A colleague recently told me that his young child, having been raised on iPhones and iPads, was amazed to find that the screen on his dad’s laptop did not respond to touch. His story reminded me of that wonderful scene in the movie Star Trek IV, where Scotty tries to use a computer by speaking into the mouse.
These real and fictional anecdotes illustrate a critical truth about interactive electronic devices: the capabilities of these devices are largely defined by their user interfaces—and especially by the mechanisms provided for user input. The first computer I used was an IMSAI 8080, which in its most basic form was programmed using a set of front-panel toggle switches. Needless to say, this was a painfully slow and error-prone process. In comparison, the keyboard offered with Radio Shack’s TRS-80, introduced in 1977, was a positive luxury.
Today, of course, most of us can’t imagine using a computer without a mouse. And apparently, the next generation will be similarly unable to imagine using a computer (or phone or videogame or what have you) without a touchscreen. Taking this evolution a step further, you may have seen demos showing how gesture-based user interfaces of the sort featured in the movie Minority Report are quickly becoming a reality (on this subject, I recommend the TED Talks presentation by John Underkoffler).
Gesture-based user interfaces may seem futuristic, but they’re really just the first of many ways in which embedded computer vision technology will fundamentally change how we interact with machines. As illustrated in John Underkoffler’s presentation, gesture-based user interfaces can be generalized to systems that recognize not only hands, but all sorts of objects, and track their positions and orientations, enabling much closer links between the physical and virtual worlds. Indeed, the day is not far off when machines will not only detect faces and recognize individual humans, but also read their emotions.
At least two companies are currently working on commercializing technology for reading emotions. In one early application, participants in consumer market research, rather than filling out surveys, can have their emotions read directly from facial expressions as they learn about or sample proposed new products. At first blush this technology may seem strange—even creepy. But I believe that it will ultimately enable profound and valuable improvements in the ways in which we interact with machines. For example, imagine an educational toy that detects whether a child is frustrated, confused, bored, or challenged—and adjusts its script accordingly.
Entertainment applications are often the drivers for the first widespread adoption of new technologies. We’ve seen this phenomenon with novel user interfaces in video games, first with the Nintendo Wii and more recently with the Microsoft Kinect, and we may see it again with emotion-reading technology. But potential applications of emotion-reading technology go far beyond toys and video games. Today, for example, I manually provide feedback to Netflix’s movie recommendation engine to tell it which movies I like, and it recommends other movies that it thinks I’ll like—with mixed success. How much more accurate would these automatically generated recommendations be if they were based not only on what movies I’ve watched, but also on whether I really enjoyed them?
More generally, what if an Internet search engine could judge the quality of its recommendations not only by whether you click on a link, but also by whether you like what I find after doing so? It seems to me that in any market where customer satisfaction matters, and any application where there’s the potential for users to get confused and frustrated, emotion-reading technology has the potential to add value. And—who knows? Emotion-reading technology may hold the key to finally transforming video surveillance technology from a technology mainly useful for after-the-fact forensics into a technology that can usefully warn of trouble before it happens.
When I think about how far we’ve come from the days of toggle-switch and punch-card programming to today’s multi-touch user interfaces, I’m amazed and humbled. When I think forward to the possibilities enabled by machines that see and understand their environments, I’m excited and optimistic about what’s coming.
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