"What’s Holding Augmented Reality Back?" is a new Modus essay by my colleague Amber Case, who makes some novel points that help explain why Magic Leap has, despite $2.6 billion in funding, only sold some 6000 units. It's not just the cost or discomfort with headsets, she argues, but the computing power required to recognize hand gestures and incorporate them into the AR display:
AR advocates often assume gesture control will be the next iteration in user interfaces since it seems so intuitive and natural to human expression. But hand-gesture models and libraries are not uniform. The ambiguous input produced by humans forces the computer to process far more information than a controller with comparatively limited function, like a touchscreen. This makes the interaction prone to a wide range of errors. The user’s background could be too dark, they could be wearing gloves, or they could have hands smaller than those that the device was tested with. This interaction model also likely requires having to train someone to use gestures they’re not yet familiar with, and not everyone will make the gestures in the same way.
... AR devices like Magic Leap and HoloLens struggle with detecting the intersection between hand movements and objects. Awash in the effluvia of reality, the headset cannot always discern that the user is, say, trying to pick up a block, and it forces them to grab it multiple times... Most augmented reality headsets were launched to early adopter enthusiasts and content creators, but even these users quickly found using these devices on a daily basis to be difficult.
I.E., it's not so much that Magic Leap only sold 6000 units, but that those 6000 Magic Leap owners find their own evangelical enthusiasm dampened by the consistent frustration of using it themselves.
The usual response to this complaint is to say greater computing power will improve the AR user interface over time. But Amber argues that this is an ever-elusive goal:
The promise of gesture control technology is that it will significantly improve over time, but in practice, its perceived accuracy basically remains the same. Like Zeno’s paradox of motion, the more our computing power and motion-sensor efficacy improves, the more our expectations for precise gesture recognition also grows. But existing computers can never have an understanding of the full spectrum of edge cases they might encounter in the real world. Even if they could, gesture recognition is cognitively expensive for machines and mostly unnecessary when a simple button would suffice.
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