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.

Episode Reference Links:

• 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.

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.

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: 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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