He is one of the leading figures in machine learning, and in 2016 Science reported him as the world's most influential computer scientist. 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. Courses Stat 210B, Theoretical Statistics, Spring 2017 Stat 210A, Theoretical Statistics, Fall 2015 CS 174, Combinatorics and Discrete Probability, Spring 2015 Alchemist. 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. 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. 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. 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. AMPLab Publications. Let us begin by considering more carefully what “AI” has been used to refer to, both recently and historically. Search UC Berkeley Directory . Such II systems can be viewed as not merely providing a service, but as creating markets. The core design goal for Anna is to avoid... Arx. 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. What we’re missing is an engineering discipline with its principles of analysis and design. 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. 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. 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. 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. 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. 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. New business models would emerge. 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. When my spouse was pregnant 14 years ago, we had an ultrasound. Jordan’s appointment is split across the Department of Statistics and the Department of EECS. 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. However, the mathematical tools are entirely different, relying on concentration, a more general tool that applies to a wide range of problems. The popular Machine Learning blog “FastML” has a recent posting from an “Ask Me Anything” session on Reddit by Mike Jordan. member of the American Academy of Arts and Sciences. 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. the ACM/AAAI Allen Newell Award in 2009. This was largely an academic enterprise. 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. These artifacts should be built to work as claimed. Charleston, S.C. (WCBD) - Classes begin Monday at the College of Charleston. CORE FACULTY AFFILIATED FACULTY GRADUATE STUDENTS VISITING RESEARCHERS POSTDOCS STAFF UNDERGRADUATE STUDENTS ALUMNI. Michael I. Jordan Professor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC Berkeley Verified email at cs.berkeley.edu - Homepage On the sufficiency side, consider self-driving cars. 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. 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. 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]. nonparametric analysis, probabilistic graphical models, spectral But an engineering discipline can be what we want it to be. Alchemist is an interface between Apache Spark applications and MPI-based libraries for... Anna. We will need well-thought-out interactions of humans and computers to solve our most pressing problems. 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. (This state of affairs is surely, however, only temporary; the pendulum swings more in AI than in most fields.). It appears whatever you were looking for is no longer here or perhaps wasn't here to begin with. They must address the difficulties of sharing data across administrative and competitive boundaries. Phone (510) 642-3806. Lowcountry Food Bank speaks about receiving donation from NBA legend Michael Jordan 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. So perhaps we should simply await further progress in domains such as these. 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. 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. 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. We need to solve IA and II problems on their own merits, not as a mere corollary to a human-imitative AI agenda. California, San Diego. Mou, J. Li, M. Wainwright, P. Bartlett, and M. I. Jordan.arxiv.org/abs/2004.04719, 2020. As with many phrases that cross over from technical academic fields into general circulation, there is significant misunderstanding accompanying the use of the phrase. Let’s broaden our scope, tone down the hype and recognize the serious challenges ahead. Prof. Jordan is a member of the National Academy 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, an Amazon Scholar, runs the Berkeley side of the collaboration. 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. AMP Lab – UC Berkeley. 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? 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. INFORMS On-line: Michael Franklin interview on “The Burgeoning Field of Big Data” October 2, 2014 Scientific American features Carat App in Podcast. Here computation and data are used to create services that augment human intelligence and creativity. Such an argument has little historical precedent. 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 we need to move beyond the particular historical perspectives of McCarthy and Wiener. Department of Statistics at the University of California, 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. Bio: Michael I. Jordan is Professor of Computer Science and Statistics at the University of California, Berkeley. His research interests bridge the computational, statistical, cognitive Joe Hellerstein hellerstein@berkeley.edu. 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. computer science, artificial intelligence, computational biology, statistics, machine learning, electrical engineering, applied statistics, optimization. “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. Anna is a low-latency, autoscaling key-value store. Raluca Ada Popa raluca@EECS.Berkeley.EDU. And, unfortunately, it distracts us. 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. 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 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. 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. 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. Computer Science 731 Soda Hall #1776 Berkeley, CA 94720-1776 Phone: (510) 642-3806 But this is not the classical case of the public not understanding the scientists — here the scientists are often as befuddled as the public. Research Description. 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 phrase is intoned by technologists, academicians, journalists and venture capitalists alike. MICHAEL JORDAN RESEARCH. Like split-conformal prediction (see the last blog post), RCPS achieve this by using a small holdout dataset. And this happened day after day until it somehow got fixed. 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. On linear stochastic approximation: Fine-grained Polyak-Ruppert and non-asymptotic concentration.W. 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. Biography. Michael I. Jordan Pehong Chen Distinguished Professor Department of EECS Department of Statistics AMP Lab Berkeley AI Research Lab University of California, Berkeley. Michael I. Jordan is the Pehong Chen Distinguished Professor in the 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? Michael I. Jordan's homepage at the University of California. and earned his PhD in Cognitive Science in 1985 from the University of We didn’t do the amniocentesis, and a healthy girl was born a few months later. systems, natural language processing, signal processing and statistical 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). Moreover, we should embrace the fact that what we are witnessing is the creation of a new branch of engineering. 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. 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.”. 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. 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. 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. 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. And, unfortunately, we are not very good at anticipating what the next emerging serious flaw will be. 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. 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. 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). 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. Michael Jordan | Berkeley, California | Professor at UC Berkeley | 245 connections | See Michael's complete profile on Linkedin and connect 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. Research Expertise and Interest. Fax (510) 642-5775 . One of its early applications was to optimize the thrusts of the Apollo spaceships as they headed towards the moon. 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. The latest videos from WCBD News 2. 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. 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 Jordan is a professor of Statistics and Computer Sciences. McCarthy, on the other hand, emphasized the ties to logic. ACM, ASA, CSS, IEEE, IMS, ISBA and SIAM. 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. Michael I. Jordan: Artificial Intelligence — The Revolution Hasn’t Happened Yet (This article has originally been published on Medium.com.) He is one of the leading figures in machine learning, and in 2016 Science reported him as the world's most influential computer scientist. 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). There was a geneticist in the room, and she pointed out some white spots around the heart of the fetus. 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. National Science Foundation Expeditions in Computing. Since the 1960s much progress has been made, but it has arguably not come about from the pursuit of human-imitative AI. 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. Michael Jordan. 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. Department of Electrical Engineering and Computer Science and the Ribbon cutting for new forensic services building in Berkeley County Toggle header content Computing-based generation of sounds and images serves as a palette and creativity enhancer for artists. There is a different narrative that one can tell about the current era. Artificial Intelligence (AI) is the mantra of the current era. I am a quantitative researcher at Citadel Securities.My research covers machine learning, statistics, and optimization. He received his Masters in Mathematics from Arizona State University, Michael JORDAN, Professor (Full) of University of California, Berkeley, CA (UCB) | Read 795 publications | Contact Michael JORDAN There are two points to make here. 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. 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 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 … And we will want computers to trigger new levels of human creativity, not replace human creativity (whatever that might mean). 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. I have interests that span the spectrum from theory to algorithms to applications. 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. Skip to content. and biological sciences, and have focused in recent years on Bayesian 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. It is not hard to pinpoint algorithmic and infrastructure challenges in II systems that are not central themes in human-imitative AI research. He has been named a Neyman Lecturer and a Medallion Lecturer by the Of course, classical human-imitative AI problems remain of great interest as well. 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. 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). II systems require the ability to manage distributed repositories of knowledge that are rapidly changing and are likely to be globally incoherent. 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. The phrase is intoned by technologists, academicians, journalists and venture capitalists alike. Boban Zarkovich May 4, 2018 blog 0 Comments, (This article has originally been published on Medium.com.). Jordan discussed how economic concepts can help advance AI as well as the challenges and opportunities of coordinating decision-making in machine learning. 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. genetics. 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. 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. 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. of Sciences, a member of the National Academy of Engineering and a 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. 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. 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. 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. 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). 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. Excellence Award in 2016, the David E. Rumelhart Prize in 2015 and 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… In this regard, as I have emphasized, there is an engineering discipline yet to emerge for the data-focused and learning-focused fields.