Research Priorities for Robust and Beneficial Artificial Intelligence
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, the cr...
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| Format: | Article |
| Language: | en_US |
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Association for the Advancement of Artificial Intelligence
2017
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| Online Access: | http://hdl.handle.net/1721.1/108478 https://orcid.org/0000-0001-7670-7190 |
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| author | Russell, Stuart Tegmark, Max Erik Dewey, Dan |
| author2 | Massachusetts Institute of Technology. Department of Physics |
| author_facet | Massachusetts Institute of Technology. Department of Physics Russell, Stuart Tegmark, Max Erik Dewey, Dan |
| author_sort | Russell, Stuart |
| collection | MIT |
| description | 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, the criterion for intelligence is related to statistical and economic notions of rationality — colloquially, the ability to make good decisions, plans, or inferences. The adoption of probabilistic representations and statistical learning
methods has led to a large degree of integration and cross-fertilization between 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 suc-
cesses in various component tasks such as speech recognition, image classification, autonomous vehicles, machine translation, legged locomotion, and question-answering systems. |
| first_indexed | 2024-09-23T11:09:42Z |
| format | Article |
| id | mit-1721.1/108478 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T11:09:42Z |
| publishDate | 2017 |
| publisher | Association for the Advancement of Artificial Intelligence |
| record_format | dspace |
| spelling | mit-1721.1/1084782022-09-27T17:34:20Z Research Priorities for Robust and Beneficial Artificial Intelligence Russell, Stuart Tegmark, Max Erik Dewey, Dan Massachusetts Institute of Technology. Department of Physics MIT Kavli Institute for Astrophysics and Space Research Dewey, Daniel Tegmark, Max Erik 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, the criterion for intelligence is related to statistical and economic notions of rationality — colloquially, the ability to make good decisions, plans, or inferences. The adoption of probabilistic representations and statistical learning methods has led to a large degree of integration and cross-fertilization between 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 suc- cesses in various component tasks such as speech recognition, image classification, autonomous vehicles, machine translation, legged locomotion, and question-answering systems. 2017-04-27T21:41:04Z 2017-04-27T21:41:04Z 2015-12 Article http://purl.org/eprint/type/JournalArticle 0738-4602 http://hdl.handle.net/1721.1/108478 Russell, Stuart; Dewey, Daniel and Tegmark, Max Erik. "Research Priorities for Robust and Beneficial Artificial Intelligence." AI Magazine 36, no. 4 (December 2015): 105-114. © 2015 Association for the Advancement of Artificial Intelligence. https://orcid.org/0000-0001-7670-7190 en_US https://aaai.org/ojs/index.php/aimagazine/article/view/2577/2521 AI Magazine Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. application/pdf Association for the Advancement of Artificial Intelligence AAAS |
| spellingShingle | Russell, Stuart Tegmark, Max Erik Dewey, Dan Research Priorities for Robust and Beneficial Artificial Intelligence |
| title | Research Priorities for Robust and Beneficial Artificial Intelligence |
| title_full | Research Priorities for Robust and Beneficial Artificial Intelligence |
| title_fullStr | Research Priorities for Robust and Beneficial Artificial Intelligence |
| title_full_unstemmed | Research Priorities for Robust and Beneficial Artificial Intelligence |
| title_short | Research Priorities for Robust and Beneficial Artificial Intelligence |
| title_sort | research priorities for robust and beneficial artificial intelligence |
| url | http://hdl.handle.net/1721.1/108478 https://orcid.org/0000-0001-7670-7190 |
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