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

In this episode, we unpack how Netflix is using cutting-edge AI—similar to the tech behind ChatGPT—to power hyper-personalized recommendations. Discover how their new foundation model moves beyond traditional algorithms, blending massive data with NLP-inspired strategies like interaction tokenization and multi-token prediction. We also explore how this personalization revolution is reshaping customer expectations across industries, drawing on insights from marketing leaders like Qualtrics, Epsilon France, and Doozy Publicity. But with great AI power comes big questions: What about privacy, ethics, and the joy of unexpected discovery?

Based on original sources and developed with the help of Google’s NotebookLM.

🎧 Main source available here: https://netflixtechblog.com/foundation-model-for-personalized-recommendation-1a0bd8e02d39

What happens when AI stops forgetting?

In this episode of IA Odyssey, we dive deep into OpenAI's rollout of memory in ChatGPT—and why it’s so much more than a feature toggle. From personalized ad agents to AI doctors learning on the job, we explore how memory transforms artificial intelligence into agentic AI: systems that adapt, personalize, and evolve. Drawing from cutting-edge research like KARMA, MeAgent Zero, and cognitive architecture frameworks, we unpack how memory lets AI learn from experience, get more accurate, and even form something close to relationships.

What happens when you put multiple AI agents together to solve a task? You might expect teamwork—but more often, you get chaos. In this episode of IA Odyssey, we dive into a groundbreaking study from UC Berkeley and Intesa Sanpaolo that reveals why multi-agent systems built on large language models are failing—spectacularly.

The researchers examined over 150 real MAS conversations and uncovered 14 unique ways these systems break down—whether it’s agents ignoring each other, forgetting their roles, or ending tasks too early. They created MASFT, the first taxonomy to map these failures, and tested whether better prompts or smarter coordination could fix things. The result? A wake-up call for anyone building AI teams.

If you've ever wondered why your squad of AIs can't seem to get along, this episode is for you.

This episode was generated using Google's NotebookLM.
Full paper here: https://arxiv.org/pdf/2503.13657

In this episode of IA Odyssey, we unpack how DeepSeek's open-source models are shaking up the AI world—matching GPT-level performance at a fraction of the cost. Drawing on insights from the research paper by Chengen Wang (University of Texas at Dallas) and Murat Kantarcioglu (Virginia Tech), we explore DeepSeek's secret sauce: memory-efficient Multi-Head Latent Attention, an evolved Mixture of Experts architecture, and reinforcement learning without supervised data. Oh, and did we mention they trained this monster on a $ave-the-GPU budget?

From hardware-aware model design to the surprisingly powerful GRPO algorithm, this episode decodes the magic that’s making DeepSeek-V3 and R1 the open-source giants to watch. Whether you're an AI enthusiast or just want to know who's giving OpenAI and Anthropic sleepless nights, you don’t want to miss this.

Crafted with help from Google's NotebookLM.
Read the full paper here: https://arxiv.org/abs/2503.11486