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To accurately and confidently answer the question 'could an AI model or system increase biorisk', it is necessary to have both a sound theoretical threat model for how AI models or systems could increase biorisk and a robust method for testing that threat model.
The cult at the end of the world
David E. Kaplan · 1996
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Biotechnology Research in an Age of Terrorism
National Academies Press · 2004
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Synthetic biology: Applying engineering to biology, 2005
European Commission · 2005
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Amerithrax investigative summary
The United States Department of Justice · 2010
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The Soviet Biological Weapons Program: A History
Milton Leitenberg, Raymond A. Zilinskas, and Jens H. Kuhn · 2012
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Seven myths & realities about do-it-yourself biology, 2013
Daniel Grushkin, Todd Kuiken, and Piers Millet · 2013
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Synthetic biology and biosecurity: Challenging the “myths”
Catherine Jefferson, Filippa Lentzos, and Claire Marris · 2014
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Biosafety levels, 2015
US Department of Health and Human Sciences · 2015
Earlier work this paper cites.
Biodefense in the Age of Synthetic Biology
National Academies of Sciences, Engineering, and Medicine · 2018
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Who should we fear more: Biohackers, disgruntled postdocs, or bad governments? a simple risk chain model of biorisk
Anders Sandberg and Cassidy Nelson · 2020
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What is dual-use research of concern?, 2020
World Health Organisation · 2020
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Highly accurate protein structure prediction with AlphaFold
John Jumper, Richard Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Olaf Ronneberger, Kathryn Tunyasuvunakool, Russ Bates, Augustin Žídek, Anna Potapenko, Alex Bridgland, Clemens Meyer, Simon A. A. Kohl, Andrew J. Ballard, Andrew Cowie, Bernardino Romera-Paredes, Stanislav Nikolov, Rishub Jain, Jonas Adler, Trevor Back, Stig Petersen, David Reiman, Ellen Clancy, Michal Zielinski, Martin Steinegger, Michalina Pacholska, Tamas Berghammer, Sebastian Bodenstein, David Silver, Oriol Vinyals, Andrew W. Senior, Koray Kavukcuoglu, Pushmeet Kohli, and Demis Hassabis · 2021
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Robust deep learning–based protein sequence design using ProteinMPNN
J. Dauparas, I. Anishchenko, N. Bennett, H. Bai, R. J. Ragotte, L. F. Milles, B. I. M. Wicky, A. Courbet, R. J. de Haas, N. Bethel, P. J. Y. Leung, T. F. Huddy, S. Pellock, D. Tischer, F. Chan, B. Koepnick, H. Nguyen, A. Kang, B. Sankaran, A. K. Bera, N. P. King, and D. Baker · 2022
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Credible pandemic virus identification willtrigger the immediate proliferation of agents as lethal as nuclear devices, 2022
Kevin M. Esvelt · 2022
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he urgent need for an overhaul of global biorisk management
F Lentzos, G.D. Koblentz, and J Rodgers · 2022
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AI-powered biological warfare is ‘biggest issue,’ former Google exec warns, 2022
Ryan Lovelace · 2022
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Evidence-based laboatory biorisk management - science and technology roadmap
National Science and Technology Council · 2022
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AlphaFold predictions are valuable hypotheses, and accelerate but do not replace experimental structure determination, 2023
Thomas C. Terwilliger, Dorothee Liebschner, Tristan I. Croll, Christopher J. Williams, Airlie J. McCoy, Billy K. Poon, Pavel V. Afonine, Robert D. Oeffner, Jane S. Richardson, Randy J. Read, and Paul D. Adams · 2022
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Advanced technology: Examining threats to national security, 2023
Gregory C Allen · 2023
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Preparing the federal response to advanced technologies, 2023
Jeff Alstott · 2023
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Frontier threats red teaming for AI safety, 2023
Anthropic · 2023
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Efficient and accurate prediction of protein structure using RoseTTAFold2, 2023
Minkyung Baek, Ivan Anishchenko, Ian R. Humphreys, Qian Cong, David Baker, and Frank DiMaio · 2023
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AI and BioRisk: An explainer, 2023
Steph Batalis · 2023
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Censoring chemical data to mitigate dual use risk, 2023
Quintina L. Campbell, Jonathan Herington, and Andrew D. White · 2023
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Statement to the US senate AI insight forum on “risk, alignment, and guarding against doomsday scenarios", 2023
Rocco Casagrande · 2023
Cited alongside, same era.
Accurate proteome-wide missense variant effect prediction with AlphaMissense
Jun Cheng, Guido Novati, Joshua Pan, Clare Bycroft, Akvilė Žemgulytė, Taylor Applebaum, Alexander Pritzel, Lai Hong Wong, Michal Zielinski, Tobias Sargeant, Rosalia G. Schneider, Andrew W. Senior, John Jumper, Demis Hassabis, Pushmeet Kohli, and Žiga Avsec · 2023
Cited alongside, same era.
Takashi Inagaki, Akari Kato, Koichi Takahashi, Haruka Ozaki, and Genki N. Kanda · 2023
Cited alongside, same era.
Bio x AI: Policy recommendations for a new frontier, 2023
Nazish Jeffery, Sarah R. Carter, Nazish Alexanian, Oliver Crook, Samuel Curtis, Richard Moulange, Shrestha Rath, Sophie Rose, and Jennifer Clark · 2023
Cited alongside, same era.
How accurately can one predict drug binding modes using AlphaFold models?, 2023
Machine learning and deep learning in synthetic biology: Key architectures, applications, and challenges
Manoj Kumar Goshisht · 2024
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Managing risks from AI-enabled biological tools, 2024
John Halstead · 2024
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Simulating 500 million years of evolution with a language model, 2024
Thomas Hayes, Roshan Rao, Halil Akin, Nicholas J. Sofroniew, Deniz Oktay, Zeming Lin, Robert Verkuil, Vincent Q. Tran, Jonathan Deaton, Marius Wiggert, Rohil Badkundri, Irhum Shafkat, Jun Gong, Alexander Derry, Raul S. Molina, Neil Thomas, Yousuf Khan, Chetan Mishra, Carolyn Kim, Liam J. Bartie, Matthew Nemeth, Patrick D. Hsu, Tom Sercu, Salvatore Candido, and Alexander Rives · 2024
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On the limitations of compute thresholds as a governance strategy, 2024
Sara Hooker · 2024
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Medical adaptation of large language and vision-language models: Are we making progress?, 2024
Daniel P. Jeong, Saurabh Garg, Zachary C. Lipton, and Michael Oberst · 2024
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Masha Karelina, Joseph Noh, and Ron O. Dror · 2023
Cited alongside, same era.
The data provenance initiative: A large scale audit of dataset licensing and attribution in ai, 2023
Shayne Longpre, Robert Mahari, Anthony Chen, Naana Obeng-Marnu, Damien Sileo, William Brannon, Niklas Muennighoff, Nathan Khazam, Jad Kabbara, Kartik Perisetla, Xinyi Wu, Enrico Shippole, Kurt Bollacker, Tongshuang Wu, Luis Villa, Sandy Pentland, and Sara Hooker · 2023
Cited alongside, same era.
Towards responsible governance of biological design tools, 2023
Richard Moulange, Max Langenkamp, Tessa Alexanian, Samuel Curtis, and Morgan Livingston · 2023
Cited alongside, same era.
Written testimony for Senate Homeland Security and Governmental Affairs Subcommittee on Emerging Threats and Spending Oversight hearing on Advanced Technology, 2023
Dewey Murdoch · 2023
Cited alongside, same era.
HyenaDNA: Long-range genomic sequence modeling at single nucleotide resolution, 2023
Eric Nguyen, Michael Poli, Marjan Faizi, Armin Thomas, Callum Birch-Sykes, Michael Wornow, Aman Patel, Clayton Rabideau, Stefano Massaroli, Yoshua Bengio, Stefano Ermon, Stephen A. Baccus, and Chris Ré · 2023
Cited alongside, same era.
AlphaFold and the future of structural biology
Randy J. Read, Edward N. Baker, Charles S. Bond, Elspeth F. Garman, and Mark J. van Raaij · 2023
Cited alongside, same era.
US senators express bipartisan alarm about AI, focusing on biological attack, 2023
Reuters · 2023
Cited alongside, same era.
Can large language models democratize access to dual-use biotechnology?, 2023
Emily H. Soice, Rafael Rocha, Kimberlee Cordova, Michael Specter, and Kevin M. Esvelt · 2023
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On the societal impact of open foundation models, 2024
Sayash Kapoor, Rishi Bommasani, Kevin Klyman, Shayne Longpre, Ashwin Ramaswami, Peter Cihon, Aspen Hopkins, Kevin Bankston, Stella Biderman, Miranda Bogen, Rumman Chowdhury, Alex Engler, Peter Henderson, Yacine Jernite, Seth Lazar, Stefano Maffulli, Alondra Nelson, Joelle Pineau, Aviya Skowron, Dawn Song, Victor Storchan, Daniel Zhang, Daniel E. Ho, Percy Liang, and Arvind Narayanan · 2024
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Shayne Longpre, Robert Mahari, Ariel N. Lee, Campbell S. Lund, Hamidah Oderinwale, William Brannon, Nayan Saxena, Naana Obeng-Marnu, Tobin South, Cole J Hunter, Kevin Klyman, Christopher Klamm, Hailey Schoelkopf, Nikhil Singh, Manuel Cherep, Ahmad Mustafa Anis, An Dinh, Caroline Shamiso Chitongo, Da Yin, Damien Sileo, Deividas Mataciunas, Diganta Misra, Emad A. Alghamdi, Enrico Shippole, Jianguo Zhang, Joanna Materzynska, Kun Qian, Kushagra Tiwary, Lester James Validad Miranda, Manan Dey, Minnie Liang, Mohammed Hamdy, Niklas Muennighoff, Seonghyeon Ye, Seungone Kim, Shrestha Mohanty, Vipul Gupta, Vivek Sharma, Vu Minh Chien, Xuhui Zhou, Yizhi LI, Caiming Xiong, Luis Villa, Stella Biderman, Hanlin Li, Daphne Ippolito, Sara Hooker, Jad Kabbara, and Alex Pentland · 2024
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Biological sequence models in the context of the AI directives, 2024
Nicole Maug · 2024
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Dozens of Top Scientists Sign Effort to Prevent A.I. Bioweapons
Cade Metz · 2024
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The operational risks of AI in large-scale biological attacks: Results of a red-team study, 2024
Christopher A. Mouton, Caleb Lucas, and Ella Guest · 2024
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Dual-use foundation models with widely available model weights, 2024
National Telecommunications and Information Administration · 2024
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Managing misuse risk for dual-use foundation models, 2024
Gaithersburg Md NIST · 2024
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White paper 3: Risks of AIxBio, 2024
NSCEB · 2024
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Barriers to Bioweapons
Sonia Ben Ouagrham-Gormley · 2024
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Open problems in technical ai governance, 2024
Anka Reuel, Ben Bucknall, Stephen Casper, Tim Fist, Lisa Soder, Onni Aarne, Lewis Hammond, Lujain Ibrahim, Alan Chan, Peter Wills, Markus Anderljung, Ben Garfinkel, Lennart Heim, Andrew Trask, Gabriel Mukobi, Rylan Schaeffer, Mauricio Baker, Sara Hooker, Irene Solaiman, Alexandra Sasha Luccioni, Nitarshan Rajkumar, Nicolas Moës, Jeffrey Ladish, Neel Guha, Jessica Newman, Yoshua Bengio, Tobin South, Alex Pentland, Sanmi Koyejo, Mykel J. Kochenderfer, and Robert Trager · 2024
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Towards quantifying the risk of LLMs raising lone-wolf bioterrorism (forthcoming)
Luca Righetti · 2024
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The near-term impact of AI on biological misuse, 2024
Sophie Rose, Richard Moulange, James Smith, and Cassidy Nelson · 2024
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Aya dataset: An open-access collection for multilingual instruction tuning, 2024
Shivalika Singh, Freddie Vargus, Daniel Dsouza, Börje F. Karlsson, Abinaya Mahendiran, Wei-Yin Ko, Herumb Shandilya, Jay Patel, Deividas Mataciunas, Laura OMahony, Mike Zhang, Ramith Hettiarachchi, Joseph Wilson, Marina Machado, Luisa Souza Moura, Dominik Krzemiński, Hakimeh Fadaei, Irem Ergün, Ifeoma Okoh, Aisha Alaagib, Oshan Mudannayake, Zaid Alyafeai, Vu Minh Chien, Sebastian Ruder, Surya Guthikonda, Emad A. Alghamdi, Sebastian Gehrmann, Niklas Muennighoff, Max Bartolo, Julia Kreutzer, Ahmet Üstün, Marzieh Fadaee, and Sara Hooker · 2024
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Science in the age of AI, 2024
The Royal Society · 2024
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AI safety institute approach to evaluations, 2024
UK AI Safety Institute · 2024
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New commitment to deepen work on severe AI risks concludes AI seoul summit, 2024
UK Government · 2024
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Specialized foundation models struggle to beat supervised baselines, 2024
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Emerging biosecurity threats and responses: A review of published and gray literature
Christopher L. Cummings, Kaitlin M. Volk, Anna A. Ulanova, Do Thuy Uyen Ha Lam, and Pei Rou Ng · 2086
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