Machine Learning, often abbreviated to ML, is a form of learning in which systems use complex computer algorithms to acquire knowledge or skill automatically without being programmed directly. It is considered as a type of AI (Artificial Intelligence) since machines are built with the idea to learn and make decisions from the available data and even improve themselves from experience without requiring human involvement. This is mainly used to maximize the machine's performance. The idea behind ML is based on mathematics, computer science, and statistics. ML comes in three types, Supervised, Reinforcement, and Unsupervised Learning.
An Approach to the Development of Technology to Empower the Elderly Edward Riseman, Allen Hanson, Roderick Grupen Computer Science Department, University of Massachusetts at Amherst Phebe Sessions, Julie Abramson, Mary Olson Smith School of Social Work, Smith College Candace Sidner, Mitsubishi Electric Research Lab The growing numbers of elderly individuals in need of support to live in the community will severely test the current services infrastructure. Part of the answer is to develop technological innovations that allow an elder population to successfully "age in place" with dignity and a sense of involvement with their community. However, we believe that it is essential to understand the needs of the target community through interdisciplinary perspectives of social science and computer science in partnership with potential elderly recipients of the technology themselves. Our team of researchers brings together social scientists and geriatric social work practitioners from Smith College and computer scientists who have expertise in computer vision, robotics, augmented and virtual reality, and intelligent user interfaces from the University of Massachussetts and Mitsubishi Electrical Research Laboratory (MERL). We believe that those who develop assistive technology should be sufficiently involved at the "ground level" with the elders themselves, their families, caregivers and service systems.
China aims to make the artificial intelligence industry a "new, important" driver of economic expansion by 2020, according to a development plan issued by the State Council. Policymakers want to be global leaders, with the AI industry generating more than 400 billion yuan ($59 billion) of output per year by 2025, according to an announcement from the Cabinet late Thursday. Key development areas include AI software and hardware, intelligent robotics and vehicles, virtual reality and augmented reality, it said. "Artificial intelligence has become the new focus of international competition," the report said. "We must take the initiative to firmly grasp the next stage of AI development to create a new competitive advantage, open the development of new industries and improve the protection of national security."
Human-machine cooperation and developing trust among robots, soldiers and civilians – these are the subjects of two, high-tech Artificial Intelligence (AI) projects from York U's Lassonde School of Engineering. They were, collectively, funded $5 million. Two York University projects led by Lassonde School of Engineering Professors Michael Jenkin (Electrical Engineering and Computer Science) and Jinjun Shan (Earth and Space Science and Engineering) were awarded funding from the Department of National Defence's Innovation for Defence Excellence and Security (IDEaS) program under Innovation Networks in October 2019. York's securing two of the six contributions from the IDEaS Program speaks to the University's leadership in this area. Each contribution is worth close to $1.5 million.
Julie A. Adams is an Assistant Professor of Computer Science and Computer Engineering in the Electrical Engineering and Computer Science Department at Vanderbilt University, where she directs the Human-Machine Teaming Laboratory. Her research focuses on distributed artificially intelligent algorithms for autonomous multiple robot coalition formation and the development of complex humanmachine systems for large human and robotic teams.She has published on topics in autonomous robotic coalition formation, human-robot interaction, cognitive task analysis for robotic systems, and human factors. She worked in Human Factors for Honeywell, Inc. and the Eastman Kodak Company from 1995 to 2000. She was an Assistant Professor of Computer Science at Rochester Institute of Technology from 2000 until 2003. She is an appointed member of the National Research Council's Army Research Laboratory Technical Assessment Review Panel on Solider Systems and is the recipient of the NSF CAREER Award.