Andrew Ng : 2013 Plenary Session

Machine learning is a very successful technology, but applying it to a new problem usually means spending a long time hand-designing the input features to feed to the learning algorithm. This is true for applications in vision, audio, and text/NLP. To address this, researchers in machine learning have recently developed "deep learning" algorithms, which can automatically learn feature representations from unlabeled data, thus bypassing most of this time-consuming engineering. These algorithms are based on building massive artificial neural networks, that were loosely inspired by cortical (brain) computations. In this talk, I describe the key ideas behind deep learning, and also discuss the computational challenges of getting these algorithms to work. I'll also present a few case studies, and report on the results from a project that I led at Google to build massive deep learning algorithms, resulting in a highly distributed neural network trained on 16,000 CPU cores, and that learned by itself to discover high level concepts such as common objects in video.

Andrew Ng's research is in the areas of machine learning and artificial intelligence. Through building very large scale cortical (brain) simulations, he is developing algorithms that can learn to sense and perceive without needing to be explicitly programed. Using these techniques, he has developed sophisticated computer vision algorithms, as well as a variety of highly capable robots, such as by far the most advanced autonomous helicopter controller, that is able to fly spectacular aerobatic maneuvers. His group also developed ROS, which is today by far the most widely used open-source robotics software platform. In 2011, he also taught an online Machine Learning class to over 100,000 students, leading to his co-founding Coursera, which is offering high quality online courses.