The 1st Forum on Frontiers of Science and Engineering
Everything towards AI
Seattle, WA, USA, May 28 - 30, 2018
A Builder’s Take on AI: magical? useful? or usable?
Recent discussions about artificial intelligence (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 mainly 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.
Eric Xing is a Professor of Computer Science at Carnegie Mellon University, and Founder and CEO of the machine learning platform startup Petuum Inc. He completed his undergraduate study at Tsinghua University, and holds a PhD in Molecular Biology and Biochemistry from the State University of New Jersey, and a PhD in Computer Science from the University of California, Berkeley. His main research interests are 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 possible worlds in artificial, biological, and social systems. Prof Xing is a board member of the International Machine Learning Society; in 2014, he served as the Program Chair of the International Conference of Machine Learning (ICML), and in 2019, he will serve as the General Chair of ICML. He is the Associate Department Head of the Machine Learning Department, founding director of the Center for Machine Learning and Health at Carnegie Mellon University, and a Fellow of the Association of Advancement of Artificial Intelligence.
AI and Security: lessons, challenges and future directions
Abstract: In this talk, I will first present recent results in the area of secure deep learning, in particular, adversarial deep learning---how deep learning systems could be easily fooled and what we need to do to address the issues. I will also talk about how AI and deep learning can help enable new capabilities in security applications. Finally, I will also talk about our recent project on privacy-preserving smart contract and towards democratization of AI using blockchain.
Dawn Song is a Professor in the Department of Electrical Engineering and Computer Science at UC Berkeley. Her research interest lies in deep learning, security, and blockchain. She has studied diverse security and privacy issues in computer systems and networks, including areas ranging from software security, networking security, distributed systems security, applied cryptography, blockchain and smart contracts, to the intersection of machine learning and security. She is the recipient of various awards including the MacArthur Fellowship, the Guggenheim Fellowship, the NSF CAREER Award, the Alfred P. Sloan Research Fellowship, the MIT Technology Review TR-35 Award, the Faculty Research Award from IBM, Google and other major tech companies, and Best Paper Awards from top conferences in Computer Security and Deep Learning. She obtained her Ph.D. degree from UC Berkeley. Prior to joining UC Berkeley as a faculty, she was a faculty at Carnegie Mellon University from 2002 to 2007.
Information Theoretic Interpretation of Deep Neural Networks
The goal of this work is to establish a theoretical foundation to deep learning by finding out what statistical quantities are being calculated and how well are these quantities calculated inside a deep neural network. For that purpose, we formulate a new problem called the "universal feature selection" problem, where we need to select from the high dimensional data a low dimensional feature that can be used to solve, not one, but a family of inference problems. We solve this problem by developing a new information metric that can be used to quantify the semantics of data, and by using a geometric analysis approach. We then show that a number of concepts in information theory and statistics such as the HGR correlation and common information are closely connected to the universal feature selection problem. At the same time, a number of learning algorithms, PCA, Compressed Sensing, FM, deep neural networks, etc., can also be interpreted as implicitly or explicitly solving the same problem, with various forms of constraints. In particular, we show that based on our approach, we can give an analytical expression to the weights computed in deep neural networks. This gives us the option of either to compute these weights with a separate routine different from the standard training procedure of neural networks, or to use the computation results of a neural network for other problems. We will show some experimental results where our theory can help us to design and use neural networks in more flexible and more rational ways.
Lizhong Zheng received the B.S and M.S. degrees, in 1994 and 1997 respectively, from the Department of Electronic Engineering, Tsinghua University, China, and the Ph.D. degree, in 2002, from the Department of Electrical Engineering and Computer Sciences, University of California, Berkeley. Since 2002, he has been working at MIT, where he is currently a professor of Electrical Engineering. His research interests include information theory, statistical inference, communications and networks theory. He received Eli Jury award from UC Berkeley in 2002, IEEE Information Theory Society Paper Award in 2003, and NSF CAREER award in 2004, and the AFOSR Young Investigator Award in 2007. He served as an associate editor for IEEE Transactions on Information Theory, and the general co-chair for the IEEE International Symposium on Information Theory in 2012. He is an IEEE fellow.
Architecture Innovations in the AI era
In the AI era, the relationship between large datasets, algorithmic training advances, and high performance hardware forms a virtuous cycle. Whenever advances are made in one, it fuels the other two to advance. The computing capability, enabled by the recent computer architecture innovations, such as better GPU and new TPU architectures, plays an important role in making AI to be pervasive. This presentation will first give an overview of the recent advance in hardware architecture designs for machine learning applications, and then present possible directions in the future architecture innovations to increase the computing capability and fuel the advance of the AI applications.
Yuan Xie is a professor in UCSB. He received the B.S. degree in electronic engineering from Tsinghua University and the M.S. and Ph.D. degrees in computer engineering from Princeton University. Before joining UCSB in 2014, He was with Pennsylvania State University, with rich industry experience in both research lab (AMD) and product team (IBM Worldwide Design Center). Xie is a IEEE Fellow, and a recipient of the NSF CAREER award. He has published more than 300 research papers in the area of computer architecture, EDA, VLSI designs, and embedded systems.
Speech and Language to AI Evolution
Abstract: Amongst all creatures the human species stands unique in Darwin’s natural selection process because of our ability to communicate, our ability to manipulate symbols, and our ability to construct language. Speech and language provides the way we communicate our collective intelligence from one generation to the next. It is no exaggeration to state that it is speech and language that differentiated human intelligence from animal intelligence in the evolution. The impact of speech and language to the evolution of AI should be as foundational as speech and language to the evolution of homo sapiens!
Xuedong Huang is a Microsoft Technical Fellow in AI and Research. He leads Microsoft’s Speech and Language Group.
In 1993, Huang joined Microsoft to found the company’s speech technology group. As the general manager of Microsoft’s spoken language efforts, he helped to bring speech to the mass market by introducing SAPI to Windows in 1995 and Speech Server to the enterprise call center in 2004. He served as General Manager for MSR Incubation and Chief Architect for Bing and Ads. In 2015, he returned to AI and Research to lead the advanced technology group. In 2016, he led the team achieving a historical conversational speech recognition human parity milestone on the Switchboard task. He helped to advance AI across Microsoft’s whole AI Stack.
Low-dimensional Structures and Deep Models for High-dimensional Data
Abstract: In this talk, we will discuss a class of models and techniques that can effectively model and extract rich low-dimensional structures in high-dimensional data such as images and videos, despite nonlinear transformation, gross corruption, or severely compressed measurements. This work leverages recent advancements in convex optimization from Compressive Sensing for recovering low-rank or sparse signals that provide both strong theoretical guarantees and efficient and scalable algorithms for solving such high-dimensional combinatorial problems. We illustrate how these new mathematical models and tools could bring disruptive changes to solutions to many challenging tasks in computer vision, image processing, and pattern recognition. We will also illustrate some emerging applications of these tools to other data types such as 3D range data, web documents, image tags, bioinformatics data, audio/music analysis, etc. Throughout the talk, we will discuss strong connections of algorithms from Compressive Sensing with other popular data-driven models such as Deep Neural Networks, providing some new perspectives to understand Deep Learning. This is joint work with John Wright of Columbia, Emmanuel Candes of Stanford, Zhouchen Lin of Peking University, Shenghua Gao of ShanghaiTech, and my former students Zhengdong Zhang of MIT, Xiao Liang of Tsinghua University, Arvind Ganesh, Zihan Zhou, Kerui Min of UIUC.
Yi Ma is a professor at the EECS Department of UC Berkeley. He has been a professor and the executive dean of the School of Information and Science and Technology, ShanghaiTech University, China from 2014 to 2017. From 2009 to early 2014, he was a Principal Researcher and the Research Manager of the Visual Computing group at Microsoft Research in Beijing. From 2000 to 2011, he was an assistant and associate professor at the Electrical & Computer Engineering Department of the University of Illinois at Urbana-Champaign. His main research interest is in computer vision, data science, and systems theory. Yi Ma received his Bachelors’ degree in Automation and Applied Mathematics from Tsinghua University (Beijing, China) in 1995, a Master of Science degree in EECS in 1997, a Master of Arts degree in Mathematics in 2000, and a PhD degree in EECS in 2000, all from the University of California at Berkeley. Yi Ma received the David Marr Best Paper Prize at the International Conference on Computer Vision 1999, the Longuet-Higgins Best Paper Prize (honorable mention) at the European Conference on Computer Vision 2004, and the Sang Uk Lee Best Student Paper Award with his students at the Asian Conference on Computer Vision in 2009. He also received the CAREER Award from the National Science Foundation in 2004 and the Young Investigator Award from the Office of Naval Research in 2005. He has written two textbooks: “An Invitation to 3-D Vision” published in 2004, and “Generalized Principal Component Analysis” published in 2016, all by Springer. He was an associate editor of IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), the International Journal of Computer Vision (IJCV), and IEEE transactions on Information Theory (TIT). He is currently an associate editor of the IMA journal on Information and Inference, SIAM journal on Imaging Sciences, SIAM journal on Mathematics of Data Science, IEEE Signal Processing Magazine. He has served as a Program Chair for ICCV 2013 and a General Chair for ICCV 2015. He is a Fellow of both IEEE and ACM. He is ranked the World's Highly Cited Researchers of 2016 by Clarivate Analytics of Thomson Reuters and is among Top 50 of the Most Influential Authors in Computer Science of the World, ranked by Semantic Scholar, reported by Science Magazine, April 2016.