Tom EverittResearch ScientistDeepMind Email: tomeveritt at google.com |
I'm a research scientist at Deepmind.
I'm working on AGI Safety,
i.e. how we can safely build and use highly intelligent AI.
My PhD thesis Towards Safe Artificial General Intelligence
is the first PhD thesis specifically devoted to this topic.
It was supervised by Marcus Hutter at the Australian National University.
Understanding Agent Incentives using Causal Influence Diagrams
Tom Everitt, Pedro A. Ortega, Elizabeth Barnes, Shane Legg
In arXiv and blog post, 2019.
Independent Chinese translation.
Scalable agent alignment via reward modeling: a research direction
Jan Leike, David Krueger, Tom Everitt, Miljan Martic, Vishal Maini, Shane Legg
In arXiv and
blog post, 2018.
Two Minute Papers video
The Alignment Problem for Bayesian History-Based Reinforcement Learners (PUBLIC DRAFT)
Tom Everitt and Marcus Hutter, 2018.
Winner of the AI Alignment Prize.
AGI Safety Literature Review
Tom Everitt, Gary Lea, and Marcus Hutter
In International Joint Conference on AI (IJCAI) and arXiv, 2018.
A full list of publications is available here and at my Google Scholar.
Below I list my papers together with some context. Many of them also appear in slightly different forms in my thesis.
An accessible and comprehensive overview of the emerging research field of AGI safety:
AGI Safety Literature Review
Tom Everitt, Gary Lea, and Marcus Hutter
In International Joint Conference on AI (IJCAI) and arXiv , 2018.
A machine learning research agenda for how to build safe AGI:
Scalable agent alignment via reward modeling: a research direction
Jan Leike, David Krueger, Tom Everitt, Miljan Martic, Vishal Maini, Shane Legg
In arXiv and
blog post , 2018.
Two Minute Papers video
The UAI/AIXI framework is a formal model of reinforcement learning in general environments. Many of my other works are based on variations of this framework:
Gridworlds make the often intangible problems of AGI safety very concrete:
AI Safety Gridworlds
Jan Leike, Miljan Martic, Victoria Krakovna, Pedro Ortega, Tom Everitt, Andrew Lefrancq, Laurent Orseau, Shane Legg
In arXiv and
GitHub, 2017.
Computerphile video.
To better understand the incentives of powerful AI systems has been the focus of most of my work.
General method. I've recently developed a general mehtod for inferring agent incentives directly from a graphical model.
Understanding Agent Incentives using Causal Influence Diagrams
Tom Everitt, Pedro A. Ortega, Elizabeth Barnes, Shane Legg
In arXiv and blog post, 2019.
Independent Chinese translation.
Reward tampering. Various ideas in the AGI safety literature can be combined to form RL-like agents without significant incentives to interfere with any aspect of its reward process, be it their reward signal, their utility function, or the online training of their reward function. (It's somewhat long, but also fairly accessibly written.)
The Alignment Problem for Bayesian History-Based Reinforcement Learners (PUBLIC DRAFT)
Tom Everitt and Marcus Hutter, 2018.
Winner of the AI Alignment Prize.
Given that the reward process can be (accidentally) corrupted, this paper explains why both richer feedback and randomized algorithms (quantlization) improve robustness to reward corruption.
Reinforcement Learning with Corrupted Reward Channel
Tom Everitt, Victoria Krakovna, Laurent Orseau, Marcus Hutter, and Shane Legg.
In IJCAI-17
and arXiv, 2017.
Blog post,
Slides,
Victoria's talk.
Self-modification. Subtly different design choices lead to systems with or without incentives to replace their goal or utilty functions:
Self-Modification of Policy and Utility Function in Rational Agents
Tom Everitt, Daniel Filan, Mayank Daswani, and Marcus Hutter.
In AGI-16 and arXiv, 2016.
Slides,
video.
Winner of the Kurzweil prize for best AGI paper.
Self-preservation and death. AIs may have an incentive not to be turned off.
There is a natural mathematical definition of death in the UAI/AIXI framework. RL agents can be suicidal:
Death and Suicide in Universal Artificial Intelligence
Jarryd Martin, Tom Everitt, and Marcus Hutter.
In AGI-16
and arxiv, 2016.
Slides
Extending the analysis of a previous paper, we determine the exact conditions for when CIRL agents ignore a shutdown signal:
A Game-Theoretic Analysis of the Off-Switch Game
Tobias Wängberg, Mikael Böörs, Elliot Catt, Tom Everitt, and Marcus Hutter
In AGI-17 and
arXiv, 2017.
Decision theory. Strangely, robots and other agents that are part of their environment may be able to infer properties of themselves from their own actions. For example, my having petted a lot of cats in the past may be evidence that I have toxoplasmosis, a disease which makes you fond of cats. Now, if I see a cat, should I avoid petting it to reduce the risk that I have the disease? (note that petting cats never causes toxoplasmosis). The two standard answers for how to reason in this situation are called CDT and EDT. We show that CDT and EDT turns into three possibilities for how to reason in sequential settings where multiple actions are interleaved with observations:
Sequential Extensions of Causal and Evidential Decision Theory.
Tom Everitt, Jan Leike,
and Marcus Hutter.
In
Algorithmic Decision Theory (ADT) and
arXiv, 2015.
Slides,
source code.
Other AI safety papers. I no longer find the approach in the following paper promising:
Avoiding Wireheading with Value Reinforcement Learning
Tom Everitt and Marcus Hutter.
In AGI-16 and arXiv, 2016.
Slides,
video.
Source code: download,
view online.
Exploration A fundamental problem in reinforcement learning is how to explore an unknown environment effectively. Ideally, an exploration strategy should direct us to regions with potentially high reward, while not being too expensive to compute. In the following paper, we find a way to employ standard function approximation techniques to estimate the novelty of different actions, which gives state-of-the-art performance in the popular Atari Learning Environment while being much cheaper to compute than most alternative strategies:
Count-Based Exploration in Feature Space for Reinforcement Learning.
Jarryd Martin, Suraj Narayanan S, Tom Everitt, and Marcus Hutter
In IJCAI-17 and arXiv, 2017.
Background. Search and optimisation are fundamental aspects of AI and of intelligence in general. Intelligence can actually be defined as optimisation ability (Legg and Hutter, Universal Intelligence: A Definition of Machine Intelligence, 2007).
(No) Free Lunch. The No Free Lunch theorems state that intelligent optimisation is impossible without knowledge about what you're trying optimise. I argue against these theorems, and show that under a natural definition of complete uncertainty, intelligent (better-than-random) optimisation is possible. Unfortunately, I was also able to show that there are pretty strong limits on how much better intelligent search can be compared to random search.
Free Lunch for Optimisation under the Universal Distribution.
Tom Everitt, Tor Lattimore,
and Marcus Hutter.
In IEEE Congress on Evolutionary Computation (CEC)
and arXiv, 2014.
Slides.
Universal Induction and Optimisation: No Free Lunch?
Tom Everitt Supervised by Tor Lattimore, Peter Sunehag, and Marcus Hutter
at ANU.
Master thesis, Department of Mathematics, Stockholm University, 2013.
Optimisation difficulty. In a related paper, we give a formal definition of how hard a function is to optimise:
Can we measure the difficulty of an optimization problem?
Tansu Alpcan, Tom Everitt, and Marcus Hutter.
In
IEEE Information Theory Workshop (ITW) PDF©IEEE, 2014.
How to search. Two of the most fundamental strategies for search is DFS and BFS. In DFS, you search depth-first; for example, you follow one path until its very end before trying something else. In BFS, you instead try to search as broadly as possible, focusing on breadth rather than depth. I calculate the expected search times for both methods, and derive some results on which method is preferable in which situations:
Analytical Results on the BFS vs. DFS Algorithm Selection Problem.
Part I, Tree Search.
Tom Everitt and Marcus Hutter.
In 28th Australasian Joint Conference on AI and
arXiv, 2015.
Slides,
Source Code.
Analytical Results on the BFS vs. DFS Algorithm Selection Problem.
Part II, Graph Search.
Tom Everitt and Marcus Hutter.
In 28th Australasian Joint Conference on AI and
arXiv, 2015.
Slides,
Source Code.
Analytical Algorithm Selection for AI Search: BFS vs. DFS
Tom Everitt and Marcus Hutter.
In preparation, 2017.
Source Code.
Automated Theorem Proving.
Tom Everitt, Supervised by Rikard Bøgvad.
Bachelor thesis, Department of Mathematics, Stockholm University, 2010.
Tutorial on Universal Reinforcement Learning
AAMAS 2018
Tutorial info and slides
AGI Safety and Understanding
Invited talk, AGI-17.
Slides.
Universal Artificial Intelligence:
Practical Agents and Fundamental Challenges.
Tutorial for AGI 2016.
Slides,
video,
and
draft writeup.
AI and the future -- Introduction to AI Safety.
ANU Regnet, ANU Learning Communities, ANU XSA (XSAC talk), LessWrong Canberra, 2016 and 2017
Slides
AI Safety -- Overview of recent models and results.
Effective Altruism Sydney retreat 2016
Slides
Jarryd Martin, Master of Computing (ANU)
Thesis: Optimism and Death in Reinforcement Learning
Papers: Count-Based Exploration in Feature Space (IJCAI-17), and Death and Suicide in Universal Artificial Intelligence (AGI-16)
Suraj Narayanan S, Master of Computing (ANU)
Thesis: Exploration in Feature Space
Paper: Count-Based Exploration in Feature Space (IJCAI-17)
Tobias Wängberg and Mikael Böörs, Bachelor of Mathematics (LiU)
Thesis: Classification by Decomposition: A Partitioning of the Space of 2x2 Symmetric Games
Paper: A Game-Theoretic Analysis of the Off-Switch Game (AGI-17)
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