Zobrazeno 1 - 10
of 37
pro vyhledávání: '"ZIEGLER, DANIEL M."'
Autor:
Hubinger, Evan, Denison, Carson, Mu, Jesse, Lambert, Mike, Tong, Meg, MacDiarmid, Monte, Lanham, Tamera, Ziegler, Daniel M., Maxwell, Tim, Cheng, Newton, Jermyn, Adam, Askell, Amanda, Radhakrishnan, Ansh, Anil, Cem, Duvenaud, David, Ganguli, Deep, Barez, Fazl, Clark, Jack, Ndousse, Kamal, Sachan, Kshitij, Sellitto, Michael, Sharma, Mrinank, DasSarma, Nova, Grosse, Roger, Kravec, Shauna, Bai, Yuntao, Witten, Zachary, Favaro, Marina, Brauner, Jan, Karnofsky, Holden, Christiano, Paul, Bowman, Samuel R., Graham, Logan, Kaplan, Jared, Mindermann, Sören, Greenblatt, Ryan, Shlegeris, Buck, Schiefer, Nicholas, Perez, Ethan
Humans are capable of strategically deceptive behavior: behaving helpfully in most situations, but then behaving very differently in order to pursue alternative objectives when given the opportunity. If an AI system learned such a deceptive strategy,
Externí odkaz:
http://arxiv.org/abs/2401.05566
Autor:
Ziegler, Daniel M., Nix, Seraphina, Chan, Lawrence, Bauman, Tim, Schmidt-Nielsen, Peter, Lin, Tao, Scherlis, Adam, Nabeshima, Noa, Weinstein-Raun, Ben, de Haas, Daniel, Shlegeris, Buck, Thomas, Nate
In the future, powerful AI systems may be deployed in high-stakes settings, where a single failure could be catastrophic. One technique for improving AI safety in high-stakes settings is adversarial training, which uses an adversary to generate examp
Externí odkaz:
http://arxiv.org/abs/2205.01663
Autor:
Wu, Jeff, Ouyang, Long, Ziegler, Daniel M., Stiennon, Nisan, Lowe, Ryan, Leike, Jan, Christiano, Paul
A major challenge for scaling machine learning is training models to perform tasks that are very difficult or time-consuming for humans to evaluate. We present progress on this problem on the task of abstractive summarization of entire fiction novels
Externí odkaz:
http://arxiv.org/abs/2109.10862
Autor:
Henighan, Tom, Kaplan, Jared, Katz, Mor, Chen, Mark, Hesse, Christopher, Jackson, Jacob, Jun, Heewoo, Brown, Tom B., Dhariwal, Prafulla, Gray, Scott, Hallacy, Chris, Mann, Benjamin, Radford, Alec, Ramesh, Aditya, Ryder, Nick, Ziegler, Daniel M., Schulman, John, Amodei, Dario, McCandlish, Sam
We identify empirical scaling laws for the cross-entropy loss in four domains: generative image modeling, video modeling, multimodal image$\leftrightarrow$text models, and mathematical problem solving. In all cases autoregressive Transformers smoothl
Externí odkaz:
http://arxiv.org/abs/2010.14701
Autor:
Stiennon, Nisan, Ouyang, Long, Wu, Jeff, Ziegler, Daniel M., Lowe, Ryan, Voss, Chelsea, Radford, Alec, Amodei, Dario, Christiano, Paul
As language models become more powerful, training and evaluation are increasingly bottlenecked by the data and metrics used for a particular task. For example, summarization models are often trained to predict human reference summaries and evaluated
Externí odkaz:
http://arxiv.org/abs/2009.01325
Autor:
Brown, Tom B., Mann, Benjamin, Ryder, Nick, Subbiah, Melanie, Kaplan, Jared, Dhariwal, Prafulla, Neelakantan, Arvind, Shyam, Pranav, Sastry, Girish, Askell, Amanda, Agarwal, Sandhini, Herbert-Voss, Ariel, Krueger, Gretchen, Henighan, Tom, Child, Rewon, Ramesh, Aditya, Ziegler, Daniel M., Wu, Jeffrey, Winter, Clemens, Hesse, Christopher, Chen, Mark, Sigler, Eric, Litwin, Mateusz, Gray, Scott, Chess, Benjamin, Clark, Jack, Berner, Christopher, McCandlish, Sam, Radford, Alec, Sutskever, Ilya, Amodei, Dario
Recent work has demonstrated substantial gains on many NLP tasks and benchmarks by pre-training on a large corpus of text followed by fine-tuning on a specific task. While typically task-agnostic in architecture, this method still requires task-speci
Externí odkaz:
http://arxiv.org/abs/2005.14165
Autor:
Ziegler, Daniel M., Stiennon, Nisan, Wu, Jeffrey, Brown, Tom B., Radford, Alec, Amodei, Dario, Christiano, Paul, Irving, Geoffrey
Reward learning enables the application of reinforcement learning (RL) to tasks where reward is defined by human judgment, building a model of reward by asking humans questions. Most work on reward learning has used simulated environments, but comple
Externí odkaz:
http://arxiv.org/abs/1909.08593
Publikováno v:
Proceedings of the National Academy of Sciences of the United States of America, 1999 Mar . 96(6), 2687-2691.
Externí odkaz:
https://www.jstor.org/stable/47424
Autor:
Ziegler, Daniel M., Ansher, Sherry S., Nagata, Toshiyuki, Kadlubar, Fred F., Jakoby, William B.
Publikováno v:
Proceedings of the National Academy of Sciences of the United States of America, 1988 Apr . 85(8), 2514-2517.
Externí odkaz:
https://www.jstor.org/stable/31464
Autor:
Ziegler, Daniel M.1 (AUTHOR)
Publikováno v:
Drug Metabolism Reviews. Aug2002, Vol. 34 Issue 3, p503-511. 9p.