Human-Machine Teams Need a Little Trust to Work Written 11 January 2018
Panelists: Moderator Bill Casebeer, senior research area manager, Human Systems and Autonomy, Lockheed Martin Advanced Technology Laboratories; Julia Badger, Robonaut project manager, Autonomous Spacecraft Management Projects, NASA’s Johnson Space Center; Eileen Liu, research scientist, Human Systems and Autonomy, Lockheed Martin Advanced Technology Laboratories; Matthias Scheutz, director, Human-Robot Interaction Lab, Tufts University; Victoria Coleman, chief technology officer, Wikimedia Foundation; Michael Casale, chief science officer, STRIVR
by Michele McDonald, AIAA Communications Manager
Work in machine intelligence crosses such disciplines as neuroscience, cognitive science, cognitive architectures, theory of mind, user experience design, human behavior modeling, systems engineering and explainable artificial intelligence. But, human-machine teams hold promise in helping with space exploration, training and boosting human performance.
The thorny issue of trust is at the heart of human-machine teams, panelists discussed Jan. 11 during the “Human-Machine Teaming” session at the 2018 AIAA SciTech Forum in Kissimmee, Florida.
Our own expectations of what machines can do, the roles machines play on a team and trust in them — too much or not enough — can get in the way of teamwork, panelists said.
Trust doesn’t come automatically, said Victoria Coleman, chief technology officer of the Wikimedia Foundation.
“In reality, there is no magic bullet,” she said.
Coleman said repetition helps as does how machines arrive at decisions.
“Trust is something you establish ... then it’s a cycle,” she said.
But, machine intelligence is finding a home in NASA and sports.
NASA is using smart machines that will work with crew members as the agency plans space stations for future missions, said Julia Badger, Robonaut project manager with Autonomous Spacecraft Management Projects at NASA’s Johnson Space Center. These complex space stations sometimes will work with crew members and other times will be on their own to solve problems, such as leaks, and maintain the station, she said.
Virtual reality and immersion are helping large companies train employees, said Michael Casale, chief science officer with STRIVR, a VR training company. Top-level athletes use the technology to help them improve their performance, he said.
The panelists said technical advancements are needed for humans and machines to become effective teammates. For example, humans and machines don’t speak the same language, and nuance is difficult for machines to process, explained Eileen Liu, a research scientist with Human Systems and Autonomy at Lockheed Martin Advanced Technology Laboratories
“I might say something, but I might mean something else,” she said.
Liu said humans are good at thinking creatively and solving problems on the fly but that we fail at repetitive tasks because our minds wander. She said that’s where machines excel and can help. But first, Liu said, they need to figure us out and understand how moods affect attention spans. Sensors may help machines get a handle on our state of mind, she said.
Humans and machines need to share mental models to become effective teammates, said Matthias Scheutz, director of the Human-Robot Interaction Lab at Tufts University. But, he said, we’re not there yet. Hammers and drills are tools, not teammates, and current technology could be described the same way, Scheutz said.
He said we expect if a machine can pick up a square, it can pick up another shape but that that’s simply not the case.
“The kind of quick inference we expect of people, we will expect of machines,” Scheutz said.
People want robots to be adaptive and better than they are, Badger said.
“My (son) was 9 months old, and he got better at grasping things than my robot did at the time. My robot should be better because he’s 8 years old,” she said, laughing.