On the use of artificial intelligence for solving inverse problems (Conference Presentation)

2019 ◽  
Author(s):  
George Barbastathis ◽  
Alexandre Goy ◽  
Shuai Li ◽  
Mo Deng ◽  
Kwabena Arthur ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Inverse Problems

scholarly journals On the task approach in artificial intelligence

2019 ◽  
Vol 17 (4) ◽  
pp. 5-25
Author(s):  
Evgeny E. Vityaev ◽  
Sergey S. Goncharov ◽  
Dmitry I. Sviridenko
Keyword(s):  
Artificial Intelligence ◽  
Theoretical Model ◽  
Inverse Problems ◽  
Important Class ◽  
Semantic Modeling ◽  
Direct And Inverse Problems ◽  
Integration Approach ◽  
Model Approach

The authors discuss the problem of the integration approach to artificial intelligence, analyzing the content and positive aspects of the integration agent approach. It is noted that this approach implicitly follows the task approach. The paper gives answers to the questions that make up the essence of the task approach - where do the tasks come from, what is the task, what should be considered a solution to the problem. It also discusses the classification of intellectual problems into direct, inverse, and hybrid. It is noted that modern artificial intelligence focuses mainly on solving direct and inverse problems, leaving a huge and important class of hybrid problems outside its scope of attention. The paper describes the theoretical model approach to solving the whole variety of intellectual problems, called semantic modeling. It analyzes the advantages of the proposed conception, including the possibility of a flexible combination when solving hybrid problems of tools already created in artificial intelligence. It also discusses the problem of creating a “strong” / “general” artificial intelligence (AGI) in the framework of the task approach.

Weak vs strong artificial intelligence

2020 ◽  
pp. 81-88
Author(s):  
A.N. Raikov
Keyword(s):  
Artificial Intelligence ◽  
Inverse Problems ◽  
Creative Process ◽  
Relativistic Theory ◽  
Quantum Physics ◽  
Evolutionary Stage ◽  
General Intelligence ◽  
Artificial General Intelligence ◽  
Strong Artificial Intelligence

It is proposed to distinguish between the traditional, so-called, weak, artificial intelligence (AI) and strong artificial intelligence, or artificial general intelligence (AGI). The latter is not considered as the evolutionary stage of the development of AI, but something else related to the construction of an informal and non-causal phenomenological space, as a kind of opposite to AI. AGI is determined by inverse problems solving on topologies, using analogies of quantum physics and optics, connecting aspects of relativistic theory. It is noted that the AGI continual power is tens of orders of magnitude greater than the AI power, however, for its release, special integration of the both, and direct inclusion of human in the process is required. Already, elements of AGI are helping to accelerate the collective creative process many times over.

Artificial Intelligence

2009 ◽  
Author(s):  
David L. Poole ◽  
Alan K. Mackworth
Keyword(s):  
Artificial Intelligence
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