IARPA in the News

Phys.org

A team of NIST scientists has devised and demonstrated a novel nanoscale memory technology for superconducting computing that could hasten the advent of an urgently awaited, low-energy alternative to power-hungry conventional data centers and supercomputers....

One promising replacement technology is superconducting (SC) computing, which offers the prospect of moving information without loss over zero-resistance channels. Instead of using semiconductor transistors to switch electronic signals, SC systems employ tiny components called Josephson junctions (JJs). JJs operate near absolute zero (in the range of 4 K to 10 K), dissipate minuscule amounts of energy (less than 10-19 joule per operation), and can be switched between states at hundreds of billions times a second (frequencies of gigahertz), compared to a few gigahertz for semiconductor computers.

To date, however, many key technologies required for a working SC computer – such as logic circuits, component interconnects, and most notably cryogenic memory – have not been developed. But the Intelligence Advanced Research Projects Activity (IARPA) has determined that, thanks to recent research progress, the "foundations for a major breakthrough" are now in place, and has launched a multi-year program to investigate the practical viability of SC computing.

Executive Gov

The Intelligence Advanced Research Projects Activity is calling on potential participants for its Machine Intelligence from Cortical Networks program that seeks to develop machine learning algorithms that leverage neural computational capabilities.

IARPA said in a FedBizOpps notice posted Jan. 8 that the program combines neuroscience and data science concepts to study cortical computing operations using brain mapping tools and build or improve algorithms based on identified knowledge gaps.

Network World

In an effort to significantly improve artificial intelligence and machine learning technologies, the research arm of the of the Office of the Director of National Intelligence recently announced a program whose chief goal is to reverse engineer human brain algorithms.

Researchers with the Intelligence Advanced Research Projects Agency (IARPA) said their five-year program called Machine Intelligence from Cortical Networks (MICrONS) would offer participants a “unique opportunity to pose biological questions with the greatest potential to advance theories of neural computation and obtain answers through carefully planned experimentation and data analysis."

HPC Wire

US intelligence officials have set in motion a five-year project to spark progress in machine learning by reverse-engineering the algorithms of the human brain. The Intelligence Advanced Research Projects Agency (IARPA) recently put out a call for innovative solutions with the greatest potential to advance theories of neural computation as part of the Machine Intelligence from Cortical Networks (MICrONS) program. The agency, known for its funding of high-risk/high-payoff research in support of national intelligence, is ultimately looking to facilitate the development of synthetic systems with brain-like performance and proficiency.

In a just-issued broad agency announcement, IARPA lays out its strategy for fostering multidisciplinary approaches at the intersection of data science and neuroscience that increase scientific understanding of the cortical computations underlying neural information processing. Although there has been much progress in modeling machine learning algorithms after neural processes, the brain remains far better-suited for a host of detection and recognition tasks.

Popular Science

Intelligence agencies, the spies and spooks and analysts grouped under three letter acronyms, exist in part to answer a difficult question that dates back to antiquity: Is it possible to predict the future, and, if so, how do we do it? A study published this month in the Journal of Experimental Psychology answers the question at least in part: Prediction is a skill, but it takes a special environment to develop that skill.

To understand how prediction works, researchers wanted to see if certain behaviors—such as making a lot of predictions, taking time to consider a question before answering it, or just having a working knowledge of politics in the region in question—effected a forecaster's accuracy.

For the experiment, participants competed in two nine-month-long forecasting tournaments. The questions for the tournament were selected by the Intelligence Advanced Research Projects Activity. Over the two years of the tournament, the forecasters were each asked a total of 199 questions, which “covered topics ranging from whether North Korea would test a nuclear device between January 9, 2012, and April 1, 2012, to whether Moody’s would downgrade the sovereign debt rating of Greece between October 3, 2011, and November 30, 2011.” Forecasters had to answer at least 25 of the questions. The vast majority of the questions had just two possible outcomes, like if a certain embattled world leader would remain in power after a given date. Other questions asked forecasters to choose one time-frame among multiple choices for a possible future event. Participants answered the questions online.

IEEE Spectrum

Early in 2014, IEEE Spectrum teamed up with SciCast, the “Bayesian combinatorial prediction market” group based at George Mason University, in Fairfax, Va. And when our January Top Tech 2015 issue hit the Web, IEEE Spectrum added something new to a few of its articles: the opportunity for readers to participate in IEEE Spectrum SciCast forecasting and match wits with experts by making their own predictions about the future of technology.

SciCast founders Robin Hanson, Kathryn Laskey, and Charles Twardy built the system to allow large numbers of forecasters (some 10,000 have signed on so far) to collectively prognosticate on technological progress. Initial support for SciCast came from the U.S. Intelligence Research Projects Activity.

IEEE Spectrum

A hardware Trojan is exactly what it sounds like: a small change to an integrated circuit that can disturb chip operation. With the right design, a clever attacker can alter a chip so that it fails at a crucial time or generates false signals. Or the attacker can add a backdoor that can sniff out encryption keys or passwords or transmit internal chip data to the outside world.

There’s good reason to be concerned. In 2007, a Syrian radar failed to warn of an incoming air strike; a backdoor built into the system’s chips was rumored to be responsible. Other serious allegations of added circuits have been made. And there has been an explosion in reports of counterfeit chips, raising questions about just how much the global supply chain for integrated circuits can be trusted....

A lot of research is still being devoted to understanding the scope of the problem. But solutions are already starting to emerge. In 2011, the United States’ Intelligence Advanced Research Projects Activity (IARPA) started a new program to explore ways to make trusted chips. As part of that program, our team at Stanford University, along with other research groups, is working on fundamental changes to the way integrated circuits are designed and manufactured.