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Bio: John Markoff is HAI’s Journalist-in-Residence. He is also a research affiliate at the Center for Advanced Study in the Behavioral Sciences or CASBS, participating in projects focusing on the future of work and artificial intelligence. He is currently researching a biography of Stewart Brand, the creator of the Whole Earth Catalog. Previously he was a Berggruen Fellow at CASBS. He has also been a staff historian at the Computer History Museum in Mountain View, Calif. Until 2017, he was a reporter at The New York Times, beginning in March 1988 as the paper’s national computer writer. Prior to joining the Times, he worked for the San Francisco Examiner. He has written about technology for Pacific News Service. He was a reporter at Infoworld and West Coast editor for Byte Magazine and wrote a column on personal computers for the San Jose Mercury. He has also been a lecturer at the University of California at Berkeley School of Journalism and an adjunct faculty member of the Stanford Graduate Program on Journalism. In 2013 he was awarded a Pulitzer Prize in explanatory reporting as part of a New York Times project on labor and automation. In 2007, he was named a fellow of the Society of Professional Journalists, the organization’s highest honor. In June of 2010, the New York Times presented him with the Nathaniel Nash Award, which is given annually for foreign and business reporting. He is the co-author of The High Cost of High Tech, published by Harper & Row. He co-wrote Cyberpunk: Outlaws and Hackers on the Computer Frontier published Simon & Schuster. Hyperion published Takedown: The Pursuit and Capture of America's Most Wanted Computer Outlaw, which he co-authored with Tsutomu Shimomura. What the Dormouse Said: How the Sixties Counterculture Shaped the Personal Computer Industry, was published by Viking Books. Machines of Loving Grace: The Quest for Common Ground Between Humans and Robots, was published by HarperCollins Ecco. Markoff grew up in Palo Alto, California, and graduated from Whitman College, Walla Walla, Washington. He attended graduate school at the University of Oregon and received a masters degree in sociology.
Bio: John Markoff is HAI’s Journalist-in-Residence. He is also a research affiliate at the Center for Advanced Study in the Behavioral Sciences or CASBS, participating in projects focusing on the future of work and artificial intelligence. He is currently researching a biography of Stewart Brand, the creator of the Whole Earth Catalog. Previously he was a Berggruen Fellow at CASBS. He has also been a staff historian at the Computer History Museum in Mountain View, Calif. Until 2017, he was a reporter at The New York Times, beginning in March 1988 as the paper’s national computer writer. Prior to joining the Times, he worked for the San Francisco Examiner. He has written about technology for Pacific News Service. He was a reporter at Infoworld and West Coast editor for Byte Magazine and wrote a column on personal computers for the San Jose Mercury. He has also been a lecturer at the University of California at Berkeley School of Journalism and an adjunct faculty member of the Stanford Graduate Program on Journalism. In 2013 he was awarded a Pulitzer Prize in explanatory reporting as part of a New York Times project on labor and automation. In 2007, he was named a fellow of the Society of Professional Journalists, the organization’s highest honor. In June of 2010, the New York Times presented him with the Nathaniel Nash Award, which is given annually for foreign and business reporting. He is the co-author of The High Cost of High Tech, published by Harper & Row. He co-wrote Cyberpunk: Outlaws and Hackers on the Computer Frontier published Simon & Schuster. Hyperion published Takedown: The Pursuit and Capture of America's Most Wanted Computer Outlaw, which he co-authored with Tsutomu Shimomura. What the Dormouse Said: How the Sixties Counterculture Shaped the Personal Computer Industry, was published by Viking Books. Machines of Loving Grace: The Quest for Common Ground Between Humans and Robots, was published by HarperCollins Ecco. Markoff grew up in Palo Alto, California, and graduated from Whitman College, Walla Walla, Washington. He attended graduate school at the University of Oregon and received a masters degree in sociology.
Bio: Marietje Schaake is an International Policy Fellow at the Stanford Institute for Human-Centered Artificial Intelligence (HAI) and the International Policy Director of the Cyber Policy Center, where she conducts policy-relevant research focused on cyber policy recommendations for industry and government. In addition to her own research, she represents the center to governments, NGOs, and the technology industry. Schaake also teaches courses on cyber policy from an international perspective, and brings to Stanford leaders from around the world to discuss cyber policy. Prior to joining Stanford, Marietje Schaake led an active career in politics and civic service. She was a representative of the Dutch Democratic Party and the Alliance of Liberals and Democrats for Europe (ALDE) in European Parliament where she was first elected in 2009. In European Parliament, Schaake focused on trade, foreign policy and technology, and as a member of the Global Commission on the Stability of Cyberspace, and founder of the European Parliament Intergroup on the European Digital Agenda, Schaake develops solutions to strengthen the rule of law online, including initiating the net neutrality law now in effect throughout Europe.
Bio: Marietje Schaake is an International Policy Fellow at the Stanford Institute for Human-Centered Artificial Intelligence (HAI) and the International Policy Director of the Cyber Policy Center, where she conducts policy-relevant research focused on cyber policy recommendations for industry and government. In addition to her own research, she represents the center to governments, NGOs, and the technology industry. Schaake also teaches courses on cyber policy from an international perspective, and brings to Stanford leaders from around the world to discuss cyber policy. Prior to joining Stanford, Marietje Schaake led an active career in politics and civic service. She was a representative of the Dutch Democratic Party and the Alliance of Liberals and Democrats for Europe (ALDE) in European Parliament where she was first elected in 2009. In European Parliament, Schaake focused on trade, foreign policy and technology, and as a member of the Global Commission on the Stability of Cyberspace, and founder of the European Parliament Intergroup on the European Digital Agenda, Schaake develops solutions to strengthen the rule of law online, including initiating the net neutrality law now in effect throughout Europe.
Abstract: New developments in Artificial Intelligence, particularly deep learning and other forms of “second-wave” AI, are attracting enormous public attention. Both triumphalists and doomsayers are predicting that human-level AI may be “just around the corner.” To assess whether that prediction is true, we need a broad understanding of intelligence, in terms of which to assess: (i) what kinds of intelligence machines currently have, and will likely have in the future; and (ii) what kinds of intelligence people currently have, and may be capable of in the future. As the first step in this direction, I distinguish two kinds of intelligence: (i) “reckoning,” the kind of calculative rationality that computers excel at, including both first- and second-wave AI; and (ii) “judgment,” a form of dispassionate, deliberative thought, grounded in ethical commitment and responsible action, that is appropriate to the situation in which it is deployed. AI will develop world-changing reckoning systems, I argue, but nothing in AI as currently conceived approaches what is required to build a system capable of judgment.
Bio: Brian Cantwell Smith is Reid Hoffman Professor of Artificial Intelligence and the Human at the University of Toronto, where he is also Professor of Information, Philosophy, Cognitive Science, and the History and Philosophy of Science and Technology, as well as being a Senior Fellow at Massey College. Smith’s research focuses on the philosophical foundations of computation, artificial intelligence, and mind, and on fundamental issues in metaphysics and epistemology. In the 1980s he developed the world’s first reflective programming language (3Lisp). He is the author of *On the Origin of Objects* (MIT Press, 1996), and of *On the Promise of Artificial Intelligence: Reckoning and Judgment* (MIT Press, 2019).
Abstract: New developments in Artificial Intelligence, particularly deep learning and other forms of “second-wave” AI, are attracting enormous public attention. Both triumphalists and doomsayers are predicting that human-level AI may be “just around the corner.” To assess whether that prediction is true, we need a broad understanding of intelligence, in terms of which to assess: (i) what kinds of intelligence machines currently have, and will likely have in the future; and (ii) what kinds of intelligence people currently have, and may be capable of in the future. As the first step in this direction, I distinguish two kinds of intelligence: (i) “reckoning,” the kind of calculative rationality that computers excel at, including both first- and second-wave AI; and (ii) “judgment,” a form of dispassionate, deliberative thought, grounded in ethical commitment and responsible action, that is appropriate to the situation in which it is deployed. AI will develop world-changing reckoning systems, I argue, but nothing in AI as currently conceived approaches what is required to build a system capable of judgment.
Bio: Brian Cantwell Smith is Reid Hoffman Professor of Artificial Intelligence and the Human at the University of Toronto, where he is also Professor of Information, Philosophy, Cognitive Science, and the History and Philosophy of Science and Technology, as well as being a Senior Fellow at Massey College. Smith’s research focuses on the philosophical foundations of computation, artificial intelligence, and mind, and on fundamental issues in metaphysics and epistemology. In the 1980s he developed the world’s first reflective programming language (3Lisp). He is the author of *On the Origin of Objects* (MIT Press, 1996), and of *On the Promise of Artificial Intelligence: Reckoning and Judgment* (MIT Press, 2019).
Rahul Panicker, Chief Innovation Officer, Wadhwani AI
Abstract: From our experience developing and deploying AI-for-social-good solutions to help healthcare workers in villages of developing countries weigh newborns using just a smartphone, cotton farmers fight pest attacks, and tuberculosis-control programs find and support TB patients, and advising organizations like the WHO, UN ITU, and governments on AI, I will share some lessons learned, and opportunities for AI to have large-scale impact across domains like health, agriculture, education, and financial inclusion. Such impact will require novel approaches across algorithms, human factors, regulatory frameworks, and systems thinking. AI-for-social-good also offers a rich source of problems for AI, spanning computer vision, weakly-supervised learning, causal reasoning, domain adaptation, uncertainty calibration, explainability, computing on low-resource devices, and privacy-preserving learning. The Wadhwani Institute for Artificial Intelligence is an independent nonprofit research institute that develops and deploys AI-for-social-good solutions in the developing world. Bio: Dr. Rahul Panicker, as Chief Innovation Officer, heads research at the Wadhwani Institute for Artificial Intelligence. Prior to this, he was co-founder of Embrace, a for-profit social enterprise that has helped over 500,000 babies worldwide through low-cost incubators that work without electricity. He is an MIT TR35 awardee, World Economic Forum Social Entrepreneur of the Year, Industrial Design Society of America Gold winner, and an Echoing Green Fellow. He holds an MS/PhD in EE from Stanford University, is an alumnus of the Stanford d.school, and has a B.Tech from IIT Madras.Rahul Panicker, Chief Innovation Officer, Wadhwani AI
Abstract: From our experience developing and deploying AI-for-social-good solutions to help healthcare workers in villages of developing countries weigh newborns using just a smartphone, cotton farmers fight pest attacks, and tuberculosis-control programs find and support TB patients, and advising organizations like the WHO, UN ITU, and governments on AI, I will share some lessons learned, and opportunities for AI to have large-scale impact across domains like health, agriculture, education, and financial inclusion. Such impact will require novel approaches across algorithms, human factors, regulatory frameworks, and systems thinking. AI-for-social-good also offers a rich source of problems for AI, spanning computer vision, weakly-supervised learning, causal reasoning, domain adaptation, uncertainty calibration, explainability, computing on low-resource devices, and privacy-preserving learning. The Wadhwani Institute for Artificial Intelligence is an independent nonprofit research institute that develops and deploys AI-for-social-good solutions in the developing world. Bio: Dr. Rahul Panicker, as Chief Innovation Officer, heads research at the Wadhwani Institute for Artificial Intelligence. Prior to this, he was co-founder of Embrace, a for-profit social enterprise that has helped over 500,000 babies worldwide through low-cost incubators that work without electricity. He is an MIT TR35 awardee, World Economic Forum Social Entrepreneur of the Year, Industrial Design Society of America Gold winner, and an Echoing Green Fellow. He holds an MS/PhD in EE from Stanford University, is an alumnus of the Stanford d.school, and has a B.Tech from IIT Madras.Abstract: The biggest challenge with the democratization of content is how to make sense of the scale. In the last decade, curation of content has consolidated into the hands of a few of the largest technology companies. Today, that curation takes the form of machine learning — often dubbed algorithms by the media. Thomas helped build and introduce the most controversial algorithms of Instagram: non-chronological feed and personalized recommendations. He will discuss challenges from the perspective of an engineer in the control room as Instagram scaled to serve over a billion people. Thomas will share a few of his thoughts about future directions as we start to form a dialogue about the responsibilities of platforms operating on a global scale.
Bio: Thomas Dimson is the original author of “The Algorithm” — the recommender systems behind Instagram's feed, stories and discovery surfaces. He joined Instagram as one of its first 50 employees in 2013, working for seven years as a principal engineer and eventually an engineering director. In that time, he also invented products such as the stories polling sticker, Hyperlapse, and engineering and was named one of the top ten most creative people in business by Fast Company. Thomas graduated from the University of Waterloo with a bachelor's of mathematics and received his master's in computer science from Stanford with a specialization in artificial intelligence.
Abstract: The biggest challenge with the democratization of content is how to make sense of the scale. In the last decade, curation of content has consolidated into the hands of a few of the largest technology companies. Today, that curation takes the form of machine learning — often dubbed algorithms by the media. Thomas helped build and introduce the most controversial algorithms of Instagram: non-chronological feed and personalized recommendations. He will discuss challenges from the perspective of an engineer in the control room as Instagram scaled to serve over a billion people. Thomas will share a few of his thoughts about future directions as we start to form a dialogue about the responsibilities of platforms operating on a global scale.
Bio: Thomas Dimson is the original author of “The Algorithm” — the recommender systems behind Instagram's feed, stories and discovery surfaces. He joined Instagram as one of its first 50 employees in 2013, working for seven years as a principal engineer and eventually an engineering director. In that time, he also invented products such as the stories polling sticker, Hyperlapse, and engineering and was named one of the top ten most creative people in business by Fast Company. Thomas graduated from the University of Waterloo with a bachelor's of mathematics and received his master's in computer science from Stanford with a specialization in artificial intelligence.
Workshop Leader: Emilie Silva
A one-day interdisciplinary workshop involving Stanford faculty and researchers, a select number of outside academics from other institutions, and a small number of private sector and governmental analysts to focus on the intersection of AI and various aspects of international security. The goal was to identify concrete research agendas and synergies, identify gaps in our understanding, and build a network of scholars and experts to address these challenges.
The past several years have seen startling advances in artificial intelligence and machine learning,
driven in part by advances in deep neural networks.3 AI-enabled machines can now meet or exceed
human abilities in a wide range of tasks, including chess, Jeopardy, Go, poker, object recognition,
and driving in some settings. AI systems are being applied to solve a range of problems in
transportation, finance, stock trading, health care, intelligence analysis, and cybersecurity. Despite
calls from prominent scientists to avoid militarizing AI,4 nation-states are certain to use AI and
machine learning tools for national security purposes.
A technology that has the potential for such sweeping changes across human society should be
evaluated for its potential effects on international stability. Many national security applications of
AI could be beneficial, such as advanced cyber defenses that can identify new malware, automated
computer security tools to find and patch vulnerabilities, or machine learning systems to uncover
suspicious behavior by terrorists. Current AI systems have substantial limitations and
vulnerabilities, however, and a headlong rush into national security applications of artificial
intelligence could pose risks to international stability. Some security related applications of AI
could be destabilizing, and competitive dynamics between nations could lead to harmful
consequences such as a “race to the bottom” on AI safety. Other security related applications of AI
could improve international stability.
CNAS is undertaking a two-year, in-depth, interdisciplinary project to examine how artificial
intelligence will influence international security and stability. It is critical for global stability to
begin to a discussion about ways to mitigate the risks while taking advantage of the benefits of
autonomous systems and artificial intelligence. This project will build a community from three
sectors of academia, business, and the policy world that often do not intersect – AI researchers in
academia and business; international security academic experts; and policy practitioners in the
government, both civilian and military. Through a series of workshops, commissioned papers, and
reports, this project will foster a community of practice and begin laying the foundations for a field
of study on AI and international security. The project will conclude with recommendations to
policymakers for ways to capitalize on the potential stabilizing benefits of artificial intelligence,
while avoiding uses that could undermine stability.
Workshop Leader: Emilie Silva
A one-day interdisciplinary workshop involving Stanford faculty and researchers, a select number of outside academics from other institutions, and a small number of private sector and governmental analysts to focus on the intersection of AI and various aspects of international security. The goal was to identify concrete research agendas and synergies, identify gaps in our understanding, and build a network of scholars and experts to address these challenges.
The past several years have seen startling advances in artificial intelligence and machine learning,
driven in part by advances in deep neural networks.3 AI-enabled machines can now meet or exceed
human abilities in a wide range of tasks, including chess, Jeopardy, Go, poker, object recognition,
and driving in some settings. AI systems are being applied to solve a range of problems in
transportation, finance, stock trading, health care, intelligence analysis, and cybersecurity. Despite
calls from prominent scientists to avoid militarizing AI,4 nation-states are certain to use AI and
machine learning tools for national security purposes.
A technology that has the potential for such sweeping changes across human society should be
evaluated for its potential effects on international stability. Many national security applications of
AI could be beneficial, such as advanced cyber defenses that can identify new malware, automated
computer security tools to find and patch vulnerabilities, or machine learning systems to uncover
suspicious behavior by terrorists. Current AI systems have substantial limitations and
vulnerabilities, however, and a headlong rush into national security applications of artificial
intelligence could pose risks to international stability. Some security related applications of AI
could be destabilizing, and competitive dynamics between nations could lead to harmful
consequences such as a “race to the bottom” on AI safety. Other security related applications of AI
could improve international stability.
CNAS is undertaking a two-year, in-depth, interdisciplinary project to examine how artificial
intelligence will influence international security and stability. It is critical for global stability to
begin to a discussion about ways to mitigate the risks while taking advantage of the benefits of
autonomous systems and artificial intelligence. This project will build a community from three
sectors of academia, business, and the policy world that often do not intersect – AI researchers in
academia and business; international security academic experts; and policy practitioners in the
government, both civilian and military. Through a series of workshops, commissioned papers, and
reports, this project will foster a community of practice and begin laying the foundations for a field
of study on AI and international security. The project will conclude with recommendations to
policymakers for ways to capitalize on the potential stabilizing benefits of artificial intelligence,
while avoiding uses that could undermine stability.
This talk develops the proposal that a central – and neglected – ethical challenge for the field of AI is demystification of the techniques and technologies that constitute it
This talk develops the proposal that a central – and neglected – ethical challenge for the field of AI is demystification of the techniques and technologies that constitute it
Join California Supreme Court Justice Cuéllar, who teaches the popular “Regulating AI” course at Stanford, Dan Ho, Associate Director of HAI and professor at the law school and political science, and Terah Lyons, the Founding Executive Director of the Partnership on AI, for a conversation on the law, regulation, and governance of AI! The three will provide a range of perspectives on the promise, challenges, and directions for AI governance.
Join California Supreme Court Justice Cuéllar, who teaches the popular “Regulating AI” course at Stanford, Dan Ho, Associate Director of HAI and professor at the law school and political science, and Terah Lyons, the Founding Executive Director of the Partnership on AI, for a conversation on the law, regulation, and governance of AI! The three will provide a range of perspectives on the promise, challenges, and directions for AI governance.
AI for Healthcare session will feature Marzyeh Ghassemi who targets “Healthy ML” focusing on creating and applying machine learning to understand and improve health. Improving health requires targeting and evidence. Marzyeh tackles part of this puzzle with machine learning. This session will cover some of the novel technical opportunities for machine learning in health challenges and the important progress to be made with a careful application to domain. She will also walk through the danger of applying methods without a robust understanding of the domain, and potential downstream uses.
AI for Healthcare session will feature Marzyeh Ghassemi who targets “Healthy ML” focusing on creating and applying machine learning to understand and improve health. Improving health requires targeting and evidence. Marzyeh tackles part of this puzzle with machine learning. This session will cover some of the novel technical opportunities for machine learning in health challenges and the important progress to be made with a careful application to domain. She will also walk through the danger of applying methods without a robust understanding of the domain, and potential downstream uses.
Recent advances of artificial intelligence and deep learning have been undoubtedly driven by a large amount of data amassed over the years, helping firms, researchers, and practitioners achieve many amazing feats, most notably in recognition tasks often surpassing human ability in several benchmarks.
Recent advances of artificial intelligence and deep learning have been undoubtedly driven by a large amount of data amassed over the years, helping firms, researchers, and practitioners achieve many amazing feats, most notably in recognition tasks often surpassing human ability in several benchmarks.
AI promises to transform how government agencies work. Where will it have the biggest impact? What are some challenges around transparency, privacy, bias, and accountability? This talk will go beyond the headlines and share highlights of a just-completed report on AI in the US Government.
AI promises to transform how government agencies work. Where will it have the biggest impact? What are some challenges around transparency, privacy, bias, and accountability? This talk will go beyond the headlines and share highlights of a just-completed report on AI in the US Government.
How can AI and machine learning be leveraged to mitigate the impact of human activities on earth’s natural systems? Learn about data science tools and strategies being used to safeguard our water supply, feed the worldwide human population, and promote greater biodiversity and global sustainability.
How can AI and machine learning be leveraged to mitigate the impact of human activities on earth’s natural systems? Learn about data science tools and strategies being used to safeguard our water supply, feed the worldwide human population, and promote greater biodiversity and global sustainability.
Bias in government automated decision systems, the future of farmwork, digital literacy, algorithms in bail decisions, and more.
Bias in government automated decision systems, the future of farmwork, digital literacy, algorithms in bail decisions, and more.
The content shared on social media is among the largest data sets on human behavior in history. I leverage this data to address questions in the psychological sciences. Specifically, I apply natural language processing and machine learning to characterize and measure psychological phenomena with a focus on mental and physical health.
The content shared on social media is among the largest data sets on human behavior in history. I leverage this data to address questions in the psychological sciences. Specifically, I apply natural language processing and machine learning to characterize and measure psychological phenomena with a focus on mental and physical health.
Mykel Kochenderfer, Assistant Professor of Aeronautics and Astronautics and Assistant Professor, by courtesy, of Computer Science at Stanford University
Mykel is Assistant Professor of Aeronautics and Astronautics and Assistant Professor, by courtesy, of Computer Science at Stanford University. He is the director of the Stanford Intelligent Systems Laboratory (SISL), conducting research on advanced algorithms and analytical methods for the design of robust decision making systems. Of particular interest are systems for air traffic control, unmanned aircraft, and automated driving where decisions must be made in uncertain, dynamic environments while maintaining safety and efficiency. Research at SISL focuses on efficient computational methods for deriving optimal decision strategies from high-dimensional, probabilistic problem representations. Prior to joining the faculty in 2013, he was at MIT Lincoln Laboratory where he worked on airspace modeling and aircraft collision avoidance. He received his Ph.D. from the University of Edinburgh in 2006 where he studied at the Institute of Perception, Action and Behaviour in the School of Informatics. He received B.S. and M.S. degrees in computer science from Stanford University in 2003. Prof. Kochenderfer is the director SAIL-Toyota Center for AI Research and a co-director of the Center for AI Safety. He is affiliated with the Stanford Artificial Intelligence Laboratory (SAIL), the Human-Centered AI (HAI) Institute, the Symbolic Systems Program, the Bio-X Institute, Wu Tsai Neurosciences Institute, and the Center for Automotive Research at Stanford (CARS). In 2017, he was awarded the DARPA Young Faculty Award. He is an associate editor of the Journal of Artificial Intelligence Research and the Journal of Aerospace Information Systems. He is an author of the textbooks Decision Making under Uncertainty: Theory and Application (MIT Press, 2015) and Algorithms for Optimization (MIT Press, 2019). He is a third-generation pilot.Bryan Casey, Legal Fellow at the Center for Automotive Research at Stanford University Bryan Casey is a Legal Fellow at the Center for Automotive Research at Stanford, a Lecturer at Stanford Law School, and an affiliate scholar at the Stanford Machine Leaning Group, CodeX: The Center for Legal Informatics, and the Transatlantic Technology Law Forum. His research covers a broad range of issues at the intersection of law and emerging artificial intelligence technologies—particularly those involving transportation systems. He was written extensively on the legal implications of machine decision making, algorithmic explanability, and the role of lawyers as gatekeepers overseeing the deployment of AI-embedded products. Bryan’s scholarship has appeared in Northwestern University Law Review, Berkeley Technology Law Journal, and Stanford Law Review Online, among other journals. He also regularly comments in media outlets including CNN, Wired Magazine, Futurism, and The Stanford Lawyer. His recent work focuses on the competing roles of legality, morality, and profit-maximization in commercial AI systems with significant social impacts. And his 2018-2019 course offerings at Stanford Law School include The Future of Algorithms and Lawyering for Innovation: Artificial Intelligence. Clark Barrett, Associate Professor (Research) of Computer Science, Stanford University Clark Barrett joined Stanford University as an Associate Professor (Research) of Computer Science in September 2016. Before that, he was an Associate Professor of Computer Science at the Courant Institute of Mathematical Sciences at New York University. His expertise is in constraint solving and its applications to system verification and security. His PhD dissertation introduced a novel approach to constraint solving now known as Satisfiability Modulo Theories (SMT). Today, he is recognized as one of the world's experts in the development and application of SMT techniques. He was also an early pioneer in the development of formal hardware verification: at Intel, he collaborated on a novel theorem prover used to verify key microprocessor properties; and at 0-in Design Automation (now part of Mentor Graphics), he helped build one of the first industrially successful assertion-based verification tool-sets for hardware. He is an ACM Distinguished Scientist. Chris Gerdes, Professor of Mechanical Engineering, Director of the Center for Automotive Research (CARS), and Director of the Revs Program, Stanford University Chris studies how cars move, how humans drive cars and how to design future cars that work cooperatively with the driver or drive themselves. When not teaching on campus, he can often be found at the racetrack with students, instrumenting historic race cars or trying out their latest prototypes for the future. Vehicles in the lab include X1, an entirely student-built test vehicle, and Shelley, an Audi TT-S capable of turning a competitive lap time around the track without a human driver. Professor Gerdes and his team have been recognized with a number of awards including the Presidential Early Career Award for Scientists and Engineers, the Ralph Teetor award from SAE International and the Rudolf Kalman Award from the American Society of Mechanical Engineers.Mykel Kochenderfer, Assistant Professor of Aeronautics and Astronautics and Assistant Professor, by courtesy, of Computer Science at Stanford University
Mykel is Assistant Professor of Aeronautics and Astronautics and Assistant Professor, by courtesy, of Computer Science at Stanford University. He is the director of the Stanford Intelligent Systems Laboratory (SISL), conducting research on advanced algorithms and analytical methods for the design of robust decision making systems. Of particular interest are systems for air traffic control, unmanned aircraft, and automated driving where decisions must be made in uncertain, dynamic environments while maintaining safety and efficiency. Research at SISL focuses on efficient computational methods for deriving optimal decision strategies from high-dimensional, probabilistic problem representations. Prior to joining the faculty in 2013, he was at MIT Lincoln Laboratory where he worked on airspace modeling and aircraft collision avoidance. He received his Ph.D. from the University of Edinburgh in 2006 where he studied at the Institute of Perception, Action and Behaviour in the School of Informatics. He received B.S. and M.S. degrees in computer science from Stanford University in 2003. Prof. Kochenderfer is the director SAIL-Toyota Center for AI Research and a co-director of the Center for AI Safety. He is affiliated with the Stanford Artificial Intelligence Laboratory (SAIL), the Human-Centered AI (HAI) Institute, the Symbolic Systems Program, the Bio-X Institute, Wu Tsai Neurosciences Institute, and the Center for Automotive Research at Stanford (CARS). In 2017, he was awarded the DARPA Young Faculty Award. He is an associate editor of the Journal of Artificial Intelligence Research and the Journal of Aerospace Information Systems. He is an author of the textbooks Decision Making under Uncertainty: Theory and Application (MIT Press, 2015) and Algorithms for Optimization (MIT Press, 2019). He is a third-generation pilot.Bryan Casey, Legal Fellow at the Center for Automotive Research at Stanford University Bryan Casey is a Legal Fellow at the Center for Automotive Research at Stanford, a Lecturer at Stanford Law School, and an affiliate scholar at the Stanford Machine Leaning Group, CodeX: The Center for Legal Informatics, and the Transatlantic Technology Law Forum. His research covers a broad range of issues at the intersection of law and emerging artificial intelligence technologies—particularly those involving transportation systems. He was written extensively on the legal implications of machine decision making, algorithmic explanability, and the role of lawyers as gatekeepers overseeing the deployment of AI-embedded products. Bryan’s scholarship has appeared in Northwestern University Law Review, Berkeley Technology Law Journal, and Stanford Law Review Online, among other journals. He also regularly comments in media outlets including CNN, Wired Magazine, Futurism, and The Stanford Lawyer. His recent work focuses on the competing roles of legality, morality, and profit-maximization in commercial AI systems with significant social impacts. And his 2018-2019 course offerings at Stanford Law School include The Future of Algorithms and Lawyering for Innovation: Artificial Intelligence. Clark Barrett, Associate Professor (Research) of Computer Science, Stanford University Clark Barrett joined Stanford University as an Associate Professor (Research) of Computer Science in September 2016. Before that, he was an Associate Professor of Computer Science at the Courant Institute of Mathematical Sciences at New York University. His expertise is in constraint solving and its applications to system verification and security. His PhD dissertation introduced a novel approach to constraint solving now known as Satisfiability Modulo Theories (SMT). Today, he is recognized as one of the world's experts in the development and application of SMT techniques. He was also an early pioneer in the development of formal hardware verification: at Intel, he collaborated on a novel theorem prover used to verify key microprocessor properties; and at 0-in Design Automation (now part of Mentor Graphics), he helped build one of the first industrially successful assertion-based verification tool-sets for hardware. He is an ACM Distinguished Scientist. Chris Gerdes, Professor of Mechanical Engineering, Director of the Center for Automotive Research (CARS), and Director of the Revs Program, Stanford University Chris studies how cars move, how humans drive cars and how to design future cars that work cooperatively with the driver or drive themselves. When not teaching on campus, he can often be found at the racetrack with students, instrumenting historic race cars or trying out their latest prototypes for the future. Vehicles in the lab include X1, an entirely student-built test vehicle, and Shelley, an Audi TT-S capable of turning a competitive lap time around the track without a human driver. Professor Gerdes and his team have been recognized with a number of awards including the Presidential Early Career Award for Scientists and Engineers, the Ralph Teetor award from SAE International and the Rudolf Kalman Award from the American Society of Mechanical Engineers.Breakthroughs in technology often have humble origins. Through it's Google AI Impact Challenge grant program, Google.org lends a helping hand to nonprofit innovators and social entrepreneurs who are using the power of AI to address social and environmental challenges. This session will feature a panel of Google.org Impact Challenge Grantees who are using AI and machine learning to tackle issues affecting the environment, educational equity, at-risk youth, and mental health.
Breakthroughs in technology often have humble origins. Through it's Google AI Impact Challenge grant program, Google.org lends a helping hand to nonprofit innovators and social entrepreneurs who are using the power of AI to address social and environmental challenges. This session will feature a panel of Google.org Impact Challenge Grantees who are using AI and machine learning to tackle issues affecting the environment, educational equity, at-risk youth, and mental health.