You’re walking home alone on a quiet street. You hear footsteps approaching quickly from behind. It’s nighttime. Your senses scramble to help your brain figure out what to do. You listen for signs of threat or glance backward. What you learn may prompt you to turn down another street, confront the person, or relax. Whether he or she turns out to be a mugger or a jogger, your brain rapidly cycled through many scenarios seeking an answer.
It’s called situational awareness. The way we respond to the world around us is so seamless that it’s almost unconscious. Our senses pull in a multitude of information, contrast it to past experience and personality traits, and present us with a set of options for how to act or react. Then, it selects and acts upon the preferred path. This process–our fundamental ability to interpret and act on the situations in which we find ourselves–has barely evolved since we were sublingual primates living on the Veldt.
Here’s the rub: Our senses aren’t attuned to modern life. A lot of the data needed to make good decisions are unreliable or nonexistent. And that’s a problem.
In the coming years, there will be a shift toward what is now known as contextual computing, defined in large part by Georgia Tech researchers Anind Dey and Gregory Abowd about a decade ago. Always-present computers, able to sense the objective and subjective aspects of a given situation, will augment our ability to perceive and act in the moment based on where we are, who we’re with, and our past experiences. These are our sixth, seventh, and eighth senses.
Hints of this shift are already arriving. Mobile devices with GPS deliver location-based services, which sets a baseline for the many ways your phone can gather information it will use to make your life easier down the line. Amazon’s and Netflix’s recommendation engines, while not magnificently intuitive, feed you book and video recommendations based on your behavior and ratings. Facebook’s and Twitter’s valuations are premised on the notion that they can leverage knowledge of your acquaintances and interests to push out relevant content and market to you in more effective ways.
These merely scratch the surface. The adoption of contextual computing–combinations of hardware, software, networks, and services that use deep understanding of the user to create tailored, relevant actions that the user can take–is contingent on the spread of new platforms. Frankly, it depends on the smartphone. Mobile technology isn’t interesting because it’s a new form factor. It’s interesting because it’s always with the user and because it’s equipped with sensors. Future platforms designed from the ground up for contextual computing will make such devices seem closer to toys than to a phone with cool tools.
For that to happen, computer scientists, technology companies, and users all need to understand and buy into the requirements and possibilities of contextual computing. It’s a cultural moment that’s not dissimilar to the way in which graphical, and then networked computing, were introduced in conceptual and technical forms 10 years before reaching commercial success.
At Jump, we’ve identified four data graphs essential to the rise of contextual computing: social, interest, behavior, and personal. Some are well-established and others have emerged seemingly out of thin air in the last few years. By mastering all four of these graphs, players seeking to dominate the next era of the web will be wildly successful.
Despite the ethical ambiguity around contextual computing, what matters is that 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.
This data set shows how you connect to other people and how they are connected to one another. It also reveals the nature and emotional relevance of those connections. Most people associate this with Facebook, but it’s actually an idea and data set that spread far beyond its walls. 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.
Only when this graph is open to a wide variety of services will it reach its potential. And all the social data in the world won’t be helpful in the slightest if you know little about a specific person’s beliefs, activities, and interests.
This is the set of data relating to a person’s deepest held beliefs, core values, and personality. It’s what makes a person unique in the world, just as the social graph helps to show what makes her similar to others. The data set is under-developed at the moment, and it’s quite difficult to design for, even conceptually.
Given that psychology still struggles to explain exactly how our personal identities function, it’s not surprising that documenting such information in a computable form is slow to emerge. There are early indicators that this will change, however. For example, Proust.com, a relatively new (and struggling) social-networking service, asks users to document intimate details of their lives and their beliefs based on the idea of the famed Proust Questionnaire. People have, quite reasonably so, been reluctant to share such information in a publicly viewable social network.
A more successful example is Evernote, which has built a large business based on making it incredibly easy and secure to document both recently consumed information and your innermost thoughts. Scraping such intimate files for data is currently the questionable realm of the NSA, however. Entirely new solutions will need to be created if the potential of the personal graph is to be reached.
Your tastes and preferences are largely organized around the subjects that tend to correlate with one another. It’s also about the overlaps in taste between the individuals whose lives closely resemble your own. Many companies have made early bets in this arena; Twitter is a fan and believes it’s well on its way to fully charting how all subjects connect to all others.
For now, such applications are notoriously narrow. For example, a book site like Goodreads.com is capable of predicting what other books you might read based on your expressed interests. What’s problematic is that the interest graph falls far short of depicting your real interests and tastes. It cannot yet tackle the way your curiosity might lead you to new directions. And it could never effectively recommend a restaurant or a vacation spot based on what it knows you read.
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.
The real potential of contextual computing isn’t about just one of these graphs. It’s about connections that resonate between them and which get tailored to different kinds of experiences. Early entrants like Google’s Now and Glass projects, Highlig.ht, and Siri are just beginning to experiment with these technologies. Just as the visionaries at Xerox PARC (who developed the foundational technologies of every desktop PC) could not have fully grasped the long-term impact of the mouse and graphical computing when they began working on them in 1973, we cannot say now which contextual applications will emerge as most vital. The way to the future will be paved on many thousands of interesting failures.
Granted, true contextual computing is a little further around the corner than the most optimistic pundits would have you believe. That should not be mistaken as a caveat that it’s unlikely to fully arrive. As Bill Gates astutely pointed out, “There’s a tendency to overestimate how much things will change in two years and underestimate how much change will occur over 10 years.” (Notably, the tablet computers he introduced in 2001 didn’t achieve commercial success until the launch of the iPad in 2010.)
Within a decade, contextual computing will be the dominant paradigm in technology. Even office productivity will move to such a model. By combining a task with broad and relevant sets of data about us and the context in which we live, contextual computing will generate relevant options for us, just as our brains do when we hear footsteps on a lonely street today. Then and only then will we have something more intriguing than the narrow visions of wearable computing that continually surface: We’ll have wearable intelligence.
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