Research Workshops - Machine Intelligence Research Institute (original) (raw)

July 20–22, 2018 – Berkeley, California

2nd Workshop on Approaches in AI Alignment

CHAI Participants Jordan Alexander
Lawrence Chan
James Drain
Aaron Tucker
Alex Turner

Unaffiliated Participants Alex Gunning

MIRI Participants Alex Appel
Daniel Demski
Evan Hubinger
Linda Linsefors
Alex Mennen
David Simmons
Alex Zhu

This weekend workshop brought together research interns from MIRI and UC Berkeley’s Center for Human-Compatible AI (CHAI) to discuss conceptual foundations and open problems in AI safety research.

November 18–19, 2017 – Berkeley, California

1st Workshop on Approaches in AI Alignment

Tsvi Benson-Tilsen (MIRI)
Paul Christiano (OpenAI)
Andrew Critch (UC Berkeley)
Wei Dai (independent)
Abram Demski (MIRI)

Sam Eisenstat (MIRI)
Scott Garrabrant (MIRI)
Richard Mallah (FLI, Cambridge Semantics)
Andreas Stuhlmüller (Stanford)
Jessica Taylor (independent)

April 1-2, 2017 – Berkeley, California

4th Workshop on Machine Learning and AI Safety

Tsvi Benson-Tilsen (MIRI)
Paul Christiano (OpenAI)
Andrew Critch (UC Berkeley)
Wei Dai (independent)
Abram Demski (MIRI)

Sam Eisenstat (MIRI)
Scott Garrabrant (MIRI)
Richard Mallah (FLI, Cambridge Semantics)
Andreas Stuhlmüller (Stanford)
Jessica Taylor (independent)

March 25–26, 2017 – Berkeley, California

Workshop on Agent Foundations and AI Safety

Alexander Appel (University of Nevada Reno)
Michael Dennis (UC Berkeley)
Sam Eisenstat (Google)
Matt Frank
Scott Garrabrant (MIRI)

Juan David Gil (MIT)
Patrick LaVictoire (MIRI)
Eliana Lorch (Thiel Fellow)
Eli Sennesh
Harry Slatyer (Google)
Alex Zhu

This two-day weekend workshop brought together researchers with interests in long-term theoretical AI safety research. The workshop covered the context and content of current AI safety research agendas and projects (with a focus on MIRI’s Agent Foundations technical agenda). It was geared for researchers who have technical backgrounds and who have not previously worked extensively with MIRI.

December 1-3, 2016 – Berkeley, California

3rd Workshop on Machine Learning and AI Safety

Ryan Carey (MIRI)
Cameron Freer (Gamalon and Borelian)
Scott Garrabrant (MIRI)
Marcello Herreshoff (Google)
Patrick LaVictoire (MIRI)

Moshe Looks (Google)
Jeremy Nixon (Spark)
Anand Srinivasan (AlphaSheets)
Jessica Taylor (MIRI)
Eliezer Yudkowsky (MIRI)

November 11-13, 2016 – Berkeley, California

9th Workshop on Logic, Probability, and Reflection

Tsvi Benson-Tilsen (UC Berkeley)
Ryan Carey (MIRI)
Andrew Critch (MIRI)
Abram Demski (USC)
Sam Eisenstat (UC Berkeley)
Benya Fallenstein (MIRI)

Jack Gallagher
Scott Garrabrant (MIRI)
Marcello Herreshoff (Google)
Patrick LaVictoire (MIRI)
Nisan Stiennon (Google)
Jessica Taylor (MIRI)
Alex Zhu (MIT)

October 21-23, 2016 – Berkeley, California

2nd Workshop on Machine Learning and AI Safety

Ryan Carey (MIRI)
Sarah Constantin
Scott Garrabrant (MIRI)
Marcello Herreshoff (Google)

Patrick LaVictoire (MIRI)
William Saunders (Google)
Jessica Taylor (MIRI)
Eliezer Yudkowsky (MIRI)

August 26-28, 2016 – Berkeley, California

1st Workshop on Machine Learning and AI Safety

This three-day workshop brought together researchers with machine learning backgrounds to work on long-term AI safety problems that can be modeled in current machine learning systems and frameworks, for instance those described in “Concrete Problems in AI Safety” and “Alignment for Advanced Machine Learning Systems”.

Topics included learning human-interpretable and causal models of the environment; engineering cost functions based on impact measures to disincentivize side effects; designing robust metrics for the quality of a purported explanation of a plan; and developing a formal model of Goodhart’s Law which yields mild optimization.

June 17, 2016 – Berkeley, California

CSRBAI Workshop on Agent Models and Multi-Agent Dilemmas

Twenty participants attended from institutions including:

The Colloquium Series on Robust and Beneficial AI included a series of workshops to facilitate conversations and collaborations between people interested in a number of different approaches to the technical challenges associated with AI robustness and reliability.

The fourth workshop of CSRBAI focused on the topics of designing agents that behave well in their environments, without ignoring the effects of the agent’s own actions on the environment or on other agents within the environment.

June 11-12, 2016 – Berkeley, California

CSRBAI Workshop on Preference Specification

Twenty participants attended from institutions including:

The Colloquium Series on Robust and Beneficial AI included a series of workshops to facilitate conversations and collaborations between people interested in a number of different approaches to the technical challenges associated with AI robustness and reliability.

The third workshop of CSRBAI focused on the topic of preference specification for highly capable AI systems, in which the perennial problem of wanting code to “do what I mean, not what I said” becomes increasingly challenging.

June 4-5, 2016 – Berkeley, California

CSRBAI Workshop on Robustness and Error-Tolerance

Fourteen participants attended from institutions including:

The Colloquium Series on Robust and Beneficial AI included a series of workshops to facilitate conversations and collaborations between people interested in a number of different approaches to the technical challenges associated with AI robustness and reliability.

The second workshop of CSRBAI focused on the topic of robustness and error-tolerance in AI systems, and how to ensure that when AI system fail, they fail gracefully and detectably.

May 28-29, 2016 – Berkeley, California

CSRBAI Workshop on Transparency

Twenty participants attended from institutions including:

The Colloquium Series on Robust and Beneficial AI included a series of workshops to facilitate conversations and collaborations between people interested in a number of different approaches to the technical challenges associated with AI robustness and reliability.

The first workshop of CSRBAI focused on the topic of transparency in AI systems, and how we can increase transparency while maintaining capabilities.