INFORMS On-line: Michael Franklin interview on “The Burgeoning Field of Big Data” October 2, 2014 Scientific American features Carat App in Podcast. Institute of Mathematical Statistics. 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. 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. While services of this kind could conceivably involve high-level reasoning and thought, currently they don’t — they mostly perform various kinds of string-matching and numerical operations that capture patterns that humans can make use of. 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). Joe Hellerstein hellerstein@berkeley.edu. In the current era, we have a real opportunity to conceive of something historically new — a human-centric engineering discipline. 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. 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. 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, Professor (Full) of University of California, Berkeley, CA (UCB) | Read 795 publications | Contact Michael JORDAN Bio: Michael I. Jordan is Professor of Computer Science and Statistics at the University of California, Berkeley. However, the mathematical tools are entirely different, relying on concentration, a more general tool that applies to a wide range of problems. Mou, J. Li, M. Wainwright, P. Bartlett, and M. I. Jordan.arxiv.org/abs/2004.04719, 2020. Department of Statistics at the University of California, Berkeley. 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. 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. 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. But amniocentesis was risky — the risk of killing the fetus during the procedure was roughly 1 in 300. Such an argument has little historical precedent. One of his recent roles is as a Faculty Partner and Co-Founder at AI@The House — a venture fund and accelerator in Berkeley. Computer Science 731 Soda Hall #1776 Berkeley, CA 94720-1776 Phone: (510) 642-3806 Did civil engineering develop by envisaging the creation of an artificial carpenter or bricklayer? Let’s broaden our scope, tone down the hype and recognize the serious challenges ahead. 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.”. Raluca Ada Popa raluca@EECS.Berkeley.EDU. 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? AMPLab Publications. 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. Jordan’s appointment is split across the Department of Statistics and the Department of EECS. Alchemist. Should chemical engineering have been framed in terms of creating an artificial chemist? Masks and social distancing will be required on campus. 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. 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. AI4ALL is a nonprofit dedicated to increasing diversity and inclusion in AI education, research, development, and policy. And this must all be done within the context of evolving societal, ethical and legal norms. 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. ACM, ASA, CSS, IEEE, IMS, ISBA and SIAM. They must address the difficulties of sharing data across administrative and competitive boundaries. 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. He is a the ACM/AAAI Allen Newell Award in 2009. 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. 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. AMP Lab – UC Berkeley. 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. 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 is one of the leading figures in machine learning, and in 2016 Science reported him as the world's most influential computer scientist. Michael Jordan is Full Professor at UC Berkeley in machine learning, statistics, and artificial intelligence. 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. 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. 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? When my spouse was pregnant 14 years ago, we had an ultrasound. MICHAEL JORDAN RESEARCH. Second, and more importantly, success in these domains is neither sufficient nor necessary to solve important IA and II problems. He is a Fellow of the AAAI, 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). 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. Artificial Intelligence (AI) is the mantra of the current era. 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. California, San Diego. and earned his PhD in Cognitive Science in 1985 from the University of “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. 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. He was a professor at MIT from 1988 to 1998. 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). These artifacts should be built to work as claimed. And, unfortunately, it distracts us. 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. methods, kernel machines and applications to problems in distributed computing Michael Jordan. Blogs; Jenkins; Search; People. Michael I. Jordan: Artificial Intelligence — The Revolution Hasn’t Happened Yet (This article has originally been published on Medium.com.) This rebranding is worthy of some scrutiny. He is one of the leading figures in machine learning, and in 2016 Science reported him as the world's most influential computer scientist. 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