This week's guest is Shashank Tiwari, a seasoned engineer and product leader who started with algorithmic systems of Wall Street before becoming Co-founder & CEO of Uno.ai, a pathbreaking autonomous security company. He started with algorithmic systems on Wall Street and then transitioned to building Silicon Valley startups, including previous stints at Nutanix, Elementum, Medallia, & StackRox. In this conversation, we discuss ML/AI, large language models (LLMs), temporal knowledge graphs, causal discovery inference models, and the Generative AI design & architectural choices that affect privacy.

Topics Covered:

Shashank describes his origin story, how he became interested in security, privacy, & AI while working on Wall Street; & what motivated him to found Uno
The benefits to using "temporal knowledge graphs," and how knowledge graphs are used with LLMs to create a "causal discovery inference model" to prevent privacy problems
The explosive growth of Generative AI, it's impact on the privacy and confidentiality of sensitive and personal data, & why a rushed approach could result in mistakes and societal harm
Architectural privacy and security considerations for: 1) leveraging Generative AI, and those to avoid certain mechanisms at all costs; 2) verifying, assuring, & testing against "trustful data" rather than "derived data;" and 3) thwarting common Generative AI attack vectors
Shashank's predictions for Enterprise adoption of Generative AI over the next several years
Shashank's thoughts on proposed and future AI-related legislation may affect the Generative AI market overall and Enterprise adoption more specifically
Shashank's thoughts on the development of AI standards across tech stacks

Resources Mentioned:

Check out episode S2E29: Synthetic Data in AI: Challenges, Techniques & Use Cases with Andrew Clark and Sid Mangalik (Monitaur.ai)

Guest Info:

Connect with Shashank on LinkedIn
Learn more about Uno.ai

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