By Sascha Brodsky
Publication Date: 2026-08-14 19:21:00
Large language models (LLMs) learn patterns from enormous collections of data and use them to generate responses. Most deployed models cannot continually update their central parameters from experience after training, Goertzel said. They can retrieve saved information or refer to earlier conversations, but those tools do not give them a continuous personal history.
“They don’t have a long-term memory of their whole life like a person does,” he said. “The result of that is they don’t know who and what they are and how they relate to the world around them.”
In his view, a generally intelligent machine would need a self-model, meaning an internal understanding of its history, abilities, limits and goals. It would also have to be able to preserve useful lessons and connect them to future decisions.
Conference participants discussed predictive coding as one possible route. The method asks a model to predict an outcome, compare its expectation with what actually occurs…
