A Civil Engineering Perspective on Artificial Intelligence From Petuum
Recent discussions about AI in both research community, and the general public have been championing a novelistic view of AI, that AI can mimic, surpass, threaten, or even destroy mankind. And such discussions are fueled by recent advances in deep learning experimentations and applications, which are however often plagued by its craftiness, un-interpretability, and poor generalizability. I will discuss a different view of AI as a rigorous engineering discipline and as a commodity, where standardization, modularity, repeatability, reusability, and transparency are commonly expected, just as in civil engineering where builders apply principles and techniques from all sciences to build reliable constructions. I will discuss how such a view sets different focus, approach, metric, and expectation for AI research and engineering, which we advocated and practiced in developing general-purpose system platform, programming tools, machine learning building blocks, and data processing modules in Petuum Inc.
Dr. Eric P. Xing is Founder, CEO and Chief Scientist at Petuum Inc. He is a Professor in the School of Computer Science at Carnegie Mellon University. He is also the Associate Department Head for Research of the Machine Learning Department and the Founding Director of the Center for Machine Learning and Health at CMU. For his distinguished contributions in AI/ML, he was elected a Fellow of the Association for the Advancement of Artificial Intelligence (AAAI).
Dr. Xing is a thought and innovation leader in Machine Learning and Artificial Intelligence. His principal research interests are in the development of machine learning and statistical methodology and large-scale computational system and architectures, for solving problems involving automated learning, reasoning, and decision-making in high-dimensional, multimodal, and dynamic complex systems. His pioneering research has created numerous AI/ML foundational techniques, such as the Parameter Server, distance metric learning, distributed network inference, dynamic networks, dynamic nonparametric Bayesian models, spectral graphical models, and variational inference. He has authored or co-authored over 300 publications, while receiving multiple Best Paper Awards.
Dr. Xing is a board member of the International Machine Learning Society, program chair and general chair of the International Conference of Machine Learning (ICML), and a former member of the U.S. Department of Defense Advanced Research Projects Agency (DARPA) Information Science and Technology (ISAT) Advisory group. He is the recipient of numerous awards including: The National Science Foundation (NSF) Career Award; Alfred P. Sloan Research Fellowship in Computer Science; United States Air Force Office of Scientific Research Young Investigator Award; and the IBM Open Collaborative Research Faculty Award.