Artificial intelligence research is ushering in a new era of sophisticated, mass-market transportation technology. While computers can already fly a passenger jet better than a trained human pilot, people are still faced with the dangerous yet tedious task of driving automobiles. Intelligent Transportation Systems (ITS) is the field that focuses on integrating information technology with vehicles and transportation infrastructure to make transportation safer, cheaper, and more efficient. Recent advances in ITS point to a future in which vehicles themselves handle the vast majority of the driving task. Once autonomous vehicles become popular, autonomous interactions amongst multiple vehicles will be possible.
Decades of research in artificial intelligence (AI) have produced formidable technologies that are providing immense benefit to industry, government, and society. AI systems can now translate across multiple languages, identify objects in images and video, streamline manufacturing processes, and control cars. The deployment of AI systems has not only created a trillion-dollar industry that is projected to quadruple in three years, but has also exposed the need to make AI systems fair, explainable, trustworthy, and secure. Future AI systems will rightfully be expected to reason effectively about the world in which they (and people) operate, handling complex tasks and responsibilities effectively and ethically, engaging in meaningful communication, and improving their awareness through experience. Achieving the full potential of AI technologies poses research challenges that require a radical transformation of the AI research enterprise, facilitated by significant and sustained investment. These are the major recommendations of a recent community effort coordinated by the Computing Community Consortium and the Association for the Advancement of Artificial Intelligence to formulate a Roadmap for AI research and development over the next two decades.
A survey released by the American Transportation Research Institute says more than half of truck drivers who have been referred to a sleep study have incurred some or all of the test costs. The survey, which was released Thursday, May 26, includes data from more than 800 commercial drivers to help quantify the costs and impact on truck drivers as they address diagnosis and a potential treatment regimen for obstructive sleep apnea. On March 10, the Federal Motor Carrier Safety Administration and Federal Railroad Administration published an advanced notice of proposed rulemaking about a possible regulation regarding sleep apnea. Specifically, the agencies requested comment on the costs and benefits of requiring motor carrier and rail transportation workers who exhibit multiple risk factors for sleep apnea to undergo evaluation and treatment by a healthcare professional with expertise in sleep disorders. The agencies have held three public listening sessions on the issue.
This book-length article combines several peer reviewed papers and new material to analyze the issues of ethical artificial intelligence (AI). The behavior of future AI systems can be described by mathematical equations, which are adapted to analyze possible unintended AI behaviors and ways that AI designs can avoid them. This article makes the case for utility-maximizing agents and for avoiding infinite sets in agent definitions. It shows how to avoid agent self-delusion using model-based utility functions and how to avoid agents that corrupt their reward generators (sometimes called "perverse instantiation") using utility functions that evaluate outcomes at one point in time from the perspective of humans at a different point in time. It argues that agents can avoid unintended instrumental actions (sometimes called "basic AI drives" or "instrumental goals") by accurately learning human values. This article defines a self-modeling agent framework and shows how it can avoid problems of resource limits, being predicted by other agents, and inconsistency between the agent's utility function and its definition (one version of this problem is sometimes called "motivated value selection"). This article also discusses how future AI will differ from current AI, the politics of AI, and the ultimate use of AI to help understand the nature of the universe and our place in it.
The 19th-century U.K. Locomotive Act, also known as the Red Flag Act, required motorized vehicles to be preceded by a person waving a red flag to signal the oncoming danger. Movies can be a good place to see what the future looks like. According to Robert Wallace, a retired director of the CIA's Office of Technical Service: "... When a new James Bond movie was released, we always got calls asking, 'Do you have one of those?' If I answered'no', the next question was, 'How long will it take you to make it?' Folks didn't care about the laws of physics or that Q was an actor in a fictional series--his character and inventiveness pushed our imagination ..."3 As an example, the CIA successfully copied the shoe-mounted spring-loaded and poison-tipped knife in From Russia With Love. It's interesting to speculate on what else Bond movies may have led to being invented. For this reason, I have been considering what movies predict about the future of artificial intelligence (AI).