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The path towards trustworthy AI (Practical AI #293)
Oct 29, 2024
Elham Tabassi, Chief AI Advisor at the U.S. National Institute of Standards & Technology (NIST), shares insights on the journey to trustworthy AI. They delve into NIST’s AI Risk Management Framework and its connection to a recent White House executive order aimed at enhancing AI safety. Tabassi emphasizes the crucial roles of explainability, reliability, and governance in advancing AI, especially in sensitive fields like healthcare. The conversation highlights the urgency for strong standards to ensure that the rapid advancements in AI remain safe and reliable.
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Quick takeaways
- NIST is pivotal in creating trustworthy AI standards, emphasizing stakeholder engagement to address security, privacy, and ethical considerations.
- The AI Risk Management Framework (AI RMF) outlines essential characteristics of trustworthy AI, including reliability, accountability, and safety, by merging insights from various disciplines.
Deep dives
Leveraging Postgres for AI Development
Postgres, a powerful open-source database, is being utilized by Timescale to enhance the development of AI applications. Developers can capitalize on their existing knowledge of Postgres to create advanced applications, including time series analytics and AI-related technologies like retrieval-augmented generation (RAG) and search agents. Timescale's PGAI project provides a roadmap for developers to transition into AI engineering without requiring them to learn new technologies. With open-source tools available for local setups, developers can easily experiment and build projects using familiar SQL query languages.
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