And, unfortunately, we are not very good at anticipating what the next emerging serious flaw will be. computer science, artificial intelligence, computational biology, statistics, machine learning, electrical engineering, applied statistics, optimization. Lowcountry Food Bank speaks about receiving donation from NBA legend Michael Jordan 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. One could simply agree to refer to all of this as “AI,” and indeed that is what appears to have happened. The popular Machine Learning blog “FastML” has a recent posting from an “Ask Me Anything” session on Reddit by Mike Jordan. Charleston, S.C. (WCBD) - Classes begin Monday at the College of Charleston. 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. 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. The phrase is intoned by technologists, academicians, journalists and venture capitalists alike. Raluca Ada Popa raluca@EECS.Berkeley.EDU. 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. The core design goal for Anna is to avoid... Arx. 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. There was a geneticist in the room, and she pointed out some white spots around the heart of the fetus. On linear stochastic approximation: Fine-grained Polyak-Ruppert and non-asymptotic concentration.W. His research interests bridge the computational, statistical, cognitive Main menu. As exciting as these latter fields appear to be, they cannot yet be viewed as constituting an engineering discipline. 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). Such labeling may come as a surprise to optimization or statistics researchers, who wake up to find themselves suddenly referred to as “AI researchers.” But labeling of researchers aside, the bigger problem is that the use of this single, ill-defined acronym prevents a clear understanding of the range of intellectual and commercial issues at play. CORE FACULTY AFFILIATED FACULTY GRADUATE STUDENTS VISITING RESEARCHERS POSTDOCS STAFF UNDERGRADUATE STUDENTS ALUMNI. This was largely an academic enterprise. It appears whatever you were looking for is no longer here or perhaps wasn't here to begin with. Research Description. 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]. “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. 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 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. 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. 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. 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. He is a Did civil engineering develop by envisaging the creation of an artificial carpenter or bricklayer? For such technology to be realized, a range of engineering problems will need to be solved that may have little relationship to human competencies (or human lack-of-competencies). Research Expertise and Interest. New business models would emerge. Most of what is being called “AI” today, particularly in the public sphere, is what has been called “Machine Learning” (ML) for the past several decades. Michael Jordan jordan@CS.Berkeley… 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 blog post will teach you an algorithm which quantifies the uncertainty of any classifier on any dataset in finite samples for free.The algorithm, called RAPS, modifies the classifier to output a predictive set containing the true label with a user-specified probability, such as 90%.This coverage level is formally guaranteed even when the dataset has a finite number of samples. Whether or not we come to understand “intelligence” any time soon, we do have a major challenge on our hands in bringing together computers and humans in ways that enhance human life. 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. 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. 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? MICHAEL JORDAN RESEARCH. Excellence Award in 2016, the David E. Rumelhart Prize in 2015 and 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.”. 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. But we need to move beyond the particular historical perspectives of McCarthy and Wiener. But I also noticed that the imaging machine used in our test had a few hundred more pixels per square inch than the machine used in the UK study. 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. 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. 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. Michael I. Jordan is a professor at Berkeley, and one of the most influential people in the history of machine learning, statistics, and artificial intelligence. It is not hard to pinpoint algorithmic and infrastructure challenges in II systems that are not central themes in human-imitative AI research. 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. Biography. And this happened day after day until it somehow got fixed. In this regard, as I have emphasized, there is an engineering discipline yet to emerge for the data-focused and learning-focused fields. 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. I am a quantitative researcher at Citadel Securities.My research covers machine learning, statistics, and optimization. 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. 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. 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. 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. INFORMS On-line: Michael Franklin interview on “The Burgeoning Field of Big Data” October 2, 2014 Scientific American features Carat App in Podcast. Michael I. Jordan's homepage at the University of California. Mou, J. Li, M. Wainwright, P. Bartlett, and M. I. Jordan.arxiv.org/abs/2004.04719, 2020. Whereas civil engineering and chemical engineering were built on physics and chemistry, this new engineering discipline will be built on ideas that the preceding century gave substance to — ideas such as “information,” “algorithm,” “data,” “uncertainty,” “computing,” “inference,” and “optimization.” Moreover, since much of the focus of the new discipline will be on data from and about humans, its development will require perspectives from the social sciences and humanities. methods, kernel machines and applications to problems in distributed computing Skip to content. 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. 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. Michael Jordan is a professor of Statistics and Computer Sciences. Such II systems can be viewed as not merely providing a service, but as creating markets. 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. This confluence of ideas and technology trends has been rebranded as “AI” over the past few years. 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. When my spouse was pregnant 14 years ago, we had an ultrasound. 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. and earned his PhD in Cognitive Science in 1985 from the University of Joseph Gonzalez jegonzal@EECS.Berkeley.EDU. 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. One of its early applications was to optimize the thrusts of the Apollo spaceships as they headed towards the moon. Michael Jordan. 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. 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. 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. 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. 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). And we will want computers to trigger new levels of human creativity, not replace human creativity (whatever that might mean). “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. Since the 1960s much progress has been made, but it has arguably not come about from the pursuit of human-imitative AI. Computer Science 731 Soda Hall #1776 Berkeley, CA 94720-1776 Phone: (510) 642-3806 Michael Jordan is Full Professor at UC Berkeley in machine learning, statistics, and artificial intelligence. 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. But this is not the classical case of the public not understanding the scientists — here the scientists are often as befuddled as the public. 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. Historically, the phrase “AI” was coined in the late 1950’s to refer to the heady aspiration of realizing in software and hardware an entity possessing human-level intelligence. Such an argument has little historical precedent. AdaHessian and PyHessian. Bio: Michael I. Jordan is Professor of Computer Science and Statistics at the University of California, Berkeley. 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. Institute of Mathematical Statistics. Like split-conformal prediction (see the last blog post), RCPS achieve this by using a small holdout dataset. AI4ALL is a nonprofit dedicated to increasing diversity and inclusion in AI education, research, development, and policy. Department of Electrical Engineering and Computer Science and the 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. 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. McCarthy, on the other hand, emphasized the ties to logic. And this must all be done within the context of evolving societal, ethical and legal norms. Michael I. Jordan is the Pehong Chen Distinguished Professor in the But an engineering discipline can be what we want it to be. Prof. Jordan is a member of the National Academy Masks and social distancing will be required on campus. We will need well-thought-out interactions of humans and computers to solve our most pressing problems. Blogs; Jenkins; Search; People. 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. 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 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. 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. A search engine can be viewed as an example of IA (it augments human memory and factual knowledge), as can natural language translation (it augments the ability of a human to communicate). And I would like to add a special thanks to Cameron Baradar at The House, who first encouraged me to contemplate writing such a piece. Ion Stoica istoica@EECS.Berkeley.EDU. systems, natural language processing, signal processing and statistical As datasets and computing resources grew rapidly over the ensuing two decades, it became clear that ML would soon power not only Amazon but essentially any company in which decisions could be tied to large-scale data. 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. Michael I. Jordan Pehong Chen Distinguished Professor Department of EECS Department of Statistics AMP Lab Berkeley AI Research Lab University of California, Berkeley. He is one of the leading figures in machine learning, and in 2016 Science reported him as the world's most influential computer scientist. Second, and more importantly, success in these domains is neither sufficient nor necessary to solve important IA and II problems. Bio: Michael I. Jordan is Professor of Computer Science and Statistics at the University of California, Berkeley. 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. 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. 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. 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 artifacts should be built to work as claimed. Artificial Intelligence (AI) is the mantra of the current era. Michael JORDAN, Professor (Full) of University of California, Berkeley, CA (UCB) | Read 795 publications | Contact Michael JORDAN Wiener had coined “cybernetics” to refer to his own vision of intelligent systems — a vision that was closely tied to operations research, statistics, pattern recognition, information theory and control theory. Artificial Intelligence (AI) is the mantra of the current era. 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. Let’s broaden our scope, tone down the hype and recognize the serious challenges ahead. In terms of impact on the real world, ML is the real thing, and not just recently. AMP Lab – UC Berkeley. Michael I. Jordan Professor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC Berkeley Verified email at cs.berkeley.edu - Homepage Ribbon cutting for new forensic services building in Berkeley County Toggle header content and biological sciences, and have focused in recent years on Bayesian They must address the difficulties of sharing data across administrative and competitive boundaries. 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. Search UC Berkeley Directory . Computing-based generation of sounds and images serves as a palette and creativity enhancer for artists. Moreover, we should embrace the fact that what we are witnessing is the creation of a new branch of engineering. But amniocentesis was risky — the risk of killing the fetus during the procedure was roughly 1 in 300. 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… There are two points to make here. 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. 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. 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