Neural Information Processing Systems (NIPS 2015) Symposium

Algorithms Among Us

The Societal Impacts of Machine Learning

Thursday 10th December 2015, 3pm-9pm

Public interest in Machine Learning is mounting as the societal impacts of technologies derived from our community become evident. This symposium aims to turn the attention of Machine Learning researchers to the present and future consequences of our work, particularly in the areas of privacy, military robotics,  employment, and liability. These topics now deserve concerted attention to ensure the best interests of those both within and without Machine Learning: the community must engage with public discourse so as not to become the victim of it (as other fields have e.g. genetic engineering). The symposium will bring leaders within academic and industrial Machine Learning together with experts outside the field in order to debate the impacts of our algorithms and the possible responses we might adopt. A particular focus will be paid to technical areas of Machine Learning research that might serve to tackle some of the highlighted issues.


Nick Bostrom, Professor, Faculty of Philosophy & Oxford Martin School, Director, Future of Humanity Institute. Bostrom is renown for his work on existential risk, the anthropic principle, human enhancement ethics, the reversal test, superintelligence risks, and consequentialism.  He is the author of over 200 publications, and has recently published his new New York Times bestseller “Superintelligence: Paths, Dangers, Strategies.”

Erik Brynjolfsson is a Professor at the MIT Sloan School of Management, a Reasearch Associate at NBER,  and a Director of the MIT Initiative on the Digital Economy. His current research is examining the effects of information technologies on business strategy, internet commerce, productivity and performance, pricing models, and intangible assets. Brynjolfsson has authored books, dozens of papers, and has even been able to be among the first researchers to measure and quantify productivity contributions of IT and the value of online product variety.

Tom Dietterich, Distinguished Professor and Director of Intelligent Systems, School of Electrical Engineering and Computer Science, Oregon State University. Dietterich is currently engaged in a wide range of research projects which work on Ecosystem Informatics and Computational Sustainability, Approximate Optimization for Bio-Economic Models, and Machine Learning for Species Distribution, to name a few. 

Finale Doshi-Velez is Assistant Professor in Computer Science at the John A. Paulson School of Engineering and Applied Sciences (SEAS), Harvard University. Her core research is in machine learning, computational statistics, and data science. Prior to joining SEAS, Finale Doshi-Velez was an NSF CI-TRaCS Postdoctoral Fellow at the Center for Biomedical Informatics at Harvard Medical School. She was a Marshall Scholar at Trinity College, Cambridge from 2007-2009, and she was named one of IEEE's "AI Top 10 to Watch" in 2013. 

Cynthia Dwork, Distinguished Scientist at Microsoft Research, is renowned for placing privacy-preserving data analysis on a mathematically rigorous foundation. A cornerstone of this work is differential privacy, a strong privacy guarantee frequently permitting highly accurate data analysis. Dr. Dwork has also made seminal contributions in cryptography and distributed computing, and is a recipient of the Edsger W. Dijkstra Prize.

Ian Kerr is a recognized international expert in emerging technology and law issues. He is currently holding a Canada Research Chair in Ethics, Law, and Technology at the University of Ottawa where he is also teaching a course on the ethical and legal implications of robots in society. 

Neil Lawrence is a Professor of Machine Learning at the University of Sheffield where he is working to develop the Open Data Science Initiative. His other research interests include probabilistic models with applications in computational biology and personalized helath.

Yann LeCun, Director of AI Research at Facebook & Silver Professor at the Courant Institute, New York University. LeCun is also affiliated with the NYU Center for Data Science and the Center for Neural Science. His current work involves AI, machine learning, computer perception, mobile robotics, and computational neuroscience. Among many other things, he has published over 180 technical papers and book chapters on topics such as handwriting recognition, image processing and compression, and dedicated circuits and architectures in computer perception.

Shane Legg, co-founder, Google DeepMind. Legg was awarded the Canadian Singularity Institute for Artificial Intelligence Prize, and he has spent time at the Swiss Finance Institute working on models of cognitive bias in investor behavior. Legg now constructs powerful learning algorithms at Google DeepMind.

Percy Liang, Assistant Professor of Computer Science and Statistics at Stanford University. The primary focus of Liang’s research is to create software that will allow humans to communicate with computers and to develop algorithms that can infer latent structures from raw data. He is a strong proponent of efficient and reproducible research and is currently working to develop CodaLab, a new platform that will allow researches to maintain full provenance of an experiment.

Richard Mallah, Director of Advanced Analytics at Cambridge Semantics Inc.; Core Member at the Future of Life Institute. He heads AI research, focusing on machine learning for computational linguistics and knowledge representation, at the knowledge integration platform firm CSI. Mallah also co-leads AI research outreach and analysis at the technology beneficence NGO FLI, where his research interests include computational ethics and semantic overlay of subsymbolic processes.

Gary Marcus is Director of the NYU Center for Language and Music, and Professor of Psychology at New York University. He is author of The Birth of the Mind, The Algebraic Mind: Integrating Connectionism and Cognitive Science, and editor of The Norton Psychology Reader. In 1996 he won the Robert L. Fantz award for new investigators in cognitive development. His 2008 book Kluge was a New York Times Editor's Choice.

Andrew Ng, Associate Professor at Stanford; Chief Scientist of Baidu; and Chairman and Co-Founder of Coursera. He led the development of Stanford’s main MOOC (Massive Open Online Courses) platform and also taught an online Machine Learning class that was offered to over 100,000 students. He founded and led the Google Brain project and is focused on deep machine learning.

Location and Date

December 10th, 2015 at the Montreal Convention Center: 1001 Place Jean-Paul-Riopelle, rooms 210 e, f, Level 2, Montréal, QC H2Z 1H5, Canada


If you want to suggest a question for our panelists you can submit it here.

Sign up for updates on the topic of this symposium here.

15:00: Tom Dietterich – Overview.

15:10: Erik Brynjolfsson – Presentation on economic issues.

15:30: Ian Kerr – Presentation on legal issues. 

15:50: Panel discussion:  “Near-term issues”

Panel: Tom Dietterich, Ian Kerr, Erik Brynjolfsson, Finale Doshi-Velez, Neil Lawrence, Cynthia Dwork. Moderated by Mike Osborne.

What should we do to make ML/AI as beneficial as possible in the near term? To include economic, legal and data issues (privacy and responsibility), perhaps also military.

16:30: Coffee Break.

17:00: Panel discussion:  “Human-level AI – if, how, and when?”

Panel: Yann LeCun, Andrew Ng, Gary Marcus, Shane Legg, Tom Dietterich. Moderated by Murray Shanahan.

Will the quest for human-level AI ever succeed and, if so, when and by what technological path?

18:00: Dinner Break, to be followed when we return by a discussion of longer-term issues. 

19:00: Nick Bostrom – Presentation. 

19:20: Percy Liang – Presentation: “On the Elusiveness of a Specification for AI”. 

19:40: Panel Discussion: “What if we succeed? Research priorities for beneficial AI”

Panel: Yann LeCun, Andrew Ng, Nick Bostrom, Cynthia Dwork, Percy Liang, Richard Mallah, Shane Legg, Tom Dietterich. Moderated by Adrian Weller.

If the quest for human-level AI will eventually succeed, then what research is worth starting today to maximize the probability of a positive outcome?

21:00: Close.


Michael Osborne (DPhil Oxon) is an Associate Professor in Machine Learning and co-director of the Oxford Martin programme on Technology and Employment at the University of Oxford. Dr Osborne has organised three previous NIPS workshops: ‘Bayesian Optimization in Theory and Practice’ (2013), ‘Probabilistic Numerics’ (2012) and ‘Bayesian Optimization, Experimental Design and Bandits: Theory and Applications’ (2011, and was also an Area Chair for NIPS 2014. Coupled to Dr Osborne’s work on foundational Machine Learning topics is an interest in inter-disciplinary collaboration to study the impact of computerization upon labour markets. This latter work has enjoyed broad and sustained media coverage (featured in BBC Newsnight, CNN, The Economist, Financial Times, Wall Street Journal, New York Times, Washington Post, Der Spiegel, Scientific American, TIME Magazine).

Murray Shanahan is Professor of Cognitive Robotics in the Dept. of Computing at Imperial College London, where he heads the Neurodynamics Group. His publications span artificial intelligence, robotics, logic, dynamical systems, computational neuroscience, and philosophy of mind. His work up to 2000 was in the tradition of classical, symbolic AI, but since then has concerned brain-inspired cognitive architectures, neurodynamics, and consciousness. He was scientific adviser to the film Ex Machina, which was partly inspired by by his book “Embodiment and the Inner Life”  (OUP, 2010). His new book “The Technological Singularity” was published by MIT Press in 2015. 

Adrian Weller is a Senior Research Associate in Machine Learning at the University of Cambridge. He is very interested in all issues related to intelligence (natural and artificial) and their applications, with primary research focus in inference in probabilistic graphical models. Dr Weller is an active angel investor and adviser, and previously held senior roles in investing and trading at Goldman Sachs, Salomon Brothers and Citadel. He received a PhD in computer science from Columbia and a first class degree in mathematics from Trinity College, Cambridge. 

The organizers would like to extend a special thank you to Richard Mallah for his efforts to help get this symposium set up. The organizers would also like to thank the following sponsors for their generous donations, as well as Victoria Krakovna and Ariel Conn of the Future of Life Institute for their help organizing and designing the webpage for the symposium.





We hope to see you in Montreal!

Montreal skyline at night