A Metamodel and Framework for Artificial General Intelligence From Theory to Practice
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| Udgivet i: | arXiv.org (Feb 11, 2021), p. n/a |
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| Andre forfattere: | , , , , , , , , |
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Cornell University Library, arXiv.org
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| Online adgang: | Citation/Abstract Full text outside of ProQuest |
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| 001 | 2488773307 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2331-8422 | ||
| 035 | |a 2488773307 | ||
| 045 | 0 | |b d20210211 | |
| 100 | 1 | |a Latapie, Hugo | |
| 245 | 1 | |a A Metamodel and Framework for Artificial General Intelligence From Theory to Practice | |
| 260 | |b Cornell University Library, arXiv.org |c Feb 11, 2021 | ||
| 513 | |a Working Paper | ||
| 520 | 3 | |a This paper introduces a new metamodel-based knowledge representation that significantly improves autonomous learning and adaptation. While interest in hybrid machine learning / symbolic AI systems leveraging, for example, reasoning and knowledge graphs, is gaining popularity, we find there remains a need for both a clear definition of knowledge and a metamodel to guide the creation and manipulation of knowledge. Some of the benefits of the metamodel we introduce in this paper include a solution to the symbol grounding problem, cumulative learning, and federated learning. We have applied the metamodel to problems ranging from time series analysis, computer vision, and natural language understanding and have found that the metamodel enables a wide variety of learning mechanisms ranging from machine learning, to graph network analysis and learning by reasoning engines to interoperate in a highly synergistic way. Our metamodel-based projects have consistently exhibited unprecedented accuracy, performance, and ability to generalize. This paper is inspired by the state-of-the-art approaches to AGI, recent AGI-aspiring work, the granular computing community, as well as Alfred Korzybski's general semantics. One surprising consequence of the metamodel is that it not only enables a new level of autonomous learning and optimal functioning for machine intelligences, but may also shed light on a path to better understanding how to improve human cognition. | |
| 653 | |a Machine learning | ||
| 653 | |a Semantics | ||
| 653 | |a Knowledge bases (artificial intelligence) | ||
| 653 | |a Reasoning | ||
| 653 | |a Cognition | ||
| 653 | |a Reasoning programs | ||
| 653 | |a Computer vision | ||
| 653 | |a Network analysis | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Natural language (computers) | ||
| 653 | |a Knowledge representation | ||
| 653 | |a Metamodels | ||
| 653 | |a Time series | ||
| 700 | 1 | |a Kilic, Ozkan | |
| 700 | 1 | |a Liu, Gaowen | |
| 700 | 1 | |a Yan, Yan | |
| 700 | 1 | |a Kompella, Ramana | |
| 700 | 1 | |a Wang, Pei | |
| 700 | 1 | |a Thorisson, Kristinn R | |
| 700 | 1 | |a Lawrence, Adam | |
| 700 | 1 | |a Sun, Yuhong | |
| 700 | 1 | |a Srinivasa, Jayanth | |
| 773 | 0 | |t arXiv.org |g (Feb 11, 2021), p. n/a | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/2488773307/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full text outside of ProQuest |u http://arxiv.org/abs/2102.06112 |