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