September 6, 2018 | International, Aerospace
SAN FRANCISCO, Sept. 5, 2018 /PRNewswire/ -- Lockheed Martin (NYSE: LMT) and Drone Racing League (DRL) today announced an innovation competition, challenging teams to develop artificial intelligence (AI) technology that will enable an autonomous drone to race a pilot-operated drone – and win. Participating teams will compete in a series of challenges for their share of over $2 million in prizes.
Lockheed Martin Chief Technology Officer Keoki Jackson announced the challenge at TechCrunch Disrupt San Francisco, kicking off a multi-year partnership with DRL, the global professional circuit for drone racing. The AlphaPilot Innovation Challenge will enlist university students, technologists, coders and drone enthusiasts to push the boundaries of AI, machine learning (ML) and fully autonomous flight.
"At Lockheed Martin, we are working to pioneer state-of-the-art, AI-enabled technologies that can help solve some of the world's most complex challenges – from fighting wildfires and saving lives during natural disasters to exploring the farthest reaches of deep space," said Jackson. "Now, we are inviting the next generation of AI innovators to join us with our AlphaPilot Innovation Challenge. Competitors will have an opportunity to define the future of autonomy and AI and help our world leverage these promising technologies to build a brighter future."
The AlphaPilot challenge aims to accelerate the development and testing of fully autonomous drone technologies. AlphaPilot participants will design an artificial intelligence/machine learning framework, powered by the NVIDIA Jetson platform for AI at the edge, capable of flying a drone – without any pre-programming or human intervention – through challenging multi-dimensional race courses in DRL's new Artificial Intelligence Robotic Racing (AIRR) Circuit.
"Since 2016, DRL has been the proving ground for the world's most talented human pilots, showcasing their abilities to race remotely piloted drones at high speeds. This challenge changes the game," said DRL CEO and Founder Nicholas Horbaczewski. "How close is AI performance to the world's best human piloting? We're excited to find out next year when AlphaPilot drones compete in adrenaline-packed, futuristic drone races on complex courses in the AIRR Circuit. Our collaboration with Lockheed Martin will both accelerate AI innovation and redefine the sport of the future."
The Lockheed Martin AlphaPilot Innovation Challenge will open for entries in November. Selected participants are eligible for over $2 million in cash prizes, including an extra $250,000 award for the first team that outperforms a professional DRL human-piloted drone. Undergraduate and graduate students, drone enthusiasts, coders and other technologists interested in learning more and applying to participate can visit lockheedmartin.com/alphapilot.
Lockheed Martin is funding the AlphaPilot Innovation Challenge through savings from the Tax Cuts and Jobs Act of 2017. The corporation is also using savings from tax reform to increase its investments in research and development and capital expenditures, employee training and educational opportunities, investment in technology startups, and STEM education programs.
About Lockheed Martin
Headquartered in Bethesda, Maryland, Lockheed Martin is a global security and aerospace company that employs approximately 100,000 people worldwide and is principally engaged in the research, design, development, manufacture, integration and sustainment of advanced technology systems, products and services. This year, the company received three Edison awards for groundbreaking innovations in autonomy, satellite technology and directed energy.
About Drone Racing League
DRL is the professional drone racing circuit for elite FPV pilots around the world. A technology, sports and media company, DRL combines world-class media and proprietary technology to create thrilling 3D drone racing content with mass appeal. Founded by Nicholas Horbaczewski in 2015, DRL is a privately held company headquartered in NYC. For more information on DRL, visit www.drl.io
SOURCE Lockheed Martin
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The inability of artificial intelligence (AI) to represent and model human partners is the single biggest challenge preventing effective human-machine teaming today. Current AI agents are able to respond to commands and follow through on instructions that are within their training, but are unable to understand intentions, expectations, emotions, and other aspects of social intelligence that are inherent to their human counterparts. This lack of understanding stymies efforts to create safe, efficient, and productive human-machine collaboration. “As humans, we are able to infer unobservable states, such as situational beliefs and goals, and use those to predict the subsequent actions, reactions, or needs of another individual,” said Dr. Joshua Elliott, a program manager in DARPA's Information Innovation Office (I2O). “Machines need to be able to do the same if we expect them to collaborate with us in a useful and effective way or serve as trusted members of a team.” Teaching machines social intelligence however is no small feat. Humans intuitively build mental models of the world around them that include approximations of the mental models of other humans – a skill called Theory of Mind (ToM). Humans use their ToM skill to infer the mental states of their teammates from observed actions and context, and are able to predict future actions based on those inferences. These models are built on each individual's existing sets of experiences, observations, and beliefs. Within a team setting, humans build shared mental models by aligning around key aspects of their environment, team, and strategies. ToM and shared mental models are key elements of human social intelligence that work together to enable effective human collaboration. DARPA's Artificial Social Intelligence for Successful Teams (ASIST) program seeks to develop foundational AI theory and systems that demonstrate the basic machine social skills necessary to facilitate effective machine-human collaboration. ASIST aims to create AI agents that demonstrate a Machine ToM, as well as the ability to participate effectively in a team by observing and understanding their environment and human partners, developing useful context-aware actions, and executing those actions at appropriate times. The agents developed under ASIST will need to operate across a number of scenarios, environments, and other variable circumstances, making the ability for them to evolve and adapt as needed critical. As such, ASIST will work to develop agents that can operate in increasingly complex environments, adapt to sudden change, and use observations to develop complex inferences and predictions. During the first phase of the program, ASIST plans to conduct experiments with single human-machine interactions to see how well the agents can infer human goals and situational awareness, using those insights to then predict their teammate's actions and provide useful recommended actions. As the program progresses, the complexity will increase with teams of up to 10 members interacting with the AI agents. During these experiments, ASIST will test the agents' ability to understand the cognitive model of the team – not just that of a single human – and use that understanding to develop appropriate situationally relevant actions. Full details on the program can be found in the Broad Agency Announcement (BAA) solicitation, which has been posted to the Federal Business Opportunities website, https://www.fbo.gov/index?s=opportunity&mode=form&id=9d4acf0aba98916288a541bd07810004&tab=core&_cview=1 https://www.darpa.mil/news-events/2019-03-21b