Paul Christiano is an American researcher in the field of artificial intelligence (AI), with a specific focus on AI alignment, which is the subfield of AI safety research that aims to steer AI systems toward human interests.[1] He serves as the Head of Safety for the U.S. Artificial Intelligence Safety Institute inside NIST.[2] He formerly led the language model alignment team at OpenAI and became founder and head of the non-profit Alignment Research Center (ARC), which works on theoretical AI alignment and evaluations of machine learning models.[3][4] In 2023, Christiano was named as one of the TIME 100 Most Influential People in AI (TIME100 AI).[4][5]
In September 2023, Christiano was appointed to the UK government's Frontier AI Taskforce advisory board.[6] He is also an initial trustee on Anthropic's Long-Term Benefit Trust.[7]
In 2012, Christiano graduated from the Massachusetts Institute of Technology (MIT) with a degree in mathematics.[10][11] At MIT, he researched data structures, quantum cryptography, and combinatorial optimization.[11]
He then went on to complete a PhD at the University of California, Berkeley.[12] While at Berkeley, Christiano collaborated with researcher Katja Grace on AI Impacts, co-developing a preliminary methodology for comparing supercomputers to brains, using traversed edges per second (TEPS).[13] He also experimented with putting Carl Shulman's donor lottery theory into practice, raising nearly $50,000 in a pool to be donated to a single charity.[14]
Career
At OpenAI, Christiano co-authored the paper "Deep Reinforcement Learning from Human Preferences" (2017) and other works developing reinforcement learning from human feedback (RLHF).[15][16] He is considered one of the principal architects of RLHF,[4][7] which in 2017 was "considered a notable step forward in AI safety research", according to The New York Times.[17] Other works such as "AI safety via debate" (2018) focus on the problem of scalable oversight – supervising AIs in domains where humans would have difficulty judging output quality.[18][19][20]
Christiano left OpenAI in 2021 to work on more conceptual and theoretical issues in AI alignment and subsequently founded the Alignment Research Center to focus on this area.[1] One subject of study is the problem of eliciting latent knowledge from advanced machine learning models.[21][22] ARC also develops techniques to identify and test whether an AI model is potentially dangerous.[4] In April 2023, Christiano told The Economist that ARC was considering developing an industry standard for AI safety.[23]
As of April 2024, Christiano was listed as the head of AI safety for the US AI Safety Institute at NIST.[24] One month earlier in March 2024, staff members and scientists at the institute threatened to resign upon being informed of Christiano's pending appointment to the role, stating that his ties to the effective altruism movement may jeopardize the AI Safety Institute's objectivity and integrity.[25]
Views on AI risks
He is known for his views on the potential risks of advanced AI. In 2017, Wired magazine stated that Christiano and his colleagues at OpenAI weren't worried about the destruction of the human race by "evil robots", explaining that "[t]hey’re more concerned that, as AI progresses beyond human comprehension, the technology’s behavior may diverge from our intended goals."[26]
However, in a widely quoted interview with Business Insider in 2023, Christiano said that there is a “10–20% chance of AI takeover, [with] many [or] most humans dead.” He also conjectured a “50/50 chance of doom shortly after you have AI systems that are human level.”[27][1]
^Christiano, Paul F; Leike, Jan; Brown, Tom; Martic, Miljan; Legg, Shane; Amodei, Dario (2017). "Deep Reinforcement Learning from Human Preferences". Advances in Neural Information Processing Systems. 30. Curran Associates, Inc.
^Ouyang, Long; Wu, Jeffrey; Jiang, Xu; Almeida, Diogo; Wainwright, Carroll; Mishkin, Pamela; Zhang, Chong; Agarwal, Sandhini; Slama, Katarina; Ray, Alex; Schulman, John; Hilton, Jacob; Kelton, Fraser; Miller, Luke; Simens, Maddie (December 6, 2022). "Training language models to follow instructions with human feedback". Advances in Neural Information Processing Systems. 35: 27730–27744. arXiv:2203.02155.
^Burns, Collin; Ye, Haotian; Klein, Dan; Steinhardt, Jacob (2022). "Discovering Latent Knowledge in Language Models Without Supervision". arXiv:2212.03827 [cs.CL].
