Based on the neural network architecture, it is designed to process and generate responses for any sequence of characters that makes sense, including different spoken languages, programming languages and mathematical equations. ChatGPT uses a vast neural network to produce the human-like language through which it communicates. But how does that process happen? Okay, so how do our typical models actually work for tasks like image recognition? The current most popular and successful approach uses neural networks. Neural networks, invented in a way markedly similar to their current use in the 1940s, can be considered simple idealizations of how the brain seems to work.
On the other hand, if ChatGPT reproduces novel text from the conversation, then, ipso facto, ChatGPT is not a neural network. But what does all this mean in the context of ChatGPT? Thanks to its training, ChatGPT has “effectively gathered a certain (quite impressive) amount of what is equivalent to semantic grammar. The original ChatGPT input is an array of numbers (until now, the embedding vectors of the tokens), and what happens when ChatGPT “executes to produce a new token” is that these numbers “go through the layers of the neural network, and each neuron “does its own thing and passes the result to the neurons of the next layer.
