Our work is crucial for developing scalable and secure machine intelligence for cloud computing, data centers and many other autonomous and semi-autonomous applications including surgical robots, imaging systems and low-power sensor networks.
Advances in the integration of hardware, software, algorithms and data are necessary for developing new generations of systems that make decisions and take actions based on data that are collected and analyzed in real time.
One key technological hurdle that must be cleared to develop these kinds of systems is the ability to analyze – on the edges of networks – massive data sets coming in from multiple sources in real time. This will require advanced machine learning algorithms to be training in real time on mobile platforms and embedded systems that are constrained by power, computational resources and bandwidth.
These machine learning algorithms will also need to process incoming data in real time, and adapt their behavior accordingly. In this way, real-time data analytics on mobile and embedded computing platforms will guide real-time decision making, and real-world actions taken by autonomous systems.
UC San Diego researchers at the new Center for Machine-Integrated Computing and Security are integrating hardware, software and massive data sets in new ways in order to invent this future. The team, for example, was the first to report real-time analysis of streaming data using machine learning algorithms running on mobile platforms and other resource-constrained platforms such as drones.