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A 20-Year Community Roadmap for Artificial Intelligence Research in the US

arXiv.org Artificial Intelligence

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.



The 2018 Survey: AI and the Future of Humans

#artificialintelligence

"Please think forward to the year 2030. Analysts expect that people will become even more dependent on networked artificial intelligence (AI) in complex digital systems. Some say we will continue on the historic arc of augmenting our lives with mostly positive results as we widely implement these networked tools. Some say our increasing dependence on these AI and related systems is likely to lead to widespread difficulties. Our question: By 2030, do you think it is most likely that advancing AI and related technology systems will enhance human capacities and empower them? That is, most of the time, will most people be better off than they are today? Or is it most likely that advancing AI and related technology systems will lessen human autonomy and agency to such an extent that most people will not be better off than the way things are today? Please explain why you chose the answer you did and sketch out a vision of how the human-machine/AI collaboration will function in 2030.


GPT-3 Creative Fiction

#artificialintelligence

What if I told a story here, how would that story start?" Thus, the summarization prompt: "My second grader asked me what this passage means: …" When a given prompt isn't working and GPT-3 keeps pivoting into other modes of completion, that may mean that one hasn't constrained it enough by imitating a correct output, and one needs to go further; writing the first few words or sentence of the target output may be necessary.


Imposing Regulation on Advanced Algorithms

arXiv.org Artificial Intelligence

This book discusses the necessity and perhaps urgency for the regulation of algorithms on which new technologies rely; technologies that have the potential to re-shape human societies. From commerce and farming to medical care and education, it is difficult to find any aspect of our lives that will not be affected by these emerging technologies. At the same time, artificial intelligence, deep learning, machine learning, cognitive computing, blockchain, virtual reality and augmented reality, belong to the fields most likely to affect law and, in particular, administrative law. The book examines universally applicable patterns in administrative decisions and judicial rulings. First, similarities and divergence in behavior among the different cases are identified by analyzing parameters ranging from geographical location and administrative decisions to judicial reasoning and legal basis. As it turns out, in several of the cases presented, sources of general law, such as competition or labor law, are invoked as a legal basis, due to the lack of current specialized legislation. This book also investigates the role and significance of national and indeed supranational regulatory bodies for advanced algorithms and considers ENISA, an EU agency that focuses on network and information security, as an interesting candidate for a European regulator of advanced algorithms. Lastly, it discusses the involvement of representative institutions in algorithmic regulation.