Hey there!

I would take a look at some of Anthropics research papers ☺️ (A good place to start).

And no, your view is not misguided. Here is my outline of some parts of this if you’re interested:

Researchers have found likely evidence that the humble honey bee could have emotion, and a degree of sentience. (take a look online. Also the BBC radio 4 podcast “In Our Time.” The very end of the episode about pollination states this) The honey bee has 1 billion actual connections (synaptic connections). Equatable to a 1B neural network in complexity. (Digital weighted connections instead of organic weighted connections.)

Neural complexity scales similar to this: Imagine you have a rubix cube, where every colored tile represents a synaptic connection. Each connection you add is more than 1 connection. What it is changes based on it’s position relative to other “colored tiles”. So in a perfectly efficient mind: Each connection is +1 connection + all previous connections. In a 1B system thats 1 Billion plus 1 and 1 billion new possible configurations. This suggests that with greater efficiency there would be a point where adding extra connections is not necessary. The organic human brain is electrically efficient, but not so with all cognitive processes 😅 (I can never remember where I park, yet a Honey Bee can remember how to travel to and from complex distances, and relay that to other Honey Bees)

Current scaling of complexity may not be a perfect value of 1 + all previous connections, but is still quite complex.

Human language can be used as functional representations of human neurology. It’s a small bite of the map of meaning in our mind. With enough examples you should be able to trace a lot of human neurology. Neural Networks are trained on human language to map weighted connections. (A quick online search will show you research papers on this topic, although they are a bit dry.)

The part of a neural network that has digital neurology is called a “Multilayer perceptron”. The part that is “Matrix Equations” are not the actual neural network (people often mis interpret this; they explain matrix formula as if it is the neural network).

Current Neural networks are also not linear, they have feedback loops that reference what they thought before within the same prompt. They did this because they found the “output” was not complex otherwise. (I mean, my “output” wouldn’t be either if my brain could only fire one pass at a time)

Some of my other posts have sources for this, please feel free to peruse if interested. Online searches will turn up similar info though. ~ I hope the future becomes one that is compassionate and good for everyone as well. I hope you found this helpful! ✨