The Future of Everything

Leanne Williams is an expert in depression. The first thing that she wants the world to know is that depression is not some sort of character flaw, but a real illness with symptoms that can impair one’s ability to function day to day. The past decade has seen remarkable advances, she says, as functional MRI has opened new avenues of understanding depression’s mechanisms and its treatments. These are hopeful times for the science of depression, Williams tells host Russ Altman on this episode of Stanford Engineering’s The Future of Everything podcast.

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Chapters:

(00:00:00) Introduction

Russ Altman introduces guest Leanne Williams, a professor of Psychiatry and Behavioral Science at Stanford University.

(00:02:13) What Is Depression?

Distinguishing clinical depression from everyday sadness.

(00:04:02) Current Depression Treatment Challenges

The trial-and-error of traditional depression treatments and their extended timelines.

(00:06:46) Brain Mapping and Circuit Dysfunctions

Advanced imaging techniques and their role in understanding depression.

(00:09:33) Diagnosing with Brain Imaging

How brain imaging can complement traditional diagnostic methods in psychiatry.

(00:10:52) Depression Biotypes

Identifying six distinct biotypes of depression through brain imaging

(00:14:41) Biotypes and Personalized Treatments

How biotypes allow for targeted therapies and improve treatment outcomes.

(00:19:33) AI in Depression Treatment

Using AI to refine biotypes and predict treatment outcomes with greater accuracy.

(00:22:45) Psychedelics in Depression Treatment

The potential for psychedelic drugs to target specific biotypes of depression.

(00:24:16) Expanding the Biotypes Framework

Integrating multimodal approaches into the biotype framework.

(00:27:59) Reducing Stigma in Depression

How showing patients their brain imaging results reduces self-blame and stigma.

(00:30:08) Conclusion

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Guest Michael Greicius is an authority on Alzheimer’s disease. He makes the case that while effective treatments have remained elusive, there are high hopes for new approaches that target tau proteins in the brain associated with the disease. In the meantime, to reduce Alzheimer’s risk stay active, eat well, and manage circulatory risks, but skip genetic testing for now until better treatments emerge, Greicius tells host Russ Altman on this episode of Stanford Engineering’s The Future of Everything podcast.

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• Stanford Profile:  Michael Greicius

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Chapters:

(00:00:00) Introduction

Russ Altman introduces guest Michael Greicius, a professor of neurology and neurological sciences at Stanford University.

(00:02:12) Understanding Alzheimer's Disease

The roles of amyloid and tau proteins in Alzheimer's disease.

(00:04:53) Challenges in Developing Treatments

The multiple hurdles in creating Alzheimer's therapies.

(00:09:07) Current Alzheimer's Drugs

The controversies and limitations of recently approved drugs.

(00:10:23) Amyloid Plaques and Their Impact

Why removing amyloid plaques hasn’t improved patient outcomes.

(00:14:29) Problems with Alzheimer's Trials

The disconnect between amyloid removal and patient outcomes.

(00:18:03) Functional Unblinding and Trial Bias

How functional unblinding affects trial results and drug evaluations.

(00:23:51) The Potential of Targeting Tau

New breakthroughs in targeting tau protein for Alzheimer’s.

(00:26:35) The Future of Prevention

Potential preemptive treatments for Alzheimer's and their administration.

(00:29:19) Lifestyle and Risk Reduction

Recommendations for reducing risk of Alzheimer’s disease.

(00:31:43) Conclusion

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Two-time guest Nate Persily is a professor of law and policy who studies the intersection of artificial intelligence and democracy. AI is creeping into democracy, he says, and 2024 saw its share of deepfakes and synthetic media, but with surprisingly little impact. His bigger concern is the opposite – politicians claiming the truth to be fake. It breeds distrust and, for democracy, that’s more pernicious, Persily tells host Russ Altman on this episode of Stanford Engineering’s The Future of Everything podcast.

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• Stanford Profile:  Nathaniel Persily

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Chapters:

(00:00:00) Introduction

Russ Altman introduces guest Nate Persily, a professor of law at Stanford University.

(00:02:49) How AI Impacts Democracy

AI's role in influencing elections and its implications for democratic systems.

(00:03:38) Deepfakes and the Election

The limited impact of deepfakes and emerging disinformation trends.

(00:04:37) Is Skepticism Beneficial?

The role of skepticism in navigating AI-driven disinformation.

(00:06:06) AI and Social Media

How AI powers social media and its implications for democratic dialogue.

(00:08:40) AI Monopoly and Democracy

Risks of concentrated AI power among tech giants.

(00:12:03) Risks of Open AI Models

Balancing innovation and misuse risks of open AI models.

(00:17:20) Regulating AI

Global efforts to regulate AI and lessons from the European AI Act.

(00:19:54) The Global AI Race

The international race to balance progress and safeguards with AI.

(00:23:35) Global South and AI

The digital divide and role of open models in under-resourced nations.

(00:25:05) AI in Legal Practice

Potential and challenges of AI in law, from drafting to adjudication.

(00:28:55) Conclusion

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The field of robotics has a long history at Stanford Engineering, and Professor Oussama Khatib has been a pioneering leader in that field, working on everything from human-interactive robots to underwater exploration, pushing the boundaries of what robots can do. Most recently, he’s led the opening of a new Robotics Center at Stanford. Today we’re bringing back the conversation we had with him about his work on OceanOneK — a humanoid robot who now has a new home in the Robotics Center. Join us as we talk about his journey, his vision for the future of robotics, and how his research is transforming the way humans interact with machines. We hope you enjoy the episode! 

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Episode Reference Links:

• Stanford Profile: Oussama Khatib
• Stanford Robotics Lab

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Chapters:

(00:00:00) Introduction

Russ Altman introduces guest Oussama Khatib, a professor of engineering at Stanford University.

(00:01:54) Underwater Robotics Advancements

Innovations in underwater robotics, including breakthroughs for deeper exploration.

(00:05:35) New Flotation Materials

The discovery of lightweight, strong flotation materials for deep-sea robots.

(00:06:25) Robot Battery Challenges

The challenges of powering robots at extreme depths.

(00:09:09) Importance of Anthropomorphic Design

Why humanoid features are essential for performing delicate underwater tasks.

(00:14:20) Robotic Design Challenges

The design of lightweight robotic arms that can withstand underwater pressure.

(00:19:51) Ease of Use for Operators

How both novices and experts can quickly adapt to controlling these robots.

(00:22:37) Applications in Biology and Archaeology

Future applications in marine biology and underwater archaeology.

(00:26:12) Search and Rescue Potential

The potential for robots to assist in search and rescue missions.

(00:27:48) Future of Deep-Sea Exploration

The future of deep-sea exploration using robotics.

(00:29:40) Conclusion

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Guest James Landay is an expert in human-centered artificial intelligence, a field all about optimizing technology for human and societal good. Landay says one of the most promising intersections is in education and AI, where the technology excels as a coaching and tutoring tool. His Smart Primer and Acorn apps use augmented reality and AI to engage children in outdoor, hands-on environmental science, and his GPT Coach is an AI-powered fitness planning tool. When it comes to AI and education, things are wide open and only just getting started, Landay tells host Russ Altman on this episode of Stanford Engineering’s The Future of Everything podcast.

Have a question for Russ? Send it our way in writing or via voice memo, and it might be featured on an upcoming episode. Please introduce yourself, let us know where you're listening from, and share your quest. You can send questions to [email protected].

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• Stanford Profile: James Landay
• Smart Primer

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Chapters:

(00:00:00) Introduction

Russ Altman introduces guest James Landay, a professor of computer science at Stanford University.

(00:02:04) Evolving AI Applications

How large language models can replicate personal coaching experiences.

(00:06:44) Role of Health Experts in AI

Integrating insights from medical professionals into AI coaching systems.

(00:10:21) Personalization in AI Coaching

How AI coaches can adapt personalities and avatars to cater to user preferences.

(00:12:51) Group Dynamics in AI Coaching

Pros and cons of adding social features and group support to AI coaching systems.

(00:14:08) Ambient Awareness in Technology

Ambient awareness and how it enhances user engagement without active attention.

(00:17:44) Using AI in Elementary Education

Narrative-driven tutoring systems to inspire kids' learning and creativity.

(00:22:59) Encouraging Student Writing with AI

Using LLMs to  motivate students to write  through personalized feedback.

(00:23:52) Scaling AI Educational Tools

The ACORN project and creating dynamic, scalable learning experiences.

(00:27:58) Human-Centered AI

The concept of human-centered AI and its focus on designing for society.

(00:30:34) Conclusion

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Climate change authority Noah Diffenbaugh says that the effects of climate change are no longer theoretical but apparent in everyday, tangible ways. Still, he says, it is not too late to better understand the effects of climate change, to mitigate them through reductions in greenhouse gas emissions and other measures, and to adapt how we live in the face of a warmer planet. Society is falling behind in its ability to deal with increasingly extreme climate events but solutions are not out of reach, Diffenbaugh tells host Russ Altman on this episode of Stanford Engineering’s The Future of Everything podcast.

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• Stanford Profile: Noah Diffenbaugh

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Chapters:

(00:00:00) Introduction

Russ Altman introduces guest Noah Diffenbaugh, a professor of Earth System Science at Stanford University.

(00:02:34) Global Impact of Climate Change

The major areas where climate change is having the greatest impact globally.

(00:03:27) Climate Phenomena and Humans

Connecting climate science with localized human impacts

(00:06:16) Understanding Climate Forcing

The concept of "climate forcing" and its significance in Noah’s research.

(00:10:00) Geoengineering and Climate Interventions

The potential and risks of intentional climate interventions.

(00:21:18) Adaptation to Climate Change

How humans are adapting to climate change and why we might be falling behind.

(00:25:19) Increase in Extreme Events

Why extreme climate events are becoming exponentially more frequent and severe.

(00:28:34) AI in Climate Research

How AI is revolutionizing climate research by enabling predictive capabilities.

(00:32:26) Conclusion

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Climate modeler Aditi Sheshadri says that while weather forecasting and climate projection are based on similar science, they are very different disciplines. Forecasting is about looking at next week, while projection is about looking at the next century. Sheshadri tells host Russ Altman how new data and techniques, like low-cost high-altitude balloons and AI, are reshaping the future of climate projection on this episode of Stanford Engineering’s The Future of Everything podcast.

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• Stanford Profile: Aditi Sheshadri

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Chapters:

(00:00:00) Introduction

Russ Altman introduces guest Aditi Sheshadri, a professor of Earth systems science at Stanford University.

(00:02:58) Climate Projection vs. Weather Forecasting

The differences between climate projection and weather forecasting.

(00:04:58) The Window of Chaos

The concept of the "window of chaos" in climate modeling.

(00:06:11) Scale of Climate Models

The limitations and scale of climate model boxes.

(00:08:19) Computational Constraints

Computational limitations on grid size and time steps in climate modeling.

(00:10:56) Parameters in Climate Modeling

Essential parameters measured, such as density, temperature, and water vapor.

(00:12:18) Oceans in Climate Models

The role of oceans in climate modeling and their integration into projections.

(00:14:35) Atmospheric Gravity Waves

Atmospheric gravity waves and their impact on weather patterns.

(00:18:51) Polar Vortex and Cyclones

Research on the polar vortex and on tropical cyclone frequency.

(00:21:53) Climate Research and Public Awareness

Communicating climate model findings to relevant audiences.

(00:23:33) New Data Sources

How unexpected data from a Google project aids climate research,

(00:25:09) Geoengineering Considerations

Geoengineering and the need for thorough modeling before intervention.

(00:28:19) Conclusion

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Halloween may be behind us in the US but here at The Future of Everything we’re not quite done with spooky season. If you’re pairing your trick-or-treat haul with some scary movies, we invite you to revisit with us a conversation Russ had with Lawrence Wein a couple years ago about the work he’s doing in forensic genetic genealogy to crack cold cases. Professor Wein shares how he’s using math to catch criminals through traces of their DNA. It’s both haunting and hopeful, and we hope you’ll take another listen.

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Episode Reference Links:

• Stanford Profile: Lawrence M. Wein
• Lawrence’s Paper: Analysis Of The Genealogy Process In Forensic Genetic Genealogy

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Chapters:

(00:00:00) Introduction

Russ Altman introduces guest Lawrence Wein, professor of management science at Stanford University.

(00:02:18) Forensic Genealogy Explained

Forensic genetic genealogy and its impact on solving unsolved crimes.

(00:04:31) Third-Party Databases in Genealogy

Insight into databases that allow law enforcement to search for criminal suspects.

(00:08:23) Math Models in Genealogy

Using mathematical models to streamline genealogy work.

(00:11:31) Components of the Genealogy Algorithm

The algorithm's methods, including ascending and descending family trees.

(00:14:12) Algorithm Efficiency and Comparison

Comparing the new algorithm's effectiveness to traditional genealogy strategies.

(00:16:53) Algorithm in Practice

Role of human input alongside the mathematical algorithm in genealogy cases.

(00:20:42) Role of Genealogists

Genealogists’ insights on balancing human skill and mathematical algorithms.

(00:22:45) DNA Databases and Ethics

The ethical and privacy concerns related to using genetic data.

(00:27:01) Background and Interest in Forensic Genealogy

Lawrence’s journey from operations management to forensic genealogy.

(00:30:16) Conclusion

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Returning guest Marco Pavone is an expert in autonomous robotic systems, such as self-driving cars and autonomous space robots. He says that there have been major advances since his last appearance on the show seven years ago, mostly driven by leaps in artificial intelligence. He tells host Russ Altman all about the challenges and progress of autonomy on Earth and in space in this episode of Stanford Engineering’s The Future of Everything podcast.

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• Stanford Profile:  Marco Pavone
• Center for AEroSpace Autonomy Research (CAESAR)

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Chapters:

(00:00:00) Introduction

Russ Altman introduces guest Marco Pavone, a professor of aeronautics and astronautics at Stanford.

(00:02:37) Autonomous Systems in Everyday Life

Advancements in the real-world applications of autonomous systems.

(00:03:51) Evolution of Self-Driving Technologies

The shift from fully autonomous cars to advanced driver assistance systems.

(00:06:36) Public Perception of Autonomous Vehicles

How people react to and accept autonomous vehicles in everyday life.

(00:07:49) AI’s and Autonomous Driving

The impact of AI advancements on autonomous driving performance.

(00:09:52) Simulating Edge Cases for Safety

Using AI to simulate rare driving events to improve safety and training.

(00:12:04) Autonomous Vehicle Communication

Communication challenges between autonomous vehicles and infrastructure.

(00:15:24) Risk-Averse Planning in Autonomous Systems

How risk-averse planning ensures safety in autonomous vehicles.

(00:18:43) Autonomous Systems in Space

The role of autonomous robots in space exploration and lunar missions.

(00:22:47) Space Debris and Collision Avoidance

The challenges of space debris and collision avoidance with autonomous systems.

(00:24:39) Distributed Autonomous Systems for Space

Using distributed autonomous systems in space missions for better coordination.

(00:28:40) Conclusion

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Physicist Matthias Kling studies photons and the things science can do with ultrafast pulses of X-rays. These pulses last just attoseconds – a billionth of a billionth of a second, Kling says. He uses them to create slo-mo “movies” of electrons moving through materials like those used in batteries and solar cells. The gained knowledge could reshape fields like materials science, ultrafast and quantum computers, AI, and medical diagnostics, Kling tells host Russ Altman on this episode of Stanford Engineering’s The Future of Everything podcast.

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• SStanford Profile: Matthias Kling
• Matthias’ Lab: SLAC National Accelerator Laboratory

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Chapters:

(00:00:00) Introduction

Russ Altman introduces guest Matthias Kling, a professor of photon science and applied physics at Stanford University.

(00:02:52) Ultrafast Electronics Overview

The technologies enabling ultrafast photonics and electronic advancements.

(00:05:32) Attosecond Science Applications

Capturing electron and molecular movements with attosecond pulses.

(00:09:31) Photoelectric Effect Insights

Attosecond science’s impact on understanding the photoelectric effect and quantum mechanics.

(00:13:27) Real-Time Molecular Measurements

Using light waves to capture images of molecules at room temperature.

(00:19:32) Future of Ultrafast Electronics

How attosecond light pulses could revolutionize computing with petahertz speed.

(00:23:28) Energy-Efficient Quantum Computing

Potential for room-temperature quantum computers using light wave electronics.

(00:26:33) AI and Machine Learning in Science

AI's role in optimizing research and data collection in ultrafast electronics.

(00:28:51) Real-Time AI Data Analysis

Machine learning enables real-time analysis of massive experimental data.

(00:32:15) Conclusion

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