Today, we’re excited to launch our very own podcast. Making a Mind is hosted by Dr. Danielle Perszyk, cognitive scientist and head of the human-computer interaction (HCI) team at Amazon’s AGI Lab. The podcast explores the science of intelligence through conversations with leading AI researchers, examining the scientific breakthroughs required to realize useful general intelligence. It’s designed for both technical audiences and general listeners curious about AI development—anyone interested in the minds behind the machines and how AI agents are being built to work alongside humans to augment our capacity. We’ve got a bunch of guests from our in-house AGI Lab as well as upcoming episodes featuring peers from across the AI industry. Here are the first two episodes:
Episode 1: A History of Modern Agents with Kelsey Szot
"Something that is 30% reliable or 60% reliable or even 80% reliable is 0% useful," Kelsey Szot, former co-founder of Adept and current product lead at the AGI Lab, explains. In this episode of Making a Mind, Kelsey traces the evolution of language models to modern agents capable of taking real-world action, reviewing a range of topics from how to solve the agent reliability problem, to generalizability, to future job transformation. You can watch the full episode with Kelsey below.
Episode 2: Building RL Gyms to Shape Agent Learning with Jason Laster
"Environments aren't a side project—they're as fundamental as the model itself," emphasizes Jason Laster, the AGI Lab's engineering lead for building RL gyms. In this episode of Making a Mind, Jason argues that training environments deserve investment equal to models and compute. In his conversation with Danielle, Jason shares the essential properties of high-fidelity simulation environments and the complexity of the world of browser training. You can watch to the full episode with Jason below. If you’re interested in learning more about RL gyms, you can also read this story from Jason about the unseen work of building AI agents.
Stay tuned for more as we publish new episodes every other week across YouTube, Amazon Music, Apple Podcasts, and Spotify.
