There is increasing evidence that enabling AI technology has the potential to aid in the aforementioned paradigm shift. Current rates of progress are insufficient, making it impossible to meet this goal without a technological paradigm shift. By the end of this century, the earth’s population is projected to increase by 45% with available arable land decreasing by 20% coupled with changes in what crops these arable lands can best support this creates the urgent need to enhance agricultural productivity by 70% before 2050. W2: AI for Agriculture and Food Systems (AIAFS)Īn increasing world population, coupled with finite arable land, changing diets, and the growing expense of agricultural inputs, is poised to stretch our agricultural systems to their limits. Yinpeng Dong (Tsinghua University, Tianyu Pang (Tsinghua University, Xiao Yang (Tsinghua University, Eric Wong (MIT, Zico Kolter (CMU, Yuan He (Alibaba, ) Additional Information Yinpeng Dong 30 Shuangqing Road, Haidian District, Tsinghua University, Beijing, China, 100084, Phone: +86 18603303421) Organizing Committee Submissions including full papers (6-8 pages) and short papers (2-4 pages) should be anonymized and follow the AAAI-22 Formatting Instructions (two-column format) at. The accepted papers will be allocated either a contributed talk or a poster presentation. We consider submissions that haven’t been published in any peer-reviewed venue (except those under review). There will be about 60~85 people to participate, including the program committee, invited speakers, panelists, authors of accepted papers, winners of the competition and other interested people. We’ll also host a competition on adversarial ML along with this workshop. This is a one-day workshop, planned with a 10-minute opening, 6 invited keynotes, ~6 contributed talks, 2 poster sessions, and 2 panel discussions. Positive applications of adversarial ML, i.e., adversarial for good.The consideration and experience of adversarial ML from industry and policy making.The positive/negative social impacts and ethical issues related to adversarial ML. ![]()
0 Comments
Leave a Reply. |