Observing how other human beings interact is so interesting that we have a name for when we do it recreationally, we call it “people-watching.” Evolution has equipped us both with a desire to people-watch and with the tools we need to do it. In social situations, we can spot patterns such as groups forming and dispersing fairly easily, but it’s harder to describe that observation process logically. If we could do that, we could make machines people-watch for us.
Modern smart phone platforms come with a growing range of sensors. They also have a (near-) ubiquitous data connection, and the ability to report user positioning via multiple methods. They are almost invariably carried everywhere on their owner’s person. Given all that, it’s now fairly easy to build large, distributed datasets from people’s smart phones—data that when collated together has a vast amount of information regarding the social graph.
We’ve developed algorithms incorporating machine learning techniques that, combined with these large spatio-temporal datasets, enable us to automatically characterize groups of people from the spatially coherent behaviour of the individuals that form them, as well as to look for other forms of interactions between people and distinguish between these different types of interactions.
These algorithms are going to give machines access to our social interactions in ways that weren’t possible before. While humans find it harder to spot social groups in large crowds because of the amount of data involved, additional data makes group identification algorithmically easier. Given enough data, machines might become better at finding patterns by people-watching than we are ourselves, and as a result give us novel insights into our own social interactions.
He is the author of a number of books, and from time to time he also stands in front of cameras. You can often find him at conferences talking about interesting things, or deploying sensors to measure them. He recently rolled out a mesh network of five hundred sensors motes covering the entire of Moscone West during Google I/O. He’s still recovering.
He sporadically writes blog posts about things that interest him, or more frequently provides commentary in 140 characters or less. He is a contributing editor for MAKE magazine, and a contributor to the O’Reilly Radar.
A few years ago he caused a privacy scandal by uncovering that your iPhone was recording your location all the time. This caused several class action lawsuits and a U.S. Senate hearing. Several years on, he still isn’t sure what to think about that.
Alasdair is a former academic. As part of his work he built a distributed peer-to-peer network of telescopes which, acting autonomously, reactively scheduled observations of time-critical events. Notable successes included contributing to the detection of±what was at the time—the most distant object yet discovered, a gamma-ray burster at a redshift of 8.2.
Zena Wood is employed as a lecturer in Computer Science at the University of Exeter, she works in the field of Applied Ontology and spatiotemporal reasoning. Entitled “Detecting and Identifying Collective Phenomena within Movement Data,” her PhD focused on identifying what is meant by the term collective, the different types of collective that exist and developing a method that allowed the identification of such phenomena within large spatiotemporal datasets.
Zena has continued to work within these fields and is currently developing similar techniques that can be used to study human and animal behaviour. In addition to her roles as lecturer, Zena also coordinates the eskills’s endorsed undergraduate and postgraduate IT Management for Business degrees and Computer Science outreach at Exeter.
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