Edge 413: Autonomous Agents and Semantic Memory

Can agents capture memory that encodes actual knowledge?

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In this issue:

1. An overview of semantic memory in autonomous agents.

2. A review of Meta AI’s MM-LLM for memory in generative video.

3. An introduction to Qdrant’s vector database platform.

💡 ML Concept of the Day: Semantic Memory in Autonomous Agents

In the last few issues of this newsletter we have been exploring different memory architectures in autonomous agents. Semantic memory is, arguably, the most common memory structure in LLMs. From a conceptual standpoint, semantic memory in autonomous agents draws inspiration from cognitive psychology and is centered around the argument that facts have meaning. For instance, listening to a specific music tune might bring back memories of places and people related to it.

 As it name indicates, the core idea of semantic memory structures is to capture knowledge of the agent’s knowledge about the world and itself. This knowledge can be used to further enrich LLM interactions combining it with in-context information.