BMW, Red Hat, and Malong Share Insights on AI and Machine Learning During Transform 2020
Denrie Caila Perez posted on August 07, 2020 |
Executives from BMW, Red Hat and Malong discuss how AI is transforming manufacturing and retail.
(From left to right) Maribel Lopez of Lopez Research, Jered Floyd of Red Hat, Jimmy Nassif of BMW Group, and Matt Scott of Malong Technologies.
(From left to right) Maribel Lopez of Lopez Research, Jered Floyd of Red Hat, Jimmy Nassif of BMW Group, and Matt Scott of Malong Technologies.

The VentureBeat 2020 conference welcomed the likes of BMW Group’s Jimmy Nassif, Red Hat’s Jered Floyd, and Malong CEO Matt Scott, who shared their insights on challenges with AI in their respective industries. Nassif, who deals primarily with robotics, and Floyd, who works in retail, both agreed that edge computing and the Internet of Things (IoT) has become powerful in accelerating production while introducing new capabilities in operations. According to Nassif, BMW’s car sales have already doubled over the past decade, with 2.5 million in 2019. With over 4,500 suppliers dealing 203,000 unique parts, logistics problems are bound to occur. In addition to that, approximately 99 percent of orders are unique, which means there are over 100 end-customer options.

Thanks to platforms such as NVIDIA’s Isaac, Jetson AXG Xavier, and DGX, BMW was able to come up with five navigation and manipulation robots that transport and manage parts around its warehouses. Two of the robots have already been deployed to four facilities in Germany. Using computer vision techniques, the robots are able to successfully identify parts, as well as people and potential obstacles. According to BMW, the algorithms are also constantly being optimized using NVIDIA’s Omniverse simulator, which BMW engineers can access anytime from any of their global facilities.

In contrast, Malong uses machine learning in a totally different playing field—self-checkout stations in retail locations. Overhead cameras are able to feed images of products as they pass the scanning bed to algorithms capable of detecting mis-scans. This includes mishaps such as occluded barcodes, products left in shopping carts, dissimilar products, and even “ticket switching,” which is when a product’s barcode is literally switched with that of a cheaper product.

These algorithms also run on NVIDIA hardware and are trained with minimal supervision, allowing them to learn and identify products using various video feeds on their own. According to Scott, edge computing is particularly significant in this area due to the necessity of storing closed-circuit footage via the cloud. Not only that, but it enables easier scalability to thousands of stores in the long term.

“Making an AI system scalable is very different from making it run,” he explained. “That’s sometimes a mirage that happens when people are starting to play with these technologies.”

Floyd also stressed how significant open platforms are when playing with AI and edge computing technology. “With open source, everyone can bring their best technologies forward. Everyone can come with the technologies they want to integrate and be able to immediately plug them into this enormous ecosystem of AI components and rapidly connect them to applications,” he said.

Malong has been working with Open Data Hub, a platform that allows for end-to-end AI and is designed for engineers to conceptualize AI solutions without needing complicated and costly machine learning workflows. In fact, it’s the very foundation of Red Hat’s data science software development stack.

All three companies are looking forward to more innovation in applications and new technologies.

Visit VentureBeat’s website for more information on Transform 2020. You can also watch the Transform 2020 sessions on demand here.

For more news and stories, check out how a machine learning system detects manufacturing defects using photos here.

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