#244 Yoav Shoham on Jamba Models, Maestro and The Future of Enterprise AI

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In this episode of Eye on AI, Yoav Shoham, co-founder of AI21 Labs, shares his insights on the evolution of AI, touching on key advancements such as Jamba and Maestro. From the early days of his career to the latest developments in AI systems, Yoav offers a comprehensive look into the future of artificial intelligence.

Yoav opens up about his journey in AI, beginning with his academic roots in game theory and logic, followed by his entrepreneurial ventures that led to the creation of AI21 Labs. He explains the founding of AI21 Labs and the company's mission to combine traditional AI approaches with modern deep learning methods, leading to innovations like Jamba—a highly efficient hybrid AI model that’s disrupting the traditional transformer architecture.

He also introduces Maestro, AI21’s orchestrator that works with multiple large language models (LLMs) and AI tools to create more reliable, predictable, and efficient systems for enterprises. Yoav discusses how Maestro is tackling real-world challenges in enterprise AI, moving beyond flashy demos to practical, scalable solutions.

Throughout the conversation, Yoav emphasizes the limitations of current large language models (LLMs), even those with reasoning capabilities, and explains how AI systems, rather than just pure language models, are becoming the future of AI. He also delves into the philosophical side of AI, discussing whether models truly "understand" and what that means for the future of artificial intelligence.

Whether you’re deeply invested in AI research or curious about its applications in business, this episode is filled with valuable insights into the current and future landscape of artificial intelligence.

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(00:00) Introduction: The Future of AI Systems

(02:33) Yoav’s Journey: From Academia to AI21 Labs

(05:57) The Evolution of AI: Symbolic AI and Deep Learning

(07:38) Jurassic One: AI21 Labs’ First Language Model

(10:39) Jamba: Revolutionizing AI Model Architecture

(16:11) Benchmarking AI Models: Challenges and Criticisms

(22:18) Reinforcement Learning in AI Models

(24:33) The Future of AI: Is Jamba the End of Larger Models?

(27:31) Applications of Jamba: Real-World Use Cases in Enterprise

(29:56) The Transition to Mass AI Deployment in Enterprises

(33:47) Maestro: The Orchestrator of AI Tools and Language Models

(36:03) GPT-4.5 and Reasoning Models: Are They the Future of AI?

(38:09) Yoav’s Pet Project: The Philosophical Side of AI Understanding

(41:27) The Philosophy of AI Understanding

(45:32) Explanations and Competence in AI

(48:59) Where to Access Jamba and Maestro