NSF CAREER grant supports the dual design of resource-friendly machine learning algorithms and learning-driven wireless networks
Artificial intelligence and machine learning are revolutionizing the ways in which we live, work, and spend our free time, from the smart devices in our homes to the tasks our phones can carry out. This transformation is being made possible by a surge in data and computing power that can help machine learning algorithms not only perform device-specific tasks, but also help them gain intelligence or knowledge over time.
In the not-so-distant future, artificial intelligence and machine learning tasks will be carried out among connected devices through wireless networks, dramatically enhancing the capabilities of future smartphones, tablets, and sensors, and achieving what’s known as distributed intelligence. As technology stands right now, however, machine learning algorithms are not efficient enough to be run over wireless networks and wireless networks are not yet ready to transmit this type of intelligence.
With the support of a National Science Foundation Faculty Early Career Development Program (CAREER) grant, Tianyi Chen, an assistant professor of electrical, computer, and systems engineering at Rensselaer Polytechnic Institute and member of the Rensselaer-IBM Artificial Intelligence Research Collaboration (AIRC), is exploring how to make such knowledge-sharing tools a reality.
“I think in the future, the main terminal of intelligence will be our phones. Our phones will be able to control our computers, our cars, our meeting rooms, our apartments,” Chen said. “This will be powered by resource-efficient machine learning algorithms and also the support of future wireless networks.”
Through his collaboration with the Lighting Enabled Systems and Applications (LESA) Center at Rensselaer, Chen will validate the algorithms he develops using the center’s smart conference room.
The conference room is equipped with devices that are capable of sensing the environment, processing that information, and efficiently sharing it with other devices on the network – the same framework the algorithms are being designed to function within.
“We need to redesign our wireless networks to support not only traditional traffic, like video and voice, but to support new traffic such as transmittable intelligence,” Chen said. “We need to design more efficient learning algorithms that are suitable for running on the wireless network.”
Chen also stressed the importance of ensuring that knowledge-sharing algorithms only extract anonymized information in order to maintain data privacy as our devices – and daily lives – become increasingly networked. While the goals of this research are foundational in nature, Chen said the potential for future applications is wide-ranging – from power grids to urban transportation systems.