Bart Selman | |
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Education | |
Awards |
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Scientific career | |
Fields | Artificial intelligence |
Institutions | |
Thesis | Tractable Default Reasoning (1991) |
Doctoral advisor | Hector Levesque |
Bart Selman is a Dutch-American professor of computer science at Cornell University. [1] He is also co-founder and principal investigator [2] of the Center for Human-Compatible Artificial Intelligence (CHAI) at the University of California, Berkeley, led by Stuart J. Russell, [3] and co-chair of the Computing Community Consortium's 20-year roadmap for AI research. [4]
Selman attended the Technical University of Delft, from where he received a master's degree in physics, graduating in 1983. [5] He received his master's and PhD in computer science from the University of Toronto in 1985 and 1991 respectively. [6]
Selman has been working at AT&T Bell Laboratories before becoming professor of computer science at Cornell University. [7]
His research areas include tractable inference, knowledge representation, stochastic search methods, theory approximation, knowledge compilation, planning, default reasoning, satisfiability solvers like WalkSAT, and connections between computer science and statistical physics, namely phase transition phenomena.
Selman co-founded in 2016 an AI alignment research organization named Center for Human-Compatible AI (CHAI), and became one of its principal investigators. [2] His role in CHAI and some of his recent lectures notably focus on the safety and ethical aspects of advanced artificial intelligence. [8] [9]
Selman has received six Best Paper Awards for his work. He also received the Cornell Stephen Miles Excellence in Teaching Award, the Cornell Outstanding Educator Award, a National Science Foundation Career Award, and an Alfred P. Sloan Research Fellowship. [10] [11] He is a Fellow of the AAAI, [12] the AAAS, [13] and the ACM. [14]
Selman is the author or co-author of more than 100 publications, [10] including:
In logic and computer science, the Boolean satisfiability problem (sometimes called propositional satisfiability problem and abbreviated SATISFIABILITY, SAT or B-SAT) is the problem of determining if there exists an interpretation that satisfies a given Boolean formula. In other words, it asks whether the variables of a given Boolean formula can be consistently replaced by the values TRUE or FALSE in such a way that the formula evaluates to TRUE. If this is the case, the formula is called satisfiable. On the other hand, if no such assignment exists, the function expressed by the formula is FALSE for all possible variable assignments and the formula is unsatisfiable. For example, the formula "a AND NOT b" is satisfiable because one can find the values a = TRUE and b = FALSE, which make (a AND NOT b) = TRUE. In contrast, "a AND NOT a" is unsatisfiable.
Eliezer S. Yudkowsky is an American artificial intelligence researcher and writer on decision theory and ethics, best known for popularizing ideas related to friendly artificial intelligence. He is the founder of and a research fellow at the Machine Intelligence Research Institute (MIRI), a private research nonprofit based in Berkeley, California. His work on the prospect of a runaway intelligence explosion influenced philosopher Nick Bostrom's 2014 book Superintelligence: Paths, Dangers, Strategies.
Allen Newell was an American researcher in computer science and cognitive psychology at the RAND Corporation and at Carnegie Mellon University's School of Computer Science, Tepper School of Business, and Department of Psychology. He contributed to the Information Processing Language (1956) and two of the earliest AI programs, the Logic Theorist (1956) and the General Problem Solver (1957). He was awarded the ACM's A.M. Turing Award along with Herbert A. Simon in 1975 for their contributions to artificial intelligence and the psychology of human cognition.
In artificial intelligence, symbolic artificial intelligence is the term for the collection of all methods in artificial intelligence research that are based on high-level symbolic (human-readable) representations of problems, logic and search. Symbolic AI used tools such as logic programming, production rules, semantic nets and frames, and it developed applications such as knowledge-based systems, symbolic mathematics, automated theorem provers, ontologies, the semantic web, and automated planning and scheduling systems. The Symbolic AI paradigm led to seminal ideas in search, symbolic programming languages, agents, multi-agent systems, the semantic web, and the strengths and limitations of formal knowledge and reasoning systems.
The Association for the Advancement of Artificial Intelligence (AAAI) is an international scientific society devoted to promote research in, and responsible use of, artificial intelligence. AAAI also aims to increase public understanding of artificial intelligence (AI), improve the teaching and training of AI practitioners, and provide guidance for research planners and funders concerning the importance and potential of current AI developments and future directions.
Peter Norvig is an American computer scientist and Distinguished Education Fellow at the Stanford Institute for Human-Centered AI. He previously served as a director of research and search quality at Google. Norvig is the co-author with Stuart J. Russell of the most popular textbook in the field of AI: Artificial Intelligence: A Modern Approach used in more than 1,500 universities in 135 countries.
Stuart Jonathan Russell is a British computer scientist known for his contributions to artificial intelligence (AI). He is a professor of computer science at the University of California, Berkeley and was from 2008 to 2011 an adjunct professor of neurological surgery at the University of California, San Francisco. He holds the Smith-Zadeh Chair in Engineering at University of California, Berkeley. He founded and leads the Center for Human-Compatible Artificial Intelligence (CHAI) at UC Berkeley. Russell is the co-author with Peter Norvig of the authoritative textbook of the field of AI: Artificial Intelligence: A Modern Approach used in more than 1,500 universities in 135 countries.
In logic and computer science, the Davis–Putnam–Logemann–Loveland (DPLL) algorithm is a complete, backtracking-based search algorithm for deciding the satisfiability of propositional logic formulae in conjunctive normal form, i.e. for solving the CNF-SAT problem.
In computer science, GSAT and WalkSAT are local search algorithms to solve Boolean satisfiability problems.
John F. Canny is an Australian computer scientist, and Paul E Jacobs and Stacy Jacobs Distinguished Professor of Engineering in the Computer Science Department of the University of California, Berkeley. He has made significant contributions in various areas of computer science and mathematics, including artificial intelligence, robotics, computer graphics, human-computer interaction, computer security, computational algebra, and computational geometry.
Toby Walsh is Chief Scientist at UNSW.ai, the AI Institute of UNSW Sydney. He is a Laureate fellow, and professor of artificial intelligence in the UNSW School of Computer Science and Engineering at the University of New South Wales and Data61. He has served as Scientific Director of NICTA, Australia's centre of excellence for ICT research. He is noted for his work in artificial intelligence, especially in the areas of social choice, constraint programming and propositional satisfiability. He has served on the Executive Council of the Association for the Advancement of Artificial Intelligence.
Eric Joel Horvitz is an American computer scientist, and Technical Fellow at Microsoft, where he serves as the company's first Chief Scientific Officer. He was previously the director of Microsoft Research Labs, including research centers in Redmond, WA, Cambridge, MA, New York, NY, Montreal, Canada, Cambridge, UK, and Bangalore, India.
Barbara J. Grosz CorrFRSE is an American computer scientist and Higgins Professor of Natural Sciences at Harvard University. She has made seminal contributions to the fields of natural language processing and multi-agent systems. With Alison Simmons, she is co-founder of the Embedded EthiCS programme at Harvard, which embeds ethics lessons into computer science courses.
Henry A. Kautz is a computer scientist, Founding Director of Institute for Data Science and Professor at University of Rochester. He is interested in knowledge representation, artificial intelligence, data science and pervasive computing.
Francesca Rossi is an Italian computer scientist, currently working at the IBM Thomas J. Watson Research Center as an IBM Fellow and the IBM AI Ethics Global Leader.
Carla Pedro Gomes is a Portuguese-American computer scientist and professor at Cornell University. She is the founding Director of the Institute for Computational Sustainability and is noted for her pioneering work in developing computational methods to address challenges in sustainability. She has conducted research in a variety of areas of artificial intelligence and computer science, including constraint reasoning, mathematical optimization, and randomization techniques for exact search methods, algorithm selection, multi-agent systems, and game theory. Her work in computational sustainability includes ecological conservation, rural resource mapping, and pattern recognition for material science.
Sheila Ann McIlraith is a Canadian computer scientist specializing in artificial intelligence (AI). She is a Professor in the Department of Computer Science, University of Toronto. She is a Canada CIFAR AI Chair, a faculty member of the Vector Institute, and Associate Director and Research Lead of the Schwartz Reisman Institute for Technology and Society.
The Center for Human-Compatible Artificial Intelligence (CHAI) is a research center at the University of California, Berkeley focusing on advanced artificial intelligence (AI) safety methods. The center was founded in 2016 by a group of academics led by Berkeley computer science professor and AI expert Stuart J. Russell. Russell is known for co-authoring the widely used AI textbook Artificial Intelligence: A Modern Approach.
Neuro-symbolic AI is a type of artificial intelligence that integrates neural and symbolic AI architectures to address the weaknesses of each, providing a robust AI capable of reasoning, learning, and cognitive modeling. As argued by Leslie Valiant and others, the effective construction of rich computational cognitive models demands the combination of symbolic reasoning and efficient machine learning. Gary Marcus argued, "We cannot construct rich cognitive models in an adequate, automated way without the triumvirate of hybrid architecture, rich prior knowledge, and sophisticated techniques for reasoning." Further, "To build a robust, knowledge-driven approach to AI we must have the machinery of symbol manipulation in our toolkit. Too much useful knowledge is abstract to proceed without tools that represent and manipulate abstraction, and to date, the only known machinery that can manipulate such abstract knowledge reliably is the apparatus of symbol manipulation."
Black in AI, formally called the Black in AI Workshop, is a technology research organization and affinity group, founded by computer scientists Timnit Gebru and Rediet Abebe in 2017. It started as a conference workshop, later pivoting into an organization. Black in AI increases the presence and inclusion of Black people in the field of artificial intelligence (AI) by creating space for sharing ideas, fostering collaborations, mentorship, and advocacy.