Huge thank you to LatticeFlow AI for sponsoring this episode. LatticeFlow AI - https://latticeflow.ai/.Dr. Petar Tsankov is a researcher and entrepreneur in the field of Computer Science and Artificial Intelligence.
MLOps podcast #218 with Petar Tsankov, Co-Founder and CEO at LatticeFlow AI, A Decade of AI Safety and Trust.
// Abstract
// Bio
Co-founder & CEO at LatticeFlow AI, building the world's first product enabling organizations to build performant, safe, and trustworthy AI systems.
Before starting LatticeFlow AI, Petar was a senior researcher at ETH Zurich working on the security and reliability of modern systems, including deep learning models, smart contracts, and programmable networks.
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// Related Links
Website: https://latticeflow.ai/
ERAN, the world's first scalable verifier for deep neural networks: https://github.com/eth-sri/eran
VerX, the world's first fully automated verifier for smart contracts: https://verx.ch
Securify, the first scalable security scanner for Ethereum smart contracts: https://securify.ch
DeGuard, de-obfuscates Android binaries: http://apk-deguard.com
SyNET, the first scalable network-wide configuration synthesis tool: https://synet.ethz.ch
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Timestamps:
[00:00] Petar's preferred coffee
[00:29] Takeaways
[03:15] Shout out to LatticeFlow for sponsoring this episode!
[03:22] Please like, share, leave a review, and subscribe to our MLOps channels!
[03:42] Expansion
[05:16] Zurich ETH
[07:06] AI Safety
[09:24] Optimizing one metric, no fixed data sets
[12:19] Trust life-changing issues
[14:59] So much interest in GenAI
[16:45] Explosion of GenAI Trust and Safety
[21:14] Red Teaming
[25:22] Trustworthy AI in Industry
[27:43] DataOps Challenges
[33:42] Trusting Third-Party Models
[37:00] Testing Open Source Models
[41:41] Specialized ML for Leasing
[43:04] Regulation and Financial Incentives
[45:30] Regulations Drive Innovation Balance
[47:23] Regulations vs Certification: Voluntary Prove
[52:24] Workflow Transparency: Trust & Efficiency
[53:20] Engineers Balance Compliance Risks
[54:53] Pushing Deep Learning Limits
[57:31] Wrap up
