Yesterday morning at the Heidelberg Laureate Forum (HLF) laureates Yoshua Bengio (2018 Turing Award), Edvard Moser (2014 Nobel Prize in Physiology or Medicine), and Leslie G. Valiant (1986 Nevanlinna Prize and 2010 Turing Award) each presented a lecture related to artificial intelligence or the modeling of the brain. Yoshua Bengio’s lecture on “Deep Learning for AI” provided a retrospective of some of the key principles behind the recent successes of deep learning. Dr. Bengio’s work has mostly been in neural networks, which are inspired by the computation found in the human brain. One of the key insights in the field came with the representation of words as vectors of numbers. This allowed relationships between words to be learned […]

Recently, the Computing Community Consortium (CCC) released the completed Artificial Intelligence (AI) Roadmap, titled A 20-Year Community Roadmap for AI Research in the US! An HTML version is available here. Slides summarizing the roadmap are now available here. We encourage you to use these when sharing the community vision. This roadmap is the result of a year long effort by the CCC and over 100 members of the research community, led by Yolanda Gil (University of Southern California and President of AAAI) and Bart Selman (Cornell University and President Elect of AAAI). Comments on a draft report of this roadmap were solicited in the Spring of 2019 and incorporated in the final report. If you would like […]

The following blog was written by Computing Community Consortium (CCC) Chair Mark D. Hill from the University of Wisconsin Madison.  This week Cerebras announced a bold design to accelerate deep neural networks with silicon that is not cut into chips. AI and Moore’s Law: Artificial Intelligence (AI) is much in the news for what it can do to today and the promise of what it can do tomorrow (CCC/AAAI 20-Year AI Roadmap). Over half a century, AI innovation has been abetted by a million-fold progress in computer system cost-performance and parallelism. For decades, computer benefits came transparently via repeated doubling of processor performance (popularly called “Moore’s Law”). For the last decade, however, AI–especially for the great successes of […]

Today, the White House, through the National Institute of Standards and Technology (NIST), issued a plan for Federal engagement in the development of technical standards for artificial intelligence (AI). The plan was called for as part of the national strategy for U.S. leadership in AI, the American AI Initiative, launched by Executive Order in February 2019. After receiving broad public input, the technical standards plan provides guidance for Federal agencies, the private sector, and the academic community in the development of technical standards and related tools in support of reliable, robust, and trustworthy AI systems, and identifies priorities for Federal government involvement and leadership in AI standards development. “The Trump […]

Recently, the White House posted a Request for Information (RFI) inviting suggestions for improvements to Federal data and models needed to accelerate AI R&D and testing. See below for more details. Your responses, and/or those of your organization, would be valuable and much appreciated. FOR IMMEDIATE RELEASE July 10, 2019 Trump Administration Takes Action to Improve Federal Data and Models for Artificial Intelligence R&D Recently, the Trump Administration continued efforts to advance American leadership in artificial intelligence (AI) with the announcement of a Request for Information (RFI) to improve Federal data and models for AI research and development (R&D) and testing. As called for by President Trump’s national AI strategy […]

Contributions to this post were provided by Monica Lam and Jen King from Stanford University.  The New York Times recently published an article titled Stanford Team Aims at Alexa and Siri With a Privacy-Minded Alternative. Professor Monica Lam and her students, Giovanni Campagna, Silei Xu, Michael Fischer, and Mehrad Moradshahi, have developed a virtual assistant called Almond that can avoid surrendering personal information to a centralized service and encourage open competition among companies. She is joined by Stanford computer science researchers Michael Bernstein, Dan Boneh,  Jen King, James Landay, Chris Manning, and David Mazières, Chris Re in a newly funded NSF research grant to expand the capabilities and privacy protection […]