Artificial Intelligence, Machine Learning, and Autonomy
Engineering with intelligence
Designing, building, and applying new technologies—especially those that include artificial intelligence—can be a double-edged sword: powerful and enabling on the one hand, but potentially biased and vulnerable to infiltration on the other.
From health care to planetary defense and national security, Johns Hopkins APL continues to make advances in AI to ensure the technology’s capabilities while identifying, minimizing, or eliminating its weaknesses.
A Laboratory-wide collaborative community of AI researchers and applied scientists works in domains from beneath the sea to outer space to innovatively incorporate autonomy, computer vision, machine learning, and other AI techniques across the breadth of our programs and projects. Internally funded AI exploration and research help us take bold steps in this realm to continue advancing AI for the good of the nation and the world.
Featured Facility
Intelligent Systems Center →
The Intelligent Systems Center (ISC) leverages APL’s broad expertise across national security, space exploration, and health to fundamentally advance the employment of intelligent systems for our nation’s critical challenges. The ISC acts as a focal point for research and development in artificial intelligence, robotics, and neuroscience at APL.
Related Projects
Accelerating Air Quality Forecasts
In the last several years, record-setting wildfires have released millions of smoke-borne contaminants into the air, setting off air quality alerts across the country. APL is using artificial intelligence to accelerate air quality forecasts and ultimately deliver a better understanding of how and where these pollutants will travel.
Agile and Intelligent Robots
Developing novel controls for robotic systems operating safely in complex environments
AlphaDogfight Trials
APL served as a core member of the Air Combat Evolution program team created by the Defense Advanced Research Projects Agency (DARPA) for the 2020 AlphaDogfight Trials, a showdown between eight AI research teams from across the United States.
Artificial Intelligence for Climate Action
Leveraging the power of artificial intelligence and mathematics to spur innovation and novel solutions to challenges at the intersection of climate change and national security
Discovering Climate Tipping Points
Artificial intelligence experts and oceanographers are integrating AI with traditional climate modeling methods to enable scientific researchers to better understand climate tipping points, critical thresholds that, once crossed, could “tip” a natural climate system into an entirely different state.
Golden Horde
Achieving networked, collaborative offensive weapons systems that will learn from their environment and autonomously work together to defeat integrated air and missile defenses.
Lifelong Learning Machines
Enabling intelligent systems that continuously adapt to changing conditions and missions in the real world
Mission-Focused Generative AI
Inventing the future of artificial intelligence for the nation by advancing frontier models that enable creativity, subject-matter expertise, and personification
Neural Interfaces
Directly interfacing with the nervous system to restore lost functions and enhance human capability
Neuroscience-Inspired Artificial Intelligence
Developing next-generation algorithms and computing substrates that leverage neurobiology to revolutionize intelligent systems
Revolutionizing Materials Discovery for National Security
APL is reimagining and accelerating the targeted discovery of materials tailored to withstand and perform in the most demanding conditions, ensuring enhanced capabilities in extreme environments.
Robust and Resilient Artificial Intelligence
Developing the next generation of intelligent systems for missions characterized by uncertain, dynamic, and adversarial environments
Swarming Uncrewed Surface Vehicles
APL, in collaboration with the Naval Air Warfare Center Port Hueneme Weapons Division, led a swarming uncrewed surface vehicle demonstration of advanced multivehicle autonomy at tactically relevant speeds.
Tracking Greenhouse Gas Emissions
Using a combination of machine learning, satellite imagery, and localized emissions data, APL advances its accurate, scalable, and easily configurable greenhouse gas monitoring framework for road transportation.
Uncrewed Surface Vessel Perception
International regulations for preventing collisions at sea require vessels to operate within certain distances based on the visual identification of other vessels.
Related News
News
Dec 4, 2024
From the Battlefield to Breast Cancer Detection, Johns Hopkins APL Technologies Find Dual Impact
APL researchers used artificial intelligence and two APL-patented imaging technologies to improve the accuracy and efficiency of breast cancer screening. The imaging technologies were originally created for satellite and military applications.
News
Nov 19, 2024
Roads to Reservoirs: Expanding the Scope of Global Emissions Tracking
Johns Hopkins APL has expanded its partnership with the Climate TRACE coalition to enhance AI-powered tracking of global greenhouse gas emissions, including new sources like wastewater treatment plants and reservoirs.
Press Release
Nov 14, 2024
A New Path to Noninvasive Brain-Computer Interface
A new high-resolution neural recording method developed by Johns Hopkins APL and the School of Medicine detects neural activity through the skull at unprecedented resolutions, expanding possibilities for nonsurgical brain-computer interface.
News
Oct 29, 2024
Johns Hopkins Teams Poised to Create Transformative Technologies
Cheap and plentiful hydrogen harvested from water. Lightning-fast biocomputers running on brain-like substrates. Widely available noninvasive brain scans for early detection of neurological disease. A visual prosthesis in the form of glasses that returns sight to people with previously incurable vision loss. These are the four boundary-pushing proposals funded in this year’s cycle of the SURPASS initiative.
News
Oct 16, 2024
Coordinating Tactical Teams of Autonomous Machines
Johns Hopkins APL researchers have made significant progress toward addressing some of the fundamental challenges that stand in the way of fieldable multirobot teams becoming reality.