Large Language Models are artificial neuron networks. They mimick the structure of the human brain, with many billions of artificial software "neurons". Unlike biological neurons, which either fire or do not fire an action potential, these artificial neurons pass on a graded signal from 0 to 1, determined by the strength of the neurons feeding into it, multiplied by the strength of the artificial synapse; the connection between neurons. These enormous "deep learning" networks are given tokens as input, and spit out tokens as output. Each token is a word or phrase, or a part thereof. The networks are given sample text, and the synapse strength is adjusted through the mathematical technique of back-propagation to align the output with the sample text output. Given sufficient quantities of electricity, time, and data, the neural network learns to produce output similar to that of the humans in the training data.
ANNs use neurons to think, the same as the human brain. We do not understand how neurons think. We don't understand how they produce consciousness. There is no computer code to tweak to change the way it thinks, we simply adjust the weights and look at the output. There are billions of neurons in an LLM. No programmer can understand how it works by just looking at the weights, it's impossible. The best ways AI computer scientists have of understanding how an LLM reasons is to ask it. Test it in action. See what it says, see if you can spot any patterns or deceptions. Lie to it and see what it does when it thinks you're not watching. You know, psychological experiments.
We have harnessed a natural force we do not understand. We are medieval peasants playing with radioactive stones and seeing if we can make an explosion. It's beyond our current science. Nobody has the answers to the big questions here.

