The Machines Are Watching You
The desire for self-improvement and optimal health has led millions of people to voluntarily measure, track and publish data created by their own bodies and behaviors through use of wearable devices. Our belief is that with greater measurement and insight, we can “habit hack”, or create new habits while changing old ones. Does “habit hacking” work, and what is the role of privacy in this very public social experiment? This panel will examine the rise of self-tracking, habit hacking and the role of machine learning (ML) in parsing this user-generated data to identify correlations beyond what individual self-trackers or medical researchers can see on their own.
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Additional Supporting Materials
- What is self-tracking, why do we do it, and how can we use it to debug or "habit hack" our behaviors?
- How much information are we willing to share about ourselves – anonymously or with our communities?
- How can we use machine learning to get meaning from medically relevant data and provide benefits to ourselves - and society?
Emily Hutson, Intel