Artificial Intelligence/Machine Learning Research at IARPA

Cyber-attack Automated Unconventional Sensor Environment (CAUSE), applies AI/ML-based models to develop novel, automated methods for event-based detection and prediction of cyber-attacks significantly earlier than existing approaches. Forecasting cyber-attack events with actionable details advances the state-of-the-art by enabling threat-specific cyber incident response and defense measures; Creation of Operationally Realistic 3D Environment (CORE3D), uses machine learning and deep learning techniques to develop methods for the construction of a fully automated high fidelity 3D model of the world using remote sensing data; Deep Intermodal Video Analytics (DIVA), leverages machine learning techniques to develop robust automatic activity detection in streaming video across multiple cameras; Finding Engineering-Linked Indicators (FELIX), uses AI for detection of engineering signatures across multiple biological organisms. The goal is to distinguish natural organisms from those that have been engineered; Functional Map of the World Challenge, developed algorithms that would quickly and accurately classify 63 classes of buildings and regions in satellite imagery. All the top participants used various forms of deep learning; Functional Genomic and Computational Assessment of Threats (Fun GCAT), develops AI/ML-based approaches to learn and classify genetic (e.g., DNA) sequence data by genetic taxonomy, sequence function, and threat potential; Mercury Challenge, asked challenge participants to make use of AI/ML approaches to forecast a variety of political events in the Middle East and North Africa region, such as non-violent civil unrest and military activity; Machine Intelligence from Cortical Networks (MICrONS), aims to revolutionize machine learning by reverse-engineering the algorithms of the brain. The program is expressly designed as a dialogue between data science and neuroscience; Machine Translation for English Retrieval of Information in Any Language (MATERIAL), develops machine learning methods to identify foreign language information from speech and text relevant to English queries, and providing evidence of relevance of the retrieved information in English in a meaningful way.