Dan Hendrycks, American Machine Learning Researcher
6.0/10
Dan Hendrycks, American Machine Learning Researcher
Dan Hendrycks is the founding director of the Centre of AI Safety, an organisation which rose to prominence after their co-signed statement on existential risks resulted in the UN Secretary-General calling for an “international AI watchdog”. Prior to focusing on AI safety specifically, Hendrycks was an accomplished AI researcher in his own right; he made significant contributions to the field, including a widely-used algorithm used in foundation models such as GPT-3.
Dan Hendrycks was mentored by Dawn Song and Jacob Steinhardt while pursuing his PhD at UC Berkeley. He currently serves as the Center for AI Safety’s director. AI safety interests Dan Hendrycks, who graduated from UChicago with a bachelor’s degree. The Open Philanthropy AI Fellowship and the NSF GRFP both fund his study. He made contributions to the distribution shift benchmarks, the out-of-distribution detection baseline, and the GELU activation function—the most popular activation in cutting-edge models like BERT, GPT, and Vision Transformers, among others.
2023
Dan Hendrycks thinks there are several ways in which technology might wipe out mankind. He told that a malicious actor may obtain access to a future iteration of generative AI, ask it for guidance on creating a biological weapon, and unleash havoc. Alternatively, he said, the effectiveness of AI may compel broad corporate adoption, subjugating the whole economy in the process; conversely, it might exacerbate the propagation of false and misleading information.
According to Hendrycks, there is a very real chance that mankind will end. “If I see international coordination doesn’t happen, or much of it, it’ll be more likely than not that we go extinct,” he stated.
Things that have most slowed down AI timelines/development:
– reviewers, by favoring of cleverness and proofs over simplicity and performance – NVIDIA, by distributing GPUs widely rather than to buyers most willing to pay – tensorflow https://t.co/KfjTRajaK2
