Department of Defense · Department of the Air Force
DEVELOPMENT OF ARTIFICIAL INTELLIGENCE & DEEP LEARNING BASED
Deep Learning
Federal deep learning contracts — neural networks, transformers, and advanced model architectures.
7 contracts · $8.19M total · across 2 agencies and 7 vendors.
Top vendors
- TEXAS A&M UNIVERSITY-CENTRAL TEXAS — $2.75M (1)
- DYNETICS, INC. — $1.93M (1)
- THE JOHNS HOPKINS UNIVERSITY APPLIED PHYSICS LABORATORY LLC — $1.50M (1)
- RAGNAROK INDUSTRIES INC — $1.25M (1)
- UNIVERSITY OF SOUTH CAROLINA — $395K (1)
- MICHIGAN TECHNOLOGICAL UNIVERSITY — $290K (1)
- AI2 INCORPORATED — $75K (1)
Top agencies
- Department of Defense — $7.51M (5)
- Department of Transportation — $685K (2)
Biggest awards
$1.93M
Department of Defense · Department of the Air Force
ARTIFICIAL INTELLIGENCE, DEEP LEARNING, AND MACHINE LEARNING FOR MUNITIONS AUTONOMOUS TARGET ACQUISITION
Department of Defense · Department of the Army
PAALE ALE WILL INVESTIGATE APPROACHES TO INTEGRATE, TEST, AND EVALUATE AI INCLUDING NEURAL NETWORK MACHINE LEARNING AT SPEEDS FASTER THAN REAL TIME AND PROBABILITY BASED SOLUTIONS IAW THE GOVERNMENTS MODULAR OPEN SYSTEMS APPROACH PRINCIPLES
$1.25M
Department of Defense · Department of the Air Force
SBIR D2P2 PHASE II PROPOSAL F2D-10399 - RADIATION-HARDENED STARTRACKER INCORPORATING MACHINE LEARNING NEURAL NETWORKS FOR ESPA MISSIONS ABOVE LEO (HODUR)
Department of Transportation · Federal Railroad Administration
THE OBJECTIVE OF THIS PROJECT IS TO DEVELOP AN INTELLIGENT RISK ASSESSMENT AND PREDICTION SYSTEM (I-RAPS) THAT WILL INTEGRATE SELF-LEARNING ARTIFICIAL INTELLIGENCE AND DEEP NEURAL NETWORK DECISION-MAKING MODELS, TRACK INSPECTION DATA, VEHICLE INFORMA
Department of Transportation · Federal Railroad Administration
IGF::OT::IGF RAILROAD ARTIFICIAL INTELLIGENCE INTRUDER LEARNING SYSTEM (RAIILS) PROPOSAL FOR THE PROCESSING OF DATA FROM HETEROGENEOUS SENSING PLATFORMS FOR THE PURPOSE OF IMPROVED DETECTION OF RAILROAD TRESPASSERS IN REAL-TIME USING CONVOLUTIONAL NEURAL NETWORKS (CNNS). FOR A SYSTEM EQUIPPED WITH CO-LOCATED NATURAL COLOR RED/GREEN/BLUE (RGB) AND THERMAL INFRARED CAMERAS, WE PROPOSE A DEEP LEARNING AI ARCHITECTURE TO DO ON-PLATFORM SENSOR FUSION. SUCH A SYSTEM WILL ALLOW FOR THE DETECTION OF RAILROAD TRESPASSERS BOTH IN REAL-TIME AND DURING THE ENTIRE 24HR CYCLE. THE PROPOSED ARCHITECTURE WOULD LEVERAGE BOTH THE RGB AND INFRARED DATA WHEN AVAILABLE AND WOULD CHANGE TO INFRARED ONLY WHEN ENVIRONMENTAL/TIME-OF-DAY CONDITIONS DO NOT PERMIT THE USE OF THE VISIBLE-SPECTRUM DATA.
$75K
Department of Defense · Department of the Air Force
SBIR PHASE I PROPOSAL FX245-PCSO1-1242 - INTERPRETABLE EXPLAINABLE ARTIFICIAL INTELLIGENCE FOR HUMAN-MACHINE TEAMING (HMT) APPLICATIONS OF DEEP LEARNING