Raluca Ada Popa raluca@EECS.Berkeley.EDU. 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. 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. Should chemical engineering have been framed in terms of creating an artificial chemist? He received his Masters in Mathematics from Arizona State University, Like split-conformal prediction (see the last blog post), RCPS achieve this by using a small holdout dataset. We need to solve IA and II problems on their own merits, not as a mere corollary to a human-imitative AI agenda. AMPLab Publications. 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. Alchemist is an interface between Apache Spark applications and MPI-based libraries for... Anna. 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. Jordan discussed how economic concepts can help advance AI as well as the challenges and opportunities of coordinating decision-making in machine learning. Research Expertise and Interest. He was a professor at MIT from 1988 to 1998. Courses Stat 210B, Theoretical Statistics, Spring 2017 Stat 210A, Theoretical Statistics, Fall 2015 CS 174, Combinatorics and Discrete Probability, Spring 2015 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. genetics. 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. 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. 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). 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. CORE FACULTY AFFILIATED FACULTY GRADUATE STUDENTS VISITING RESEARCHERS POSTDOCS STAFF UNDERGRADUATE STUDENTS ALUMNI. McCarthy, on the other hand, emphasized the ties to logic. 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. Such II systems can be viewed as not merely providing a service, but as creating markets. jordan@cs.berkeley.edu. 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. Mou, J. Li, M. Wainwright, P. Bartlett, and M. I. Jordan.arxiv.org/abs/2004.04719, 2020. Summary. One could simply agree to refer to all of this as “AI,” and indeed that is what appears to have happened. 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. 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. Computer Science 731 Soda Hall #1776 Berkeley, CA 94720-1776 Phone: (510) 642-3806 Michael Jordan, an Amazon Scholar, runs the Berkeley side of the collaboration. 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. This rebranding is worthy of some scrutiny. It appears whatever you were looking for is no longer here or perhaps wasn't here to begin with. 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. 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. Phone (510) 642-3806. AI4ALL is a nonprofit dedicated to increasing diversity and inclusion in AI education, research, development, and policy. Bio: Michael I. Jordan is Professor of Computer Science and Statistics at the University of California, Berkeley. ACM, ASA, CSS, IEEE, IMS, ISBA and SIAM. 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. 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. 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 this happened day after day until it somehow got fixed. Michael Jordan is a professor of Statistics and Computer Sciences. 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… 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. Ion Stoica istoica@EECS.Berkeley.EDU. 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. Michael Jordan is Full Professor at UC Berkeley in machine learning, statistics, and artificial intelligence. 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. You might want to try starting over from the homepage to see if you can find what you're after from there. 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. (This state of affairs is surely, however, only temporary; the pendulum swings more in AI than in most fields.). He has been named a Neyman Lecturer and a Medallion Lecturer by the 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. 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. 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.”. As exciting as these latter fields appear to be, they cannot yet be viewed as constituting an engineering discipline. 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. This was largely an academic enterprise. 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. 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. Blogs; Jenkins; Search; PROJECTS. 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 (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]. Artificial Intelligence (AI) is the mantra of the current era. He received the IJCAI Research They must address the difficulties of sharing data across administrative and competitive boundaries. Moreover, we should embrace the fact that what we are witnessing is the creation of a new branch of engineering. Michael Jordan jordan@CS.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. Of course, classical human-imitative AI problems remain of great interest as well. 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. 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. Masks and social distancing will be required on campus. We didn’t do the amniocentesis, and a healthy girl was born a few months later. Prof. Jordan is a member of the National Academy 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. systems, natural language processing, signal processing and statistical nonparametric analysis, probabilistic graphical models, spectral On the sufficiency side, consider self-driving cars. One of his recent roles is as a Faculty Partner and Co-Founder at AI@The House — a venture fund and accelerator in Berkeley. Michael I. Jordan is the Pehong Chen Distinguished Professor in the New business models would emerge. 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 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. methods, kernel machines and applications to problems in distributed computing Alchemist. 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. Lowcountry Food Bank speaks about receiving donation from NBA legend Michael Jordan Fellow of the American Association for the Advancement of Science. 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). Michael I. Jordan's homepage at the University of California. Michael I. Jordan Pehong Chen Distinguished Professor Department of EECS Department of Statistics AMP Lab Berkeley AI Research Lab University of California, 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. 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. Second, and more importantly, success in these domains is neither sufficient nor necessary to solve important IA and II problems. 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. Jordan’s appointment is split across the Department of Statistics and the Department of EECS. 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). 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. In this regard, as I have emphasized, there is an engineering discipline yet to emerge for the data-focused and learning-focused fields. I have interests that span the spectrum from theory to algorithms to applications. 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). But this is not the classical case of the public not understanding the scientists — here the scientists are often as befuddled as the public. He is a Fellow of the AAAI, and biological sciences, and have focused in recent years on Bayesian “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. 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. 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. 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 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. It is not hard to pinpoint algorithmic and infrastructure challenges in II systems that are not central themes in human-imitative AI research. There is a different narrative that one can tell about the current era. MICHAEL JORDAN RESEARCH. 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. But we need to move beyond the particular historical perspectives of McCarthy and Wiener. 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. 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. He is one of the leading figures in machine learning, and in 2016 Science reported him as the world's most influential computer scientist. On linear stochastic approximation: Fine-grained Polyak-Ruppert and non-asymptotic concentration.W. Michael Jordan. II systems require the ability to manage distributed repositories of knowledge that are rapidly changing and are likely to be globally incoherent. 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. Blogs; Jenkins; Search; People. But humans are in fact not very good at some kinds of reasoning — we have our lapses, biases and limitations. The popular Machine Learning blog “FastML” has a recent posting from an “Ask Me Anything” session on Reddit by Mike Jordan. 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. 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. Here computation and data are used to create services that augment human intelligence and creativity. 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. 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 artifacts should be built to work as claimed. 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. 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. The core design goal for Anna is to avoid... Arx. 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. 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. 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. computer science, artificial intelligence, computational biology, statistics, machine learning, electrical engineering, applied statistics, optimization. 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. 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 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. Joseph Gonzalez jegonzal@EECS.Berkeley.EDU. Michael I. Jordan Professor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC Berkeley Verified email at cs.berkeley.edu - Homepage 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 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. But an engineering discipline can be what we want it to be. 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, Professor (Full) of University of California, Berkeley, CA (UCB) | Read 795 publications | Contact Michael JORDAN In terms of impact on the real world, ML is the real thing, and not just recently. The phrase is intoned by technologists, academicians, journalists and venture capitalists alike. 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. and earned his PhD in Cognitive Science in 1985 from the University of Department of Statistics at the University of California, Berkeley. Fax (510) 642-5775 . And, unfortunately, it distracts us. In the current era, we have a real opportunity to conceive of something historically new — a human-centric engineering discipline. Did civil engineering develop by envisaging the creation of an artificial carpenter or bricklayer? 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. “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. California, San Diego. What we’re missing is an engineering discipline with its principles of analysis and design. member of the American Academy of Arts and Sciences. 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 … 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. 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 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. But amniocentesis was risky — the risk of killing the fetus during the procedure was roughly 1 in 300. 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. 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. 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. Michael I. Jordan: Artificial Intelligence — The Revolution Hasn’t Happened Yet (This article has originally been published on Medium.com.) And we will want computers to trigger new levels of human creativity, not replace human creativity (whatever that might mean). 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? 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. AdaHessian and PyHessian. Bio: Michael I. Jordan is Professor of Computer Science and Statistics at the University of California, Berkeley. 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. AMP Lab – UC Berkeley. We will need well-thought-out interactions of humans and computers to solve our most pressing problems. Michael Jordan | Berkeley, California | Professor at UC Berkeley | 245 connections | See Michael's complete profile on Linkedin and connect 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. He is a This confluence of ideas and technology trends has been rebranded as “AI” over the past few years. INFORMS On-line: Michael Franklin interview on “The Burgeoning Field of Big Data” October 2, 2014 Scientific American features Carat App in Podcast. 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. CYCLADES: Conflict-free Asynchronous Machine Learning; A Variational Perspective on Accelerated Methods in Optimization; A Linearly-Convergent Stochastic L-BFGS Algorithm