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Ready for a deep dive into the fascinating world of large language models?

In this episode, we push AI chatbots to their conversational limits—spanning hundreds of turns, multiple sessions, and even images—to find out how well they remember and understand context over time.

We delve into a groundbreaking dataset called “Locomo” that evaluates an AI’s ability to recall events, summarize complex stories, and navigate tricky, adversarial questions.

We also discuss how giving these models structured notes (or “observations”) can dramatically improve their performance—and why they still struggle with understanding time, cause and effect, and cleverly worded “gotcha” questions.

Finally, we look ahead at emerging possibilities when AI gains access to richer, multimodal inputs like audio and video.

Join us for a thought-provoking conversation on what it takes to give AI a more human-like sense of memory, context, and experience—and why it matters for the future of technology and society.

Beyond the Short Chat: Exploring Long-Term Memory in AI

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