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Making a Mind

Making a Mind

Von: Amazon AGI Lab
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What happens when brains meet code? Join cognitive scientist Dr. Danielle Perszyk as she unravels the science of intelligence, dissects the latest AI research, and explores the consequences of training autonomous systems. Breaking down concepts from AI hallucinations and reinforcement learning, to complex reasoning and relational dynamics, this podcast features conversations with researchers, scientists and industry peers about how we can build AI agents that augment human capacity. Thoughtful, insightful, and a little playful—this is for anyone curious about the minds behind the machines.

Wissenschaft
  • Developing Agent Learning Curriculums with Anirudh Chakravarthy
    Feb 18 2026

    What if the key to building intelligent agents isn't just better models, but better teachers? Cognitive scientist Dr. Danielle Perszyk sits down with AI researcher Anirudh (Ani) Chakravarthy from Amazon's AGI Lab to explore how agents learn—not through memorization of data sets, but through structured experience.

    Drawing parallels to human development, Ani introduces a training approach where two AI agents work together: one explores the web to discover tasks at the frontier of its capabilities, while the other learns from these challenges—a new approach to self-play. Together, Ani and Danielle discuss how this process points to a form of embodied intelligence distinct from language models—and what it could mean for the future of human-AI collaboration.

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    42 Min.
  • Improving Agent Reliability with Reinforcement Learning with Deniz Birlikci
    Feb 4 2026

    A system that succeeds once is a demo. A system that succeeds every time is a breakthrough. Dr. Danielle Perszyk sits down with AI researcher Deniz Birlikci from Amazon's AGI Lab to explore how reinforcement learning (RL) is transforming AI agents from impressive demos into dependable tools that work consistently in real-world environments.

    Danielle and Deniz discuss why reliability, not accuracy, is the true bottleneck for web agents, the critical role of a robust verification system, failure models that RL attempts to fix, and the extraordinary complexity of orchestrating live browsers with perception and actuation stacks. Discover how RL is building the foundation for agents that can handle complex workflows reliably alongside humans.

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    43 Min.
  • Giving Agents the Ability to See with Matthew Elkherj
    Jan 21 2026

    Before an AI agent can reason or plan, it has to see. Dr. Danielle Perszyk and AI researcher Matthew Elkherj explore why user interface (UI) understanding is one of the most underestimated challenges in building autonomous agents—and why it’s foundational to creating reliable AI teammates.


    Danielle and Matthew discuss the distinct reliability requirements of agents, how perceptual hallucinations can be a feature (rather than a bug), and the role of synthetic gym environments in training. Together, they explain why building reliable agents requires solving interconnected challenges—from how agents perceive digital interfaces to how they learn from mistakes, handle real-world complexity, and ultimately augment human capacity.


    Please note: this podcast was recorded in August 2025.

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    58 Min.
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