Four U.S. technology companies take on self-aware artificial intelligence (AI) and machine learning
ARLINGTON, Va. – Artificial intelligence (AI) experts at four U.S. companies are helping military researchers determine if autonomous machines are self-aware of their own competencies and limitations to carry out assigned tasks.
Officials of the U.S. Defense Advanced Research Projects Agency (DARPA) have announced four contracts cumulatively worth $20.9 million for the Competency-Aware Machine Learning (CAML) project.
Companies involved are SRI International in Menlo Park, Calif.; Raytheon BBN Technologies Corp. in Cambridge, Mass.; Teledyne Scientific & Imaging LLC in Camarillo, Calif.; and the BAE Systems Electronic Systems segment in Burlington, Mass.
How does a person know if he's smart enough to do the job ... if he has a skill set that's adequate for the task at hand? It sounds simple, but it's a fundamental ability necessary for trust and team building.
Now apply the same question to artificial intelligence (AI) technology and machine learning? How does a machine know if it's smart enough to do the job? That's what DARPA and the CAML contractors are aiming at.
The CAML project focuses on competency-awareness machine learning, where an autonomous system can self-assess its task competency and strategy, and express both in a human-understandable form.
SRI International won a $4.7 million CAML contract on 25 Sept. 2019; Raytheon BBN won a $6 million CAML contract on 27 Sept. 2019; Teledyne Scientific & Imaging won a $5.4 million CAML contract on 10 Oct. 2019; and BAE Systems won a $4.9 million CAML contract on 10 Oct. 2019.
This competency-awareness capability contributes to the goal of transforming autonomous systems from tools into trusted, collaborative partners, DARPA officials say. Competency-aware machine learning will enable machines to control their behaviors to match user expectations and enable human operators quickly and accurately to gain insight into a system’s competence in complex, time-critical, dynamic environments. CAML, in short, seeks to improve human-machine teaming.
State-of-the-art machine learning systems today operate in a complex space, and continuously develop behaviors based on their experiences. Nevertheless, these kinds of smart machines with trusted computing capabilities are unable to communicate their task strategies, the completeness of their training on a given task, what might influence their actions, or how likely they are to succeed under specific conditions.
Verifying a machine's competence increasingly is unrealistic for human operators. This can be a big problem for the military, where machines often deal with high-stake decisions, and must cope with dynamic, fast-changing conditions.
CAML seeks to improve human-machine teaming capabilities by creating a fundamentally new machine-learning approach, and help human operators choose the right smart machines based on the machines' experience and expertise.
CAML is a four-year program divided into a three-year research first phase, and a one-year technology-demonstration second phase. It focuses on four technology areas: self-knowledge or experience; self-knowledge of task strategies; competency-aware learning; and capability demonstrations.
Self-knowledge of Experiences will develop mechanisms for learning systems to discover conditions encountered during operation, and maintain a memory of experiences.
Self-knowledge of task strategies will enable a machine learning system to analyze its task behaviors, summarize them into generalized patterns, and identify what controls its behavior.
Competency-aware learning integrates component technologies into a competency-aware learning framework that is able to communicate in human-understandable statements. It will conclude with a demonstration on a proposer-provided platform. Capability demonstrations will show competency-aware machine learning systems on military platforms.
For more information contact SRI International online at www.sri.com; Raytheon BBN at www.raytheon.com; Teledyne Scientific & Imaging at www.teledyne-si.com; BAE Systems Electronic Systems at www.baesystems.com; or DARPA at www.darpa.mil.
John Keller | Editor-in-Chief
John Keller is the Editor-in-Chief, Military & Aerospace Electronics Magazine--provides extensive coverage and analysis of enabling electronics and optoelectronic technologies in military, space and commercial aviation applications. John has been a member of the Military & Aerospace Electronics staff since 1989 and chief editor since 1995.
