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.

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Episode Reference Links:

• 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.

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 thefutureofeverything@stanford.edu.

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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.

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 thefutureofeverything@stanford.edu.

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• 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.

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 thefutureofeverything@stanford.edu.

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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.

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 thefutureofeverything@stanford.edu.

Episode Reference Links:

• 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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We’re just weeks away from a national election, and in our polarized society, we all know it can be difficult to find and create spaces for thoughtful policy discussions. A couple of years ago, James Fishkin, a professor of communication at Stanford, joined the podcast. He talked about a system called deliberative polling that can serve as a model for structuring small group discussions to help bring people together and bridge differences in conversations about some of the most politically fraught issues. It’s an opportune time to bring this conversation back for another listen and we hope our discussion helps as you go about your conversations through this political season and beyond.

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 thefutureofeverything@stanford.edu.

Episode Reference Links:

• Stanford Profile: James Fishkin
• James’ Lab: Deliberative Democracy Lab

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Chapters:

(00:00:00) Introduction

Russ Altman introduces guest James Fishkin, a professor of communication at Stanford.

(00:01:31) What is Deliberative Democracy?

The concept of deliberative democracy and how it addresses political divides.

(00:03:47) Managing Balance in Deliberation

The importance of balanced group discussions and strategies for avoiding conflict.

(00:04:55) Scaling Deliberation for Large Groups

The logistics and technology behind scaling to larger groups, both online and in person.

(00:06:54) Deciding Which Questions to Address

How tough issues are selected for deliberation in different locations.

(00:10:54) The Human Element in Deliberation

The surprising effectiveness of online platforms for fostering connection.

(00:13:13) Automated Moderators in Deliberation

The development and success of automated moderators in online deliberations.

(00:19:20) Applying Deliberative Democracy to the U.S.

Whether deliberative polling could help address political deadlock in the U.S.

(00:25:30) The Future of Deliberative Polling

The future possibilities of scaling deliberative polling to larger populations.

(00:27:23) Conclusion

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Astronautics professor Grace Gao is an authority on the Global Positioning System. GPS has long been key to navigation on Earth, she says, but science is now shifting its focus outward to the frontiers of space. Gao is working on a GPS-like system for the Moon. To keep costs low, this lunar positioning system will leverage Earth-based satellites complemented by a network of smaller satellites in lunar orbit. It could lead to autonomous vehicles on the moon and a new era of lunar exploration, Gao 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 thefutureofeverything@stanford.edu.

Episode Reference Links:

• Stanford Profile: Grace Gao
• Grace’s Labe:

Stanford NAV Lab

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Chapters:

(00:00:00) Introduction

Russ Altman introduces Grace Gao, a professor of aeronautics and astronautics at Stanford University.

(00:02:15) GNSS vs. GPS

The difference between GPS and GNSS, and the different global navigation systems.

(00:03:09) How Does GPS Work?

GPS operation, including the role of satellites, ground monitoring stations, and user receivers.

(00:04:07) GPS Signal and Satellites

How GPS uses multiple satellites and how the different global systems collaborate to improve accuracy.

(00:05:23) GPS Challenges in Cities

Issues with GPS in urban environments and the importance of reliability and safety.

(00:07:53) Improving GPS Accuracy

Multimodal sensor fusion helps enhance GPS accuracy in challenging environments.

(00:10:11) Collaboration Among Autonomous Vehicles

The potential for autonomous vehicles to share information for better navigation and safety.

(00:14:07) GPS Safety and Signal Jamming

GPS safety concerns and real-world signal disruption examples.

(00:18:56) GPS in Space Travel

How GNSS and Earth-based GPS systems can support space missions.

(00:25:05) Designing Lunar GPS

The cost and coverage challenges of creating a lunar navigation system.

(00:27:13) Autonomous Moon Rovers

NASA’s plans for collaborative autonomous rovers on the Moon.

(00:30:42) Conclusion

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