Theory and Practice

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."

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.

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.

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.

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.

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 will delve into many aspects of AI: from communication to robotic surgery and decision-making.

In episode 3, we explore how humans will control computers in the future with Dr. Thomas Reardon. Dr. Reardon founded CTRL-Labs and is an early pioneer in exploring the relationship between humans and machines. Now at Meta’s Reality Labs, Dr. Reardon continues his work on non-invasive neural interfaces that detect activity in the human nervous system. 

Dr. Reardon explains how these systems encourage co-adaptation between man and machine. This human-computer interaction is core to understanding how human-like AI is changing humanity. 

Alex also describes the complexity of haptics — how computers try to relay the depth of human senses. He explains why understanding touch is essential for the future of human-like robotics.

On Season 4 of the Theory and Practice podcast, GV’s Anthony Philippakis and Osmo’s Alex Wiltschko explore being human in the age of AI. Guests this season dive into areas including AI communication, robotic surgery, and decision-making.

Episode 2 explores how machine learning evolved to where it is today. Anthony and Alex’s guest is Dr. Claire Cui, a computer scientist from Google DeepMind. They discuss the underlying architecture of LLMs, how self-supervising algorithms work, and the technological developments that have driven innovation. 

How do we empower the next generation of LLMs with greater deduction skills and efficiency? We explore a future where introspection is added to LLMs, and Dr. Cui gives broader context to our current thinking about AI’s vast potential.

On Season 4 of the Theory and Practice podcast, hosts Anthony Philippakis and Alex Wiltschko explore the many aspects of what it means to be human in the new era of artificial intelligence: from communication to robotic surgery and decision-making.

In episode 1, Dr. Greg Corrado, Distinguished Scientist and Head of Health AI at Google Health, explains how to responsibly introduce AI into healthcare. AI has proven itself in detecting diabetic eye disease, managing the risk of cardiovascular disease, and even encoding medical knowledge to answer patient queries, among many new and exciting applications.

Greg discusses safety concepts in AI: bias, robustness, transparency, explainability, and groundedness. He also discusses developing and maintaining datasets reflecting real-world patient realities and values. 

Following this conversation, Anthony and Alex discuss Brian Christian’s book “The Most Human Human.”

For millennia, humans have believed that aging is inevitable. Yet thirty years ago, the work of Professor Cynthia Kenyon and her colleagues showed that a single gene mutation in a worm doubled its lifespan and postponed the diseases of aging. Recent work on the naked mole rat, a mammal like us, has shown that risk of death need not increase with age.

In the final episode of this season of Theory and Practice, we explore the genetic, cellular, and molecular basis of aging with Professor Kenyon and ask what harnessing this knowledge means for the future of healthcare.

Theory and Practice is a presentation of GV and Google AI.

This season we'll dive deep into the languages of life through explorations of the "dark genome", genome editing, protein folding, the future of aging, and more.

Hosted by Anthony Philippakis (Venture Partner at GV) and Alex Wiltschko (Staff Research Scientist with Google AI), Theory and Practice opens the doors to the cutting edge of biology and computer science through conversations with leaders in the field.

What is a thought? Some may think that question is quite abstract, but it has huge implications for science and computer design.

If we cannot define a human thought, how can we know if a computer can think? Only then can true Artificial Intelligence be achieved.

This week we speak to the “godfather of deep learning”, Professor Geoffrey Hinton, a cognitive psychologist and computer scientist. He is now an emeritus Professor at the University of Toronto, and an engineering fellow at Google.

Our wide-ranging discussion reflects on Professor Hinton’s journey into this field, his instrumental role in the deep learning revolution, and an analysis of when, if ever, computers might achieve the next level of intelligence.

Theory and Practice is a presentation of GV and Google AI.

This season we'll dive deep into the languages of life through explorations of the "dark genome", genome editing, protein folding, the future of aging, and more.

Hosted by Anthony Philippakis (Venture Partner at GV) and Alex Wiltschko (Staff Research Scientist with Google AI), Theory and Practice opens the doors to the cutting edge of biology and computer science through conversations with leaders in the field.