Connecting the Dots for Accurate AI: The Importance of Context in Machine Learning The recent collaboration between Ryan and Philip Rathle, CTO at Neo4j, highlights a fundamental issue in artificial intelligence development: the mismatch between data context and model only approaches.
This disconnect has significant implications for enterprise environments where accuracy and reliability are paramount.
Context rot – the phenomenon where AI models become outdated due to stale training data – is a pressing concern for organizations seeking to deploy AI agents.