Welcome back to the first episode of season 3 of the Talking AI podcast, formerly known as the Built Right podcast!
We’re kicking off with Amir Behbehani, Founder and Chief AI Engineer at Memra, a platform that helps build, deploy, and automate workflow through AI. He and host Matt Paige look at some of the hurdles AI needs to overcome to fully integrate into enterprise, such as lack of proprietary data access and memory.
Amir delves into the science behind these challenges and potential solutions. He introduces the concept of ‘agentic AI’ and the emergent AI stack, made up of five layers, aimed at enhancing AI functionality through memory management, data orchestration, and agentic reasoning.
They also discuss the role of vector and graph databases, the importance of memory and context, and exploring future use cases in a marketplace with agentic AI. Amir also speculates on the transformative potential of AI in the future labor market - hinting at more entrepreneurial endeavors as individuals leverage AI for improved productivity and automation.
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Key links:
Memra: https://www.memra.co/
Connect with Amir on LinkedIn: https://www.linkedin.com/in/aimlengineer/
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Key moments:
00:00 Introduction and Podcast Overview
00:40 Generative AI and LLMs Overview
01:18 Guest Introduction: Amir Babani
02:32 Challenges with LLMs and AI Integration
05:39 Emergent AI Stack Introduction
06:44 Foundation Layer: LLMs and Models
09:31 Data Layer: Vector and Knowledge Databases
13:53 Memory Management in AI
16:20 Orchestration Layer: Context and Agents
19:07 Master and Subordinate Agents
24:21 Applications and Use Cases
27:33 Impact on Jobs and Future of Work
45:35 Conclusion and Contact Information
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