What you described could work in principle, but in practice, "mini" training on a single sample is not likely to produce good results, unless the sample is very

What you described could work in principle, but in practice, "mini" training on a single sample is not likely to produce good results, unless the sample is very large. Also, this finetuning would most likely be quite resource intensive. I recall older speech recognition systems where they would ask you to read a specific text sample to adapt the model to your voice, so yes, this can work. If you can fit a large generative model (e.g. an rnn or a transformer) in the codec, you might be able to offer something like "prompt engineering" [1], where the weights of the model don't change, but the hidden state vectors are adjusted using the current input. So, using your analogy, weights would be DNA, and the hidden state vectors would be the "mental state". By talking to this person you adjust their mental state to hopefully steer the conversation in the right direction. [1] https://www.gwern.net/GPT-3#prompts-as-programming https://www.gwern.net/GPT-3#prompts-as-programming