Chang, Nancy
Reports on the 2017 AAAI Spring Symposium Series
Bohg, Jeannette (Max Planck Institute for Intelligent Systems) | Boix, Xavier (Massachusetts Institute of Technology) | Chang, Nancy (Google) | Churchill, Elizabeth F. (Google) | Chu, Vivian (Georgia Institute of Technology) | Fang, Fei (Harvard University) | Feldman, Jerome (University of California at Berkeley) | González, Avelino J. (University of Central Florida) | Kido, Takashi (Preferred Networks in Japan) | Lawless, William F. (Paine College) | Montaña, José L. (University of Cantabria) | Ontañón, Santiago (Drexel University) | Sinapov, Jivko (University of Texas at Austin) | Sofge, Don (Naval Research Laboratory) | Steels, Luc (Institut de Biologia Evolutiva) | Steenson, Molly Wright (Carnegie Mellon University) | Takadama, Keiki (University of Electro-Communications) | Yadav, Amulya (University of Southern California)
Reports on the 2017 AAAI Spring Symposium Series
Reports on the 2017 AAAI Spring Symposium Series
Bohg, Jeannette (Max Planck Institute for Intelligent Systems) | Boix, Xavier (Massachusetts Institute of Technology) | Chang, Nancy (Google) | Churchill, Elizabeth F. (Google) | Chu, Vivian (Georgia Institute of Technology) | Fang, Fei (Harvard University) | Feldman, Jerome (University of California at Berkeley) | González, Avelino J. (University of Central Florida) | Kido, Takashi (Preferred Networks in Japan) | Lawless, William F. (Paine College) | Montaña, José L. (University of Cantabria) | Ontañón, Santiago (Drexel University) | Sinapov, Jivko (University of Texas at Austin) | Sofge, Don (Naval Research Laboratory) | Steels, Luc (Institut de Biologia Evolutiva) | Steenson, Molly Wright (Carnegie Mellon University) | Takadama, Keiki (University of Electro-Communications) | Yadav, Amulya (University of Southern California)
It is also important to remember that having a very sharp distinction of AI A rise in real-world applications of AI has stimulated for social good research is not always feasible, and significant interest from the public, media, and policy often unnecessary. While there has been significant makers. Along with this increasing attention has progress, there still exist many major challenges facing come a media-fueled concern about purported negative the design of effective AIbased approaches to deal consequences of AI, which often overlooks the with the difficulties in real-world domains. One of the societal benefits that AI is delivering and can deliver challenges is interpretability since most algorithms for in the near future. To address these concerns, the AI for social good problems need to be used by human symposium on Artificial Intelligence for the Social end users. Second, the lack of access to valuable data Good (AISOC-17) highlighted the benefits that AI can that could be crucial to the development of appropriate bring to society right now. It brought together AI algorithms is yet another challenge. Third, the researchers and researchers, practitioners, experts, data that we get from the real world is often noisy and and policy makers from a wide variety of domains.
Linguistic Wisdom from the Crowd
Chang, Nancy (Google) | Lee-Goldman, Russell (Google) | Tseng, Michael (Google)
Crowdsourcing for linguistic data typically aims to replicate expert annotations using simplified tasks. But an alternative goal — one that is especially relevant for research in the domains of language meaning and use — is to tap into people's rich experience as everyday users of language. Research in these areas has the potential to tell us a great deal about how language works, but designing annotation frameworks for crowdsourcing of this kind poses special challenges. In this paper we define and exemplify two approaches to linguistic data collection corresponding to these differing goals (model-driven and user-driven) and discuss some hybrid cases in which they overlap. We also describe some design principles and resolution techniques helpful for eliciting linguistic wisdom from the crowd.