What if your AI didn't just follow instructions… but coordinated a whole team to solve complex problems on its own?

In this episode, we dive into the fascinating shift from traditional AI Agents to a bold new paradigm: Agentic AI. Based on the eye-opening paper “AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications and Challenges”, we unpack why single-task bots like AutoGPT are already being outpaced by swarms of intelligent agents that collaborate, strategize, and adapt—almost like digital organizations.

Discover how these systems are transforming research, medicine, robotics, and cybersecurity, and why Google’s new A2A protocol could be a game-changer. From hallucination traps to multi-agent breakthroughs, this is the frontier of AI you haven’t heard enough about.

Synthesized with help from Google’s NotebookLM.
Full paper here 👇
https://arxiv.org/abs/2505.10468

In this episode, we explore “The Illusion of Thinking”, a thought-provoking study from Apple researchers that dives into the true capabilities—and surprising limits—of Large Reasoning Models (LRMs). Despite being designed to "think harder," these advanced AI models often fall short when problem complexity increases, failing to generalize reasoning and even reducing effort just when it’s most needed.

Using controlled puzzle environments, the authors reveal a curious three-phase behavior: standard language models outperform LRMs on simple tasks, LRMs shine on moderately complex ones, but both collapse entirely under high complexity. Even with access to explicit algorithms, LRMs struggle to follow logical steps consistently.

This paper challenges our assumptions about AI reasoning and suggests we're still far from building models that trulythink. Generated using Google’s NotebookLM.

🎧 Listen in and learn why scaling up “thinking” might not be the answer we thought it was.

🔗 Read the full paper: https://ml-site.cdn-apple.com/papers/the-illusion-of-thinking.pdf
📚 Authors: Parshin Shojaee, Iman Mirzadeh, Keivan Alizadeh, Maxwell Horton, Samy Bengio, Mehrdad Farajtabar (Apple)

Prompting AI just got smarter. In this episode, we dive into Local Prompt Optimization (LPO) — a breakthrough approach that turbocharges prompt engineering by focusing edits on just the right words. Developed by Yash Jain and Vishal Chowdhary from Microsoft, LPO refines prompts with surgical precision, dramatically improving accuracy and speed across reasoning benchmarks like GSM8k, MultiArith, and BIG-bench Hard.

Forget rewriting entire prompts. LPO reduces the optimization space, speeding up convergence and enhancing performance — even in complex production environments. We explore how this technique integrates seamlessly into existing prompt optimization methods like APE, APO, and PE2, and how it delivers faster, smarter, and more controllable AI outputs.

This episode was generated using insights synthesized in Google’s NotebookLM.

Read the full paper here: https://arxiv.org/abs/2504.20355

AI is everywhere—but what is it, really? In this episode, we cut through the noise to explore the fundamentals of artificial intelligence, from narrow AI and reactive systems to generative models, AI agents, and the emerging frontier of agentic AI. Using insights from expert sources, articles, and research papers, we break down key concepts in simple, accessible terms.

You'll learn how tools like ChatGPT work under the hood, why generative AI felt like such a leap, and what it actually means for an AI to be an agent—or part of a multi-agent system. We explore the real capabilities and limits of today’s AI, as well as the ethical and societal questions shaping its future.

What if an AI could become smarter without being taught anything? In this episode, we dive into Absolute Zero, a groundbreaking framework where an AI model trains itself to reason—without any curated data, labeled examples, or human guidance. Developed by researchers from Tsinghua, BIGAI, and Penn State, this radical approach replaces traditional training with a bold form of self-play, where the model invents its own tasks and learns by solving them.

The result? Absolute Zero Reasoner (AZR) surpasses existing models that depend on tens of thousands of human-labeled examples, achieving state-of-the-art performance in math and code reasoning tasks. This paper doesn’t just raise the bar—it tears it down and rebuilds it.

Get ready to explore a future where models don’t just answer questions—they ask them too.

Original research by Andrew Zhao, Yiran Wu, Yang Yue, and colleagues. Content powered by Google’s NotebookLM.

Read the full paper: https://arxiv.org/abs/2505.03335

What if AI agents could collaborate as seamlessly as devices do over the Internet? In this episode, we dive into "A Survey of AI Agent Protocols" by Yingxuan Yang and colleagues from Shanghai Jiao Tong University, a landmark paper that tackles the missing piece in today’s intelligent agent landscape: standardized communication protocols. As large language model (LLM) agents spread across industries—from customer service to healthcare—they still operate in silos, struggling to integrate with tools or with one another. This paper proposes a two-dimensional classification of agent protocols and explores a future where agents form coalitions, speak common languages, and evolve into a decentralized, intelligent network. Expect insights on leading protocols like MCP, A2A, and ANP, a vision for “Agent Internets,” and a compelling case for why protocol design may shape the next era of AI collaboration.

This podcast was generated using insights from the original paper and synthesized via Google’s NotebookLM.

🔗 Read the full paper: https://arxiv.org/abs/2504.16736

What happens when generative AI collides with human creativity? In this episode, we dive into the extraordinary transformation sweeping across visual arts, music, film, and writing—powered by tools like DALL·E, Midjourney, Suno, and ChatGPT. From text-to-image magic and AI-composed music to VFX breakthroughs and story co-writing, we explore how these innovations are democratizing access, supercharging workflows, and sparking heated debates over ethics, copyright, and what it means to be an artist. Drawing on a wide range of sources—made accessible with help from Google’s NotebookLM—we unpack how individuals and industries are adapting, and what the future of artistic expression might look like.

In this episode of IA Odyssey, we go beyond the AI hype and into the trenches with real-world business stories from OpenAI’s “AI in the Enterprise” guide. From Morgan Stanley's precision evals to Klarna's rapid-fire customer service, and BBVA’s bottom-up innovation strategy, we explore seven powerful lessons that show how companies are embedding AI into their workflows—not just for efficiency, but for transformation. You’ll hear how organizations are improving personalization, accelerating operations, and unlocking their teams’ potential.

Whether you're curious, cautious, or already deploying AI, this deep dive offers insights you can actually use. Content generated with help from Google’s NotebookLM. Original article and full guide here:

Sources:

🔗 http://cdn.openai.com/business-guides-and-resources/ai-in-the-enterprise.pdf

🔗 http://cdn.openai.com/business-guides-and-resources/a-practical-guide-to-building-agents.pdf

🔗 http://cdn.openai.com/business-guides-and-resources/identifying-and-scaling-ai-use-cases.pdf