The Intelligence Advanced Research Projects Activity (IARPA) is an organization within the Office of the Director of National Intelligence that funds high-risk, high-payoff research to overcome difficult challenges relevant to the United States Intelligence Community. IARPA was given the mandate to conduct cross-community research, target new opportunities and innovations, and generate revolutionary capabilities for national intelligence.
IARPA is modeled after the Defense Advanced Research Projects Agency, which develops new technology for the military. “IARPA does for NSA what DARPA does for the military,” said James A. Lewis, director and senior fellow of the technology and public policy program at the Center for Strategic and International Studies. “A lot of their programs are black,” he told CIO Journal, meaning that they’re classified and funded from a classified budget.
“A larger part of IARPA’s portfolio in the last year has been devoted to improving our biosecurity, our ability to detect and prevent biological weapons or disease outbreaks,” said Jason Matheny, director of the Intelligence Advanced Research Projects Activity, in an interview with FCW.
In August 2018, Dixon IARPA’s deputy director was tapped by Director of National Intelligence Dan Coats to lead the organization, replacing its current director, Jason Matheny. China’s outsize investment in technology industries and priority research areas like synthetic biology and artificial intelligence may mean top talent that might have remained in the United States is instead lured overseas, according to the newly named director of the Intelligence Advanced Research Project Activity (IARPA), Stacey Dixon. “We’re talking billion-dollar investments,” Dixon said, “and it’s scary, because those are large investments that can really shift what research is accomplished.”
Among IARPA’s four areas of focus are analysis, anticipatory intelligence, collection and computing. Generally, Dixon said, improving analytical capabilities is about making better use of data to, in turn, make better decisions. Anticipatory intelligence is essentially signals-informed forecasting.
The IARPA Mercury Challenge is looking to automatically predict the occurrence of critical events, such as military action and non-violent civil unrest events, and infectious diseases such as MERS in eight countries in the Middle East and North Africa (MENA).
IARPA funds academic and industry research across a broad range of technical areas, including mathematics, computer science, physics, chemistry, biology, neuroscience, linguistics, political science, and cognitive psychology. Notable IARPA investments include quantum computing, superconducting computing, and forecasting tournaments.
IARPA’s programs run for three to five years, and once the technical problems are solved, the technology is distributed to relevant agencies. Some of the tech becomes immediately deliverable and operable, but other solutions are only 80 percent complete when they are passed to an agency to finish.
Technology from defense and intelligence research labs has often been criticized for ending up in the “valley of death” between research and commercialization instead of in the field. Matheny said IARPA has those struggles too but tries to test field viability in the lab first to prevent tech from “gathering dust on a shelf.”
Seventy percent of IARPA’s developments make it over the valley of death, Matheny said, which includes programs that failed to meet the agency’s goals but were still better than the current state of the art. “Our partners throughout government still want things that work better than the state of the art even if it didn’t achieve the milestones that it set for the program,” he said.
Predict the Unpredictable
The Agency focuses on characterizing and reducing uncertainty through anticipatory intelligence. IARPA is known for its programs to fund research into anticipatory intelligence, using data science to make predictions about future events ranging from the political elections to disease outbreaks to cyberattacks, some of which focus on open-source intelligence.
“Anticipatory intelligence is part of our national intelligence strategy; it means gaining a lead time on warning against certain kinds of events or conditions that might change in the world,” said Jason Matheny, IARPA director. “The kinds of events we’re interested in anticipating include things like political instability, disease events, economic crises and cyberattacks. So we’ve focused on those events in our programs.”
“There’s a lot of information out there publicly available that really you can derive signals from,” Dixon explained. A growing volume of conversations on social media may indicate pending civil unrest or economic instability; restaurant cancellations or pharmacy visits may suggest disease outbreak, she said. “We take the forecasting all the way to geopolitical events,” Dixon told Morell, “to try to predict who is going to win foreign elections, whether other countries are going to do things like launch missiles.”
Collection, she said, “is all about just the signals themselves…getting information from an area that you couldn’t collect from before,” including by using synthetic biology to enhance biological sensors. “So — what are the signals that are going to let us know that someone has been handling narcotics or handling explosives?” Dixon explained. “We’re really trying to do an entirely different way of collecting the data.”
“In order to deliver forecasts to decision-makers we have to continuously monitor the environment for indicators of change. That might be indicators of political unrest in a region, indicators that a disease outbreak is occurring or indicators that a cyberattack is under way.”
One such program is Open Source Indicators, which reviews a range of publicly available sources, such as Tweets, Web queries, oil prices and daily stock market activity, to gauge the likelihood of certain “significant societal events,” according to a program announcement posted on FedBizOpps.gov. The goal of the program is to develop continuously automated systems that use information from these sources to predict when and where a disease outbreak, riot, political crisis or mass violence might occur.
There have been a number of successes said Jason Matheny IARPA’s New Director, “A team in ourOpen Source Indicators (OSI) program was the first to notify U.S. public health officials about the Ebola outbreak in West Africa. It identified the outbreak from automated detection of news reports of an undefined hemorrhagic fever in West Africa. That program also accurately forecasted the Brazilian Spring, which was a series of nationwide protests in Brazil in 2013.”
“The project was also able to accurately predict a Hantavirus outbreak in Argentina last year, said Mr. Ramakrishna, who is leading a team of about 60 people on the project. The team is trying to find which pieces of data most accurately forecast an outbreak. For example, project team members are looking at the numbers of cars parked in hospital parking lots from satellite images and trying to figure out if an increased number of cars in the parking lot can be an early indication of an increase in disease outbreak, he said. IARPA keeps a log of all the predictions forecast by researchers and checks them against articles in local newspapers to determine which ones were accurate. Each month, his group gets a report card from IARPA telling the researchers how well they performed.
The Intelligence Advanced Research Projects Activity (IARPA) launched Geopolitical Forecasting Challenge 2 recently with the goal of furthering the science of forecasting, IARPA said in a statement released May 2019. The collective effort of GF Challenge 2 stimulates breakthroughs in the science of forecasting, leading to greater strategic advantages for maintaining global security, predicting economic trends, and directing the need for humanitarian efforts. Individuals and teams earn prizes by creating methods that can successfully forecast a wide variety of global events, such as political elections, disease outbreaks, and macro-economic indicators.
“Accurate geopolitical forecasts are crucial to making informed, effective policy. IARPA is interested in identifying the most effective ways to integrate human judgment with other types of data,” says IARPA Program Manager, Dr. Seth Goldstein.
Mercury Challenge launched in 2018
In an effort to provide early warning capabilities, the Department of Defense’s Integrated Crisis Early Warning System (ICEWS) and IARPA’s Open Source Indicators (OSI) programs want to leverage novel statistical and machine learning techniques using publicly available data sources to forecast societal such as civil unrest and disease outbreaks with a high degree of accuracy.
Participants are encouraged to develop and test innovative forecasting methods that ingest and process publicly available data sources to predict Military Activity, Non-violent Civil Unrest, and Infectious Disease in specific places of interest.
Participant forecasts will be scored across a three-month rolling window. Every month, IARPA will score those participants who have sent forecasts for three consecutive months. Scoring will be based on four metrics including forecast lead time, location accuracy, date accuracy, and facet actor/event-type matching. This challenge is scheduled to run from July 2018 until early 2019 when winners are announced.
Technologists, data scientists, and machine learning engineers who are skilled at breaking down complex data are encouraged to join. Individuals ranging from private industry and academia are all eligible to participate and win prizes. The Mercury Challenge Team believes success in this challenge can prove to be a strong addition to any data science practitioner’s portfolio.
IARPA Launches COVID-19 Seedling BAA in May 2020
The BAA solicits proposals for developing new tools and technologies that provide rapid capabilities against the current COVID-19 pandemic, as well as enhanced warning and response capacity for future similar events. The solicitation focuses on technologies that can support: detection and sensing; supply chain management and integrity; geo-spatio-temporal monitoring and mapping with privacy protection; information reliability and collaboration tools; and modeling, simulation and predictive analytics.
“Technology solutions for COVID-19 will require creative, multidisciplinary methods, paradigm changing thinking, and transformative approaches,” said IARPA Deputy Director for Research Dr. Catherine Cotell. “Our goal is to advance ground-breaking technologies that will help the Intelligence Community and the country prepare for and recover from pandemic events.”
In order to develop methods that are good at forecasting, we run forecasting tournaments in which teams made up of universities and industry labs are actually forecasting real-world events before the occur. And then we keep score – who got it right, who got it wrong, what distinguished the good forecasts from the bad forecasts.
Key research areas for OAS include forecasting events related to science and technology (S&T); social, political, and economic crises; epidemiology and biosecurity; counterintelligence; and cybersecurity. IARPA has pursued its objectives not only through traditional funding programs but also through tournaments and prizes
IARPA holds many forecasting tournaments including the Aggregative Contingent Estimation program (ACE), which ran from June 2010 until June 2015, program to forecast cyberattacks, a program to forecast disease outbreaks and political instability, a program to forecast military mobilization and terrorism, and a program to forecast insider threats.
The goal of ACE was to develop advanced techniques that combine the judgments of many analysts in ways that would enhance the accuracy, precision, and timeliness of intelligence forecasts. This was the world’s largest forecasting experiment. It involved more than 20,000 people collecting over 2 million judgments that were crowdsourced on hundreds of geopolitical topics. It asked thousands of participants to forecast who would win a political election, for example, or which countries would go to war. We kept score of whose forecasts were right, whose were wrong, what distinguished the good forecasters from the not-so-good forecasters, and discovered ways of combining the judgments from individuals to create better forecasts than any single individual.
There is also a program called Forecasting Science & Technology (ForeST). As far as we know, ForeST was the world’s largest tournament in science and technology forecasting. We had about 15,000 people over a two-year period forecast around 1,000 different science and technology milestones. What would be the most advanced photovoltaic cell by the end of 2014? What would be the top of the top 500 supercomputers by the end of 2013? IEEE was a partner in some of that work.
IARPA lauched advanced radio eavesdropping defenses in Aug 2020
IARPA released a request for information for the Securing Compartmented Information with Smart Radio Systems (SCISRS) Research Program on Aug. 5. The virtual proposers’ day on Aug. 20 will explain the goal of developing smart radio technology that can detect the radio wave equivalent of a snapping twig, or other sign someone is trying to listen in on radio communications generated by the intelligence agencies and Department of Defense (DOD) operations’ remote sites.
IARPA said it wants to find elusive radio frequency irregularities in increasingly complex radio environments, including low probability of intercept signals (LPIs), altered or mimicked signals, and abnormal unintended emissions using smart radio technologies. Intelligence Community and DOD missions require that information and data be generated, stored, used, transmitted, and received in secure facilities as well as “in the wild,” said IARPA’s announcement. Although the federal government and private sector have provided high levels of security for their enterprise data facilities, IARPA said remote places outside those enterprise walls don’t have a lot of protections.
The announcement doesn’t specifically name remote applications where the smart radio detection technology might be used. It includes photos of a U.S. port with nearby naval vessels, a data collection site in a desert, as well as a photo of former President Barack Obama and other officials talking on telephones in what looks to be a tarp-covered temporary operations center. “In environments such as those illustrated…, where there is potentially much less control, data security becomes more challenging,” the document states.
Video and Speech Analysis and Recognition
Other projects involve analysis of images or video that lacks metadata by directly analyzing the media’s content itself. Examples given by IARPA include determining the location of an image by analyzing features such as placement of trees or a mountain skyline, or determining whether a video is of a baseball game or a traffic jam.
“We try to find the problems that aren’t going to be solved by industry or academia without our investment. We stay updated on what industry is already doing so we can identify where we can make advances that industry isn’t pursuing,” said Jason Matheny IARPA’s New Director.
For example, Google searches video for tags but doesn’t actually look at the content of the video itself. For most of the videos that Google indexes, that’s enough. The problem for us is that the videos we need to find might be martyrdom videos of people planning a suicide bombing, or IED-placement videos. Those videos are not tagged because the people posting them want them to be found only after the fact, or only by those who were sent a link. They don’t want them to be searchable in the same way.
So we need to be able to index the video content as opposed to just the tags that are associated with the video. That’s meant a completely different approach to video search. We’ve led the way through a program called Aladdin Video, which indexes based on what’s happening inside the video. For example, it can characterize that a video is of a baseball game, or of a traffic jam, without any tags associated with the videos.
Another example is a program called Finder, which geolocates images. If you don’t know where the picture was taken, can you figure that out by analyzing the features that are in the picture, like the arrangement of trees or a certain mountain skyline? There aren’t a whole lot of commercial applications for that because people usually want you to know where photos were taken, and they’ve tagged it themselves. But the classic problem for us would be the images of Osama bin Laden. Can we tell which cave this is, which part of Pakistan this is?
IARPA has announced the UG2+ Prize Challenge, a competition that leverages a unique computer vision data set of unmanned aerial vehicle (UAV), glider, and ground (UG2) data. This marks the second edition of this challenge, which aims to advance the analysis of images collected by small UAVs by improving the performance of image-restoration and algorithm technologies.
The advantages of collecting images from outdoor camera platforms, like UAVs, surveillance cameras and outdoor robots, are evident and clear. For instance, man-portable UAV systems can be launched from safe positions to survey difficult or dangerous terrain, acquiring hours of video without putting human lives at risk. What is unclear is how to automate the interpretation of these images – a necessary measure in the face of millions of frames containing artifacts unique to the operation of the sensor and optics platform in outdoor, unconstrained, and usually visually degraded environments.
IARPA program manager Lars Ericson said of the competition: “Last year this prize challenge showed that this is an active area of research, but that the problem is still unsolved. This second iteration aims to further engage the community to advance techniques needed to aid analysts in processing and understanding the large amounts of imagery they receive on a daily basis.”
Original high-quality contributions are solicited on the following topics:
- Novel algorithms for robust object detection, segmentation or recognition on outdoor mobility platforms, such as UAVs, gliders, autonomous cars, outdoor robots, etc.
- Novel algorithms for robust object detection and/or recognition in the presence of one or more real-world adverse conditions, such as haze, rain, snow, hail, dust, underwater, low-illumination, low resolution, etc.
- The potential models and theories for explaining, quantifying, and optimizing the mutual influence between the low-level computational photography (image reconstruction, restoration, or enhancement) tasks and various high-level computer vision tasks.
- Novel physically grounded and/or explanatory models, for the underlying degradation and recovery processes, of real-world images going through complicated adverse visual conditions.
- Novel evaluation methods and metrics for image restoration and enhancement algorithms, with a particular emphasis on no-reference metrics, since for most real outdoor images with adverse visual conditions it is hard to obtain any clean “ground truth” to compare with.
Another program focuses on developing speech recognition tools that can transcribe arbitrary languages.
Computing makes up a broader research category, Dixon said, and spans everything from enhancing the security of microchips and software to emerging, high-performance computing concepts. “Whether it’s quantum computing, cryo computing or neuromorphic computing,” she said, “those are all things that we are investing in because we know that the traditional classical methods that we’re using for computing aren’t going to last forever.”
IARPA’s Exascale Supercomputer Initiative
At the end of July 2015, US President Obama, in a new executive order, demanded for a new initiative dedicated exclusively on supercomputing research by establishing the National Strategic Computing Initiative (NSCI).
The next big leap in scientific computing is the race to exascale capability: supercomputer capable of performing 1 million trillion floating-point operations per second (1 exaflops). Currently the fastest systems in the world perform between ten and 33 petaflops, or ten to 33 million billion calculations per second – roughly one to three percent the speed of exascale.
Exascale computers are required by Intelligence agencies like NSA and GCHQ for counter terrorism operations. They collect vast amounts of signal intelligence like phone calls of an entire nation; listen to satellites and radio communication to identify patterns of behavior or connections between individuals and/or events that are relevant to national security. Handling of this big data is becoming an increasingly important part of the intelligence services’ surveillance programs worldwide.
American intelligence agency IARPA had launched “Cryogenic Computer Complexity” (C3) program to develop a exascale supercomputer, with “a simplified cooling infrastructure and a greatly reduced footprint.” The goal of the program is to create a computer that requires only 20 percent or less of the energy used by a traditional supercomputer.
The project has awarded contracts to three major technology companies: International Business Machines, Raytheon BBN Technologies and Northrop Grumman.
“Today’s supercomputers can’t go up to an exascale without requiring a football field of computers and a power plant large enough to supply a midsize city. With cryogenic computing, you reduce to zero the energy cost of moving bits around because you’re using superconductors rather than semiconductors. However, this requires a whole new kind of chip. Rather than using CMOS, you’re using niobium, a superconductor. And you’re using a different kind of logic and memory, so it requires a different way of programing the computers,” explains Jason Matheny.
Quantum Programs at IARPA
As part of its mission to address some of the most difficult challenges in the Intelligence Community by investing in high-risk, high-payoff research, IARPA sponsors several applied research programs that explore the potential and possibilities in quantum computing. Quantum computers shall be able to simulate quantum systems efficiently, crack modern encryption codes, search through huge databases, as well as solve a wide range of optimization problems.
Quantum computing programs include:
Coherent Superconducting Qubits (CSQ) which is designed to demonstrate a reproducible, ten-fold increase in coherence times in superconducting qubits;
Logical Qubits (LogiQ) which aims to build the first logical qubit;
Multi-Qubit Coherent Operations (MQCO) which is working to develop the foundation of an error-free quantum computer;
Quantum Computer Science (QCS) which developed the world’s first high-level quantum programming language and compilers; and
Quantum Enhanced Optimization (QEO), which seeks to harness quantum effects required to enhance quantum annealing solutions to hard combinatorial optimization problems.
IARPA Award to IBM
IBM announced earlier that the U.S. Intelligence Advanced Research Projects Activity (IARPA) program has notified IBM that it will award its scientists a major multi-year research grant to advance the building blocks for a universal quantum computer. The spy agencies are now giving thrust to development of Quantum computers which can break this encryption used by terrorists.
Under the LogiQ program, IBM’s research team will continue to pursue the leading approach for building a universal quantum computer by using superconducting qubits. By encoding the superconducting qubits into a logical qubit, one should then be able to perform true quantum computation. These logical qubit designs will be foundational to future, more complex quantum computing systems. The award is funded under the Logical Qubits (LogiQ) program of IARPA led by Dr. David Moehring. The LogiQ Program seeks to overcome the limitations of current quantum systems by building a logical qubit from a number of imperfect physical qubits.
IARPA Announces Launch of the Molecular Information Storage Program in Jan 2020
Molecular Information Storage (MIST) program. MIST is a multi-year research effort to develop next-generation data storage technologies that can scale into the exabyte (1 million terabyte) regime, and beyond, with significantly reduced physical footprint, power and cost requirements, relative to conventional approaches. The program will pursue this goal by using synthetic DNA as a data storage medium and developing a new category of devices that can write information to, and read from, synthetic DNA media at scale.
The scale and complexity of the world’s big data problems are rapidly increasing. Use cases requiring storage and random access from an exabyte of data are well-established in the private sector and increasingly relevant to the public sector. Meeting these requirements poses logistical and financial challenges. Today’s exabyte-scale data centers, for example, occupy large warehouses, consume megawatts of power, and cost hundreds of millions of dollars to build, operate and maintain. This resource-intensive model limits the availability of exascale storage and future scalability.
“The MIST program is a data storage moonshot to develop technologies that allow us to shrink an exabyte-scale data warehouse down to a tabletop form factor, with equally large reductions in operation and maintenance costs,” said IARPA Program Manager David Markowitz. “This would be a transformative capability for big data stakeholders in government and industry.”
Through a competitive Broad Agency Announcement, IARPA awarded MIST research contracts to teams led by the Georgia Tech Research Institute, as well as the Broad Institute of MIT and Harvard University. Los Alamos National Laboratory, Sandia National Laboratories and the U.S. Army Research Laboratory will work together to independently test the new systems — drawing on expertise in DNA synthesis, sequencing, nanofabrication, information theory and large-scale file systems.
IARPA ramps up biosecurity investments in 2018 and beyond
“The biotech industry has advanced significantly in the last few years. On the upside, there’s great potential for medicine, for agriculture, for energy, for materials. On the downside, that means the ability for an individual to develop a biological weapon either potentially or accidentally that could kill millions of people has increased,” he said.
IARPA has a few bio-centered research programs that will start this year that could help with biosecurity. FELIX, the Finding Engineering-Linked Indicators program, will focus on developing new tools for detecting whether an organism has been engineered, such as whether a genome has been edited. If successful, FELIX could help in the rapid detection of manipulated biologics that could be used in bioweapons.
IARPA’s Proteos program aims to develop a technology that would use proteins, rather than DNA, to identify individuals. “In a lot of cases, including law enforcement cases, we don’t obtain a DNA sample that can be used for identification or prosecution,” Matheny said. “DNA degrades in the environment quickly, and it’s very difficult to obtain from things like shell casings or surfaces that have been touched. Proteins are much more plentiful but it’s been a hard technical challenge to use proteins for identification.”
IARPA’s Standoff ILluminator for Measuring Absorbance and Reflectance Infrared Light Signatures (SILMARILS)
Standoff chemical detection is a ubiquitous need across the Intelligence Community (IC) for applications ranging from forensic crime scene analysis to border and facility protection to stockpile and production monitoring. However, current systems do not provide the sensitivity, specificity, and low false-alarm rates that are needed to enable effective use in a cluttered, realworld environment.
The SILMARILS program aims to develop a portable system for realtime standoff detection and identification of trace chemical residues on surfaces using active infrared spectroscopy at a 30 meter range.
Block MEMS (Marlborough, MA) has been awarded Phase II of the Intelligence Advanced Research Programs Activity (IARPA) program for the standoff detection of explosives and toxic chemical threats. Block was chosen for Phase II in a competitive down-selection process. Under Phase I, Block demonstrated the ability to detect trace quantities of explosives and other threats on multiple surfaces at 1 and 5 meter standoff distances in a few seconds.
A critical achievement under Phase I has been the development of a benchtop system based on quantum-cascade lasers (QCLs) and a novel chemical detection algorithm. The algorithm combines data-processing techniques, simulations of light/material interactions, and modeling of anticipated detected signatures to eliminate the effect of clutter, reduce false-alarm rates, and improve limits of detection.
“The achievements of the previous phase have taken a significant step towards the ultimate goal of the SILMARILS program to be able to detect explosives and other chemicals at standoff distances of 30 and 50 meters,” says Anish Goyal, Block’s VP of technology and principal investigator of the SILMARILS program at Block. “The ability to not only detect chemical-warfare agents (CWAs), but explosives and pharmaceutical based agents (such as fentanyl) as well at these standoff distances is addressing a strong need within the intelligence community, the Defense Department, and the Department of Homeland Security.”
A critical area for tapping that wisdom is cybersecurity. As high-profile cyberattacks become increasingly common, including in government, the prospect of predicting them before they occur is promising.
IARPA’s CAUSE program, or Cyber-attack Automated Unconventional Sensor Environment, seeks to predict coming cyberattacks by studying behavioral data from unconventional sources. The Security and Privacy Assurance Research (SPAR) program targets encryption of data even when users need to manipulate the data, such as running queries against it, something that’s been tough to do in the past.
“The IARPA mission begins and ends with research itself,” Matheny said. “What the [agencies] then do is really not up to us; it’s up to them to decide how to best tailor the research methods that are developed under our programs to their particular need. Cybersecurity is one where the conditions on the networks and in the organizations are different, the actors are different, but the approaches we try to develop are meant to be generalizable or highly tunable to any particular organization that might need it.”