Deep learning enables us to create intelligence from large amounts of complex data by using vast amounts of virtual neurons stacked in layers on top of each other. Enjoy our deep learning as a service model in selected verticals and read more about our value proposition.
For specific tracks related to hardware development, Robovision works together with Robovision Integrated Solutions to combine the power of AI with the next generation of robots, building cost-efficient prototypes in a limited amount of time.
Robovision is at its best when we can serve large robotic farms in an industrial environment. We usually work with one powerful GPU machine (local cloud or ‘fog’).
Agriculture is a very good match for AI and deep learning.
There are so many types of crops that it is impossible to keep doing heuristic programming.
Robovision specializes in human behaviour detection in video streams. From safety applications to the processing of a television stream.
Manufacturing
To assemble a car, the car manufacturer needs a solid and validated flow of components. High tech scanning technology can guarantee that a specific compontent will arrive just in time on the production line. Audi relies on Robovision to fulfill this need.
By applying 3D deep learning on manufactured pieces Robovision checks if the right subtype is selected, and if a set of components is complete for a specific car type. In this case RVAI ensures that the WZB (the kit you use when you have a flat tyre) is complete and validated. Thanks to the strong GPUs from NVIDIA we keep the cycle time fast and snappy.
Manufacturing
Pepsico manufactures and packages dorito chips in the highlands above Mexico City. With package branding changing so frequently, a powerful AI based central system is needed to teach branding very fast. The RVAI is the solution here. Reflective, blown up and unpredictable, recognizing orientation and branding at high speed is not for the faint hearted in this application.
Agriculture
The next horizon in our agricultural disruption is 3D. Natural products have a tendency to be highly unpredictable, in contrast to manufactured pieces. By configuring convolutional neural networks to 3D data we want to help the industry automate the handling of difficult products. A Jumbo 747 coming from Africa flies frequently to Amsterdam, full of Chrysanthemum stems. We want to grow them here in Western Europe and manipulate them with smart robots (that pick the stems from the mother plant. By doing so we can use the fertile ground of Ethiopia instead to feed the hungry in the region. The AI revolution can generate powerful secondary effects, disrupting the flow to cheap labour and creating a more harmonious world order.
Deep learning applied to the process industry
We apply deep learning to processes. By mounting a series of proprietary sensors on machinery, we are able to generate data that are used as input for a deep learning architecture. In this way we can teach the system what normal stationary behaviour is and how to properly detect anomalies.
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