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.

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. To preserve the viability of the GSPAS ontology and to make it easily usable for other applications within Ford, we needed to re-engineer and convert the KL-ONE ontology into a semantic web OWL/RDF format. 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.

Human mobility modeling for either transportation system development or individual location based services has a tangible impact on people's everyday experience. In recent years cell phone data has received a lot of attention as a promising data source because of the wide coverage, long observation period, and low cost. The challenge in utilizing such data is how to robustly extract people's trip sequences from sparse and noisy cell phone data and endow the extracted trips with semantic meaning, i.e., trip purposes.In this study we reconstruct trip sequences from sparse cell phone records. Next we propose a Bayesian trip purpose classification method and compare it to a Markov random field based trip purpose clustering method, representing scenarios with and without labeled training data respectively. This procedure shows how the cell phone data, despite their coarse granularity and sparsity, can be turned into a low cost, long term, and ubiquitous sensor network for mobility related services.

For over twenty five years Ford has been utilizing an AI-based system to manage process planning for vehicle assembly at our 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 (Engine 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. To preserve the viability of the GSPAS ontology and to make it easily usable for other applications within Ford, we needed to re-engineer and convert the KL-ONE ontology into a semantic web OWL/RDF format. In this paper, we will discuss the process by which we re-engineered the existing GSPAS KL-ONE ontology and deployed semantic web technology in our application.

We developed a tool to solve a problem of position assignment within the IT Ford College Graduate program. This position assignment tool was first developed in 2012 and has been used successfully since then. The tool has since evolved for use with several other position assignment and related tasks with other similar programs in Ford Motor Company. This paper will describe the creation of this tool and how we have applied it, focusing on the need for developing such a tool, and how the continued development of this tool will benefit its users and the company.

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 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.

This paper reports on a mutual information (MI) based algorithm for automatic extrinsic calibration of a 3D laser scanner and optical camera system. By using MI as the registration criterion, our method is able to work in situ without the need for any specific calibration targets, which makes it practical for in-field calibration. The calibration parameters are estimated by maximizing the mutual information obtained between the sensor-measured surface intensities. We calculate the Cramer-Rao-Lower-Bound (CRLB) and show that the sample variance of the estimated parameters empirically approaches the CRLB for a sufficient number of views. Furthermore, we compare the calibration results to independent ground-truth and observe that the mean error also empirically approaches to zero as the number of views are increased. This indicates that the proposed algorithm, in the limiting case, calculates a minimum variance unbiased (MVUB) estimate of the calibration parameters. Experimental results are presented for data collected by a vehicle mounted with a 3D laser scanner and an omnidirectional camera system.

This issue of AI Magazine provides extended versions of several papers that were recently presented at the Innovative Applications of Artificial Intelligence Conference (IAAI-2010). We present three articles reflecting deployed applications of AI, one describing a unique, emerging application, plus an article based on the invited talk by Jay M. Tenenbaum, who was the 2010 Engelmore Award recipient.

We are proud to continue this tradition with the presentation of five articles from the Twenty Second IAAI conference that was held in Atlanta, Georgia, from July 11-14, 2010. We were especially honored to have Jay M. (Marty) Tenenbaum accept the Robert S. Engelmore Memorial Award for his exceptional contributions to AI in computer vision and manufacturing as well as his visionary role in the birth of electronic commerce. This issue of AI Magazine includes an article based on his lecture Cancer: A Computational Disease That AI Can Cure. In this article, Jay Tenenbaum and Jeff Shrager provide a personal view of their work in the development of an AIbased system that addresses the challenge of helping to find a cure for cancer. As a cancer survivor himself, Tenenbaum has a unique insight into the shortcomings of current approaches to treating this disease.