IARPA in the News

Washington Post

How well do people make intuitive predictions? Researchers say the answer is disappointing. Medical diagnoses are sometimes wrong, economic forecasts are often mistaken, and many stock market pickers generate returns that fall below the market average. Is this also true with geopolitical forecasts? From instability in the Middle East to the ongoing conflict between Russia and Ukraine to continuing concern about economic and security implications of the rise of China, understanding what is most likely to happen – and why – is an issue of vital interest for the U.S. government, as well as other governments and companies around the world. Yet, one long-term study showed that people were frequently hard-pressed to beat simple actuarial models even in areas of their own expertise.

What can we do to improve such predictions? Could we improve accuracy by bringing forecasters together, training them, taking advantage of the wisdom of crowds and applying other insights from the decision sciences? We decided to try, working with an interdisciplinary group of scholars, to improve geopolitical forecasting accuracy as part of a multi-year forecasting tournament funded by the Intelligence Advanced Research Projects Activity (IARPA). IARPA, the experimental research and development arm of the intelligence community, wanted to find new ways to generate accurate forecasts. They selected five university and industry programs to compete to find the best possible ways of identifying better forecasters, eliciting predictions and aggregating predictions across forecasters.

The Almanac

An experiment organized by the national intelligence community and set to end this year is attempting to demonstrate that reliable forecasts can be made about economic and geopolitical events. And that some people, including Woodside resident and investment adviser Bob Sawyer, are good enough at it to earn the title of super forecaster.

Mr. Sawyer is one of some 12,000 volunteers throughout the world participating in the Good Judgment Project. For the past four years, small teams of about a dozen people each have been competing for the honor of accurately answering yes-or-no questions like these...

These forecasting tournaments, with about 100 questions a year, are sponsored by the Intelligence Advanced Research Projects Activity (IARPA), which is overseen by the Office of the Director of National Intelligence. IARPA, with its numerous and varied programs, may be at the cutting edge of advances in intelligence gathering and analysis.

Military & Aerospace Electronics

U.S. intelligence experts are ready to kick off a program to find ways of forecasting cyber warfare attacks and other cyber security issues to assist cyber defenders with the earliest possible modes of detection.

Officials of the U.S. Intelligence Advanced Projects Agency (IARPA) in Washington, say they soon will release a formal solicitation for the Cyber-attack Automated Unconventional Sensor Environment (CAUSE) program.


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.