As part of a series of longitudinal studies on AI, the Stanford HAI has come out with the new AI100 report titled'Gathering Strength, Gathering Storms: The One Hundred Year Study on Artificial Intelligence (AI100) 2021 Study Panel Report.' The report evaluates AI's most significant concerns in the previous five years. As part of a series of longitudinal study on AI, the new #AI100report examines AI's most pressing dangers in the last five years. Much has been written on the state of artificial intelligence and its effects on society since the initial AI100 report. Despite this, AI100 is unusual in that it combines two crucial features.

Artificial intelligence has reached a critical turning point in its evolution, according to a new report by an international panel of experts assessing the state of the field. Substantial advances in language processing, computer vision and pattern recognition mean that AI is touching people's lives on a daily basis -- from helping people to choose a movie to aiding in medical diagnoses. With that success, however, comes a renewed urgency to understand and mitigate the risks and downsides of AI-driven systems, such as algorithmic discrimination or use of AI for deliberate deception. Computer scientists must work with experts in the social sciences and law to assure that the pitfalls of AI are minimized. Those conclusions are from a report titled "Gathering Strength, Gathering Storms: The One Hundred Year Study on Artificial Intelligence (AI100) 2021 Study Panel Report," which was compiled by a panel of experts from computer science, public policy, psychology, sociology and other disciplines.

A newly published report on the state of artificial intelligence says the field has reached a turning point where attention must be paid to the everyday applications of AI technology -- and to the ways in which that technology are being abused. The report, titled "Gathering Strength, Gathering Storms," was issued today as part of the One Hundred Year Study on Artificial Intelligence, or AI100, which is envisioned as a century-long effort to track progress in AI and guide its future development . AI100 was initiated by Eric Horvitz, Microsoft's chief scientific officer, and hosted by the Stanford University Institute for Human-Centered Artificial Intelligence. The project is funded by a gift from Horvitz, a Stanford alumnus, and his wife, Mary. The project's first report, published in 2016, downplayed concerns that AI would lead to a Terminator-style rise of the machines and warned that fear and suspicion about AI would impede efforts to ensure the safety and reliability of AI technologies.

A newly published report on the state of artificial intelligence says the field has reached a turning point where attention must be paid to the everyday applications of AI technology -- and to the ways in which that technology are being abused. The report, titled "Gathering Strength, Gathering Storms," was issued today as part of the One Hundred Year Study on Artificial Intelligence, or AI100, which is envisioned as a century-long effort to track progress in AI and guide its future development . AI100 was initiated by Eric Horvitz, Microsoft's chief scientific officer, and hosted by the Stanford University Institute for Human-Centered Artificial Intelligence. The project is funded by a gift from Horvitz, a Stanford alumnus, and his wife, Mary. The project's first report, published in 2016, downplayed concerns that AI would lead to a Terminator-style rise of the machines and warned that fear and suspicion about AI would impede efforts to ensure the safety and reliability of AI technologies.

Compared to other countries, the USA has been relatively slow to develop a national strategy pertaining specifically to Artificial Intelligence. However that has not slowed down the rate of progress in American academia and industry that has led to many noteworthy AI technical advances over the past several years, both in fundamental algorithms and in practical applications. This high rate of AI-related technological progress shows no sign of slowing down. Meanwhile, the federal government has recently become more proactive in its organization of a national strategy and providing guidance and possibly new resources for AI development. Artificial Intelligence (AI) is changing rapidly in all ways.

In September 2016, Stanford's "One Hundred Year Study on Artificial Intelligence" project (AI100) issued the first report of its planned long-term periodic assessment of artificial intelligence (AI) and its impact on society. The report, entitled "Artificial Intelligence and Life in 2030," examines eight domains of typical urban settings on which AI is likely to have impact over the coming years: transportation, home and service robots, healthcare, education, public safety and security, low-resource communities, employment and workplace, and entertainment. It aims to provide the general public with a scientifically and technologically accurate portrayal of the current state of AI and its potential and to help guide decisions in industry and governments, as well as to inform research and development in the field. This article by the chair of the 2016 Study Panel and the inaugural chair of the AI100 Standing Committee describes the origins of this ambitious longitudinal study, discusses the framing of the inaugural report, and presents the report's main findings. It concludes with a brief description of the AI100 project's ongoing efforts and planned next steps.

Shivaram Kalyanakrishnan is an assistant professor in the department of computer science and engineering at the Indian Institute of Technology-Bombay. He specialises in artificial intelligence (AI) and is the only author from India who is part of an 18-member study panel of the Stanford University-hosted report titled Artificial Intelligence and Life. Kalyanakrishnan's expertise broadly fits in the area of machine learning. Called reinforcement learning, it defines what actions software agents should take to maximize a certain type of reward after learning from reward and punishment. In an interview, he urges people to be more optimistic about the things AI can do rather than be obsessed with the fear around AI machines.

Shivaram Kalyanakrishnan is an assistant professor in the department of computer science and engineering at the Indian Institute of Technology-Bombay. He specialises in artificial intelligence (AI) and is the only author from India who is part of an 18-member study panel of the Stanford University-hosted report titled Artificial Intelligence and Life. Kalyanakrishnan's expertise broadly fits in the area of machine learning. Called reinforcement learning, it defines what actions software agents should take to maximize a certain type of reward after learning from reward and punishment. In an interview, he urges people to be more optimistic about the things AI can do rather than be obsessed with the fear around AI machines.

And see also this great piece from Mashable on what manufacturers are up to next. In the near future, sensing algorithms will achieve super-human performance for capabilities required for driving. Automated perception, including vision, is already near or at human-performance level for well-defined tasks such as recognition and tracking. Advances in perception will be followed by algorithmic improvements in higher level reasoning capabilities such as planning. Beyond self-driving cars, we'll have a variety of autonomous vehicles including robots and drones.

And see also this great piece from Mashable on what manufacturers are up to next. In the near future, sensing algorithms will achieve super-human performance for capabilities required for driving. Automated perception, including vision, is already near or at human-performance level for well-defined tasks such as recognition and tracking. Advances in perception will be followed by algorithmic improvements in higher level reasoning capabilities such as planning. Beyond self-driving cars, we'll have a variety of autonomous vehicles including robots and drones. AI also has the potential to transform city transportation planning, but is being held back by a lack of standardisation in the sensing infrastructure and AI techniques used. Accurate predictive models of individuals' movements, their preferences, and their goals are likely to emerge with the greater availability of data. That last sentence is worth reflecting on for a while. It does indeed seem highly likely to happen, but that doesn't mean we have to like what it might mean for society.