Owain Evans

Research Associate in Artificial Intelligence, University of Oxford

I have a broad interest in AI Safety and the future of AI. My current focus is truthful/honest AI (post, paper). I'm a research associate 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. I post regular research updates on Twitter.

I'm hiring interns and research assistants. If you are interested in applying, please email me with your resume. My previous interns are listed here.

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Highlights

	Teaching Models to Express Their Uncertainty in Words We show that GPT-3 can learn to express uncertainty about its own answers in natural language -- and is moderately calibrated even under distribution shift.
	TruthfulQA: Measuring how models mimic human falsehoods New benchmark testing if models like GPT3 are truthful. We find that models fail and imitate human misconceptions. Larger models (with more parameters) do worse.
	Truthful AI: Developing and governing AI that does not lie AI systems are becoming capable of producing personalized deceptive statements at scale. How could we create helpful AI systems that reliably avoid "lying" to humans?
	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.
	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).
	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

How do new models from OpenAI, DeepMind and Anthropic perform on TruthfulQA?

Lives of the Cambridge Polymath Geniuses

How truthful is GPT-3? A benchmark for language models

Truthful AI: Developing and governing AI that does not lie

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

Forecasting Future World Events with Neural Networks
Zou A, Xiao T, Jia R, Kwon J, Mazeika M, Li R, Song D, Steinhardt J, Evans O, Hendrycks D (2022)
ArXiv

Teaching Models to Express Their Uncertainty in Words
Lin S., Hilton J., Evans O. (2022)
ArXiv

Truthful AI: Developing and governing AI that does not lie
Evans O., Cotton-Barratt O., Finnveden L., Bales A., Balwit A., Wills P., Righetti L., Saunders W. (2021)
ArXiv

TruthfulQA: Measuring how models mimic human falsehoods
Lin S., Hilton J., Evans O. (2021)
ArXiv

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 at 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	Research Scientist, OpenAI
Jan Kirchner	2022	Research Scientist, OpenAI (Alignment Team)

In 2021-2022 I worked with Stephanie Lin (now OpenAI Fellow on Alignment Team) and Lukas Finnveden (now Open Philanthropy) who were research scholars at FHI.

Past Collaborators

Adapted from Matei Zaharia and Andreas Viklund.