CAREER: Towards the Design of Models and Systems for Efficient Prediction

University Of California-Berkeley, Berkeley CA

Investigators

Abstract

Advances in artificial intelligence (AI) are enabling new services and applications including voice-enabled digital assistants, machine translation, personalized medicine, fraud detection, and autonomous vehicles. This new class of intelligent services and applications are critical to the economy and transforming century-old industries ranging from commerce and banking to transportation and healthcare. In this research, the problem of deploying advanced machine learning models and prediction pipelines is studied. A holistic approach is taken for the design of the computer systems needed to render predictions and the models and algorithms that they run. Models are developed that can scale their complexity to avoid overthinking and thereby reduce the energy required to make predictions along with algorithms and systems to automatically configure complex prediction pipelines to simplify model deployment while also improving efficiency and reliability. This research will also produce and promote open-source software systems that can be used across industries. Building these new AI-powered applications will also require new skills and education. A key part of this proposal is open-source course development. A new Data Science major is being designed and this grant will fund new open modules in the data science courses. These courses are taken by students in engineering as well as across the entire campus and provide the fundamental skills needed to understand how machine learning can be used to solve real-world problems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.