System Bits: April 23

HoloClean tool; hacking AVs; NeuroAutonomy.


AI tool can clean up dirty data
Researchers at the University of Waterloo, collaborating with colleagues at the University of Wisconsin and Stanford University, came up with HoloClean, an artificial intelligence tool to comb through dirty data and to detect information errors.

“More and more machines are making decisions for us, so all our lives are touched by dirty data daily,” said Ihab Ilyas, a professor in Waterloo’s David R. Cheriton School of Computer Science. “If organizations like banks or utility companies are working with bad data, it could negatively impact things such as credit scores or mortgage approvals.”

After the AI is trained, it can then independently figure out what’s an error, what’s not, and if there’s an error determine the most probable value for the missing data. Users will then have a cleaner dataset to use in their analytics which will produce more trustworthy results.

“This work deviates from the old way of manually trying to clean the data, which was expensive, didn’t scale, and does not meet the current needs for cleaning the data,” said Ilyas of Waterloo’s Faculty of Mathematics. “This system addresses the problem where the information is out there, and people are using it to run analytics, but it is not correct. It doesn’t provide information that was not there, but instead corrects information you assume is correct.”

Image credit: University of Waterloo

The next step for the researchers is to pair error detection and data repair in one end-to-end solution for the ultimate data quality dashboard.

Protecting AVs from cyberattacks
The concept of the fully autonomous vehicle is enticing – a place where you could catch up on email, check the news of the day, or even play in augmented-reality games while commuting to work, as the vehicle does all the driving.

Nicola Bezzo works at the intersection of the physical and cyberworlds. An assistant professor in the University of Virginia’s Department of Systems and Information Engineering with a dual appointment in Electrical and Computer Engineering, he researches autonomous systems and assesses the threats to them.

He started his career developing robotics and autonomous machines, then moved into protecting them from cyberattacks.

“About 50% of my work is related to how to detect cyberattacks on modern vehicles, such as the car you drive every day, or robotic systems in airborne vehicles,” he said. “I try to understand the state of these systems, to see if they have been corrupted or not and how to give some defense mechanism. The other 50% of my work is dedicated to making such systems more and more autonomous and smarter.”

“Modern vehicles are not built with cybersecurity in mind,” Bezzo said. “They have a lot of computers with a lot of sensors, and they work great; driving comfort is increasing and there are a lot of safety features.

“But an attacker can compromise these sensors, or the computer, and can drive you wherever he wants. They can take over the brakes of the car, or some sensor like the GPS, or he can take over the lights in the vehicle.”

One strategy Bezzo uses to counter these stealth attacks is building redundancy into systems, where multiple sensors monitor the operation of the system, and subtle variations in one sensor can indicate an intrusion on another.

“At the end of the day, we like automations,” he said. “We want to improve the quality of our lives; we want to make our lives easy. The more automations you add to your car, usually the safer is your vehicle, [and] the more you can relax and concentrate on other activities. If you commute every day and you can rely on your autonomous vehicle, you can use the time to do something else, like answering email. You can get rid of wasted time.”

But while an autonomous vehicle can provide more free time, how secure are its systems from intrusions?

“I believe we are never going to be able to completely solve this problem,” Bezzo said. “There is always a way to compromise your systems, to do something that you did not take into account.”

Pentagon grant funds Project NeuroAutonomy
The U.S. Department of Defense awarded a $7.5 million grant to academic researchers working on bioinspired control systems that would enable the self-navigation of vehicles on the land, in the air, and at sea.

The Multidisciplinary University Research Initiative funding will be shared by Boston University, the Massachusetts Institute of Technology, and Australian research universities.

Autonomous vehicles that can maneuver themselves around any city are already out on our public roads, says Yannis Paschalidis, but operating off-road remains a challenge.

“These vehicles are designed for very structured environments, within roads and lanes,” says Paschalidis, a Boston University engineer who uses data science and machine learning to develop new software algorithms and control systems. “They are only programmed to recognize a small number of different types of objects.”

Paschalidis, a BU professor of biomedical, systems, and electrical and computer engineering, has a vision for self-driving vehicles that would launch them from the mundane world of suburban commuting to the most dynamic (and sometimes harsh) places around the globe. “We are interested in developing fundamental principles that can be applied to autonomous vehicles capable of navigating themselves on the ground, underwater, and in the air,” he says.

“Our team spans two continents and brings together some of the preeminent experts in neuroscience—with emphasis on localization, mapping, and navigation functions—with experts in robotics, computer vision, control systems, and algorithms,” says Paschalidis, the team’s principal investigator. “We’re essentially going to use insights from neuroscience to better organize and control engineered systems.”

Their goal? To investigate how the brains of living organisms—namely ants, animals, and humans—process their spatial environments to derive meaningful navigation information. The international research team calls their efforts Project NeuroAutonomy.

“The research that we’ll be doing under this MURI is focused on the most interesting control system out there—the brain and its coordination of the neurosensory and neuromuscular systems in the body,” says co–principal investigator John Baillieul, a BU Distinguished Professor of mechanical, systems, and electrical and computer engineering.

The Australian collaborators, particularly insect navigation expert Ken Cheng of Macquarie University, will draw insight from the way that ants use visual cues to move around. In the United States, BU collaborators will lead teams that examine animal and human spatial navigation.

“This project offers the potential for some major theoretical breakthroughs for understanding cognition,” says co–principal investigator Michael Hasselmo, director of BU’s Center for Systems Neuroscience and a professor of psychological and brain sciences.
Hasselmo will lead the team’s investigation of how rodents navigate their environment. He says although this project is focusing on navigation, elements of the algorithm the team plans to develop could eventually be applied “to a broad range of different types of intelligent behavior.”

To develop the algorithm, the team will zero in on three big gaps between the navigation prowess of current autonomous vehicle technology and biological organisms, says co–principal investigator Chantal Stern, director of BU’s Cognitive Neuroimaging Center. Stern will lead team members in using functional MRI to investigate how humans develop a map of their environment and detect changing elements of their surroundings.

“An example that comes directly from robotics is known as the loop closure problem,” Stern says. “When you wander around in a circle through your house and come back to the kitchen, you know you are back in the kitchen; you have mapped your environment and recognize that you have returned to a location you were in before. In robotics, that’s a difficult problem for an autonomous system. An autonomous system will keep mapping a location it returns to, in the same way a Roomba vacuum keeps cleaning the same spot when it comes back around to the same location.”

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