It's no secret that Ford has been lagging when it comes to the'futuristic' tech that its rivals are already on top of. Meanwhile, Volkswagen has recently announced its cars will be'talking' to each other within as little as two years, while Tesla, a company focused on tech innovation, last year delivered less than 80,000 cars compared to Ford's 6.7 million, and has still managed to overtake Ford in terms of market value. This renewed focus is certainly attributable to his vision -- and Ford is better-positioned to realize this now it's working in partnership with self-driving tech company Argo AI. Of course, car manufacturers are already focusing their efforts on innovating their technology so the announcement doesn't give Ford any lead.

For over twenty-five years Ford Motor Company has been utilizing an AI-based system to manage process planning for vehicle assembly at its assembly plants around the world. The scope of the AI system, known originally as the Direct Labor Management System and now as the Global Study Process Allocation System (GSPAS), has increased over the years to include additional functionality on Ergonomics and Powertrain Assembly (Engines and Transmission plants). The knowledge about Ford's manufacturing processes is contained in an ontology originally developed using the KL-ONE representation language and methodology. In this article, we will discuss the process by which we re-engineered the existing GSPAS KL-ONE ontology and deployed semantic web technology in our application.

The road to self-driving cars got a little more crowded Tuesday, as Mobileye (MBLY) announced it will partner with General Motors (GM) supplier Delphi Automotive (DLPH) to jointly develop off-the-shelf autonomous driving technology for automakers. Mobileye, a Tesla supplier of advanced driver-assistance systems (ADAS), last month disclosed that its partnership with Tesla was ending. Mobileye is a leader in the development and production in computer vision systems, mapping and machine learning, while Delphi, a General Motors spinoff, is a leader in automated driving software, sensors and systems integration. Mobileye recently announced that it would team with BMW (BMWYY) and Intel (INTC) to develop a fully automated driving system, a partnership that one analyst called a "turning point" for autonomous vehicles.

In 2007 we presented a paper that described the application of Natural Language Processing (NLP) and Machine Translation (MT) for the automated translation of process build instructions from English to other languages to support Ford's assembly plants in non-English speaking countries. This project has continued to evolve with the addition of new languages and improvements to the translation process. However, we discovered that there was a large demand for automated language translation across all of Ford Motor Company and we decided to expand the scope of our project to address these requirements. This paper will describe our efforts to meet all of Ford's internal translation requirements with AI and MT technology and focus on the challenges and lessons that we learned from applying advanced technology across an entire corporation.

In this article I discuss a deployed application at Ford Motor Company that utilizes AI technology for the analysis of potential ergonomic concerns at Ford's assembly plants. The manufacture of motor vehicles is a complex and dynamic problem, and the costs related to workplace injuries and lost productivity due to bad ergonomic design can be very significant. Ford has developed two separate ergonomic analysis systems that have been integrated into the process planning for manufacturing system at Ford known as the Global Study and Process Allocation System (GSPAS). GSPAS has become the global repository for standardized engineering processes and data for assembling all Ford vehicles, including parts, tools, and standard labor time.

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