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The Decentralized Future of Private AI with Illia Polosukhin - #749
Manage episode 509318959 series 2355587
In this episode, Illia Polosukhin, a co-author of the seminal "Attention Is All You Need" paper and co-founder of Near AI, joins us to discuss his vision for building private, decentralized, and user-owned AI. Illia shares his unique journey from developing the Transformer architecture at Google to building the NEAR Protocol blockchain to solve global payment challenges, and now applying those decentralized principles back to AI. We explore how Near AI is creating a decentralized cloud that leverages confidential computing, secure enclaves, and the blockchain to protect both user data and proprietary model weights. Illia also shares his three-part approach to fostering trust: open model training to eliminate hidden biases and "sleeper agents," verifiability of inference to ensure the model runs as intended, and formal verification at the invocation layer to enforce composable guarantees on AI agent actions. Finally, Illia shares his perspective on the future of open research, the role of tokenized incentive models, and the need for formal verification in building compliance and user trust.
The complete show notes for this episode can be found at https://twimlai.com/go/749.
769 에피소드
The Decentralized Future of Private AI with Illia Polosukhin - #749
The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
Manage episode 509318959 series 2355587
In this episode, Illia Polosukhin, a co-author of the seminal "Attention Is All You Need" paper and co-founder of Near AI, joins us to discuss his vision for building private, decentralized, and user-owned AI. Illia shares his unique journey from developing the Transformer architecture at Google to building the NEAR Protocol blockchain to solve global payment challenges, and now applying those decentralized principles back to AI. We explore how Near AI is creating a decentralized cloud that leverages confidential computing, secure enclaves, and the blockchain to protect both user data and proprietary model weights. Illia also shares his three-part approach to fostering trust: open model training to eliminate hidden biases and "sleeper agents," verifiability of inference to ensure the model runs as intended, and formal verification at the invocation layer to enforce composable guarantees on AI agent actions. Finally, Illia shares his perspective on the future of open research, the role of tokenized incentive models, and the need for formal verification in building compliance and user trust.
The complete show notes for this episode can be found at https://twimlai.com/go/749.
769 에피소드
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