Filtering by: Morning1

[Keynote] Rise of the Cognitive Machines — Mac Devine (IBM)
Oct
11
9:35 AM09:35

[Keynote] Rise of the Cognitive Machines — Mac Devine (IBM)

Mac Devine is an IBM Fellow currently serving as Vice President and CTO for Emerging Technologies. He is also a faculty member for the Cloud and Internet-of-Things Expo, and a member of the IoT Community Advisory Board. 

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[Keynote] Computational Privacy: The privacy bounds of human behavior  — Yves-Alexandre de Montjoye (MIT Media Lab)
Oct
11
9:05 AM09:05

[Keynote] Computational Privacy: The privacy bounds of human behavior — Yves-Alexandre de Montjoye (MIT Media Lab)

We're living in an age of big data, a time when most of our movements and actions are collected and stored in real time. Large-scale mobile phone, credit card, or browsing datasets dramatically increase our capacity to measure, understand, and potentially affect the behavior of individuals and collectives. The use of this data, however, raise legitimate privacy concerns. In this talk, I will first show how the mere absence of obvious identifiers such as name or phone number is often not enough to prevent re-identification. I will then discuss how, as the use of this data progress, it will become increasingly important to consider whether sensitive information can be inferred from apparently innocuous data. Finally, I will discuss the impact of metadata on society and some of solutions we have been developing to allow metadata to be used in a privacy-conscientious way.

Yves-Alexandre de Montjoye is a Research Scientist at the MIT Media Lab (and was previously a postdoctoral researcher at Harvard IQSS). His research aims at understanding how the unicity of human behavior impacts the privacy of individuals in large-scale metadata datasets. (My work has been covered in The New York Times, BBC News, CNN, Wall Street Journal, Harvard Business Review, Le Monde, Die Spiegel, Die Zeit, El Pais, and in reports of the World Economic Forum, United Nations, OECD, FTC, and the European Commission, as well as in my talks at TEDxLLN and TEDxULg.)

Twitter: @yvesalexandre - Linkedin - Website

 

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[Panel Discussion] Building intelligent applications with ML
Oct
10
9:50 AM09:50

[Panel Discussion] Building intelligent applications with ML

Panel discussion moderated by Andy Thurai, Program Director at IBM and Local Chair at PAPIs '16 (Twitter - Linkedin)

Panelists:

 

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[Keynote] Regulating Greed Over Time: An Important Lesson For Practical Recommender Systems — Cynthia Rudin (Duke & MIT)
Oct
10
9:20 AM09:20

[Keynote] Regulating Greed Over Time: An Important Lesson For Practical Recommender Systems — Cynthia Rudin (Duke & MIT)

There is an important aspect of practical recommender systems that we noticed while competing in the ICML Exploration-Exploitation 3 data mining competition. The goal of the competition was to build a better recommender system for Yahoo!'s Front Page, which provides personalized new article recommendations. The main strategy we used was to carefully control the balance between exploiting good articles and exploring new ones in the multi-armed bandit setting. This strategy was based on our observation that there were clear trends over time in the click-through-rates of the articles. At certain times, we should explore new articles more often, and at certain times, we should reduce exploration and just show the best articles available. This led to dramatic performance improvements. As it turns out, the observation we made in the Yahoo! data is in fact pervasive in settings where recommender systems are currently used...

Cynthia Rudin is an associate professor of statistics at the Massachusetts Institute of Technology associated with the Computer Science and Artificial Intelligence Laboratory and the Sloan School of Management, and directs the Prediction Analysis Lab. Her interests are in machine learning, data mining, applied statistics, and knowledge discovery (Big Data). Her application areas are in energy grid reliability, healthcare, and computational criminology.

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