and biological sciences, and have focused in recent years on Bayesian Focusing narrowly on human-imitative AI prevents an appropriately wide range of voices from being heard. And, unfortunately, we are not very good at anticipating what the next emerging serious flaw will be. And we will want computers to trigger new levels of human creativity, not replace human creativity (whatever that might mean). 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. 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. 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. AMP Lab – UC Berkeley. Bio: Michael I. Jordan is Professor of Computer Science and Statistics at the University of California, Berkeley. Michael Jordan. 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. One of his recent roles is as a Faculty Partner and Co-Founder at AI@The House — a venture fund and accelerator in Berkeley. 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. Lowcountry Food Bank speaks about receiving donation from NBA legend Michael Jordan The phrase is intoned by technologists, academicians, journalists and venture capitalists alike. 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. 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. National Science Foundation Expeditions in Computing. 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.”. 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. 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. 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 … 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. Let us begin by considering more carefully what “AI” has been used to refer to, both recently and historically. He is one of the leading figures in machine learning, and in 2016 Science reported him as the world's most influential computer scientist. 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. Here computation and data are used to create services that augment human intelligence and creativity. Michael I. Jordan's homepage at the University of California. 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 But this is not the classical case of the public not understanding the scientists — here the scientists are often as befuddled as the public. 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). Second, and more importantly, success in these domains is neither sufficient nor necessary to solve important IA and II problems. 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. But humans are in fact not very good at some kinds of reasoning — we have our lapses, biases and limitations. Fellow of the American Association for the Advancement of Science. 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. 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. 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. Jordan’s appointment is split across the Department of Statistics and the Department of EECS. New business models would emerge. The phrase is intoned by technologists, academicians, journalists and venture capitalists alike. So perhaps we should simply await further progress in domains such as these. 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. And this happened day after day until it somehow got fixed. Moreover, we should embrace the fact that what we are witnessing is the creation of a new branch of engineering. Excellence Award in 2016, the David E. Rumelhart Prize in 2015 and 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. Phone (510) 642-3806. 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. He has been named a Neyman Lecturer and a Medallion Lecturer by the MICHAEL JORDAN RESEARCH. What we’re missing is an engineering discipline with its principles of analysis and design. Boban Zarkovich May 4, 2018 blog 0 Comments, (This article has originally been published on Medium.com.). On the sufficiency side, consider self-driving cars. 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. “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. We need to solve IA and II problems on their own merits, not as a mere corollary to a human-imitative AI agenda. 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. In terms of impact on the real world, ML is the real thing, and not just recently. I am a quantitative researcher at Citadel Securities.My research covers machine learning, statistics, and optimization. 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 Jordan | Berkeley, California | Professor at UC Berkeley | 245 connections | See Michael's complete profile on Linkedin and connect AdaHessian and PyHessian. (This state of affairs is surely, however, only temporary; the pendulum swings more in AI than in most fields.). 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. 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. jordan@cs.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 In the current era, we have a real opportunity to conceive of something historically new — a human-centric engineering discipline. member of the American Academy of Arts and Sciences. Since the 1960s much progress has been made, but it has arguably not come about from the pursuit of human-imitative AI. 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. 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. 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. genetics. Computer Science 731 Soda Hall #1776 Berkeley, CA 94720-1776 Phone: (510) 642-3806 The core design goal for Anna is to avoid... Arx. 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. 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]. 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. 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? INFORMS On-line: Michael Franklin interview on “The Burgeoning Field of Big Data” October 2, 2014 Scientific American features Carat App in Podcast. He is a 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. Let’s broaden our scope, tone down the hype and recognize the serious challenges ahead. 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. One of its early applications was to optimize the thrusts of the Apollo spaceships as they headed towards the moon. 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. of Sciences, a member of the National Academy of Engineering and a In this regard, as I have emphasized, there is an engineering discipline yet to emerge for the data-focused and learning-focused fields. Like split-conformal prediction (see the last blog post), RCPS achieve this by using a small holdout dataset. California, San Diego. 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). Should chemical engineering have been framed in terms of creating an artificial chemist? There is a different narrative that one can tell about the current era. methods, kernel machines and applications to problems in distributed computing Michael I. Jordan: Artificial Intelligence — The Revolution Hasn’t Happened Yet (This article has originally been published on Medium.com.) Core Faculty. 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. 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. Mou, J. Li, M. Wainwright, P. Bartlett, and M. I. Jordan.arxiv.org/abs/2004.04719, 2020. 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. Ribbon cutting for new forensic services building in Berkeley County Toggle header content Being a statistician, I determined to find out where these numbers were coming from. This rebranding is worthy of some scrutiny. Artificial Intelligence (AI) is the mantra of the current era. 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. Emails: EECS Address: University of California, Berkeley EECS Department 387 Soda Hall #1776 Berkeley, CA 94720-1776 Statistics Address: University of California, Berkeley Statistics Department 427 Evans Hall #3860 Berkeley… Michael Jordan is a professor of Statistics and Computer Sciences. 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. The popular Machine Learning blog “FastML” has a recent posting from an “Ask Me Anything” session on Reddit by Mike Jordan. 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. Jordan discussed how economic concepts can help advance AI as well as the challenges and opportunities of coordinating decision-making in machine learning. 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. Of course, classical human-imitative AI problems remain of great interest as well. But an engineering discipline can be what we want it to be. As exciting as these latter fields appear to be, they cannot yet be viewed as constituting an engineering discipline. 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. 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. Artificial Intelligence (AI) is the mantra of the current era. nonparametric analysis, probabilistic graphical models, spectral We didn’t do the amniocentesis, and a healthy girl was born a few months later. 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. Ion Stoica istoica@EECS.Berkeley.EDU. And, unfortunately, it distracts us. 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. McCarthy, on the other hand, emphasized the ties to logic. 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. 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. 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). 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. Research Expertise and Interest. II systems require the ability to manage distributed repositories of knowledge that are rapidly changing and are likely to be globally incoherent. Main menu. Search UC Berkeley Directory . Department of Statistics at the University of California, Berkeley. 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 Jordan is Full Professor at UC Berkeley in machine learning, statistics, and artificial intelligence. Michael Jordan jordan@CS.Berkeley… 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. He received the IJCAI Research 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. 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. I have interests that span the spectrum from theory to algorithms to applications. His research interests bridge the computational, statistical, cognitive 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. He is a Fellow of the AAAI, systems, natural language processing, signal processing and statistical 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. 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. 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. 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. Joe Hellerstein hellerstein@berkeley.edu. Department of Electrical Engineering and Computer Science and the 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. 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. and earned his PhD in Cognitive Science in 1985 from the University of Charleston, S.C. (WCBD) - Classes begin Monday at the College of Charleston. 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. 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? 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? 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. But amniocentesis was risky — the risk of killing the fetus during the procedure was roughly 1 in 300. 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. 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. 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. One could simply agree to refer to all of this as “AI,” and indeed that is what appears to have happened. On linear stochastic approximation: Fine-grained Polyak-Ruppert and non-asymptotic concentration.W. 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. Blogs; Jenkins; Search; People. Anna is a low-latency, autoscaling key-value store. “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. Research Description. Biography. It appears whatever you were looking for is no longer here or perhaps wasn't here to begin with. But we need to move beyond the particular historical perspectives of McCarthy and Wiener. 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