The Future Of
Technology Isn’t Mobile, It’s Contextual
Next
up: Machines that understand you
and everything you care about, anticipate your
behavior and emotions, absorb your social graph, interpret your intentions, and
make life, um, "easier."
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.
Future
platforms designed for contextual computing will make mobile tech seem closer
to toys than to a phone with cool tools.
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.
There
are legitimate ethical concerns about each of these graphs. 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. We’re all trying to understand what to do about this from an
individual standpoint, let alone a legal one.
The
interest graph can turn your passions into a marketing campaign.
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.
by: Pete Mortensen
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