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AAAI AI-Alert for Jun 9, 2019


Vision Online

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Advances in 3D imaging have allowed vision users to overcome some challenging inspection tasks. In the machine vision marketplace, 3D imaging continues to mature, tackling applications 2D imaging cannot. "In a manufacturing setting, the fusion of 2D with 3D is necessary to measure how well components go together into an assembly and assess the product for final fit, finish, and packaging," says Terry Arden, CEO of LMI Technologies. According to David Dechow, Principal Vision Systems Architect at Integro Technologies, a systems integrator specializing in machine vision technologies with broad experience in helping companies implement 3D and 2D imaging for industrial automation, accuracy has improved as well. And with inspection tasks in 3D space, which may include measurement or reconstruction, precision is even more essential than with most tasks in robotic guidance or bin picking.


Ford Self-Driving Vans Will Use Legged Robots to Make Deliveries

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Ford is adding legs to its robocars--sort of. The automaker is announcing today that its fleet of autonomous delivery vans will carry more than just packages: Riding along with the boxes in the back there will be a two-legged robot. Digit, Agility Robotics' humanoid unveiled earlier this year on the cover of IEEE Spectrum, is designed to move in a more dynamic fashion than regular robots do, and it's able to walk over uneven terrain, climb stairs, and carry 20-kilogram packages. Ford says in a post on Medium that Digit will bring boxes from the curb all the way to your doorstep, covering those last few meters that self-driving cars are unable to. The company plans to launch a self-driving vehicle service in 2021.


The AI Breakthrough Will Require Researchers Burying Their Hatchets

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At their core, neural networks are complex mathematical functions composed of thousands of variables. During the "training" phase, the network ingests numerous labeled examples and tunes its variables based on the common patterns it finds among each class of examples. Afterward, when you run a new piece of data through the network, it can classify the data based on its statistical similarity to examples the network has previously seen. Neural networks are especially efficient at tasks such as image classification, voice recognition, and natural language processing, areas where rule-based AI has historically struggled.