Image Credit: stefano carniccio / Shutterstock
This is an exciting time for those of us in computer vision — we’re seeing it merge with AI to enable all kinds of new possibilities. At the LDV Vision Summit in New York a few weeks ago, I came away with five key insights about where computer vision will impact AI:
1. Smart assistants will battle it out over vision
AI needs data with which to learn and process, and as we move closer to more “human”-like AI, it will increasingly need visual data. “This is one of the reasons all the major companies are at war to own the visual data of our activities,” said LDV Capital’s Evan Nisselson. “To do that, they need to own the camera.” Amazon recently added a camera to its Alexa-powered Echo, for example, and Google (Lens) and Facebook recently made new recent augmented reality announcements.
2. Optics alone could be enough to direct self-driving cars
We are seeing debate over whether self-driving cars need LiDAR or can depend solely on optical solutions. Tesla CEO Elon Musk, for example, doesn’t think that LiDAR, a bulky and expensive device that uses lasers to maps its environment in real time, is necessary for fully-autonomous driving. Wheras Humatics CTO Gregory Charvat said at the vent that cars “need more than just optical sensor platforms [cameras], they also need LiDAR, radar, and high-precision radio navigation more precise than differential GPS.”
LiDAR and radar work by pinpointing actual objects in the surrounding environment by range and angle, whereas deep learning-based camera solutions need to run images through algorithms and are ultimately still predictions. Optical solutions are nevertheless better at actually identifying what something is — for example, a pedestrian versus a bunch of pixels that look like a Christmas tree, as Auto X Founder and CEO Jianxiong Xiao showed during a demo of his company’s impressive and low-cost self-driving solution that only uses cameras.
Technology pros and cons aside, car companies typically work five years in advance, so the necessary hardware would need to be purchased now to make a 2021 deadline. For now, LiDAR and more advanced forms of radar are still expensive ($80,000 is considered cheap for the former) and bulky. Meanwhile, operating all these optical and sensor technologies in a fused way needs supercomputers small enough…
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