Argonne National Laboratory will use Aurora for machine learning and simulation problems in space research, drug response predictions and weather forecasting.
The Real-Time Machine Learning Grand Challenge aims to create processors that can interpret and learn from data in real-time with the energy efficiency of the human brain.
Research with generative adversarial networks may explain how neural networks learn and make decisions.
Carnegie Mellon University will lead a consortium of universities developing solutions to support autonomous processing and highly effective human-machine teams.
Predictive analytics can give agencies and their contractors a better understanding of supplier risks over time so they can make more informed decisions.
Factoring humans’ decisions -- and confidence level in those choices -- together with algorithmic judgments, yields a more accurate result than people or machines can deliver independently.
The Army wants to incorporate tech solutions usually seen in smart cities at its military installations.
If AI algorithms pass decisions they have less confidence in to humans, they can still safely perform the bulk of the decision-making work.
Agencies must embrace automation and a hybrid cloud network model that allows critical services to remain up and running even with reduced staffing.
Advances in artificial intelligence are rendering current methods of protecting biometric data obsolete.
The National Science Foundation is working with the Bureau of Fiscal Service to automate payment processes.
Researchers are working on developing technology that mimics the behavior of a human rescuer, looking briefly at wide areas and quickly choosing specific regions to examine more closely.
Agencies, industry and individuals presented digital tools using open federal data and artificial intelligence at the March 1 demo day.
The Defense Innovation Unit is on the lookout for a system to detect, identify and mitigate attacks from swarms of drones.