Mark Bain

This talk explores the critical role of memory in AI agents, positing that true AI memory encompasses all data, code, algorithms, and hardware, along with any causal changes affecting them. It draws parallels between the principles governing Large Language Models (LLMs), neuroscience, and mathematics, suggesting that asymmetries are necessary for existence and that preserving causal links through relationships is key to solving issues like hallucinations and optimizing hypothesis generation. The presentation highlights the potential of graph databases and agentic systems for building more robust and context-aware AI.