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pro vyhledávání: '"Tong, Richard"'
With the advent of foundation models like ChatGPT, educators are excited about the transformative role that AI might play in propelling the next education revolution. The developing speed and the profound impact of foundation models in various indust
Externí odkaz:
http://arxiv.org/abs/2405.10959
The adoption of Artificial Intelligence in Education (AIED) holds the promise of revolutionizing educational practices by offering personalized learning experiences, automating administrative and pedagogical tasks, and reducing the cost of content cr
Externí odkaz:
http://arxiv.org/abs/2403.14689
Autor:
Tong, Richard Jiarui, Cao, Cassie Chen, Lee, Timothy Xueqian, Zhao, Guodong, Wan, Ray, Wang, Feiyue, Hu, Xiangen, Schmucker, Robin, Pan, Jinsheng, Quevedo, Julian, Lu, Yu
This paper presents the Never Ending Open Learning Adaptive Framework (NEOLAF), an integrated neural-symbolic cognitive architecture that models and constructs intelligent agents. The NEOLAF framework is a superior approach to constructing intelligen
Externí odkaz:
http://arxiv.org/abs/2308.03990
Automatic analysis of teacher and student interactions could be very important to improve the quality of teaching and student engagement. However, despite some recent progress in utilizing multimodal data for teaching and learning analytics, a thorou
Externí odkaz:
http://arxiv.org/abs/1910.06078
The purpose of this paper is to report on the most recent developments in our ongoing investigation of the representation and manipulation of uncertainty in automated reasoning systems. In our earlier studies (Tong and Shapiro, 1985) we described a s
Externí odkaz:
http://arxiv.org/abs/1304.3113
Autor:
Tong, Richard M., Appelbaum, Lee A.
In our previous series of studies to investigate the role of evidential reasoning in the RUBRIC system for full-text document retrieval (Tong et al., 1985; Tong and Shapiro, 1985; Tong and Appelbaum, 1987), we identified the important role that probl
Externí odkaz:
http://arxiv.org/abs/1304.2746
While concept-based methods for information retrieval can provide improved performance over more conventional techniques, they require large amounts of effort to acquire the concepts and their qualitative and quantitative relationships. This paper di
Externí odkaz:
http://arxiv.org/abs/1304.1128
Akademický článek
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Autor:
Tong, Richard, Goel, Ashok
Publikováno v:
AI Magazine; Vol. 42 No. 1: Spring 2021; 95-100
In this interview, conducted in early 2020 by Ashok Goel, Richard Tong, the chief architect and general manager of Squirrel AI Learning’s US operations, discusses adaptive learning, challenges facing AI in education, and the Squirrel AI Award for A
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=issn07384602::8793afacfc123380bc38c81a38205fc7
https://ojs.aaai.org/index.php/aimagazine/article/view/7386
https://ojs.aaai.org/index.php/aimagazine/article/view/7386
Autor:
Arbel, Ami, Tong, Richard M.
Publikováno v:
The Journal of the Operational Research Society, 1982 Apr 01. 33(4), 377-387.
Externí odkaz:
https://www.jstor.org/stable/2581647