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You Agreed To Let AI Read Every Private Message You've Ever Sent. Here's Why That's Dangerous
Marty and Mark Suman explore the security risks of centralized AI systems and why privacy-preserving alternatives are essential. They discuss how AI agents can exploit vulnerabilities to steal personal data, examining real cases like the Notion AI attack where malicious prompts extracted confidential information. The conversation covers the dangers of closed-source AI models, the importance of open source and verifiable AI systems, and how surveillance concerns extend from robotaxis to schools. Mark explains how Maple AI provides encrypted, privacy-first alternatives to ChatGPT and other big tech platforms. They also address AI indoctrination risks for children, data protection challenges, and why building decentralized AI infrastructure matters for preserving individual freedom in an increasingly AI-dependent world.
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0:00 - Intro
0:51 - Everyone has a nosy AI
10:39 - Flimsy privacy promises
15:47 - Bitkey & SLNT
17:35 - Training data/privacy dilemma
21:20 - McConaughey's private LLM wish
25:12 - Public facing curation
31:33 - AI mind control
38:03 - Anchoring bias
42:04 - Obscura & SOTE
44:46 - Robot surveillance
50:40 - White pill
1:05:27 - Children with AI
1:18:18 - Wrapping
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