Smart Transport Robot carrying roller containers through the logistics hall at BMW Group Plant Wackersdorf.
Smart Transport Robot at BMW Group Plant Regensburg.
Standard camera takes live picture from model designation plate, which AI compares with image data base.
Employee marks approved combinations of model designation and further identification plates.
Infrared camera takes live picture from a component, which AI compares with data base.
Employee marks defect in component, labeling needed for image database /neural network.
BMW Group makes AI algorithms for smart production public

BWM Group says the publicly available data will allow software developers all over the world to view, change, use and improve the source code.

13 Dec 2019 | 4807 Views | By Autocar Pro News Desk

German luxury carmaker, BMW Group has deployed a extensive number of artificial intelligence (AI) applications in production, for instance, AI relieves workers of monotonous tasks such as checking whether the warning triangle is placed in the right spot in the trunk. This task is now performed by a camera and self-learning software that compares the camera’s live images with hundreds of stored images in milliseconds and can detect any deviations from the standard. And to further enhance smarter production, the company has shared selected algorithms from this area of artificial intelligence on an open source platform (github.com/BMW-InnovationLab). The algorithms are part of various AI applications, in particular in automated image recognition and image tagging.

BMW says making these publicly available allows software developers all over the world to view, change, use and improve the source code. "With the algorithms we are now publishing, the BMW Group has significantly reduced the development time for neural networks for autonomous transport systems and robots," said Dirk Dreher, Head of Logistics Planning. Neural networks independently compare live images in production and logistics with image databases to detect any deviations from the target state.   

The open source approach benefits both interested software developers and the company. “We provide elements of our innovative digital image tagging software, which has proven effective in multiple AI applications; in turn, we receive support in taking our AI software to the next level of development. Also, this allows us to focus more strongly on advancing specific AI applications in production and logistics,” said Christian Patron, Head of Innovation, Digitalisation, Smart Data Analytics.

Kai Demtroder, Head of Artificial Intelligence, Data Platforms at BMW Group IT said: “We are making major investments in artificial intelligence. By sharing our algorithms with the global developer community, we want to do our part and make AI accessible to a broad group of users. We expect the further open source development to lead to a rapid and agile advancement of the software.”

The company says as part of the open source approach, all users of the algorithms are guaranteed anonymity. Any flaws in the algorithms can be identified quickly; in this process, automated functions provided by the platform operators can also be used, if needed. For quality assurance purposes, the BMW Group will check all incoming user suggestions before they are put into productive use or shared. The model – in other words, the actual AI application being developed with these algorithms – always remains protected. All users are free to decide whether they want to make their models accessible to partners, such as suppliers.

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