Machine learning is becoming increasingly important in the world of technology. As computers become more advanced and powerful, they can process data faster and more accurately than ever. Recent developments in machine learning have increased interest in using coordinate-based neural networks that parametrize the physical properties of scenes or objects across space and time to solve visual computing problems. These methods, known as neural fields, have been used successfully for synthesizing 3D shapes, human body animation, 3D reconstruction, and pose estimation. The Neural Radiance Fields (NeRF) model, which learns to represent the local opacity and view-dependent radiance of a static scene from sparse calibrated images, is one of the most recent works using neural fields.

Not so long ago, watching a movie on a smartphone seemed impossible. Vivienne Sze was a graduate student at MIT at the time, in the mid 2000s, and she was drawn to the challenge of compressing video to keep image quality high without draining the phone's battery. The solution she hit upon called for co-designing energy-efficient circuits with energy-efficient algorithms. Sze would go on to be part of the team that won an Engineering Emmy Award for developing the video compression standards still in use today. Now an associate professor in MIT's Department of Electrical Engineering and Computer Science, Sze has set her sights on a new milestone: bringing artificial intelligence applications to smartphones and tiny robots.

"Using an optical chip to perform neural network computations more efficiently than is possible with digital computers could allow more complex problems to be solved," said research team leader Shanhui Fan of Stanford University. "This would enhance the capability of artificial neural networks to perform tasks required for self-driving cars or to formulate an appropriate response to a spoken question, for example. It could also improve our lives in ways we can't imagine now." An artificial neural network is a type of artificial intelligence that uses connected units to process information in a manner similar to the way the brain processes information. Using these networks to perform a complex task, for instance voice recognition, requires the critical step of training the algorithms to categorize inputs, such as different words.