Lockheed Martin partners with Uni of Adelaide on machine learning

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Technology and innovation company Lockheed Martin Australia has become the first Foundation Partner with the University of Adelaide's new Australian Institute for Machine Learning. The strategic partnership will deliver world-leading machine learning research for national security, the space industry, business, and the broader community. Machine learning is a form of artificial intelligence that enables computers and machines to learn how to do complex tasks without being programmed by humans. This technology is driving what is known as the "fourth industrial revolution". The University's new Australian Institute for Machine Learning (AIML) – which builds on decades of expertise in artificial intelligence and computer vision – will be based in the South Australian Government's new innovation precinct at Lot Fourteen (the old Royal Adelaide Hospital site).


SELLING ARTIFICIAL INTELLIGENCE

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Artificial intelligence, the science of making computers ''think,'' has long been the preserve of theoreticians who were little concerned with practical applications. ''When they said'real things,' they meant computers that can play chess,'' said Dr. Roger Schank, chairman of the computer science department at Yale University. ''They were not going to talk to Wall Street, let alone own a suit.'' Now, however, business is taking an interest in artificial intelligence, or A.I., and some professors, such as Dr. Schank, are forming or joining companies to capitalize on the expected boom. But the new move toward commercialization is disrupting the academic community and provoking fears that university research will be hurt.


Cluster Hiring: AI

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Successful candidates will have a Doctoral degree (Ph.D.), publications, and demonstrated research competencies and capabilities commensurate with appointment levels in the department(s) of interest, as well as demonstrated interest in and experience with collaborative teaming and/or transdisciplinary efforts Successful candidates will be expected to develop and maintain externally funded research programs (individual and collaborative), engage in both undergraduate and graduate education, and contribute their leadership, partnering and innovative thinking towards global prominence in their respective discipline. Teaching opportunities will vary by department and teaching qualifications will be considered for fit within respective department(s).


Letters to the Editor

AI Magazine

In this context, "intelligence" is related to statistical and economic notions of rationality -- colloquially, the ability to make good decisions, plans, or inferences. The adoption of probabilistic and decisiontheoretic representations and statistical learning methods has led to a large degree of integration and cross-fertilization among AI, machine learning, statistics, control theory, neuroscience, and other fields. The establishment of shared theoretical frameworks, combined with the availability of data and processing power, has yielded remarkable successes in various component tasks such as speech recognition, image classification, auton omous vehicles, machine translation, legged locomotion, and question-answering systems. As capabilities in these areas and others cross the threshold from laboratory research to economically valuable technologies, a virtuous cycle takes hold whereby even small improvements in performance are worth large sums of money, prompting greater investments in research. There is now a broad consensus that AI research is progressing steadily, and that its impact on society is likely to increase.


Letter to the Editor: Research Priorities for Robust and Beneficial Artificial Intelligence: An Open Letter

AI Magazine

Artificial intelligence (AI) research has explored a variety of problems and approaches since its inception, but for the last 20 years or so has been focused on the problems surrounding the construction of intelligent agents — systems that perceive and act in some environment. In this context, "intelligence" is related to statistical and economic notions of rationality — colloquially, the ability to make good decisions, plans, or inferences. The adoption of probabilistic and decision-theoretic representations and statistical learning methods has led to a large degree of integration and cross-fertilization among AI, machine learning, statistics, control theory, neuroscience, and other fields. The establishment of shared theoretical frameworks, combined with the availability of data and processing power, has yielded remarkable successes in various component tasks such as speech recognition, image classification, autonomous vehicles, machine translation, legged locomotion, and question-answering systems. As capabilities in these areas and others cross the threshold from laboratory research to economically valuable technologies, a virtuous cycle takes hold whereby even small improvements in performance are worth large sums of money, prompting greater investments in research. There is now a broad consensus that AI research is progressing steadily, and that its impact on society is likely to increase. The potential benefits are huge, since everything that civilization has to offer is a product of human intelligence; we cannot predict what we might achieve when this intelligence is magnified by the tools AI may provide, but the eradication of disease and poverty are not unfathomable. Because of the great potential of AI, it is important to research how to reap its benefits while avoiding potential pitfalls. The progress in AI research makes it timely to focus research not only on making AI more capable, but also on maximizing the societal benefit of AI. Such considerations motivated the AAAI 2008–09 Presidential Panel on Long-Term AI Futures and other projects on AI impacts, and constitute a significant expansion of the field of AI itself, which up to now has focused largely on techniques that are neutral with respect to purpose. We recommend expanded research aimed at ensuring that increasingly capable AI systems are robust and beneficial: our AI systems must do what we want them to do. The attached research priorities document [see page X] gives many examples of such research directions that can help maximize the societal benefit of AI. This research is by necessity interdisciplinary, because it involves both society and AI. It ranges from economics, law and philosophy to computer security, formal methods and, of course, various branches of AI itself. In summary, we believe that research on how to make AI systems robust and beneficial is both important and timely, and that there are concrete research directions that can be pursued today.