Artificial intelligence not so intelligent, claim human scientists

#artificialintelligence

"Today's forecast for San Antonio is..." "The rapper Sisqó was born in Baltimore, Maryland." Sometimes, talking to an artificially intelligent robot is, well, not so intelligent. That's part of a conclusion reached by a team of Stanford University professors who are tracking the technological progress of artificial intelligence. Yohav Shoham is the Stanford computer science professor who conceived of the idea for the index. "AI has made truly amazing strides in the past decade, but computers still can't exhibit the common sense or the general intelligence of even a 5-year-old," said Shoham, who studied at Yale University and in his native Israel at both the Technion Institute and the Weizmann Institute of Science.


Heuristic Search and Information Visualization Methods for School Redistricting

AI Magazine

We describe an application of AI search and information visualization techniques to the problem of school redistricting, in which students are assigned to home schools within a county or school district. This is a multicriteria optimization problem in which competing objectives, such as school capacity, busing costs, and socioeconomic distribution, must be considered. Because of the complexity of the decision-making problem, tools are needed to help end users generate, evaluate, and compare alternative school assignment plans. A key goal of our research is to aid users in finding multiple qualitatively different redistricting plans that represent different trade-offs in the decision space. We present heuristic search methods that can be used to find a set of qualitatively different plans, and give empirical results of these search methods on population data from the school district of Howard County, Maryland. We show the resulting plans using novel visualization methods that we have developed for summarizing and comparing alternative plans.


Heuristic Search and Information Visualization Methods for School Redistricting

AAAI Conferences

We describe an application of AI search and information visualization techniques to the problem of school redistricting, in which students are assigned to home schools within a county or school district. This is a multicriteria optimization problem in which competing objectives must be considered, such as school capacity, busing costs, and socioeconomic distribution. Because of the complexity of the decision-making problem, tools are needed to help end users generate, evaluate, and compare alternative school assignment plans. A key goal of our research is to aid users in finding multiple qualitatively different redistricting plans that represent different tradeoffs in the decision space. We present heuristic search methods that can be used to find a set of qualitatively different plans, and give empirical results of these search methods on population data from the school district of Howard County, Maryland. We show the resulting plans using novel visualization methods that we have developed for summarizing and comparing alternative plans.


MouseAge.Org: Artificial intelligence for photographic biomarkers in mice

#artificialintelligence

IMAGE: MouseAge.Org provides tools for cross-species analysis, and provide correlations between health and appearance. Tuesday, 29th of August, 2017, Baltimore, MD - Insilico Medicine, Inc, a Baltimore-based next-generation artificial intelligence company, today announced its participation in the MouseAge.org The scientists from Insilico Medicine will collaborate with scientists from Harvard, Oxford, Youth Laboratories, the Biogerontology Research Foundation, and other institutions to enable scientists worldwide to derive more information from rodent studies, develop novel biomarkers of aging and various diseases in mice, develop tools for cross-species analysis, and provide correlations between health and appearance. The project campaign has been launched today at research crowdfunding platform Lifespan.io. The project was conceived by Vadim Gladyshev, Professor of Medicine at Brigham and Women's Hospital, Harvard Medical School, and Alex Zhavoronkov, CEO of Insilico Medicine.


Panelists Talk Machine Learning and the Future of Mathematics at ICIAM 2019

#artificialintelligence

The excitement and activity surrounding the field of machine learning was clearly evident at the 9th International Congress on Industrial and Applied Mathematics (ICIAM 2019), which took place this summer in Valencia, Spain. Over 25 minisymposia--as well as several prize lectures and invited talks--touched on the theme of "learning," while other invited presentations addressed important mathematical research challenges necessary to advance the field. Panelists Hans De Sterck (University of Waterloo), Gitta Kutyniok (Technische Universität Berlin), James Nagy (Emory University), and Eitan Tadmor (University of Maryland, College Park) represented various core areas of computational and applied mathematics that develop and utilize machine learning techniques, including computational science and engineering, imaging science, linear algebra, and partial differential equations. Discussion broached a variety of issues surrounding machine learning, such as the obvious fact that machine learning will remain, as mathematician Ali Rahimi stated, "an area comparable to alchemy" without new mathematical understanding and developments. Deep learning is among the most transformative technologies of our time, and its many potential applications--from driverless cars to drug discovery--can have tremendous societal impact.