“Until recently, the idea of robot rights had been left to the realms of science fiction. Perhaps that’s because the real machines surrounding us have been relatively unsophisticated. Nobody feels bad about chucking away a toaster or a remote-control toy car. Yet the arrival of social robots changes that. They display autonomous behaviour, show intent and embody familiar forms like pets or humanoids, says Darling. In other words, they act as if they are alive. It triggers our emotions, and often we can’t help it.”
Nuance is placing priorities on persona and emotional connection. It’s fine drawing on third parties for some databases.
(via Stephen Wolfram Blog : Data Science of the Facebook World)
All of this post is very interesting, but this visualization of the author’s 15-year-old daughter’s Facebook network and the ones that follow caught my attention the most. It illustrates, to some extent, where the source of her connections on the medium come from. The analysis that follows is worth a look.
It’s of course logical that we have different clusters of friends on social networks, probably particularly so in the case of Facebook. It’s neat here, however, that data may suggest there’s a most typical number of clusters that make up the majority of an individual’s network: three.
Years ago, I also went to one-time events like summer camps, and I am still friends with most of those folks on Facebook. I’ve probably neglected the size of those resulting clusters in my own network. But odds are that years later — after a handful of schools, jobs and one-time events like conferences — the show choir camp I attended in high school doesn’t make up one of my major three clusters. But I’m willing to bet a cluster I don’t think about or engage with in real life all the time definitely does.
(And who knows, maybe it actually is that show choir group. For someone who is involved pretty heavily in journalism, I do see a lot of news in my stream that deals not with great free tools for online storytelling, but instead, something like who won grand champions at a random competition in Iowa.)
It’s interesting, in general, to think about what those three(ish) clusters may be for every individual user on the platform. There is some level of filter bubble and we do see content from people similar to us in a place like Facebook. But perhaps it’s worth opening up and examining which “people like us” we see most. Or, at the very least, determining the ones that have a shot, sticking around and gaining influence in our network because of their sheer size.
The more I read about Google Now and Sherpa, the more I can’t help but think about how the same predictive intelligence for news apps would be so effective. If done right, it could fill many “jobs to be done.”
A smart, predictive news app would be a step above my “OpenMoment” idea/desire, which outlines a mobile app that sorts news by real-life context. Rather than have to select “morning commute” and filter news that fits your preferences for a long Metro ride — like I thought by itself would be pretty cool — smart technology could use your location and time data to automatically suggest news material your most likely to read and enjoy.
It could do that for a commute, a lunch line, the “lean back” hours of the evening, etc., always suggesting the most appropriate content for each context. I’d sign up right away. It’d save me some time and potentially lend itself to discovery I may have missed out on.
I know some people are experimenting with this realm of mobile location + news (Kon*Fab comes to mind). Perhaps experimentation will become easier as Google Now and Sherpa get going and we’ll be able to see some news outlets get into the game, too.
Location and time data spliced together could offer pathways to answering big relevancy questions in media (in the information overload we all recognize, how do we serve stories or information readers will want to see). And I’m sure there are a hefty plenty of possibilities for targeted advertising based on both location and time of day…