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They throw into relief the larger questions of privacy policy we’re currently wrestling with as a culture: Too much disclosure of the social graph can lead to friends feeling that you’re tattling on them to a corporation. The interest graph can turn your passions into a marketing campaign. The behavior graph can allow people who wish you harm to know where you are and what you’re doing. And revealing the personal graph can make it feel like an outside entity is quite literally reading your mind.

companies are actively constructing these graphs already. These products and services are in the market today, but most in existence target only one or two of these graphs. Few are pursuing all four, both given the immaturity of the space and a lack of clear targets to shoot for. This has the unintentional effect of highlighting the risks of using such services, without demonstrating their benefits. For the potential of contextual computing to be realized, these data sets must be integrated.

In an ideal contextual computing state, this graph would be complete--so gentle nudges by software and services can bring together two people who are strangers but who could get along brilliantly and are in the same place at the same time. It could be two people who share a friend and who simultaneously move to Omaha, where neither person knows a soul.

It’s easy for data to depict what you actually do instead of what you claim to do. Sensors do the job. So do, if less elegantly, self-reporting mechanisms. This data can sit in pivotal contrast to the interest graph, allowing computers to know, perhaps better than you, how likely you are to go for a jog. It would be useful, too, for a travel site that notes how you tell friends you’d like to visit China but records that you only vacation in Europe. Rather than uselessly recommending vacation deals to Beijing, a smart travel app would instead feed you deals to Paris or Berlin. The behavior graph provides the foundation, to some extent, of Google Search, Netflix recommendations, Amazon recommendations, iTunes Genius, Nike+ run tracking, FourSquare, FitBit, and the entire "quantified self" movement. When mashed against the other three graphs, there’s a potential for real insight.

Within a decade, contextual computing will be the dominant paradigm in technology.

The combination of automated agents, contextual search and a sea of data from our devices, services and the Internet of Things, search is poised to become vastly more useful and efficient than it already is. The pieces are getting there with agents like Siri and contextual search like Google Now. If it all works as promised, information we need will be delivered to us just when we need it, without our having to invest time and effort looking for it.

Foursquare will only provide notifications that are relevant to you personally. The application learns your behavior based on previous check-ins and recommendations. You won’t get notifications everywhere you go; rather, when you’re at a restaurant, Foursquare will crawl the tips and if there is one that fits your profile, you will be notified. Additionally, a new swipeable carousel of suggestions at the top of the application’s home page will show location-based suggestions, such as deals around the corner or something saved to your to-do list nearby.