Publications

Managing AI Risks in an Era of Rapid Progress

Yoshua Bengio

Geoffrey Hinton

Andrew Yao

Dawn Song

Pieter Abbeel

Yuval Noah Harari

Ya-Qin Zhang

Lan Xue

Shai Shalev-Shwartz

Gillian K. Hadfield

Jeff Clune

Tegan Maharaj

Frank Hutter

Atilim Güneş Baydin

Sheila McIlraith

Qiqi Gao

Ashwin Acharya

David Scott Krueger

Anca Dragan

Philip Torr … (see 4 more)

Stuart Russell

Daniel Kahneman

Jan Brauner

Sören Mindermann

In this short consensus paper, we outline risks from upcoming, advanced AI systems. We examine large-scale social harms and malicious uses, … (see more)as well as an irreversible loss of human control over autonomous AI systems. In light of rapid and continuing AI progress, we propose priorities for AI R&D and governance.

2023-10-26

ArXiv (preprint)

doi.org

arxiv.org

Managing AI Risks in an Era of Rapid Progress

Yoshua Bengio

Geoffrey Hinton

Andrew Yao

Dawn Song

Pieter Abbeel

Yuval Noah Harari

Trevor Darrell

Ya-Qin Zhang

Lan Xue

Shai Shalev-Shwartz

Gillian K. Hadfield

Jeff Clune

Tegan Maharaj

Frank Hutter

Atilim Güneş Baydin

Sheila McIlraith

Qiqi Gao

Ashwin Acharya

David Scott Krueger

Anca Dragan … (see 5 more)

Philip Torr

Stuart Russell

Daniel Kahneman

Jan Brauner

Sören Mindermann

2023-10-26

Science (published)

doi.org

arxiv.org

Managing AI Risks in an Era of Rapid Progress

Yoshua Bengio

Geoffrey Hinton

Andrew Yao

Dawn Song

Pieter Abbeel

Yuval Noah Harari

Ya-Qin Zhang

Lan Xue

Shai Shalev-Shwartz

Gillian K. Hadfield

Jeff Clune

Tegan Maharaj

Frank Hutter

Atilim Güneş Baydin

Sheila McIlraith

Qiqi Gao

Ashwin Acharya

David Scott Krueger

Anca Dragan

Philip Torr … (see 4 more)

Stuart Russell

Daniel Kahneman

Jan Brauner

Sören Mindermann

In this short consensus paper, we outline risks from upcoming, advanced AI systems. We examine large-scale social harms and malicious uses, … (see more)as well as an irreversible loss of human control over autonomous AI systems. In light of rapid and continuing AI progress, we propose priorities for AI R&D and governance.

2023-10-26

ArXiv (preprint)

doi.org

arxiv.org

Managing AI Risks in an Era of Rapid Progress

Yoshua Bengio

Geoffrey Hinton

Andrew Yao

Dawn Song

Pieter Abbeel

Yuval Noah Harari

Ya-Qin Zhang

Lan Xue

Shai Shalev-Shwartz

Gillian K. Hadfield

Jeff Clune

Tegan Maharaj

Frank Hutter

Atilim Güneş Baydin

Sheila McIlraith

Qiqi Gao

Ashwin Acharya

David Scott Krueger

Anca Dragan

Philip Torr … (see 4 more)

Stuart Russell

Daniel Kahneman

Jan Brauner

Sören Mindermann

In this short consensus paper, we outline risks from upcoming, advanced AI systems. We examine large-scale social harms and malicious uses, … (see more)as well as an irreversible loss of human control over autonomous AI systems. In light of rapid and continuing AI progress, we propose priorities for AI R&D and governance.

2023-10-26

ArXiv (preprint)

doi.org

arxiv.org

Surrogate Minimization: An Optimization Algorithm for Training Large Neural Networks with Model Parallelism

Reza Asad

Reza Babanezhad Harikandeh

Issam Hadj Laradji

Nicolas Le Roux

Sharan Vaswani

2023-10-26

NeurIPS.cc/2023/Workshop/OPT (poster)

openreview.net

The value of standards for health datasets in artificial intelligence-based applications

Anmol Arora

Joseph E. Alderman

Joanne Palmer

Shaswath Ganapathi

Elinor Laws

Melissa D. McCradden

Lauren Oakden-Rayner

Stephen R. Pfohl

Marzyeh Ghassemi

Francis McKay

Darren Treanor

Negar Rostamzadeh

Bilal Mateen

Jacqui Gath

Adewole O. Adebajo

Stephanie Kuku

Rubeta Matin

Katherine Heller

Elizabeth Sapey

Neil J. Sebire … (see 4 more)

Heather Cole-Lewis

Melanie Calvert

Alastair Denniston

Xiaoxuan Liu

2023-10-26

Nature Medicine (published)

doi.org

PDB-Struct: A Comprehensive Benchmark for Structure-based Protein Design

Chuanrui Wang

Bozitao Zhong

Zuobai Zhang

Narendra Chaudhary

Sanchit Misra

Jian Tang

Structure-based protein design has attracted increasing interest, with numerous methods being introduced in recent years. However, a univers… (see more)ally accepted method for evaluation has not been established, since the wet-lab validation can be overly time-consuming for the development of new algorithms, and the

2023-10-25

NeurIPS.cc/2023/Workshop/AI4D3 (poster)

openreview.net

PDB-Struct: A Comprehensive Benchmark for Structure-based Protein Design

Chuanrui Wang

Bozitao Zhong

Zuobai Zhang

Narendra Chaudhary

Sanchit Misra

Jian Tang

Structure-based protein design has attracted increasing interest, with numerous methods being introduced in recent years. However, a univers… (see more)ally accepted method for evaluation has not been established, since the wet-lab validation can be overly time-consuming for the development of new algorithms, and the

2023-10-25

NeurIPS.cc/2023/Workshop/AI4D3 (poster)

openreview.net

Understanding Graph Neural Networks with Generalized Geometric Scattering Transforms

Michael Perlmutter

Alexander Tong

Feng Gao

Guy Wolf

Matthew Hirn

The scattering transform is a multilayered wavelet-based deep learning architecture that acts as a model of convolutional neural networks. R… (see more)ecently, several works have introduced generalizations of the scattering transform for non-Euclidean settings such as graphs. Our work builds upon these constructions by introducing windowed and non-windowed geometric scattering transforms for graphs based upon a very general class of asymmetric wavelets. We show that these asymmetric graph scattering transforms have many of the same theoretical guarantees as their symmetric counterparts. As a result, the proposed construction unifies and extends known theoretical results for many of the existing graph scattering architectures. In doing so, this work helps bridge the gap between geometric scattering and other graph neural networks by introducing a large family of networks with provable stability and invariance guarantees. These results lay the groundwork for future deep learning architectures for graph-structured data that have learned filters and also provably have desirable theoretical properties.

2023-10-25

SIAM Journal on Mathematics of Data Science (published)

doi.org

arxiv.org

Improving Robustness and Reliability in Medical Image Classification with Latent-Guided Diffusion and Nested-Ensembles

Xing Shen

Hengguan Huang

Brennan Nichyporuk

Tal Arbel

2023-10-24

ArXiv (preprint)

doi.org