Zobrazeno 1 - 10
of 21
pro vyhledávání: '"Poursabzi-Sangdeh, Forough"'
Autor:
Vidgen, Bertie, Agrawal, Adarsh, Ahmed, Ahmed M., Akinwande, Victor, Al-Nuaimi, Namir, Alfaraj, Najla, Alhajjar, Elie, Aroyo, Lora, Bavalatti, Trupti, Bartolo, Max, Blili-Hamelin, Borhane, Bollacker, Kurt, Bomassani, Rishi, Boston, Marisa Ferrara, Campos, Siméon, Chakra, Kal, Chen, Canyu, Coleman, Cody, Coudert, Zacharie Delpierre, Derczynski, Leon, Dutta, Debojyoti, Eisenberg, Ian, Ezick, James, Frase, Heather, Fuller, Brian, Gandikota, Ram, Gangavarapu, Agasthya, Gangavarapu, Ananya, Gealy, James, Ghosh, Rajat, Goel, James, Gohar, Usman, Goswami, Sujata, Hale, Scott A., Hutiri, Wiebke, Imperial, Joseph Marvin, Jandial, Surgan, Judd, Nick, Juefei-Xu, Felix, Khomh, Foutse, Kailkhura, Bhavya, Kirk, Hannah Rose, Klyman, Kevin, Knotz, Chris, Kuchnik, Michael, Kumar, Shachi H., Kumar, Srijan, Lengerich, Chris, Li, Bo, Liao, Zeyi, Long, Eileen Peters, Lu, Victor, Luger, Sarah, Mai, Yifan, Mammen, Priyanka Mary, Manyeki, Kelvin, McGregor, Sean, Mehta, Virendra, Mohammed, Shafee, Moss, Emanuel, Nachman, Lama, Naganna, Dinesh Jinenhally, Nikanjam, Amin, Nushi, Besmira, Oala, Luis, Orr, Iftach, Parrish, Alicia, Patlak, Cigdem, Pietri, William, Poursabzi-Sangdeh, Forough, Presani, Eleonora, Puletti, Fabrizio, Röttger, Paul, Sahay, Saurav, Santos, Tim, Scherrer, Nino, Sebag, Alice Schoenauer, Schramowski, Patrick, Shahbazi, Abolfazl, Sharma, Vin, Shen, Xudong, Sistla, Vamsi, Tang, Leonard, Testuggine, Davide, Thangarasa, Vithursan, Watkins, Elizabeth Anne, Weiss, Rebecca, Welty, Chris, Wilbers, Tyler, Williams, Adina, Wu, Carole-Jean, Yadav, Poonam, Yang, Xianjun, Zeng, Yi, Zhang, Wenhui, Zhdanov, Fedor, Zhu, Jiacheng, Liang, Percy, Mattson, Peter, Vanschoren, Joaquin
This paper introduces v0.5 of the AI Safety Benchmark, which has been created by the MLCommons AI Safety Working Group. The AI Safety Benchmark has been designed to assess the safety risks of AI systems that use chat-tuned language models. We introdu
Externí odkaz:
http://arxiv.org/abs/2404.12241
Autor:
Dibia, Victor, Fourney, Adam, Bansal, Gagan, Poursabzi-Sangdeh, Forough, Liu, Han, Amershi, Saleema
Large language models have demonstrated great potential to assist programmers in generating code. For such human-AI pair programming scenarios, we empirically demonstrate that while generated code is most often evaluated in terms of their functional
Externí odkaz:
http://arxiv.org/abs/2210.16494
Autor:
Poursabzi-Sangdeh, Forough, Goldstein, Daniel G., Hofman, Jake M., Vaughan, Jennifer Wortman, Wallach, Hanna
With machine learning models being increasingly used to aid decision making even in high-stakes domains, there has been a growing interest in developing interpretable models. Although many supposedly interpretable models have been proposed, there hav
Externí odkaz:
http://arxiv.org/abs/1802.07810
Autor:
Hofman, Jake M., Goldstein, Daniel G., Sen, Siddhartha, Poursabzi-Sangdeh, Forough, Allen, Jennifer, Dong, Ling Liang, Fried, Brenda, Gaur, Harpreet, Hoq, Adnan, Mbazor, Emeka, Moreira, Naomi, Muso, Cindy, Rapp, Etta, Terrero, Roymil
Publikováno v:
In Organizational Behavior and Human Decision Processes May 2021 164:192-202
Publikováno v:
Communications of the ACM; Dec2018, Vol. 61 Issue 12, p62-67, 6p, 1 Color Photograph
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Autor:
Fok, Raymond, Weld, Daniel S.
Publikováno v:
AI Magazine; Sep2024, Vol. 45 Issue 3, p317-332, 16p
Publikováno v:
Journal of Politics. Apr2022, Vol. 84 Issue 2, p1132-1148. 17p.
This book examines how people make decisions under risk and uncertainty in operational settings and opens the black box by specifying the cognitive processes that lead to human behavior. Drawing on economics, psychology and artificial intelligence, t
Autor:
S. Scott Graham
For years, technologists and computer scientists have promised an AI revolution that would transform the very basis of how we imagine and administer modern medicine. AI-driven advancements in medical error rates, diagnostic accuracy, or disease outbr