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 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.
The Latest on: Beneficial Artificial Intelligence
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The Latest on: Beneficial Artificial Intelligence
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- Artificial intelligence is reshaping financeon November 19, 2020 at 8:10 am
Instead, the German tie-up’s real significance is as a tiny, but unusually visible, sign of a feverish race under way at banks and tech companies to find ways to use big data and artificial ...
- Enterprise Artificial Intelligence Market to Witness Huge Growth by 2025 | SAP, Microsoft, IBMon November 18, 2020 at 8:59 pm
AI can be beneficial to enterprises in a number of ways that ... and Wipro Technologies (India). The Global Enterprise Artificial Intelligence market is gaining huge competition due to involvement of ...
- Dada Group Receives Grants of Funds by 2020 Shanghai Artificial Intelligence Development Programon November 17, 2020 at 8:22 pm
"Integrated Product Recommendation System Based on Deep Learning Algorithm for Local On-demand Retail" (Product Recommendation System) is created by JDDJ, the on-demand retail platform of Dada Group ...
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