Theory and Practice

• S4E8: Dave Munichiello on Investing in AI’s Future

  Throughout the fourth season of Theory and Practice, we explored emerging human-like artificial intelligence and robots. We asked if we could learn as much about ourselves as we do about the machines we use. The series has covered safety guardrails for AI, empathic AI communication, communication between minds and machines, robotic surgery, computers that smell, and using AI to understand human vision. The most recent episode with Google DeepMind's Dr. Clément Farabet illuminates how computers might demonstrate understanding and reasoning on par with humans. In the final episode, we reflect on investing in artificial intelligence's future with the leader of GV’s Digital Investing Team, Dave Munichiello, who has a long-standing history with AI and robotics. Dave was an early technologist at Kiva Systems, purchased by Amazon and ultimately becoming Amazon Robotics. Over the past decade-plus at GV, Dave has been leading investments across two major categories: Platforms Empowering Developers (GitLab, Segment, Slack, RedPanda, etc) and Platforms Powering AI Systems (Determined, Modular, SambaNova, Snorkel AI, etc), along with others. Dave’s first AI investment, Lattice (bought by Apple’s Siri team) was seven years before the hype of generative AI. We asked, from a seasoned AI investor's perspective, where does AI hold the most promise? To answer this, Dave returns to the themes we've investigated over the last eight weeks — including AI trust and safety, which Google Health's Greg Corrado raised in the first episode. Together, we explore how AI will change how we work, the nature of jobs, and how an investing team with a culture focused on having more questions than answers is well positioned for AI’s future.Dave rounds out the discussion with a picture of how artificial intelligence, with real-life use cases, will move research lab theory to real-world practice. He also walks us through his hopes for AI, including a world where humans and computers exist as co-pilots.Ultimately, Dave shares an optimistic and rational view of AI's future. “AI has the potential to democratize the very creation of technology," he reflects. "With AI-assistance, folks across the country will no longer need to rely on software programmers to solve everyday digital problems – they’ll be able to create these tools themselves. That is incredibly exciting, and I'm honored to be a part of that journey."

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• S4E7: Google DeepMind’s Clément Farabet on AI Reasoning

  In this season of Theory and Practice, we explore newly emerging human-like artificial intelligence and robots — and how we can learn as much about ourselves, as humans, as we do about the machines we use. As we near the end of Season 4, we explore whether decision-making and judgment are still the final preserve of humans.Our guest for Episode 7 is Dr. Clément Farabet, VP of Research at Google DeepMind. For the past 15 years, Dr. Farabet’s work has been guided by a central mission: figuring out how to build AI systems that can learn on their own — and ultimately redefine how we write software. We discuss the conundrum in the Chinese Room Argument to explore whether computers can achieve artificial general intelligence. Dr. Farabet outlines four modules required for computers to demonstrate understanding. These modules include a predictive model of its environment that can create a representation of its world and an ability to store memories. He also points to the ability to perform reasoning about possible futures from its representation and memories. And finally, he explains how the ability to act in the world is key to illustrating understanding.Dr. Fabaret believes that we can build computers to become more human-like than most people may realize, but the overarching goal should be to build systems that improve human life.

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• S4E6: MIT’s James DiCarlo on Reverse-Engineering Human Sight with AI

  Season 4 of our Theory and Practice podcast investigates the powerful new world of AI applications and what it means to be human in the age of human-like artificial intelligence. Episode 6 explores what happens when AI is explicitly used to understand humans.In this episode, we're joined by James DiCarlo, the Peter de Florez Professor of Neuroscience at Massachusetts Institute of Technology and Director of the MIT Quest for Intelligence. Trained in biomedical engineering and medicine, Professor DiCarlo brings a technical mindset to understanding the machine-like processes in human brains. His focus is on the machinery that enables us to see. "Anything that our brain achieves is because there's a machine in there. It's not magic; there's some kind of machine running. So that means there is some machine that could emulate what we do. And our job is to figure out the details of that machine. So the problem is someday tractable. It's just a question of when."Professor DiCarlo unpacks how well convolutional neural networks (CNNs), a form of deep learning, mimic the human brain. These networks excel at finding patterns in images to recognize objects. One key difference with humans is that our vision feeds information into different areas of the brain and receives feedback. Professor DiCarlo argues that CNNs help him and his team understand how our brains gather vast amounts of data from a limited field of vision in a millisecond glimpse.Alex and Anthony also discuss the potential clinical applications of machine learning — from using an ECG to determine a person's biological age to understanding a person's cardiovascular health from retina images.

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• S4E5: Mapping the World of Smell to Broaden Diagnostics in Healthcare

  On Season 4 of Theory and Practice, Anthony Philippakis and Alex Wiltschko explore newly emerging human-like artificial intelligence and robots — and how we can learn as much about ourselves, as humans, as we do about the machines we use. The series has delved into many aspects of AI, from safety guardrails to empathic communication to robotic surgery and how computers can make decisions.In episode 5, we explore how machine learning helped create a map of odor and how that technology will train computers to smell. Anthony Philippakis visits Dr. Alex Wiltschko’s lab at Osmo, where scientists are dedicated to digitizing our sense of smell.

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• S4E4: Moravec’s Paradox and the Evolution of Surgical Robotics

  In Season 4 of the Theory and Practice podcast, we’ve been investigating the powerful new world of AI applications. We’ve explored how to build safety guardrails into AI-driven healthcare, what the future holds for empathetic AI communication, and how humans can control computers with imperceptible movements of their hands.For episode 4, we turn to surgical robots with the help of Dr. Catherine Mohr, President of the Intuitive Foundation, who played an integral role in developing the DaVinci surgical robot system. Before we explore the limits of robotic-assisted surgery, we discuss Moravec’s paradox: computers are good at things we find complicated, including complex calculations and handling large amounts of data, but not as good at perception and mobility tasks.This context explains why Dr. Mohr does not think that haptics, and the process of providing tactile feedback, is a breakthrough — humans have a very sophisticated tactile sense. She posits that we do not need to recapitulate evolution by having robots mimic human physicality. Instead, she asks, “What is the best technology I can use to solve that problem?” She believes a promising future for surgical robotics is to augment the surgeon’s hands: finding the cellular edges of a cancerous tumor by lighting up a nest of cells at its margins or helping the surgeon grasp a bleeding artery when the field is obscured by blood.Further down the line, she believes we will be able to move away from extensive surgery apart from trauma and move to maintenance surgery. For example, routinely doing “precision excision,” where tumors in their earliest form can be detected and removed at the cellular level, and “precision installment” — adding regenerative cells before organs and joints are damaged irrevocably.

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