On linear stochastic approximation: Fine-grained Polyak-Ruppert and non-asymptotic concentration.W. Masks and social distancing will be required on campus. 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. I’m also a computer scientist, and it occurred to me that the principles needed to build planetary-scale inference-and-decision-making systems of this kind, blending computer science with statistics, and taking into account human utilities, were nowhere to be found in my education. 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. And this happened day after day until it somehow got fixed. Blogs; Jenkins; Search; People. Artificial Intelligence (AI) is the mantra of the current era. California, San Diego. The core design goal for Anna is to avoid... Arx. and biological sciences, and have focused in recent years on Bayesian 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. Should chemical engineering have been framed in terms of creating an artificial chemist? 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. For example, returning to my personal anecdote, we might imagine living our lives in a “societal-scale medical system” that sets up data flows, and data-analysis flows, between doctors and devices positioned in and around human bodies, thereby able to aid human intelligence in making diagnoses and providing care. 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. 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. Thus, just as humans built buildings and bridges before there was civil engineering, humans are proceeding with the building of societal-scale, inference-and-decision-making systems that involve machines, humans and the environment. 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. 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. 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. Computer Science 731 Soda Hall #1776 Berkeley, CA 94720-1776 Phone: (510) 642-3806 Let us begin by considering more carefully what “AI” has been used to refer to, both recently and historically. While a trained human might be able to work all of this out on a case-by-case basis, the issue was that of designing a planetary-scale medical system that could do this without the need for such detailed human oversight. 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. What we’re missing is an engineering discipline with its principles of analysis and design. Boban Zarkovich May 4, 2018 blog 0 Comments, (This article has originally been published on Medium.com.). 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. As exciting as these latter fields appear to be, they cannot yet be viewed as constituting an engineering discipline. 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. of Sciences, a member of the National Academy of Engineering and a His research interests bridge the computational, statistical, cognitive This was largely an academic enterprise. So perhaps we should simply await further progress in domains such as these. However, the mathematical tools are entirely different, relying on concentration, a more general tool that applies to a wide range of problems. 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… Michael Jordan jordan@CS.Berkeley… 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. Let’s broaden our scope, tone down the hype and recognize the serious challenges ahead. Research Expertise and Interest. He is a member of the American Academy of Arts and Sciences. Department of Statistics at the University of California, Berkeley. And, unfortunately, we are not very good at anticipating what the next emerging serious flaw will be. He has been named a Neyman Lecturer and a Medallion Lecturer by the Core Faculty. These artifacts should be built to work as claimed. ACM, ASA, CSS, IEEE, IMS, ISBA and SIAM. 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. 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. I'm most interested in problems that arise when working with non-traditional data types; examples I've worked with include document corpora, graphs, protein structures, phylogenies and multi-media signals. But humans are in fact not very good at some kinds of reasoning — we have our lapses, biases and limitations. Raluca Ada Popa raluca@EECS.Berkeley.EDU. Blogs; Jenkins; Search; PROJECTS. This rebranding is worthy of some scrutiny. The popular Machine Learning blog “FastML” has a recent posting from an “Ask Me Anything” session on Reddit by Mike Jordan. 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. We will need well-thought-out interactions of humans and computers to solve our most pressing problems. Main menu. Michael I. Jordan's homepage at the University of California. There are domains such as music, literature and journalism that are crying out for the emergence of such markets, where data analysis links producers and consumers. The phrase is intoned by technologists, academicians, journalists and venture capitalists alike. 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. Michael I. Jordan Pehong Chen Distinguished Professor Department of EECS Department of Statistics AMP Lab Berkeley AI Research Lab 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). New business models would emerge. Second, and more importantly, success in these domains is neither sufficient nor necessary to solve important IA and II problems. Mou, J. Li, M. Wainwright, P. Bartlett, and M. I. Jordan.arxiv.org/abs/2004.04719, 2020. 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. 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. Fax (510) 642-5775 . However, the current focus on doing AI research via the gathering of data, the deployment of “deep learning” infrastructure, and the demonstration of systems that mimic certain narrowly-defined human skills — with little in the way of emerging explanatory principles — tends to deflect attention from major open problems in classical AI. 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. CORE FACULTY AFFILIATED FACULTY GRADUATE STUDENTS VISITING RESEARCHERS POSTDOCS STAFF UNDERGRADUATE STUDENTS ALUMNI. AdaHessian and PyHessian. Here computation and data are used to create services that augment human intelligence and creativity. Ribbon cutting for new forensic services building in Berkeley County Toggle header content Alchemist. 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.”. He was a professor at MIT from 1988 to 1998. Michael Jordan, an Amazon Scholar, runs the Berkeley side of the collaboration. 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. 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. 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. genetics. It is not hard to pinpoint algorithmic and infrastructure challenges in II systems that are not central themes in human-imitative AI research. Prof. Jordan is a member of the National Academy Institute of Mathematical Statistics. Michael Irwin Jordan (born February 25, 1956) is an American scientist, professor at the University of California, Berkeley and researcher in machine learning, statistics, and artificial intelligence. Fellow of the American Association for the Advancement of Science. One could simply agree to refer to all of this as “AI,” and indeed that is what appears to have happened. “Those are markers for Down syndrome,” she noted, “and your risk has now gone up to 1 in 20.” She further let us know that we could learn whether the fetus in fact had the genetic modification underlying Down syndrome via an amniocentesis. And this must all be done within the context of evolving societal, ethical and legal norms. Consider the following story, which involves humans, computers, data and life-or-death decisions, but where the focus is something other than intelligence-in-silicon fantasies. Michael I. Jordan Professor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC Berkeley Verified email at cs.berkeley.edu - Homepage Moreover, we should embrace the fact that what we are witnessing is the creation of a new branch of engineering. INFORMS On-line: Michael Franklin interview on “The Burgeoning Field of Big Data” October 2, 2014 Scientific American features Carat App in Podcast. and earned his PhD in Cognitive Science in 1985 from the University of About; People; Papers; Projects; Software; Blog; Sponsors; Photos; Login; Le Monde: “Michael Jordan : Une approche transversale est primordiale pour saisir le monde actuel” Posted on December 6, 2015 by AMP Lab. I have interests that span the spectrum from theory to algorithms to applications. 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. CYCLADES: Conflict-free Asynchronous Machine Learning; A Variational Perspective on Accelerated Methods in Optimization; A Linearly-Convergent Stochastic L-BFGS Algorithm AMPLab Publications. Alchemist is an interface between Apache Spark applications and MPI-based libraries for... Anna. And we will want computers to trigger new levels of human creativity, not replace human creativity (whatever that might mean). 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. AMP Lab – UC Berkeley. 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. Excellence Award in 2016, the David E. Rumelhart Prize in 2015 and But amniocentesis was risky — the risk of killing the fetus during the procedure was roughly 1 in 300. Michael Jordan. He received his Masters in Mathematics from Arizona State University, Such II systems can be viewed as not merely providing a service, but as creating markets. And it occurred to me that the development of such principles — which will be needed not only in the medical domain but also in domains such as commerce, transportation and education — were at least as important as those of building AI systems that can dazzle us with their game-playing or sensorimotor skills. Moreover, critically, we did not evolve to perform the kinds of large-scale decision-making that modern II systems must face, nor to cope with the kinds of uncertainty that arise in II contexts. There are two points to make here. 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. Even more polemically: if our goal was to build chemical factories, should we have first created an artificial chemist who would have then worked out how to build a chemical factory? 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. On the other hand, while the humanities and the sciences are essential as we go forward, we should also not pretend that we are talking about something other than an engineering effort of unprecedented scale and scope — society is aiming to build new kinds of artifacts. Joe Hellerstein hellerstein@berkeley.edu. 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. You might want to try starting over from the homepage to see if you can find what you're after from there. But we need to move beyond the particular historical perspectives of McCarthy and Wiener. He is a Fellow of the AAAI, Michael Jordan (aussi appelé par ses initiales MJ), né le 17 février 1963 à Brooklyn (), est un joueur de basket-ball américain ayant évolué dans le championnat nord-américain professionnel de basket-ball, la National Basketball Association (NBA), de 1984 à 2003.Selon la BBC et la NBA, « Michael Jordan est le plus grand joueur de basket-ball de tous les temps » [1], [4]. Bio: Michael I. Jordan is Professor of Computer Science and Statistics at the University of California, Berkeley. 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. Research Description. 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. “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. 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. Biography. We didn’t do the amniocentesis, and a healthy girl was born a few months later. The latest videos from WCBD News 2. We need to solve IA and II problems on their own merits, not as a mere corollary to a human-imitative AI agenda. One of its early applications was to optimize the thrusts of the Apollo spaceships as they headed towards the moon. On the sufficiency side, consider self-driving cars. Artificial Intelligence (AI) is the mantra of the current era. 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. Anna is a low-latency, autoscaling key-value store. Jordan discussed how economic concepts can help advance AI as well as the challenges and opportunities of coordinating decision-making in machine learning. McCarthy, on the other hand, emphasized the ties to logic. They must address the difficulties of sharing data across administrative and competitive boundaries. One of his recent roles is as a Faculty Partner and Co-Founder at AI@The House — a venture fund and accelerator in Berkeley. The term “engineering” is often invoked in a narrow sense — in academia and beyond — with overtones of cold, affectless machinery, and negative connotations of loss of control by humans. Skip to content. Jordan’s appointment is split across the Department of Statistics and the Department of EECS. 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. Summary. Joseph Gonzalez jegonzal@EECS.Berkeley.EDU. But this is not the classical case of the public not understanding the scientists — here the scientists are often as befuddled as the public. When my spouse was pregnant 14 years ago, we had an ultrasound. 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 … As with many phrases that cross over from technical academic fields into general circulation, there is significant misunderstanding accompanying the use of the phrase. Ion Stoica istoica@EECS.Berkeley.EDU. 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. Michael I. Jordan: Artificial Intelligence — The Revolution Hasn’t Happened Yet (This article has originally been published on Medium.com.) Lowcountry Food Bank speaks about receiving donation from NBA legend Michael Jordan nonparametric analysis, probabilistic graphical models, spectral It is those challenges that need to be in the forefront, and in such an effort a focus on human-imitative AI may be a distraction. 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. the ACM/AAAI Allen Newell Award in 2009. methods, kernel machines and applications to problems in distributed computing While related academic fields such as operations research, statistics, pattern recognition, information theory and control theory already existed, and were often inspired by human intelligence (and animal intelligence), these fields were arguably focused on “low-level” signals and decisions. Michael Jordan is a professor of Statistics and Computer Sciences. We need to realize that the current public dialog on AI — which focuses on a narrow subset of industry and a narrow subset of academia — risks blinding us to the challenges and opportunities that are presented by the full scope of AI, IA and II. 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. Courses Stat 210B, Theoretical Statistics, Spring 2017 Stat 210A, Theoretical Statistics, Fall 2015 CS 174, Combinatorics and Discrete Probability, Spring 2015 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). In this regard, as I have emphasized, there is an engineering discipline yet to emerge for the data-focused and learning-focused fields. 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 Being a statistician, I determined to find out where these numbers were coming from. This confluence of ideas and technology trends has been rebranded as “AI” over the past few years. 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. Bio: Michael I. Jordan is Professor of Computer Science and Statistics at the University of California, Berkeley. 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. 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 is Full Professor at UC Berkeley in machine learning, statistics, and artificial intelligence. 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. We do not want to build systems that help us with medical treatments, transportation options and commercial opportunities to find out after the fact that these systems don’t really work — that they make errors that take their toll in terms of human lives and happiness. Michael I. 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