Hackers can use lasers to silently “speak” to any computer that receives voice commands, these include smartphones, Amazon Echo speakers, Google Homes, and Facebook’s Portal video chat devices. Former Computing Community Consortium (CCC) Council member Kevin Fu, from the University of Michigan, and his collaborator Takeshi Sugawara, from the University of Tokyo, discovered that it is possible to make microphones respond to light as if it were sound.  This means that anything that acts on sound commands will act on light commands. They found that when they pointed a laser at a microphone and changed the intensity, the light would somehow perturb the microphone’s membrane at that same frequency. The […]

Significant contributions were provided by CRA Director of Government Affairs Peter Harsha and CCC Director Ann Schwartz Drobnis.   Yesterday at a conference of the National Security Commission on AI (NSCAI) in DC, a bipartisan collection of Congressional and agency leaders spoke of the urgent need for the United States to retain its leadership role in Artificial Intelligence in the face of dramatically increased competition from U.S. adversaries. Senate Minority Leader Chuck Schumer (D-NY), noting Russian President Vladimir Putin’s declaration that the nation that leads AI will rule the world, affirmed the priority he believes AI research and education ought to enjoy by teasing a legislative proposal that would create a new […]

The following is a message to the community from the Computing Research Association (CRA) Executive Director Andrew Bernat.  Dear Colleague, I am pleased to announce that CRA’s Career Mentoring Workshop will take place February 27-28, 2020, in Washington, DC. The application link is here. The deadline to apply (for guaranteed attendance for CRA member institution participants) is October 31st. Appropriate participants from CRA-member institutions are automatically accepted (until we run out of physical space); we will accept participants from non-CRA member institutions if there is space (at a higher registration fee).If you have attended or know someone who has, then you know that this workshop gets raves as a kickstart […]

The following blog was written by Computing Community Consortium (CCC) Vice-Chair Liz Bradley from University of Colorado Boulder and CCC Chair Mark D. Hill from the University of Wisconsin Madison. Times in Artificial Intelligence are or should be changing. See Bob Dylan’s 1964 lyrics below.  Last week the New York Times published an article titled “A.I. Researchers See Danger of Haves and Have-Nots.” Modern AI research, which demands enormous computational resources, large data sets, and significant human expertise, is becoming increasingly difficult for anyone outside the large tech companies like Google, Microsoft, Amazon and Facebook. This includes university labs—which, as the article points out, have traditionally been a wellspring of […]

The National Science Foundation (NSF) has a number of upcoming funding opportunities. Computational and Data-Enabled Science and Engineering  (CDS&E) (PD 12-8084) Full Proposal Window: September 30, 2019 Advanced computational infrastructure and the ability to perform large-scale simulations and accumulate massive amounts of data have revolutionized scientific and engineering disciplines. The goal of the CDS&E program is to identify and capitalize on opportunities for major scientific and engineering breakthroughs through new computational and data analysis approaches. See more at this website here. Computer and Information Science and Engineering (CISE): Core Programs (NSF 19-589) Full Proposal Window: September 30, 2019 The NSF CISE Directorate supports research and education projects that develop new […]

The National Science Foundation (NSF) is holding a webinar on September 11, 2019 at 2:00 PM ET for the NSF/Intel Partnership on Machine Learning for Wireless Networking Systems (MLWiNS) solicitation NSF 19-591, submission requirements, and program updates. The NSF/Intel Partnership on Machine Learning for Wireless Networking Systems (MLWiNS) seeks to accelerate fundamental, broad-based research on wireless-specific machine learning (ML) techniques, towards a new wireless system and architecture design, which can dynamically access shared spectrum, efficiently operate with limited radio and network resources, and scale to address the diverse and stringent quality-of-service requirements of future wireless applications. In parallel, this program also targets research on reliable distributed ML by addressing the challenge […]