats a very vague way of framing it. What exactly do you mean by an algorithm? Most people who use that word usually mean heuristics. It's like saying if you can improve your vision with machine learning.
Yes you can potentially map it out and simulate it, and has been done with single neurons and neural nets of simple organisms such as an earthworm.
What's more trippier is that scientists have copied the entirety of the neural nets of a mouse brain chemically.
This becomes more tricky as your brain is just part of your overall neural net. 90% of neurons reside outside the brain, distributed all over the body.
You could simulate the brain in a very powerful computer, but that's a huge assumption. There's a lot more that we (or at least, I) dont know about the brain than the very little we do.
Even if it were possible to simulate the brain, it wouldn't make much sense to reconstruct it in the same manner that it existed in the body, for the same reason you dont see planes with feathers and flapping wings.
Also, your base reality is destroyed once you restart consciousness in a different form (unless you transfer into a synth brain, slowly teetering off of the main brain),
your consciousness is the most real thing in your existence, even the universe is up for skepticism next to it.
Even neural mapping isn't representative of the biology in most cases, You have to make a distinction between Artificial Neural Networks (ANN) and Biological Neural Networks (BNN). BNN operates using dendrites, synapses, neurons and axons. Neurons are the singular nodes, Dendrites and axons are bridges with dendrites being neural receptors and axons neural transmitters. Synapses are the location where a chemical is transmitted from one neuron to another, otherwise known as a synpatic junction.
Then you have ANN, which operates for the most part in layers: You have the input layer, 2 hidden layers and usually a discrete (0,1) to determine the computation: So layer 1 would be
inputs, layer 2 would take the inputs of layer 1 and give them a corresponding
weight, unlike BNN where where synapses are strengthened/weakened, in an ANN we use the hidden layer and weights to simulate this process of reinforcement/deselection. The nodes in layer two can be be connected to 9 nodes (Ratio of 1:9), given a weight, then layer 3 has the same nodes as layer 2, the values are put through again with an associated weight and then finally you reach layer 4, where the output is predicted. (That's enough to explain an analogy),
So as you can see, representing Biological neural networks is quite difficult, we haven't had true representation as much as we've created models that simulate the brain. Creating a computer as daddychaos said sensitive to the billions of biochemical processes in the brain, nevermind the body, is just insane to even map and ruins the purpose of a computer, which is it's a logical binary simplification of 1s and 0s, compared to the transmutation, atomic necessity for x molecule and so on. On this precedent I don't think we'll get an AGI, which is really what you're describing given an accurate Neural network, for a long, long time. AGI will be a long-field away, mainly because our brain has been designed over millions of years, most of our cognitive faculties like vision, pattern recognition, depth perception, being able to pinpoint the origin of a sound, are all very complex cognitive behaviours for a machine to even emulate. AI does good with pattern recognition now, but let's say you had an image of a cow but put a red circle on its stomach from microsoft paint, and a green circle on it's hooves, it cannot disambiguate the layers of the image with accuracy, while any of us will be able to eliminate the red/green circle from our representation and understand it is indeed, a cow.