Join Xiao-Li Meng, PhD ’90, statistics, and the editors of the Harvard Data Science Review (HDSR) for a stimulating virtual panel entitled, “Future Shock: Grappling with the Generative AI Revolution." The conversation, and the HDSR publication of the same name scheduled to be published in January, will explore the broad spectrum of questions raised by recent advancements in foundation models and generative AI tools like ChatGPT, including:
- To what extent are these advancements presenting contemporary society with dangers of future shock?
- To what degree and how is the accelerating pace of the generative AI revolution putting novel, and potentially unsustainable, pressures on accepted norms and practices of scientific research, teaching, scholarship, and academic publication?
- How is the hasty industrialization of this set of technologies impacting broader social, cultural, economic, political, and legal structures, dynamics, and institutions?
- How can policymakers, regulators, civil society organizations, and members of the public swiftly and effectively respond to the far-reaching risks posed by foundation models and generative AI?
Xiao-Li Meng, PhD ’90, statistics
Xiao-Li Meng, the founding editor-in-chief of Harvard Data Science Review and the Whipple V. N. Jones Professor of Statistics is well known for his depth and breadth in research, his innovation and passion in pedagogy, his vision and effectiveness in administration, as well as for his engaging and entertaining style as a speaker and writer. Meng was named the best statistician under the age of 40 by the Committee of Presidents of Statistical Societies (COPSS) in 2001, and he is the recipient of numerous awards and honors for his more than 150 publications in at least a dozen theoretical and methodological areas, as well as in areas of pedagogy and professional development. In 2020, he was elected to the American Academy of Arts and Sciences. He has delivered more than 400 research presentations and public speeches on these topics, and he is the author of “The XL-Files," a thought-provoking and entertaining column in the Institute of Mathematical Statistics (IMS) Bulletin. His interests range from the theoretical foundations of statistical inferences (e.g., the interplay among Bayesian, fiducial, and frequentist perspectives; frameworks for multi-source, multi-phase and multi-resolution inferences) to statistical methods and computation (e.g., posterior predictive p-value; expectation–maximization (EM) algorithm; Markov chain Monte Carlo; bridge and path sampling) to applications in natural, social, and medical sciences and engineering (e.g., complex statistical modeling in astronomy and astrophysics, assessing disparity in mental health services, and quantifying statistical information in genetic studies). Meng received his BS in mathematics from Fudan University in 1982 and his PhD in statistics from Harvard in 1990. He was on the faculty of the University of Chicago from 1991 to 2001 before returning to Harvard, where he served as the chair of the Department of Statistics (2004–2012) and the dean of the Graduate School of Arts and Sciences (2012–2017).
Panelists
Francine Berman is the director of Public Interest Technology and Stuart Rice Research Professor in the Manning College of Information and Computer Sciences at the University of Massachusetts, Amherst. She is a faculty associate at the Berkman Klein Center for Internet and Society at Harvard University and was selected as the 2019–2020 Katherine Hampson Bessell Fellow at the Radcliffe Institute for Advanced Study at Harvard University. She is a fellow of the American Association for the Advancement of Science (AAAS), the Institute of Electrical and Electronics Engineers (IEEE), and the Association for Computing Machinery (ACM). Berman is a founder of the Research Data Alliance, a community-driven international organization created to accelerate research data sharing. Her research interests include data cyberinfrastructure, stewardship, preservation and policy, and the social and ethical impact of the Internet of Things.
Ralf Herbrich is a professor of Computer Science at Hasso Plattner Institute and the University of Potsdam and chair of the research group on Artificial Intelligence and Sustainability. Previously, he served as senior vice president of Artificial Intelligence at Zalando (2020-2022) and director of Machine Learning at Amazon in Berlin (2013-2020 ) after starting and leading Facebook’s Unified Ranking and Allocation team from 2011–2012. From 2000 to 2011, Ralf worked at Microsoft Research in Cambridge, UK. Ralf’s areas of research span from Bayesian inference and decision-making, game theory, information retrieval, natural language processing, computer vision, and distributed systems to learning theory, knowledge representation and reasoning, and programming languages.
David Leslie is the director of Ethics and Responsible Innovation Research at The Alan Turing Institute and professor of Ethics, Technology, and Society at Queen Mary University of London. He previously taught at Princeton’s University Center for Human Values, Yale’s program in Ethics, Politics, and Economics, and at Harvard’s Committee on Degrees in Social Studies, where he received over a dozen teaching awards including the 2014 Stanley Hoffman Prize for Teaching Excellence. David is the author of the UK government’s official guidance on the responsible design and implementation of AI systems in the public sector.
Join Xiao-Li Meng, PhD ’90, statistics, and the editors of the Harvard Data Science Review (HDSR) for a stimulating virtual panel entitled, “Future Shock: Grappling with the Generative AI Revolution." The conversation, and the HDSR publication of the same name scheduled to be published in January, will explore the broad spectrum of questions raised by recent advancements in foundation models and generative AI tools like ChatGPT, including:
- To what extent are these advancements presenting contemporary society with dangers of future shock?
- To what degree and how is the accelerating pace of the generative AI revolution putting novel, and potentially unsustainable, pressures on accepted norms and practices of scientific research, teaching, scholarship, and academic publication?
- How is the hasty industrialization of this set of technologies impacting broader social, cultural, economic, political, and legal structures, dynamics, and institutions?
- How can policymakers, regulators, civil society organizations, and members of the public swiftly and effectively respond to the far-reaching risks posed by foundation models and generative AI?
Xiao-Li Meng, PhD ’90, statistics
Xiao-Li Meng, the founding editor-in-chief of Harvard Data Science Review and the Whipple V. N. Jones Professor of Statistics is well known for his depth and breadth in research, his innovation and passion in pedagogy, his vision and effectiveness in administration, as well as for his engaging and entertaining style as a speaker and writer. Meng was named the best statistician under the age of 40 by the Committee of Presidents of Statistical Societies (COPSS) in 2001, and he is the recipient of numerous awards and honors for his more than 150 publications in at least a dozen theoretical and methodological areas, as well as in areas of pedagogy and professional development. In 2020, he was elected to the American Academy of Arts and Sciences. He has delivered more than 400 research presentations and public speeches on these topics, and he is the author of “The XL-Files," a thought-provoking and entertaining column in the Institute of Mathematical Statistics (IMS) Bulletin. His interests range from the theoretical foundations of statistical inferences (e.g., the interplay among Bayesian, fiducial, and frequentist perspectives; frameworks for multi-source, multi-phase and multi-resolution inferences) to statistical methods and computation (e.g., posterior predictive p-value; expectation–maximization (EM) algorithm; Markov chain Monte Carlo; bridge and path sampling) to applications in natural, social, and medical sciences and engineering (e.g., complex statistical modeling in astronomy and astrophysics, assessing disparity in mental health services, and quantifying statistical information in genetic studies). Meng received his BS in mathematics from Fudan University in 1982 and his PhD in statistics from Harvard in 1990. He was on the faculty of the University of Chicago from 1991 to 2001 before returning to Harvard, where he served as the chair of the Department of Statistics (2004–2012) and the dean of the Graduate School of Arts and Sciences (2012–2017).
Panelists
Francine Berman is the director of Public Interest Technology and Stuart Rice Research Professor in the Manning College of Information and Computer Sciences at the University of Massachusetts, Amherst. She is a faculty associate at the Berkman Klein Center for Internet and Society at Harvard University and was selected as the 2019–2020 Katherine Hampson Bessell Fellow at the Radcliffe Institute for Advanced Study at Harvard University. She is a fellow of the American Association for the Advancement of Science (AAAS), the Institute of Electrical and Electronics Engineers (IEEE), and the Association for Computing Machinery (ACM). Berman is a founder of the Research Data Alliance, a community-driven international organization created to accelerate research data sharing. Her research interests include data cyberinfrastructure, stewardship, preservation and policy, and the social and ethical impact of the Internet of Things.
Ralf Herbrich is a professor of Computer Science at Hasso Plattner Institute and the University of Potsdam and chair of the research group on Artificial Intelligence and Sustainability. Previously, he served as senior vice president of Artificial Intelligence at Zalando (2020-2022) and director of Machine Learning at Amazon in Berlin (2013-2020 ) after starting and leading Facebook’s Unified Ranking and Allocation team from 2011–2012. From 2000 to 2011, Ralf worked at Microsoft Research in Cambridge, UK. Ralf’s areas of research span from Bayesian inference and decision-making, game theory, information retrieval, natural language processing, computer vision, and distributed systems to learning theory, knowledge representation and reasoning, and programming languages.
David Leslie is the director of Ethics and Responsible Innovation Research at The Alan Turing Institute and professor of Ethics, Technology, and Society at Queen Mary University of London. He previously taught at Princeton’s University Center for Human Values, Yale’s program in Ethics, Politics, and Economics, and at Harvard’s Committee on Degrees in Social Studies, where he received over a dozen teaching awards including the 2014 Stanley Hoffman Prize for Teaching Excellence. David is the author of the UK government’s official guidance on the responsible design and implementation of AI systems in the public sector.