scholarly journals Artificial intelligence as structural estimation: Deep Blue, Bonanza, and AlphaGo

2020 ◽  
Vol 23 (3) ◽  
pp. S1-S24
Author(s):  
Mitsuru Igami
Keyword(s):  
Artificial Intelligence ◽  
Value Function ◽  
Structural Estimation ◽  
Structural Models ◽  
Policy Network ◽  
Value Network ◽  
Deep Blue ◽  
Dynamic Structural Models ◽  
Similarities And Differences ◽  
Cross Fertilization

Summary This article clarifies the connections between certain algorithms to develop artificial intelligence (AI) and the econometrics of dynamic structural models, with concrete examples of three 'game AIs'. Chess-playing Deep Blue is a calibrated value function, whereas shogi-playing Bonanza is an estimated value function via Rust’s nested fixed-point (NFXP) method. AlphaGo’s 'supervised-learning policy network' is a deep-neural-network implementation of the conditional-choice-probability (CCP) estimation reminiscent of Hotz and Miller's first step; the construction of its 'reinforcement-learning value network' is analogous to their conditional choice simulation (CCS). I then explain the similarities and differences between AI-related methods and structural estimation more generally, and suggest areas of potential cross-fertilization.

Synthetic Biology and Artificial Intelligence: Toward Cross-Fertilization

2018 ◽  
Vol 27 (3) ◽  
pp. i-vii
Author(s):  
Luisa Damiano ◽  
◽  
Yutetsu Kuruma ◽  
Pasquale Stano ◽  
◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Synthetic Biology ◽  
Cross Fertilization

MANAGERIAL USE OF DYNAMIC STRUCTURAL MODELS

1977 ◽  
Vol 8 (1) ◽  
pp. 73-94 ◽  
Author(s):  
Elwood S. Buffa ◽  
James S. Dyer
Keyword(s):  
Structural Models ◽  
Dynamic Structural Models

Data, Information, and Knowledge

2015 ◽  
pp. 85-96
Author(s):  
G. Scott Erickson ◽  
Helen N. Rothberg
Keyword(s):  
Big Data ◽  
Knowledge Management ◽  
Conceptual Development ◽  
Intangible Assets ◽  
Business Analytics ◽  
Management Perspective ◽  
Successful Employment ◽  
Similarities And Differences ◽  
Global Strategies ◽  
Cross Fertilization

This chapter examines the similarities and differences between big data and knowledge management. Big data has relatively little conceptual development, at least from a strategy and management perspective. Knowledge management has a lengthy literature and decades of practice but has always explicitly focused only on knowledge assets as opposed to precursors like data and information. Even so, there are considerable opportunities for cross-fertilization. Consequently, this chapter considers data from McKinsey Global Strategies on data holdings, by industry, and contrasts that with data on knowledge development, essentially the intangible assets found in the same industries. Using what we know about the variables influencing the application of intangible assets such as knowledge and intelligence, we can then better identify where successful employment of big data might take place. Further, we can identify specific variables with the potential to grant competitive advantage from the application of big data and business analytics.

Does Deep Blue use Artificial Intelligence?1

ICGA Journal ◽  
1997 ◽  
Vol 20 (4) ◽  
pp. 243-245
Author(s):  
R.E. Korf
Keyword(s):  
Artificial Intelligence ◽  
Deep Blue

scholarly journals Deep Blue Cannot Play Checkers: The Need for Generalized Intelligence for Mobile Robots

2010 ◽  
Vol 2010 ◽  
pp. 1-8 ◽  
Author(s):  
Troy D. Kelley ◽  
Lyle N. Long
Keyword(s):  
Artificial Intelligence ◽  
Development Time ◽  
Systems Approach ◽  
Intelligent System ◽  
System Of Systems ◽  
Chess Player ◽  
Cognitive Architectures ◽  
Learned Behavior ◽  
Deep Blue ◽  
And Task

Generalized intelligence is much more difficult than originally anticipated when Artificial Intelligence (AI) was first introduced in the early 1960s. Deep Blue, the chess playing supercomputer, was developed to defeat the top rated human chess player and successfully did so by defeating Gary Kasporov in 1997. However, Deep Blue only played chess; it did not play checkers, or any other games. Other examples of AI programs which learned and played games were successful at specific tasks, but generalizing the learned behavior to other domains was not attempted. So the question remains: Why is generalized intelligence so difficult? If complex tasks require a significant amount of development, time and task generalization is not easily accomplished, then a significant amount of effort is going to be required to develop an intelligent system. This approach will require a system of systems approach that uses many AI techniques: neural networks, fuzzy logic, and cognitive architectures.

scholarly journals ON ARTIFICIAL INTELLIGENCE TALENT CULTIVATION IN CHINA FROM CASE STUDY

2021 ◽  
Author(s):  
XIAO-LONG LI
Keyword(s):  
Artificial Intelligence ◽  
Case Analysis ◽  
Decision Makers ◽  
Research Centers ◽  
Incentive Mechanisms ◽  
Four Dimensions ◽  
Academic Conference ◽  
Similarities And Differences

Through the case analysis of Carnegie Mellon University in the field of artificial intelligence in America. the similarities and differences of the above university in artificial intelligence talent cultivation were obtained from four dimensions: length of learning and degree, enrollment requirements, staff force construction and the offered curriculum. The conclusion could support the suggestions and advice for domestic policymakers and decision makers as follows: to promulgate the document of artificial intelligence talent cultivation pertinently as the guideline; to promote the new institutes construction of artificial intelligence and update the research centers of artificial intelligence; to improve the supporting incentive mechanisms such as scholarships, competitions and academic conference grants for the students in the direction of artificial intelligence.

scholarly journals DESIGN OF DYNAMIC STRUCTURAL MODELS OF INFORMATION MANAGEMENT SYSTEM OF MOVING OBJECTS

2013 ◽  
Vol 3 (4) ◽  
pp. 78-80
Author(s):  
Maksym Korobchynskyi ◽  
Oleg Mashkov
Keyword(s):  
Information Management ◽  
Management System ◽  
Moving Objects ◽  
Structural Model ◽  
Structural Models ◽  
Information Management System ◽  
Management Systems ◽  
Dynamic Structural Model ◽  
Dynamic Structural Models ◽  
Time Dependencies

In the following work the authors attempt to find the best way to design a dynamic structural model of information management system of moving objects. This structural model allows organizing various management systems of moving objects, considering the spatial and time dependencies between the key components or parameters of the said management system. An example of such system may be a group of UAVs.

Sign in / Sign up

Export Citation Format

Share Document