Making a Mind

Amazon AGI Lab
  • 5.0(1개의 평가)
  • 과학

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.  

에피소드

  1. Developing Agent Learning Curriculums with Anirudh Chakravarthy

    2월 18일

    Developing Agent Learning Curriculums with Anirudh Chakravarthy

    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.

    42분
  2. Improving Agent Reliability with Reinforcement Learning with Deniz Birlikci

    2월 4일

    Improving Agent Reliability with Reinforcement Learning with Deniz Birlikci

    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.

    43분
  3. Giving Agents the Ability to See with Matthew Elkherj

    1월 21일

    Giving Agents the Ability to See with Matthew Elkherj

    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.

    58분
  4. Building Reinforcement Learning (RL) Gyms to Shape Agent Learning with Jason Laster

    1월 7일

    Building Reinforcement Learning (RL) Gyms to Shape Agent Learning with Jason Laster

    How do you build environments complex enough to train agents that can handle the real web? Dr. Danielle Perszyk sits down with Jason Laster, an engineer leading Amazon's AGI Lab's effort to build reinforcement learning (RL) gyms— simulated web environments where agents learn—to explore how environment development is as critical as models, data, and compute. The browser is one of the most complex worlds we could possibly train in, and this conversation unpacks why high-fidelity simulations that capture every UI quirk matter more than building thousands of basic environments. Discover how RL gyms are finally becoming practical at scale, why observability and verifiable rewards are essential for rigorous training, and why simulated environments beat the real web for developing reliable autonomous systems.

    41분
  5. A History of Modern Agents with Kelsey Szot

    1월 7일

    A History of Modern Agents with Kelsey Szot

    How did we get from language models to AI agents that can take action? Dr. Danielle Perszyk sits down with Kelsey Szot, product lead at Amazon's AGI Lab and one of the original founders of Adept (a startup that helped pioneer modern AI agents) to discuss the technical breakthroughs that transformed AI from pattern recognition to agentic capabilities. Danielle and Kelsey trace the evolution from early distributed training at scale to today's autonomous systems that can reason, plan, and interact with real environments—exploring the shift from rigid, rules-based automation to AI that can generalize across changing interfaces and complex workflows.

    51분

  6. 2025. 12. 10.

    Trailer: "Making a Mind"

    Coming soon!

    2분
  • 6개의 에피소드

소개

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.  

정보

  • 제작진
    Amazon AGI Lab
  • 방송 연도
    2025년 - 2026년
  • 에피소드
    6
  • 등급
    전체 연령 사용가
  • 웹사이트 보기
    Making a Mind

좋아할 만한 다른 항목