Ford Calls On Non-IT Professionals To Expand AI Expertise

Ford engine Co.

puts artificial intelligence development tools in the hands of engineers, designers and other non-IT professionals with the goal of helping the automaker harness the technology for a range of pressing issues, including the challenges of the supply chain.

The automaker has trained more than 1,000 employees over the past year on a platform housing developed and purchased AI development tools, said Gil Gur Arie, Ford director of data and analytics .

Ford has a long history of using AI throughout its organization, including its factories, where AI analyzes data from equipment sensors for preventive maintenance; on its assembly lines, where intelligent robots build cars; and across the business to help manage parts inventory and other activities. In 2017, it acquired a stake in startup Argo AI as part of its efforts to develop an autonomous car.

Gil Gur Arie, Ford Data and Analytics Manager


Ford Motor Co.

With complex AI applications, such as autonomous driving, left to professional data scientists and other AI engineers, those trained in new AI tools are turning to new areas to deploy the technology, a said Mr. Gur Arie, a retired Israeli military intelligence colonel. who joined Ford in May 2020.

“In other cases where it is simpler, we can democratize this power,” he said.

Ford last week reported a sharp drop in third-quarter profits as a shortage of computer chips resulted in lower output at the plant, although the company said supply chain disruptions are expected to occur. ‘slowly improve in the fourth quarter and throughout next year. Still, Ford said, dealer lots will be almost bare until 2022.

To help reduce the pain, Ford’s AI makers are working on an AI optimization model that will help the company decide which vehicles should be shipped to which European countries so that car inventory is optimized. to maximize sales, according to Ford. The model takes into account thousands of variables, including the carbon dioxide emissions of each type of vehicle, the emission standards of each country, the number of kilometers traveled by the citizens of a given country, as well as the adoption of electric vehicles and vehicle size. preferred in each country. Ford said the number of variables analyzed requires the use of AI, which is designed to handle large data sets.

Other applications developed under the program include an application to discover the shapes of air ducts that make car cabs quieter as well as a machine learning model, built by a logistics team, designed to streamline air ducts. parts shipments from warehouses to its more than 60 factories around. the world.

“It’s a pretty complicated problem, although you think it can be quite simple,” said Mr. Gur Arie, who said the logistics application saves the company tens of millions of dollars. dollars in shipping costs.

Ford is hoping that opening up AI development to a wider range of employees can dramatically reduce the average time it takes to develop many apps, in some cases from months to weeks, or even days.

Philipp Kampshoff, a senior partner at McKinsey & Co. who runs the company’s Center for Future Mobility, said automakers could unlock billions of dollars in savings through automation and intelligence-driven analytics artificial if they could find a way to evolve the technology.

One way to do this is to expand access to AI development tools and training beyond data scientists and other AI experts, he said, a move widely known as the democratization of AI.

About 15 to 20 percent of companies that have adopted artificial intelligence have launched efforts to democratize AI, said Ritu Jyoti, vice president of the Global Research Group on Artificial Intelligence and Automation at International Data Corp.

The democratization of AI presents challenges, Ms. Jyoti said. Models developed by these AI builders, for example, need to be validated and tested extensively to avoid unexpected results. Controls and governance must also be put on the data assets and models that these AI builders, or citizen data scientists as they are often called, operate and use to ensure data integrity and validation metrics. appropriate such as accuracy, fairness and explainability, she said. .

Ford said it has put measures in place. It puts safeguards around its data resources. It has established testing and validation procedures for all models developed by its AI builders. And it sometimes combines its AI builders with its best data scientists on more complex applications to ensure quality.

Ford’s AI builders use a platform that includes, among other tools, Domino Data Lab Inc.’s Machine Learning Operations Platform, which provides a central hub for leveraging compute resources as well as to catalog and track models once they’re deployed, and an in-house developed “feature store”, or repository, of reusable artificial intelligence building blocks.

Write to John McCormick at [email protected]

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