Search Results for author: David Manheim

Found 9 papers, 0 papers with code

Modeling Transformative AI Risks (MTAIR) Project -- Summary Report

no code implementations19 Jun 2022 Sam Clarke, Ben Cottier, Aryeh Englander, Daniel Eth, David Manheim, Samuel Dylan Martin, Issa Rice

This report outlines work by the Modeling Transformative AI Risk (MTAIR) project, an attempt to map out the key hypotheses, uncertainties, and disagreements in debates about catastrophic risks from advanced AI, and the relationships between them.

Arguments about Highly Reliable Agent Designs as a Useful Path to Artificial Intelligence Safety

no code implementations9 Jan 2022 Issa Rice, David Manheim

Several different approaches exist for ensuring the safety of future Transformative Artificial Intelligence (TAI) or Artificial Superintelligence (ASI) systems, and proponents of different approaches have made different and debated claims about the importance or usefulness of their work in the near term, and for future systems.

Compositional modelling of immune response and virus transmission dynamics

no code implementations3 Nov 2021 William Waites, Matteo Cavaliere, Vincent Danos, Ruchira Datta, Rosalind M. Eggo, Timothy B. Hallett, David Manheim, Jasmina Panovska-Griffiths, Timothy W. Russell, Veronika I. Zarnitsyna

Transmission models for infectious diseases are typically formulated in terms of dynamics between individuals or groups with processes such as disease progression or recovery for each individual captured phenomenologically, without reference to underlying biological processes.

Forecasting AI Progress: A Research Agenda

no code implementations4 Aug 2020 Ross Gruetzemacher, Florian Dorner, Niko Bernaola-Alvarez, Charlie Giattino, David Manheim

This paper describes the development of a research agenda for forecasting AI progress which utilized the Delphi technique to elicit and aggregate experts' opinions on what questions and methods to prioritize.

Rule-based epidemic models

no code implementations22 Jun 2020 William Waites, Matteo Cavaliere, David Manheim, Jasmina Panovska-Griffiths, Vincent Danos

This paper gives an introduction to rule-based modelling applied to topics in infectious diseases.

Decision Making Epidemiology

Oversight of Unsafe Systems via Dynamic Safety Envelopes

no code implementations22 Nov 2018 David Manheim

This paper reviews the reasons that Human-in-the-Loop is both critical for preventing widely-understood failure modes for machine learning, and not a practical solution.

BIG-bench Machine Learning

Multiparty Dynamics and Failure Modes for Machine Learning and Artificial Intelligence

no code implementations16 Oct 2018 David Manheim

An important challenge for safety in machine learning and artificial intelligence systems is a~set of related failures involving specification gaming, reward hacking, fragility to distributional shifts, and Goodhart's or Campbell's law.

BIG-bench Machine Learning

Categorizing Variants of Goodhart's Law

no code implementations13 Mar 2018 David Manheim, Scott Garrabrant

There are several distinct failure modes for overoptimization of systems on the basis of metrics.

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