Below are the first 10 and last 10 pages of uncorrected machine-read text (when available) of this chapter, followed by the top 30 algorithmically extracted key phrases from the chapter as a whole.
Intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text on the opening pages of each chapter. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.
Do not use for reproduction, copying, pasting, or reading; exclusively for search engines.
OCR for page 23
5 Global and Transnational Issues T here are significant real-world problems, Bradshaw proposed, that by their very nature are international in scope and would benefit by partici- pation from researchers from different countries and different disciplines. Sonenberg added that for many large-scale (and potentially international or global) problems, great opportunities exist for collaboration and coordination to meet shared goals. Today, researchers are tapping into the potential to exploit the Web to collect, integrate, and share data in useful ways to support the flow of data from information to knowledge. In addition, new technological capabili- ties, such as large-scale and massively distributed sensor systems, are allowing researchers to explore new, and potentially global, scales where the “field” has become the “laboratory.” She also referred to the scenario discussions on cross-cultural issues that addressed differing norms regarding “personal space,” gender roles and preferences, safety and trust in automation, and communication. Addressing these cultural differences, she noted, would benefit from national and local—as well as global–expertise. From an international manufacturing and assembly collaboration per- spective, Don Mottaz described the challenges of translating process information into other languages and cultures. While current efforts focus on teaching hu- mans, he proposed that machines may one day be used to teach humans from a variety of different cultural backgrounds. Thus spoken, written, tactile, and other teaching strategies will help to incorporate cross-cultural human-machine inter- action requirements. Lakmal Seneviratne discussed the increasing global use of automated tools in surgical environments, as well as long-distance teleoperated robotic sur- geries. In addition to time zone differences, technological challenges from com- puter-robot delays (above 1/100 of a second), operating room team dynamics and hierarchies, and social acceptance of robot surgery tools across cultures by both medical practitioners and patients still present significant obstacles. In addition to robotic surgery applications, Wagner proposed that med- ical doctors might beam into rural or underserved hospitals and clinics to con- 23
OCR for page 24
24 INTELLIGENT HUMAN-MACHINE COLLABORATION duct physical exams and deploy further specialization. He would like to see these tasks move beyond “skype-on-wheels.” Oron-Gilad suggested that envi- ronmental context is also an important factor in remote presence. For example, remote participants may not realize that they have beamed into a stressful, un- predictable, or dangerous environment, such as a war zone, thus underappreciat- ing or underutilizing the context in which the local staff is operating. Ramchurn identified energy management as a global issue in which agents and machines will play a role. As nations shift their focus to renewable energy sources, he suggested, intermittent sources and supply/demand con- straints may require agents to have some control of devices (e.g., washing ma- chines) to influence energy usage patterns. In these circumstances, humans would actually be adapting their behavior to agents. For search and rescue missions, Kruijff commented that cultural con- siderations come into play when local, national, and regional agencies or organi- zations need to deliver, share, and coordinate information. For this reason, Tadokoro emphasized multiculturally sensitive data-gathering and -sharing strategies. Lastly, Sonenberg commented that effectively addressing the social and cultural implications of human-machine collaboration will call for social scien- tists and anthropologists to work together with engineers. Further, many of these kinds of collaborations will be studied more effectively in natural settings than in the lab.