The Minds of the New Machines

Machine learning has been around for decades, but the advent of big data and more powerful computers has increased its impact significantly — ­moving machine learning beyond pattern recognition and natural language processing into a broad array of scientific disciplines.

A subcategory of artificial intelligence, machine learning deals with the construction of algorithms that enable computers to learn from and react to data rather than following explicitly programmed instructions. “Machine-learning algorithms build a model based on inputs and then use that model to make other hypotheses, predictions, or decisions,” explained Irfan Essa, professor and associate dean in Georgia Tech’s College of Computing who also directs the Institute’s Center for Machine Learning.

Established in June 2016, the Center for Machine Learning is comprised of researchers from six colleges and 13 schools at Georgia Tech — a number that keeps growing. “Among our goals is to better coordinate research efforts across campus, serve as a home for machine learning leaders, and train the next generation of leaders,” Essa said, referring to Georgia Tech’s new Ph.D. program in machine learning.

Within the center, researchers are striving to advance both basic and applied science. “For example, one foundational goal is to really understand deep learning at its core,” Essa said. “We want to develop new theories and innovative algorithms, rather than just using deep learning as a black box for inputs and outputs.” And on the applied research front, the center has seven focal areas: health care, education, logistics, social networks, the financial sector, information security, and robotics.

Automating the art of interruption

Today’s robots are heavily programmed and don’t learn very much. This can work well enough in factories, but if robots are going to operate in offices, schools, and homes, they must be able to adapt to certain environments — and the specifications of particular users, points out Sonia Chernova, an assistant professor in Georgia Tech’s School of Interactive Computing, who specializes in machine learning and human-robot interactions.

Among projects in Chernova’s lab, graduate students are investigating interruptibility, an area where little robotics research has been conducted.

Interactive robots are more effective if they can occasionally ask for direction or verify whether something should be done, Chernova points out. “Suppose you have a robot that cleans houses. Not everyone has the same type of house — and not everyone wants their house cleaned the same way.”

“Yet while humans are amazingly good at knowing when to interrupt, robots can be pretty rude. If robots perceive a human in the environment, they will approach, regardless of what the person is doing. And that interruption can take a toll if the person is engaged in a challenging task.”

With that in mind, the researchers are looking at when robots should interrupt a human and who they should interrupt if there are multiple people nearby.

To collect training and testing data for the project, the researchers asked five people to engage in a series of activities such as drinking coffee, talking, or working on laptops. During each instance, the robot moved through a series of waypoints that enabled it to observe the group from different perspectives. Based on such social cues as body position and face gaze, along with sounds and what kind of objects the humans were holding, the robot determined on a scale of one to four how interruptible people were.

Four different types of temporal machine-learning models were tested: three types of conditional random fields (CRFs) and a hidden Markov model (HMM). Of these, the researchers showed that latent dynamic CRFs did the best job of predicting interruptibility. CRFs were expected to have an upper hand, because they are discriminative models and perform better for classification tasks.