The Intersection of Memory and Grounding in AI Systems

Memory in language models refers to the ability of AI systems to retain and recall pertinent information, contributing to its ability to reason and continuously learn from its experiences. There are four categories of memory: short term memory, short long term memory, long term memory, and working memory. Short term memory retains information for a very brief period of time, often seconds to minutes, and is used to reference recent messages and generate relevant responses. Short long term memory retains information for a moderate period, such as minutes to hours, and is used to manage sessions and keep conversation history current. Long term memory retains information for an indefinitely long period and is used to understand subjects a student performs well in and where they struggle. Working memory is a component of the language model itself, enabling the model to hold information, manipulate it, and refine it, improving the model's ability to reason. Grounding measures the ability of a model to produce an output that is contextually relevant and meaningful. The process of grounding a language model can be a combination of language model training, fine-tuning, and external processes, including memory.