Go offline with the Player FM app!
S2E30: "LLMs, Knowledge Graphs, & GenAI Architectural Considerations" with Shashank Tiwari (Uno)
Manage episode 378661332 series 3407760
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:
Privado.ai
Privacy assurance at the speed of product development. Get instant visibility w/ privacy code scans.
Shifting Privacy Left Media
Where privacy engineers gather, share, & learn
Disclaimer: This post contains affiliate links. If you make a purchase, I may receive a commission at no extra cost to you.
Copyright © 2022 - 2024 Principled LLC. All rights reserved.
Chapters
1. S2E30: "LLMs, Knowledge Graphs, & GenAI Architectural Considerations" with Shashank Tiwari (Uno) (00:00:00)
2. Shashank describes his origin story, why he became interested in security, privacy, & AI; and what motivated him to found Uno (00:01:56)
3. The benefits to using "temporal knowledge graphs;" how knowledge graphs are used with LLMs to create, what Shashank calls, "causal discovery;" and how this approach helps privacy (00:04:53)
4. Shashank's thoughts on how the explosive growth of Generative AI has impacted privacy & confidentiality (00:13:14)
5. Architectural choices to consider when leveraging Generative AI, and those to avoid certain mechanisms at all costs (00:20:59)
6. Architectural considerations for verifying, assuring, & testing against "trustful data;" and what makes data "trustful" (00:28:26)
7. Architectural considerations for thwarting common Generative AI attack vectors (00:35:29)
8. Shashank's predictions for Enterprise adoption of Generative AI over the next few years (00:43:56)
9. Shashank's thoughts on proposed and future AI-related legislation may affect the Generative AI market overall and Enterprise adoption more specifically (00:50:34)
10. Shashank's thoughts on the development of AI standards across tech stacks (00:54:36)
62 episodes
Manage episode 378661332 series 3407760
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:
Privado.ai
Privacy assurance at the speed of product development. Get instant visibility w/ privacy code scans.
Shifting Privacy Left Media
Where privacy engineers gather, share, & learn
Disclaimer: This post contains affiliate links. If you make a purchase, I may receive a commission at no extra cost to you.
Copyright © 2022 - 2024 Principled LLC. All rights reserved.
Chapters
1. S2E30: "LLMs, Knowledge Graphs, & GenAI Architectural Considerations" with Shashank Tiwari (Uno) (00:00:00)
2. Shashank describes his origin story, why he became interested in security, privacy, & AI; and what motivated him to found Uno (00:01:56)
3. The benefits to using "temporal knowledge graphs;" how knowledge graphs are used with LLMs to create, what Shashank calls, "causal discovery;" and how this approach helps privacy (00:04:53)
4. Shashank's thoughts on how the explosive growth of Generative AI has impacted privacy & confidentiality (00:13:14)
5. Architectural choices to consider when leveraging Generative AI, and those to avoid certain mechanisms at all costs (00:20:59)
6. Architectural considerations for verifying, assuring, & testing against "trustful data;" and what makes data "trustful" (00:28:26)
7. Architectural considerations for thwarting common Generative AI attack vectors (00:35:29)
8. Shashank's predictions for Enterprise adoption of Generative AI over the next few years (00:43:56)
9. Shashank's thoughts on proposed and future AI-related legislation may affect the Generative AI market overall and Enterprise adoption more specifically (00:50:34)
10. Shashank's thoughts on the development of AI standards across tech stacks (00:54:36)
62 episodes
All episodes
×Welcome to Player FM!
Player FM is scanning the web for high-quality podcasts for you to enjoy right now. It's the best podcast app and works on Android, iPhone, and the web. Signup to sync subscriptions across devices.