Open Source Privacy
Open source models offer significant advantages for privacy and confidentiality by allowing organizations to run them on-premises, thus avoiding exposure of sensitive data to external providers. However, challenges arise in maintaining information boundaries within organizations, especially when sensitive queries could inadvertently reveal confidential information. The concept of a data mesh, which promotes distributed data access with strict rules, complicates matters further in the context of LLMs, where traditional boundaries become blurred.In this clip
From this podcast
Super Data Science: ML & AI Podcast with Jon Krohn
701: Generative A.I. without the Privacy Risks — with Prof. Raluca Ada Popa
Related Questions
Why is openness important for artificial intelligence, as discussed in the episode DisTrO and the Quest for Community-Trained AI Models and the clip Open Source AI?
Which is better for the future of enterprise search and AI-powered work productivity: open source or closed source AI, as discussed in the episode Chris Lattner: Compilers, LLVM, Swift, TPU, and ML Accelerators | Lex Fridman Podcast #21 and the clip Open Source Revolution?