The following post is based on the latest Radical Talks episode with host Molly Welch, Partner at Radical Ventures, and featuring Ted Mabrey, Head of Palantir Commercial. Listen to the podcast on Spotify, Apple Podcasts, or YouTube.

Radical Partners Molly Welch and Aaron Rosenberg are joined by General Reasoning Co-founder and CEO Ross Taylor to discuss the AI approach powering today's reasoning revolution: reinforcement learning.

The AI revolution is changing nearly every industry, from software to content creation, yet one crucial area remains largely untouched: the design of physical things. While AI is great at manipulating digital bits, the physical world — from skyscrapers to spacecraft — still relies on traditional engineering approaches that haven’t fundamentally changed in decades.

This gap represents a huge, untapped opportunity. Physical systems could benefit enormously from AI-assisted design, but it’s a tough problem to solve. Current AI models struggle with quantitative and spatial reasoning, and training data is scarce. P-1 AI, a company co-founded by former Airbus CTO Paul Eremenko, is working to bridge this gap by developing an agent capable of mastering the physics and quantitative reasoning needed for physical design.

While consumer voice AI has long frustrated users with clunky interactions, enterprise voice AI has reached a turning point. The architectural improvements over the past 12-18 months have enabled natural, contextual conversations that are revolutionizing industries from recruiting to healthcare. 

Sanjana and Arsham join host Molly Welch to discuss why voice represents the most natural human interface, the breakthrough technology stack that's finally working, and how companies like Ribbon are already deploying voice agents that interview candidates 24/7 with Fortune 500 customers.

In this week's Radical Talks podcast, Radical Partner Rob Toews joins host Molly Welch to discuss AI’s curse of catastrophic forgetting and the challenge of building systems capable of continual learning.

Rob digs into why this limitation exists and the early work being done to solve it, exploring the emerging AI paradigm that could bridge the gap between static models and truly adaptive intelligence. Continual learning holds the promise of AI systems that grow smarter with every interaction, creating unprecedented competitive moats and bringing us closer to truly adaptive machine intelligence.

Coming soon, Radical Talks cuts through AI hype to showcase what's truly driving innovation. Hosted by Radical Ventures partner Molly Welch, each episode features intimate conversations with researchers, founders, and investors at AI's cutting edge. Discover contrarian perspectives and insider insights you won't find anywhere else, and subscribe wherever you get your podcasts.

In honour of Geoffrey Hinton winning the Nobel Prize in Physics, we bring you an exclusive Radical AI Founders Masterclass talk between, Geoffrey Hinton and Fei-Fei Li, hosted and moderated by Jordan Jacobs, Managing Partner and Co-Founder of Radical Ventures.

The conversation marked the first time Geoff Hinton and Fei-Fei Li have shared a stage, and featured a dramatic recounting of the 2012 ImageNet competition when their professional careers first intersected. It was a pivotal moment in the history of AI, when neural networks proved capable of ‘solving’ computer vision.

Recognized as the “godfather of AI,” Hinton candidly voices his apprehensions about the unforeseen challenges of advanced AI, including concerns that innovations could one day yield superior intelligence. However, Hinton also noted that he believes his message of caution is getting through. Li, a Stanford University Professor, Co-Director of Stanford’s Human-Centered AI Institute (HAI), and Radical Ventures Scientific Partner, stressed the need for responsible AI stewardship through human-centred approaches, while grounding the conversation in a sense of optimism about its potential.

The discussion was held in front of a live audience at the University of Toronto.

In the same paper where Alan Turing outlined his famous criteria for determining when a computer is capable of thinking like a human being, he also shared advice on building a device that might one day pass the ‘Turing Test.’ “Instead of trying to produce a programme to simulate the adult mind,” the pioneer of computer science writes,“why not rather try to produce one which simulates the child's?”

The deep learning breakthroughs by Geoffrey Hinton and the architects of neural networks created technologies that have gone on to out-perform oncologists in cancer detection and help self-driving cars navigate our roads. However, to build systems that are capable of broadly generalizing across different use cases and experiences, researchers are looking to children for inspiration.

In this episode of Radical Talks, renowned psychologist Alison Gopnik explores how AI systems may benefit from a better understanding of the way children learn and play. Gopnik, who runs the Cognitive Development and Learning Lab at the University of California, Berkeley is also the best-selling author of The Philosophical Baby, and The Gardener & The Carpenter.

Like scientists, children constantly test hypotheses to better understand the world. Gopnik argues that the causal inference demonstrated by a child offers clues into how to build more resilient AI systems. Current AI technologies are a bit like the children of ‘helicopter’ parents – they’re task-focused and good at doing one thing well. As is the case with raising a well-rounded child, to broaden the potential of AI there may be value in nurturing and rewarding curiosity in AI models.

If researchers are successful in building an AI that demonstrates child-like curiosity, a question emerges around the need for computational caregivers to keep AIs from causing damage to themselves or others and to ensure they benefit society more broadly. As Gopnik puts it, “AIs need moms.”

A feature conversation with AI pioneer Fei-Fei Li. As WIRED magazine put it:  “She’s one of a tiny group of scientists—a group perhaps small enough to fit around a kitchen table—who are responsible for AI’s recent remarkable advances.” The Stanford Computer Science Professor and Co-Director of the Stanford Institute for Human-Centered AI, joins Radical Ventures Managing Partner, Jordan Jacobs, for a feature conversation on the enormous opportunities to improve healthcare with AI, the state of computer vision, and how we should be thinking about the human values of AI systems. Fei-Fei Li is a co-founder of Dawnlight, a Radical portfolio company.

For more thoughts and insights from Radical Ventures,  visit www.radical.vc.

A feature interview with Pieter Abbeel,  an innovator at the cutting edge of where AI meets robotics. In this episode of Radical Talks, Radical Ventures Managing Partner Jordan Jacobs chats with Pieter about the impact of COVID19 on automation, how AI has revolutionized the field of robotics and they also share a few thoughts on how professional sports should be thinking about robot refs.

Pieter Abbeel is a professor of electrical engineering and computer science at the University of California, Berkeley where he is also director of the Robot Learning Lab. Peter is also co-founder of Covariant - a company that has built a universal AI platform for robotics (Radical Ventures is an investor in Covariant).

For more thoughts and insights from Radical Ventures,  visit www.radical.vc.