Making a Mind

BONUS - Building agents that work in the real world: Live from SXSW

While we gear up for Season 2, we're sharing a special episode to bridge the seasons. Recorded live at SXSW, this conversation examines how AI agents are moving out of controlled research environments and into real-world consumer applications—and what it takes to make them reliable enough to matter. Cognitive scientist Danielle Perszyk is joined by Amanda Doerr, VP of Core Shopping at Amazon, Michael Giannangeli, Head of Agentic AI for Amazon Nova, and Michael Reiczyk, VP of Technology at Bandsintown, to discuss the gap between agentic capability and customer trust, including where users are willing to delegate decisions and where they pull back. The conversation covers reinforcement learning as a tool for improving model reliability, the shift from web-actuated to API-driven agentic shopping, and how human-in-the-loop design is shaping deployment across retail, live events, and foundation model development. Across all three domains, the panel finds that durable customer problems remain constant—even as the technical approaches to solving them change rapidly.

Cyborg Psychology with Dr. Pat Pataranutaporn

What would it mean to design AI for human flourishing? In the final episode of this season of “Making a Mind,” Cognitive scientist Dr. Danielle Perszyk sits down with Dr. Pat Pataranutaporn, Assistant Professor at the MIT Media Lab and founder of the Cyborg Psychology group, to explore how we move beyond optimizing models—and toward optimizing human development.    They discuss intelligence augmentation (IA) versus artificial intelligence (AI), why benchmarking model capability isn’t enough, and how we might instead measure AI by its impact on curiosity, learning, and collective well-being. From interdisciplinary meta-science to the risks of dehumanizing people while humanizing machines, they examine how AI can help us get smarter at getting smarter—without undermining what makes us human.

Creating Tools That Use Tools with Vibhaa Sivaraman

Human minds are scaffolded by the tools we create, but what happens when we build tools that can use other tools?    Cognitive scientist Danielle Perszyk sits down with AI researcher Vibhaa Sivaraman to discuss agent tool use and the future of the digital world. They unpack what it really means to build computer-use agents—not just chatbots with function calls—by exploring personalization, multi-agent collaboration, and the idea of agents as the next “cognitive technology.” As agents begin navigating the web on our behalf, they examine how digital environments might evolve and whether agents should think like us, or complement us in entirely new ways.

Eliciting Agent Reasoning with Meiqi Sun

Pre-trained language models already contain vast knowledge—the challenge is producing the reasoning needed to handle ambiguous, multi-step tasks. Cognitive scientist Dr. Danielle Perszyk sits down with Amazon AI researcher, Meiqi Sun, to explore the shift from simple action execution to high-reasoning agents.  Drawing parallels to human cognitive development, they discuss how reinforcement learning enables models to generate and refine their own chains of thought rather than relying on rigid, human-written templates. Together, they unpack why teaching agents to reason requires the freedom to explore, struggle, and self-correct.

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.