Owain Evans

Senior Research Fellow in Artificial Intelligence, University of Oxford

I'm interested in AI safety, cognitive science, and philosophy. I'm a research fellow at the Future of Humanity Institute, Oxford University and got my PhD at MIT. I previously worked at Ought, where I still serve on the Board of Directors.

If you are interested in interning with me, fill out the form here or email me. My previous interns are listed here.

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Highlights

Link When Will AI Exceed Human Performance? Evidence from Experts

We conducted the first large, representative survey of ML researchers on when AI will reach human level on various tasks. The aggregate forecast (median) was 2026 for high-school essays, 2027 for truck-driving, and 2049 for writing a NYT bestseller.

Link Sensory Optimization: Neural Nets as a Model for Understanding and Creating Art

A cognitive science model for how humans understand and create visual art. Artists optimize paintings to be evocative to their own visual system (analagous to Deep Dream and Style Transfer for CNNs).

Link Trial without Error: Towards Safe Reinforcement Learning via Human Intervention

How can an RL agent learn a task without making a single dangerous error? We train a Deep RL agent with a human in the loop and show how to reduce human labor by training a supervised learner to imitate the human.

Blog posts

Solving Math Problems with Relay Teams: An Experiment in Factored Cognition
(w/ Ben Goldhaber)

Evaluating Arguments One Step at a Time
(w/ Ought team)

Quantifying Household Transmission of Covid

Neural nets as a model for how humans make and understand visual art

Model Mis-specification and Inverse Reinforcement Learning: Obstacles to Inferring Preferences from Behavior
(w/ Jacob Steinhardt)

Papers

Modelling the health and economic impacts of population-wide testing, contact tracing and isolation (PTTI) strategies for Covid-19
Colbourn T. et al. (2020)
SSRN Preprint

Estimating Household Transmission of SARS-CoV-2
Curmei M., Ilyas A., Evans O., Steinhardt J. (2020)
Medrxiv Preprint

Evaluating arguments one step at a time
Saunders, W., Rachbach, B., Evans, O., Miller, Z., Byun, J., Stuhlmüller A. (2020)
Ought.org Technical report

Sensory Optimization: Neural Networks as a Model for Understanding and Creating Art
Evans, O. (2019)
Arxiv
(PDF version)

Generalizing from a few environments in safety-critical reinforcement learning
Kenton Z., Filos A., Evans O., Gal Y. (2019)
ICLR 2019 (Safe ML Workshop)

Machine Learning Projects for Iterated Distillation and Amplification
Evans O., Saunders W., Stuhlmüller A. (2019)
FHI Technical Report

Predicting Human Deliberative Judgments with Machine Learning
Evans O., Stuhlmüller A., Cundy C., Carey R., Kenton, Z., McGrath T., Schreiber A. (2018)
FHI Technical Report

Active Reinforcement Learning with Monte-Carlo Tree Search
Schulze S., Evans O. (2018)
Arxiv

The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation
Brundage M., Avin S., Clark J., et al. (2018)
Arxiv

Trial without Error: Towards Safe Reinforcement Learning via Human Intervention
Saunders S., Sastry G., Stuhlmüller A., Evans O. (2017)
AAMAS 2018
(Blogpost, Atari Videos, Slides)

When Will AI Exceed Human Performance? Evidence from AI Experts.
Grace K., Salvatier J., Zhang B., Dafoe A., Evans O. (2017)
Journal of AI Research (JAIR) 2018.
(Covered by BBC News, New Scientist, Newsweek, and more)

Model Mis-specification and Inverse Reinforcement Learning.
(Essay co-authored with Jacob Steinhardt, 2017).

Agentmodels.org: Modeling Agents with Probabilistic Programs.
Evans O., Stuhlmüller A., Salvatier J., Filan D. (2017)
Online Book and Open-source Library

Agent-Agnostic Human-in-the-Loop Reinforcement Learning.
Abel D., Salvatier J., Stuhlmüller A., Evans O. (2016)
NIPS Workshop

Active Reinforcement Learning: Observing Rewards at a Cost.
Krueger D., Leike J, Salvatier J., Evans O. (2016)
NIPS Workshop

Learning the Preferences of Ignorant, Inconsistent Agents.
Evans O., Stuhlmüller A., Goodman N. (2016)
AAAI

Learning the Preferences of Bounded Agents.
Evans O., Stuhlmüller A., Goodman N. (2015)
NIPS Workshop

Learning Structured Preferences.
Evans O., Bergen L., Tenenbaum J. (2012)
Proceedings of Cognitive Science Society Conference

Help or hinder: Bayesian models of social goal inference.
Ullman T., Baker C., Macindoe O., Evans O., Goodman N., & Tenenbaum J. (2010)
NIPS

Bayesian Computational Models for Inferring Preferences (2015)
MIT Dissertation

Video and slides

Predicting the future of AI (YouTube link)
(Towards Data Science Podcast, 2020)

Synergies Between Near-term and Long-term AI Safety (YouTube)
(Future of Life Institute Conference, 2019 in Puerto Rico)

Predicting Slow Judgment
(Slides for talk at "Aligning AI" workshop at NIPS 2017 in Long Beach.)

Careers in AI safety (YouTube)
(Effective Altruist Global Conference, 2017 in London)

Trial without Error: Towards Safe Reinforcement Learning via Human Intervention
(Slides for talks at Cambridge Centre for the Future of Intelligence and Google Deepmind)

Automated Corporations and AI Risk
(Informal talk at Oxford University)

Agent-agnostic Human-in-the-loop Reinforcement Learning
(Slides for talks at U. Toronto and Deepmind)

Learning the Preferences of Ignorant, Inconsistent Agents
(Slides for oral presentation at AAAI 2016)

Learning Human Preferences
(Short talk at MIT)

Past Interns

Name Year Current role
Daniel Filan 2016 PhD student in ML, UC Berkeley (CHAI)
John Salvatier 2016 Independent researcher
David Abel 2016 Research Scientist, DeepMind (London)
David Krueger 2016 Lecturer in ML, University of Cambridge
William Saunders 2017 Research Engineer, OpenAI (Alignment Team)
Girish Sastry 2017 Researcher, OpenAI (Policy Team)
Neal Jean 2017 Founder of YC startup Beacons
Ryan Carey 2017 PhD student in ML (Oxford) and Researcher at FHI
Chris Cundy 2017 PhD student in ML, Stanford
Tom McGrath 2018 Research Scientist in AI Safety, DeepMind (London)
Zac Kenton 2018 Research Scientist in AI Safety, DeepMind (London)
Richard Ngo 2018 PhD student, University of Cambridge

Past Collaborators

Adapted from Matei Zaharia and Andreas Viklund.