Michael Jordan, an Amazon Scholar, runs the Berkeley side of the collaboration. 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 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. The popular Machine Learning blog “FastML” has a recent posting from an “Ask Me Anything” session on Reddit by Mike Jordan. 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. It appears whatever you were looking for is no longer here or perhaps wasn't here to begin with. 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). genetics. 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. And this happened day after day until it somehow got fixed. But amniocentesis was risky — the risk of killing the fetus during the procedure was roughly 1 in 300. Alchemist is an interface between Apache Spark applications and MPI-based libraries for... Anna. Here computation and data are used to create services that augment human intelligence and creativity. Mou, J. Li, M. Wainwright, P. Bartlett, and M. I. Jordan.arxiv.org/abs/2004.04719, 2020. 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 California, San Diego. He is a Fellow of the AAAI, 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. Bio: Michael I. Jordan is Professor of Computer Science and Statistics at the University of California, Berkeley. So perhaps we should simply await further progress in domains such as these. “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 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. 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. He is one of the leading figures in machine learning, and in 2016 Science reported him as the world's most influential computer scientist. In this regard, as I have emphasized, there is an engineering discipline yet to emerge for the data-focused and learning-focused fields. Jordan’s appointment is split across the Department of Statistics and the Department of EECS. Let us begin by considering more carefully what “AI” has been used to refer to, both recently and historically. Summary. Anna is a low-latency, autoscaling key-value store. 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. The phrase is intoned by technologists, academicians, journalists and venture capitalists alike. 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. 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. In terms of impact on the real world, ML is the real thing, and not just recently. 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. 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. There is a different narrative that one can tell about the current era. 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. 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. He was a professor at MIT from 1988 to 1998. Michael Jordan | Berkeley, California | Professor at UC Berkeley | 245 connections | See Michael's complete profile on Linkedin and connect 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). 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. nonparametric analysis, probabilistic graphical models, spectral CORE FACULTY AFFILIATED FACULTY GRADUATE STUDENTS VISITING RESEARCHERS POSTDOCS STAFF UNDERGRADUATE STUDENTS ALUMNI. 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? of Sciences, a member of the National Academy of Engineering and a The phrase is intoned by technologists, academicians, journalists and venture capitalists alike. But we need to move beyond the particular historical perspectives of McCarthy and Wiener. Such an argument has little historical precedent. In the current era, we have a real opportunity to conceive of something historically new — a human-centric engineering discipline. 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. Moreover, we should embrace the fact that what we are witnessing is the creation of a new branch of engineering. member of the American Academy of Arts and Sciences. 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. 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. 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. Department of Electrical Engineering and Computer Science and the 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. Such II systems can be viewed as not merely providing a service, but as creating markets. systems, natural language processing, signal processing and statistical Fax (510) 642-5775 . But an engineering discipline can be what we want it to be. jordan@cs.berkeley.edu. 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. They must address the difficulties of sharing data across administrative and competitive boundaries. There was a geneticist in the room, and she pointed out some white spots around the heart of the fetus. 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. 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. 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. He is a 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. 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. I am a quantitative researcher at Citadel Securities.My research covers machine learning, statistics, and optimization. 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. He received the IJCAI Research 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. Michael Jordan is Full Professor at UC Berkeley in machine learning, statistics, and artificial intelligence. Being a statistician, I determined to find out where these numbers were coming from. Phone (510) 642-3806. Masks and social distancing will be required on campus. 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. AMP Lab – UC Berkeley. Indeed, that ML would grow into massive industrial relevance was already clear in the early 1990s, and by the turn of the century forward-looking companies such as Amazon were already using ML throughout their business, solving mission-critical back-end problems in fraud detection and supply-chain prediction, and building innovative consumer-facing services such as recommendation systems. 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. Michael I. Jordan is the Pehong Chen Distinguished Professor in the Blogs; Jenkins; Search; PROJECTS. On linear stochastic approximation: Fine-grained Polyak-Ruppert and non-asymptotic concentration.W. 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. 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. One could simply agree to refer to all of this as “AI,” and indeed that is what appears to have happened. 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. We didn’t do the amniocentesis, and a healthy girl was born a few months later. 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. Lowcountry Food Bank speaks about receiving donation from NBA legend Michael Jordan Skip to content. Let’s broaden our scope, tone down the hype and recognize the serious challenges ahead. 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. 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]. 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. AI4ALL is a nonprofit dedicated to increasing diversity and inclusion in AI education, research, development, and policy. 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. His research interests bridge the computational, statistical, cognitive 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. Raluca Ada Popa raluca@EECS.Berkeley.EDU. ML is an algorithmic field that blends ideas from statistics, computer science and many other disciplines (see below) to design algorithms that process data, make predictions and help make decisions. Michael Jordan jordan@CS.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. McCarthy, on the other hand, emphasized the ties to logic. 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. We need to solve IA and II problems on their own merits, not as a mere corollary to a human-imitative AI agenda. Computer Science 731 Soda Hall #1776 Berkeley, CA 94720-1776 Phone: (510) 642-3806 What we’re missing is an engineering discipline with its principles of analysis and design. Michael I. Jordan: Artificial Intelligence — The Revolution Hasn’t Happened Yet (This article has originally been published on Medium.com.) ACM, ASA, CSS, IEEE, IMS, ISBA and SIAM. Excellence Award in 2016, the David E. Rumelhart Prize in 2015 and Bio: Michael I. Jordan is Professor of Computer Science and Statistics at the University of California, Berkeley. 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. 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. 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. 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. This rebranding is worthy of some scrutiny. “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. We will need well-thought-out interactions of humans and computers to solve our most pressing problems. 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 … 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. 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. And, unfortunately, we are not very good at anticipating what the next emerging serious flaw will be. II systems require the ability to manage distributed repositories of knowledge that are rapidly changing and are likely to be globally incoherent. INFORMS On-line: Michael Franklin interview on “The Burgeoning Field of Big Data” October 2, 2014 Scientific American features Carat App in Podcast. However, the mathematical tools are entirely different, relying on concentration, a more general tool that applies to a wide range of problems. Michael Jordan is a professor of Statistics and Computer Sciences. 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. Department of Statistics at the University of California, Berkeley. Michael I. Jordan Pehong Chen Distinguished Professor Department of EECS Department of Statistics AMP Lab Berkeley AI Research Lab University of California, Berkeley. Did civil engineering develop by envisaging the creation of an artificial carpenter or bricklayer? 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. 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. 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? Since the 1960s much progress has been made, but it has arguably not come about from the pursuit of human-imitative AI. These artifacts should be built to work as claimed. This was largely an academic enterprise. National Science Foundation Expeditions in Computing. 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. 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. Search UC Berkeley Directory . One of his recent roles is as a Faculty Partner and Co-Founder at AI@The House — a venture fund and accelerator in Berkeley. 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. 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. But humans are in fact not very good at some kinds of reasoning — we have our lapses, biases and limitations. 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). Institute of Mathematical Statistics. 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. Core Faculty. 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. Research Expertise and Interest. 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. (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. 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 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. 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