An APL engineer modifies a robot to test  its navigation algorithms

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.

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Related Projects

Forest fire (Credit: Bigstock)

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.
Learn more about Accelerating Air Quality Forecasts
Robotics researchers Craig Knuth and Adam Polevoy test machine learning algorithms for estimating traversable regions in complex environments. Such algorithms are necessary to enable robotic systems to autonomously traverse complex terrain.

Agile and Intelligent Robots

Developing novel controls for robotic systems operating safely in complex environments.
Learn more about Agile and Intelligent Robots
AlphaDogfight

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.
Learn more about AlphaDogfight Trials
ISC researchers Elizabeth Reilly and Jason Lee apply mathematical models to understand how different shocks impact food security.

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
Learn more about Artificial Intelligence for Climate Action
Jay Brett and Jennifer Sleeman

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.
Learn more about Discovering Climate Tipping Points
Golden Horde

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.
Learn more about Golden Horde
Erik Johnson, a machine learning researcher at Johns Hopkins APL, demonstrating how agents are evaluated for lifelong learning on ISC-developed L2Explorer.

Lifelong Learning Machines

Enabling intelligent systems that continuously adapt to changing conditions and missions in the real world.
Learn more about Lifelong Learning Machines
Optical physicist Michael Fitch models a prototype high-density diffuse optical imaging system, described in JJ Wathen et al. 2021.

Neural Interfaces

Directly interfacing with the nervous system to connect humans with intelligent systems.
Learn more about Neural Interfaces
Raphael Norman-Tenazas, neuro-AI researcher, tests a robot navigation strategy inspired by the fruit fly connectome.

Neuroscience-Inspired Artificial Intelligence

Developing next-generation learning algorithms that draw inspiration from biological nervous systems to revolutionize intelligent systems.
Learn more about Neuroscience-Inspired Artificial Intelligence
Visualization of machine learning

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.
Learn more about Revolutionizing Materials Discovery for National Security
Neil Fendley, machine learning researcher, demonstrates a backdoor adversarial attack he embedded in a computer vision application.

Robust and Resilient Artificial Intelligence

Developing intelligent systems for missions characterized by uncertain and adversarial environments.
Learn more about Robust and Resilient Artificial Intelligence
Swarming unmanned surface vehicles

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.
Learn more about Swarming Uncrewed Surface Vehicles
Earth (Credit: Bigstock)

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.
Learn more about Tracking Greenhouse Gas Emissions
Autonomous swarming unmanned surface vessels (SUSVs) — equipped with Johns Hopkins APL-developed hardware and autonomy software

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.
Learn more about Uncrewed Surface Vessel Perception

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