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[2017-11-24] Dr. Edward Chang, HTC, "On Artificial Happiness in the Era of Artificial Intelligence”

專題討論演講公告
張貼人:Seminar專用帳號2公告日期:2017-11-17

Title: On Artificial Happiness in the Era of Artificial Intelligence
Date: 2017-11-24  02:20pm-03:30pm

Location: R103, CSIE

Speaker:  Dr. Edward Chang, HTC 

Hosted by: Prof. Winston Hsu

Abstract

Aristotle defines happiness to be "living well with complete and sufficient good."  During my trip to the Himalayas, plenty of evidence demonstrated that people in developing countries could live happier lives than those in developed countries because of fewer material desires. However, with the advent of the mobile Internet, whether one lives in a major city or remote monastery, they are now both just a click away from temptation.

 

The Internet economy largely lives on raising users’ desires, and then maximizing users’ happiness via satisfying those desires.  Shopping sites, for example, suggest attractive merchandise to achieve higher click through rates. Video game companies sell boosters and weapons so a player can instantly become mightier and defeat the toughest bosses. Most, if not
all, companies aim to maximize revenue, and high revenue correlates with high degrees of user happiness.

At the same time, AI has been instrumental in generating the real happiness that Aristotle described. In the field of medicine for instance, AI can facilitate accurate diagnosis and effective treatment of diseases, leading to a patients' increased well-being and quality of life. The
happiness that comes with such an application is long lived and real, while happiness generated by user desires are often short lived and artificial.

This talk surveys how AI has fueled happiness, both artificial and real. I will also consider how we could use AI to arm users against being tempted by, or becoming addicted to, attaining artificial happiness.

 

Biography

Edward Chang currently serves as the President of Research and Healthcare (DeepQ) at HTC. Ed's most notable work is co-leading the DeepQ project (with Prof. CK Peng at Harvard), working with a team of physicians, scientists, and engineers to design and develop mobile wireless diagnostic instruments. Such instruments can help consumers make their own reliable health diagnoses anywhere at any time. The project entered the Tricorder XPRIZE competition in 2013 with 310 other entrants and was awarded second place in April 2017 with 1M USD prize. The deep architecture that powers DeepQ is also applied to power Vivepaper, an AR product Ed's team launched in 2016 to support immersive augmented reality experiences (for education, training, and entertainment).

Prior to his HTC post, Ed was a director of Google Research for 6.5 years,
leading research and development in several areas including scalable
machine learning, indoor localization, social networking and search integration, and Web search (spam fighting). His contributions in parallel machine learning algorithms and data-driven deep learning (US patents
8798375 and 9547914) are recognized through several keynote invitations
and the developed open-source codes have been collectively downloaded over 30,000 times. His work on IMU calibration/fusion with project X was first
deployed via Google Indoor Maps (see XINX paper and ASIST/ACM SIGIR/ICADL keynotes) and is now widely used on mobile phones and VR/AR devices. Ed's team also developed the Google Q&A system (codename Confucius), which was launched in over 60 countries.

Prior to Google, Ed was a full professor of Electrical Engineering at the
University of California, Santa Barbara (UCSB). He joined UCSB in 1999
after receiving his PhD from Stanford University, and was tenured in 2003
and promoted to full professor in 2006. Ed has served on ACM (SIGMOD, KDD,MM, CIKM), VLDB, IEEE, WWW, and SIAM conference program committees, and co-chaired several conferences including MMM, ACM MM, ICDE, and WWW. He is a recipient of the NSF Career Award, IBM Faculty Partnership Award, and Google Innovation Award. He is also an IEEE Fellow for his contributions to scalable machine learning.

 

 

最後修改時間:2017-11-17 AM 11:36

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