Artificial Intelligence is rapidly embedding itself within militaries, economies, and societies, reshaping their very foundations. Given the depth and breadth of its consequences, it has never been more pressing to understand how to ensure that AI systems are safe, ethical, and have a positive societal impact. This book aims to provide a comprehensive approach to understanding AI risk. Our primary goals include consolidating fragmented knowledge on AI risk, increasing the precision of core ideas, and reducing barriers to entry by making content simpler and more comprehensible. The book has been designed to be accessible to readers from diverse backgrounds. You do not need to have studied AI, philosophy, or other such topics. The content is skimmable and somewhat modular, so that you can choose which chapters to read. We introduce mathematical formulas in a few places to specify claims more precisely, but readers should be able to understand the main points without these.

Lincoln celebrates its 100th anniversary with the'Model L100 Concept' taking center stage at the Pebble Beach Concours d'Elegance this week. Created as an autonomous vehicle with an intelligent driving experience, connectivity, and software-driven innovations, the new concept model pushes the boundaries of the brand's'Quiet Flight' design to create connected experiences that reimagine the ultimate vehicle sanctuary of tomorrow. 'Lincoln has been one of the most enduring and stylish automotive brands in the world, and in many ways, it is perfectly positioned for a second century defined by great design, zero-emissions, and technology-led experiences,' comments Bill Ford, executive chair at Ford Motor Company. 'Lincoln has always been special to my family, especially my father and grandfather. If there is one secret to Lincoln's longevity, it is its ability to balance its core values with a desire to innovate and create the future.'

At Google Cloud, our mission is to accelerate manufacturers' digital transformation by bridging data silos, and turning every engineer into a data scientist with easy-to-use AI technologies and an industry data platform. Manufacturers across all domains, from discrete manufacturing to energy and supply chain, are adopting Google's products and purpose-built industry solutions because of their ease-of-use and scalability, using analytics and machine learning to remotely monitor and optimize operations and make better-informed business decisions. Our customers and partners have made great progress on their data transformation journey and on May 5th they will share their perspective and insights at the Manufacturer Spotlight. Join us to hear leading manufacturers and technology partners discuss how they transformed operations and empowered their teams to solve productivity challenges and build competitive and sustainable businesses with Google Cloud. You will hear from thought leaders how a data platform, data analytics and AI models can create a solid foundation for scaling smart factory initiatives and enable unique optimization models.

DEARBORN, Mich., JULY 21, 2021 – In an industry-first collaboration, Argo AI, Lyft and Ford Motor Company are working together to commercialize autonomous ride hailing at scale. The unique collaboration brings together all of the parts necessary to create a viable autonomous ride hailing service, including the self-driving technology, vehicle fleet and transportation network needed to support a scalable business and deliver an exceptional experience for riders. "This collaboration marks the first time all the pieces of the autonomous vehicle puzzle have come together this way," Lyft co-founder and CEO Logan Green said. "Each company brings the scale, knowledge and capability in their area of expertise that is necessary to make autonomous ride-hailing a business reality." As vehicles are deployed, Lyft users within the defined service areas will be able to select a Ford self-driving vehicle to hail a ride.

Oct. 6, 2020--Partners for Automated Vehicle Education (PAVE) held a Sept. 23 webinar on promoting autonomous vehicle technology. The guest speakers, Lucinda Yuen with Ford Motor Company, and Rebecca Posner with the Center for Connected Autonomous Vehicles (CCAV) in the United Kingdom, spoke about AV museum exhibits that they'd helped create. Both exhibits aim to get people comfortable with the idea of AV technology and to inform the public about the power, versatility, and potential of the tech. The exhibits are at the Michigan Science Center in Detroit, and the Science Museum in London. Both are interactive exhibits that allow people to engage with and learn from the technology in a personal way.

Machine translation (MT) was one of the first applications of artificial intelligence technology that was deployed to solve real-world problems. Since the early 1960s, researchers have been building and utilizing computer systems that can translate from one language to another without requiring extensive human intervention. In the late 1990s, Ford Vehicle Operations began working with Systran Software Inc. to adapt and customize its machine-translation technology in order to translate Ford's vehicle assembly build instructions from English to German, Spanish, Dutch, and Portuguese. The use of machine translation was made necessary by the vast amount of dynamic information that needed to be translated in a timely fashion. The assembly build instructions at Ford contain text written in a controlled language as well as unstructured remarks and comments.

Job Description Automated Driving Engineer position offered by Ford Motor Company (Dearborn, MI). Design and implement computer and sensor architectures and interfaces for Level 4 Autonomous vehicles. Conduct system integration of data acquisition and data processing for sensors including camera, lidar, radar, ultrasonics and related. Develop software toolsets for data collection, analysis and visualization. Develop, implement and test algorithms for environmental / situational awareness.

In the not too distant future, robots may be dispatched as last-mile delivery vehicles to drop your takeout order, package, or meal-kit subscription at your doorstep -- if they can find the door. Standard approaches for robotic navigation involve mapping an area ahead of time, then using algorithms to guide a robot toward a specific goal or GPS coordinate on the map. While this approach might make sense for exploring specific environments, such as the layout of a particular building or planned obstacle course, it can become unwieldy in the context of last-mile delivery. Imagine, for instance, having to map in advance every single neighborhood within a robot's delivery zone, including the configuration of each house within that neighborhood along with the specific coordinates of each house's front door. Such a task can be difficult to scale to an entire city, particularly as the exteriors of houses often change with the seasons.

We are very pleased to republish here extended versions of a sample of the papers drawn from the Innovative Applications of Artificial Intelligence Conference (IAAI-06), which was held July 17-20, 2006, in Boston, Massachusetts. Three of these articles describe deployed applications and two describe emerging applications. By this measure, artificial intelligence is going strong. Evidence comes from the annual Conference on Innovative Applications of Artificial Intelligence (IAAI), the premier conference on AI applications. The papers presented at the conference provide compelling case studies of the value and impact of AI technology.

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. One of the more significant benefits of GSPAS is the use of a controlled language, known as Standard Language, which is used throughout Ford to write the process assembly instructions.