michael jordan berkeley blog

member of the American Academy of Arts and Sciences. Ion Stoica istoica@EECS.Berkeley.EDU. While this challenge is viewed by some as subservient to the creation of “artificial intelligence,” it can also be viewed more prosaically — but with no less reverence — as the creation of a new branch of engineering. The phrase “Data Science” began to be used to refer to this phenomenon, reflecting the need of ML algorithms experts to partner with database and distributed-systems experts to build scalable, robust ML systems, and reflecting the larger social and environmental scope of the resulting systems. McCarthy, on the other hand, emphasized the ties to logic. Lowcountry Food Bank speaks about receiving donation from NBA legend Michael Jordan Moreover, in this understanding and shaping there is a need for a diverse set of voices from all walks of life, not merely a dialog among the technologically attuned. Phone (510) 642-3806. Michael Jordan | Berkeley, California | Professor at UC Berkeley | 245 connections | See Michael's complete profile on Linkedin and connect Second, and more importantly, success in these domains is neither sufficient nor necessary to solve important IA and II problems. Computer Science 731 Soda Hall #1776 Berkeley, CA 94720-1776 Phone: (510) 642-3806 Finally, and of particular importance, II systems must bring economic ideas such as incentives and pricing into the realm of the statistical and computational infrastructures that link humans to each other and to valued goods. Hoping that the reader will tolerate one last acronym, let us conceive broadly of a discipline of “Intelligent Infrastructure” (II), whereby a web of computation, data and physical entities exists that makes human environments more supportive, interesting and safe. nonparametric analysis, probabilistic graphical models, spectral He is one of the leading figures in machine learning, and in 2016 Science reported him as the world's most influential computer scientist. However, the mathematical tools are entirely different, relying on concentration, a more general tool that applies to a wide range of problems. Although not visible to the general public, research and systems-building in areas such as document retrieval, text classification, fraud detection, recommendation systems, personalized search, social network analysis, planning, diagnostics and A/B testing have been a major success — these are the advances that have powered companies such as Google, Netflix, Facebook and Amazon. Search UC Berkeley Directory . A related argument is that human intelligence is the only kind of intelligence that we know, and that we should aim to mimic it as a first step. We now come to a critical issue: Is working on classical human-imitative AI the best or only way to focus on these larger challenges? And, unfortunately, we are not very good at anticipating what the next emerging serious flaw will be. The system would incorporate information from cells in the body, DNA, blood tests, environment, population genetics and the vast scientific literature on drugs and treatments. Much like civil engineering and chemical engineering in decades past, this new discipline aims to corral the power of a few key ideas, bringing new resources and capabilities to people, and doing so safely. II systems require the ability to manage distributed repositories of knowledge that are rapidly changing and are likely to be globally incoherent. California, San Diego. This confluence of ideas and technology trends has been rebranded as “AI” over the past few years. Bio: Michael I. Jordan is Professor of Computer Science and Statistics at the University of California, Berkeley. Alchemist. Research Expertise and Interest. Rather, as in the case of the Apollo spaceships, these ideas have often been hidden behind the scenes, and have been the handiwork of researchers focused on specific engineering challenges. Michael JORDAN, Professor (Full) of University of California, Berkeley, CA (UCB) | Read 795 publications | Contact Michael JORDAN Jordan discussed how economic concepts can help advance AI as well as the challenges and opportunities of coordinating decision-making in machine learning. Let us begin by considering more carefully what “AI” has been used to refer to, both recently and historically. Indeed, the famous “backpropagation” algorithm that was rediscovered by David Rumelhart in the early 1980s, and which is now viewed as being at the core of the so-called “AI revolution,” first arose in the field of control theory in the 1950s and 1960s. While the building blocks have begun to emerge, the principles for putting these blocks together have not yet emerged, and so the blocks are currently being put together in ad-hoc ways. Such infrastructure is beginning to make its appearance in domains such as transportation, medicine, commerce and finance, with vast implications for individual humans and societies. Fellow of the American Association for the Advancement of Science. Here computation and data are used to create services that augment human intelligence and creativity. So perhaps we should simply await further progress in domains such as these. Michael I. Jordan is the Pehong Chen Distinguished Professor in the Department of Electrical Engineering and Computer Science and the Department of Statistics at the University of California, Berkeley. The Center for Data Innovation spoke with Michael I. Jordan, a professor at the University of California, Berkeley whose research spans the computational, statistical, cognitive, and social sciences. Department of Electrical Engineering and Computer Science and the Computing-based generation of sounds and images serves as a palette and creativity enhancer for artists. I went back to tell the geneticist that I believed that the white spots were likely false positives — that they were literally “white noise.” She said “Ah, that explains why we started seeing an uptick in Down syndrome diagnoses a few years ago; it’s when the new machine arrived.”. Core Faculty. Michael I. Jordan is the Pehong Chen Distinguished Professor in the Department of Electrical Engineering and Computer Science and the Department of Statistics at the University of California, Berkeley. MICHAEL JORDAN RESEARCH. Michael Jordan, a leading UC Berkeley faculty researcher in the fields of computer science and statistics, is the 2015 recipient of the David E. Rumelhart Prize, a prestigious honor reserved for those who have made fundamental contributions to the theoretical foundations of human cognition. Editor’s Note: The following blog is a special guest post by a recent graduate of Berkeley BAIR’s AI4ALL summer program for high school students. We need to solve IA and II problems on their own merits, not as a mere corollary to a human-imitative AI agenda. The problem that this episode revealed wasn’t about my individual medical care; it was about a medical system that measured variables and outcomes in various places and times, conducted statistical analyses, and made use of the results in other places and times. Department of Statistics at the University of California, Berkeley. Such systems must cope with cloud-edge interactions in making timely, distributed decisions and they must deal with long-tail phenomena whereby there is lots of data on some individuals and little data on most individuals. National Science Foundation Expeditions in Computing. We will use the phrase “human-imitative AI” to refer to this aspiration, emphasizing the notion that the artificially intelligent entity should seem to be one of us, if not physically at least mentally (whatever that might mean). One of its early applications was to optimize the thrusts of the Apollo spaceships as they headed towards the moon. Focusing narrowly on human-imitative AI prevents an appropriately wide range of voices from being heard. He is a Fellow of the AAAI, It is not hard to pinpoint algorithmic and infrastructure challenges in II systems that are not central themes in human-imitative AI research. Biography. He is one of the leading figures in machine learning, and in 2016 Science reported him as the world's most influential computer scientist. Emails: EECS Address: University of California, Berkeley EECS Department 387 Soda Hall #1776 Berkeley, CA 94720-1776 Statistics Address: University of California, Berkeley Statistics Department 427 Evans Hall #3860 Berkeley… First, although one would not know it from reading the newspapers, success in human-imitative AI has in fact been limited — we are very far from realizing human-imitative AI aspirations. It would not just focus on a single patient and a doctor, but on relationships among all humans — just as current medical testing allows experiments done on one set of humans (or animals) to be brought to bear in the care of other humans. The problem had to do not just with data analysis per se, but with what database researchers call “provenance” — broadly, where did data arise, what inferences were drawn from the data, and how relevant are those inferences to the present situation? Previously, I got my Ph.D. in Statistics from UC Berkeley, where I was fortunate to be advised by Michael I. Jordan and Martin J. Wainwright.During my graduate study, I was a member in the Berkeley Artificial Intelligence Research (BAIR) Lab. This was largely an academic enterprise. Joe Hellerstein hellerstein@berkeley.edu. Unfortunately the thrill (and fear) of making even limited progress on human-imitative AI gives rise to levels of over-exuberance and media attention that is not present in other areas of engineering. methods, kernel machines and applications to problems in distributed computing I am a quantitative researcher at Citadel Securities.My research covers machine learning, statistics, and optimization. One could argue that an AI system would not only imitate human intelligence, but also “correct” it, and would also scale to arbitrarily large problems. He has been cited over 170,000 times and has mentored many of the world-class researchers defining the field of AI today, including Andrew Ng, Zoubin Ghahramani, Ben Taskar, and Yoshua Bengio. Michael I. Jordan: Artificial Intelligence — The Revolution Hasn’t Happened Yet (This article has originally been published on Medium.com.) He has worked for over three decades in the computational, inferential, cognitive and biological sciences, first as a graduate student at UCSD and then as a faculty member at MIT and Berkeley. computer science, artificial intelligence, computational biology, statistics, machine learning, electrical engineering, applied statistics, optimization. Mou, J. Li, M. Wainwright, P. Bartlett, and M. I. Jordan.arxiv.org/abs/2004.04719, 2020. The core design goal for Anna is to avoid... Arx. The ability of, say, a squirrel to perceive the three-dimensional structure of the forest it lives in, and to leap among its branches, was inspirational to these fields. Michael I. Jordan Pehong Chen Distinguished Professor Department of EECS Department of Statistics AMP Lab Berkeley AI Research Lab University of California, Berkeley. MICHAEL JORDAN RESEARCH Michael I. Jordan Pehong Chen Distinguished Professor Department of EECS Department of Statistics AMP Lab Berkeley AI Research Lab University of California, Berkeley Michael Jeffrey Jordan: biography Michael Jeffery Jordan was born February 17, 1963, in Brooklyn, New York to Deloris and James R. Jordan. Michael Jordan jordan@CS.Berkeley… September 17, 2014 Berkeley.edu: Ken Goldberg – Pushing the Boundaries of Art and Technology (and Haberdashery) September 14, 2014 FastML Blog: Mike Jordan’s Thoughts on Deep Learning These are classical goals in human-imitative AI, but in the current hubbub over the “AI revolution,” it is easy to forget that they are not yet solved. Courses Stat 210B, Theoretical Statistics, Spring 2017 Stat 210A, Theoretical Statistics, Fall 2015 CS 174, Combinatorics and Discrete Probability, Spring 2015 As for the necessity argument, it is sometimes argued that the human-imitative AI aspiration subsumes IA and II aspirations, because a human-imitative AI system would not only be able to solve the classical problems of AI (as embodied, e.g., in the Turing test), but it would also be our best bet for solving IA and II problems. In terms of impact on the real world, ML is the real thing, and not just recently. But this is not the classical case of the public not understanding the scientists — here the scientists are often as befuddled as the public. It was John McCarthy (while a professor at Dartmouth, and soon to take a position at MIT) who coined the term “AI,” apparently to distinguish his budding research agenda from that of Norbert Wiener (then an older professor at MIT). And, unfortunately, it distracts us. Anna is a low-latency, autoscaling key-value store. The overall transportation system (an II system) will likely more closely resemble the current air-traffic control system than the current collection of loosely-coupled, forward-facing, inattentive human drivers. CHARLESTON, S.C. (WCBD) - The Lowcountry Food Bank (LCFB) announced Tuesday that it is one of the recipients of NBA Hall of Famer Michael Jordan's November 2020 donation to … Michael I. Jordan is the Pehong Chen Distinguished Professor in the Department of Electrical Engineering and Computer Science and the Department of Statistics at the University of California, Berkeley. The past two decades have seen major progress — in industry and academia — in a complementary aspiration to human-imitative AI that is often referred to as “Intelligence Augmentation” (IA). You might want to try starting over from the homepage to see if you can find what you're after from there. And, while one can foresee many problems arising in such a system — involving privacy issues, liability issues, security issues, etc — these problems should properly be viewed as challenges, not show-stoppers. But we are now in the realm of science fiction — such speculative arguments, while entertaining in the setting of fiction, should not be our principal strategy going forward in the face of the critical IA and II problems that are beginning to emerge. Bio: Michael I. Jordan is Professor of Computer Science and Statistics at the University of California, Berkeley. Did civil engineering develop by envisaging the creation of an artificial carpenter or bricklayer? This scope is less about the realization of science-fiction dreams or nightmares of super-human machines, and more about the need for humans to understand and shape technology as it becomes ever more present and influential in their daily lives. In an interesting reversal, it is Wiener’s intellectual agenda that has come to dominate in the current era, under the banner of McCarthy’s terminology. Ray: A Distributed Framework for Emerging AI Applications, RLlib: Abstractions for Distributed Reinforcement Learning, A Berkeley View of Systems Challenges for AI, Finite-Size Corrections and Likelihood Ratio Fluctuations in the Spiked Wigner Model, Breaking Locality Accelerates Block Gauss-Seidel, Real-Time Machine Learning: The Missing Pieces, Decoding from Pooled data: Phase Transitions of Message Passing, Decoding from Pooled data: Sharp Information-Theoretic Bounds, Universality of Mallows’ and degeneracy of Kendall’s kernels for rankings. He is a professor of machine learning, statistics, and AI at UC Berkeley, and in 2016 was recognized as the world’s most influential computer scientist by Science magazine. AMP Lab – UC Berkeley. But the episode troubled me, particularly after a back-of-the-envelope calculation convinced me that many thousands of people had gotten that diagnosis that same day worldwide, that many of them had opted for amniocentesis, and that a number of babies had died needlessly. I have interests that span the spectrum from theory to algorithms to applications. The developments which are now being called “AI” arose mostly in the engineering fields associated with low-level pattern recognition and movement control, and in the field of statistics — the discipline focused on finding patterns in data and on making well-founded predictions, tests of hypotheses and decisions. “AI” was meant to focus on something different — the “high-level” or “cognitive” capability of humans to “reason” and to “think.” Sixty years later, however, high-level reasoning and thought remain elusive. The popular Machine Learning blog “FastML” has a recent posting from an “Ask Me Anything” session on Reddit by Mike Jordan. I will resist giving this emerging discipline a name, but if the acronym “AI” continues to be used as placeholder nomenclature going forward, let’s be aware of the very real limitations of this placeholder. INFORMS On-line: Michael Franklin interview on “The Burgeoning Field of Big Data” October 2, 2014 Scientific American features Carat App in Podcast. We will need well-thought-out interactions of humans and computers to solve our most pressing problems. (This state of affairs is surely, however, only temporary; the pendulum swings more in AI than in most fields.). Acknowledgments: There are a number of individuals whose comments during the writing of this article have helped me greatly, including Jeff Bezos, Dave Blei, Rod Brooks, Cathryn Carson, Tom Dietterich, Charles Elkan, Oren Etzioni, David Heckerman, Douglas Hofstadter, Michael Kearns, Tammy Kolda, Ed Lazowska, John Markoff, Esther Rolf, Maja Mataric, Dimitris Papailiopoulos, Ben Recht, Theodoros Rekatsinas, Barbara Rosario and Ion Stoica. This fund aims to support not only AI activities, but also IA and II activities, and to do so in the context of a university environment that includes not only the engineering disciplines, but also the perspectives of the social sciences, the cognitive sciences and the humanities. The phrase is intoned by technologists, academicians, journalists and venture capitalists alike. It would help maintain notions of relevance, provenance and reliability, in the way that the current banking system focuses on such challenges in the domain of finance and payment. These artifacts should be built to work as claimed. These problems include the need to bring meaning and reasoning into systems that perform natural language processing, the need to infer and represent causality, the need to develop computationally-tractable representations of uncertainty and the need to develop systems that formulate and pursue long-term goals. While industry will continue to drive many developments, academia will also continue to play an essential role, not only in providing some of the most innovative technical ideas, but also in bringing researchers from the computational and statistical disciplines together with researchers from other disciplines whose contributions and perspectives are sorely needed — notably the social sciences, the cognitive sciences and the humanities. Charleston, S.C. (WCBD) - Classes begin Monday at the College of Charleston. To cut a long story short, I discovered that a statistical analysis had been done a decade previously in the UK, where these white spots, which reflect calcium buildup, were indeed established as a predictor of Down syndrome. Let’s broaden our scope, tone down the hype and recognize the serious challenges ahead. But humans are in fact not very good at some kinds of reasoning — we have our lapses, biases and limitations. of Sciences, a member of the National Academy of Engineering and a But amniocentesis was risky — the risk of killing the fetus during the procedure was roughly 1 in 300. He received the IJCAI Research He received his Masters in Mathematics from Arizona State University, and earned his PhD in Cognitive Science in 1985 from the University of California, San Diego. genetics. Being a statistician, I determined to find out where these numbers were coming from. This emergence sometimes arises in conversations about an “Internet of Things,” but that effort generally refers to the mere problem of getting “things” onto the Internet — not to the far grander set of challenges associated with these “things” capable of analyzing those data streams to discover facts about the world, and interacting with humans and other “things” at a far higher level of abstraction than mere bits. It will be vastly more complex than the current air-traffic control system, specifically in its use of massive amounts of data and adaptive statistical modeling to inform fine-grained decisions. The idea that our era is somehow seeing the emergence of an intelligence in silicon that rivals our own entertains all of us — enthralling us and frightening us in equal measure. The current public dialog about these issues too often uses “AI” as an intellectual wildcard, one that makes it difficult to reason about the scope and consequences of emerging technology. IA will also remain quite essential, because for the foreseeable future, computers will not be able to match humans in their ability to reason abstractly about real-world situations. Some of the most heralded recent success stories of ML have in fact been in areas associated with human-imitative AI — areas such as computer vision, speech recognition, game-playing and robotics. AdaHessian and PyHessian. Excellence Award in 2016, the David E. Rumelhart Prize in 2015 and While services of this kind could conceivably involve high-level reasoning and thought, currently they don’t — they mostly perform various kinds of string-matching and numerical operations that capture patterns that humans can make use of. He received his Masters in Mathematics from Arizona State University, There was a geneticist in the room, and she pointed out some white spots around the heart of the fetus. Moreover, we should embrace the fact that what we are witnessing is the creation of a new branch of engineering. And this must all be done within the context of evolving societal, ethical and legal norms. Just as early buildings and bridges sometimes fell to the ground — in unforeseen ways and with tragic consequences — many of our early societal-scale inference-and-decision-making systems are already exposing serious conceptual flaws. Fact that what we want it to be globally incoherent the particular historical perspectives of mccarthy Wiener! Trends has been named a Neyman Lecturer and a Medallion Lecturer by the Institute of Mathematical Statistics as... We ’ re missing is an engineering discipline with its principles of analysis and design emerge the... Hype and recognize the serious challenges ahead, M. Wainwright, P.,! Conflict-Free Asynchronous machine learning, Statistics, and not just recently civil engineering develop by envisaging creation. Professor at MIT from 1988 to 1998 exciting as these latter fields appear to be, they can not be! 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