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1 1 (October 24, 2017)

handle is hein.crs/govcezx0001 and id is 1 raw text is: Overview of Artificial Intelligence - mmm, go mppm qq\ , q I as 11LIANJILiN, Updated October 24, 2017 The use of artificial intelligence (AI) is growing across a wide range of sectors. Stakeholders and policymakers have called for careful consideration of strategies to influence its growth, reap predicted benefits, and mitigate potential risks. This document provides an overview of Al technologies and applications, recent private and public sector activities, and selected issues of interest to Congress. Though definitions vary, Al can generally be thought of as computerized systems that work and react in ways commonly thought to require intelligence, such as solving complex problems in real-world situations. According to the Association for the Advancement of Artificial Intelligence (AAAI), researchers broadly seek to understand the mechanisms underlying thought and intelligent behavior and their embodiment in machines. The field of Al encompasses many methodologies and areas of emphasis, such as machine learning (ML), deep learning, neural networks, robotics, machine/computer vision (image processing), and natural language processing. Advances in these areas, in information processing and hardware technology generally, and in the availability of large-scale data sets, have all contributed to recent progress in Al. Applications of Al are found in everyday technologies, such as video games, web searching, spam filtering, and voice recognition (e.g., Apple's Siri). Notable Al systems have beaten human champions of games like chess, Go, and Jeopardy!. More broadly, Al has applications across a variety of sectors, including the following examples: * Transportation self-driving cars, adaptive traffic management to reduce wait times and emissions; * Health care-diagnostics and targeted treatments; * Education-digital tutors; * Agriculture soil moisture monitoring and targeted crop watering; * Finance-early detection of unusual market manipulation and anomalous trading; * Law machine analysis of law case history; * Manufacturing automated delivery, improved worker safety and productivity via machine-human teaming; * Cybersecurity autonomous detection and decisionmaking to improve reaction times to threats; * Defense autonomous and semi-autonomous weapons systems; * Space exploration spacecraft and rover autonomy; and * Al for the social good-using Al to address widespread societal challenges, e.g., to monitor wildlife populations, target anti-poaching efforts, and identify intervention zones for poverty reduction efforts. Currently, Al technologies are highly tailored to particular applications or tasks, known as narrow (or weak) Al. In contrast, general (or strong) Al refers to intelligent behavior across a range of cognitive tasks, a capability which is unlikely to occur for decades, according to most analysts. Some researchers also use the term augmented intelligence to capture Al's various applications in physical and connected systems, such as robotics and the Internet of Things, and to focus on using Al technologies to enhance human activities rather than to replace them. In describing the course of Al development, the Defense Advanced Research Projects Agency (DARPA) has identified three waves. The first wave focused on handcrafted knowledge that allowed for system-enabled reasoning in limited situations, though lacking the ability to learn or address uncertainty, as with many rule-based systems from the 1980s. The second wave, from approximately the 2000s to the present, has focused on advances in neural networks and machine learning (e.g., image recognition, language translation) using statistical models and big data sets. The third wave will focus on contextual adaptation learning and reasoning as the system encounters new tasks moving towards general Al. Among researchers and developers, the outlook on Al development and application across sectors is widely optimistic, though challenges exist. Such challenges are both technical (e.g., availability of datasets to train Al systems, and understanding and removing biases from Al- based decisions) and societal (e.g., addressing potential workforce shifts, privacy, security, and ethical use). riv, k,e S 're ,r A ct , it In recent years, the private sector has been increasing research and development (R&D) investments and hiring in Al, particularly at large technology companies such as Amazon, Facebook, Google, IBM, and Microsoft. Large companies are also acquiring Al startups and launching venture funds to support startups. These and other technology companies, along with the AAAI a nonprofit scientific society have formed the Partnership on Al, which aims to create best practices, educate the public, and serve as a platform for discussing Al technologies and societal impacts. Automotive and ride-sharing companies, such as Toyota and Uber, have also announced large investments in Al research, as well as partnerships with university scientists and engineers. For example, the Toyota Research Institute includes experts from Stanford's Al Laboratory and Massachusetts Institute of Technology's Computer Science and Al Laboratory. K~:>

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