The US (IARPA) is looking “to develop systems capable of modeling population movement patterns around the globe” using AI and sensors connected to the Internet of Things (IoT) and smart cities. Intelligence Advanced Research Projects Activity According to the Office of the Director of National Intelligence ( ), IARPA’s “Hidden Activity Signal and Trajectory Anomaly Characterization ( ) aims to establish ‘normal’ movement models across times, locations, and populations and determine what makes an activity atypical. ODNI HAYSTAC program “Expansive data from the Internet of Things and infrastructures provides opportunities to build new models that understand human dynamics at unprecedented resolution and creates the responsibility to understand privacy expectations for those moving through this sensor-rich world.” Smart City “This an unprecedented opportunity to understand how humans move, and HAYSTAC’s goal will be to build an understanding of what normal movement looks like at any given time and place” Dr. Jack Cooper, IARPA Leading the four-year research program is , who joined IARPA in 2020 after a stint at the National Geospatial-Intelligence Agency (NGA) in the Research Directorate, where he was a senior staff scientist for predictive analytics. HAYSTAC Dr. Jack Cooper For the program manager, represents “ .” HAYSTAC an unprecedented opportunity to understand how humans move, and HAYSTAC’s goal will be to build an understanding of what normal movement looks like at any given time and place https://www.youtube.com/watch?v=UDkCthIoc2M&embedable=true “With , we have the opportunity to leverage machine learning and advances in artificial intelligence to understand mobility patterns with exceptional clarity,” said Dr. Cooper in a statement to the ODNI. HAYSTAC “ ,” he added. The more robustly we can model normal movements, the more sharply we can identify what is out of the ordinary and foresee a possible emergency , “Current human mobility modeling techniques can provide high-level insight into human movement for the study of disease spread or population migration.” According to IARPA However, “They don’t provide the complex, fine-grained modeling the Intelligence Community (IC) needs to identify more subtle anomalies with confidence.” That’s where and Dr. Cooper come in. HAYSTAC “With HAYSTAC, we have the opportunity to leverage machine learning and advances in artificial intelligence to understand mobility patterns with exceptional clarity” Dr. Jack Cooper, IARPA Dr. Cooper is also the program manager for at least two other IARPA research programs focused on detecting and characterizing human activities, which include: ( ), which is using satellite imagery to detect, monitor, and characterize human construction projects, as well as natural processes like crop growth. Space-Based Machine Automated Recognition Technique SMART ( ), which is creating automatic activity detectors that can watch hours of video and highlight the few seconds when a person or vehicle does a specific activity (e.g., carry something heavy, load it into a vehicle, then drive away). Deep Intermodal Video Analytics DIVA “Internet of Thing devices are a growing source of data that can be collected to learn intent” Dr. Catherine Marsh, IARPA Speaking at the Department of Defense Intelligence Information System (DoDIIS) Worldwide Conference back in December, 2021, IARPA director the coming program when she said: Dr. Catherine Marsh foreshadowed HAYSTAC “Internet of Thing devices are a growing source of data that can be collected to learn intent. “Developing these new sensors and detectors, as well as thinking about clever ways to collect multi-modal data to reveal what our adversaries are attempting to hide from us, is at the very core of what our collection programs are aimed at doing.” For its program, IARPA has already awarded several contracts to big defense contractors and consulting firms with ties to academia, NGOs, and tech companies. HAYSTAC These contracts went to: Raytheon Technologies Research Center L3Harris Technologies, Inc. STR Kitware, Inc. Leidos, Inc. Novateur Research Solutions Deloitte Consulting LLP Raytheon BBN “As the systems mature, they will be evaluated based on probability of detection and false alarm performance in creating relevant alerts, ultimately seeking to identify 80% of anomalous activity while generating normal activity that is only 10% detectable,” according to the . HAYSTAC program description This article was originally published by Tim Hinchliffe on The Sociable.
