| Creators: |
Grabinski, Michael and Klinkova, Galiya |
| Title: |
Artificial Intelligence Differs Strikingly from Human Thinking Due to Quantitative Reasons |
| Item Type: |
Article or issue of a publication series |
| Journal or Series Title: |
Theoretical Economics Letters |
| Page Range: |
pp. 1095-1110 |
| Additional Information: |
open access |
| Date: |
28 June 2024 |
| Divisions: |
Wirtschaftswissenschaften |
| Abstract (ENG): |
Artificial intelligence (AI) is hailed as a new revolution, especially in business and economics with all the opportunities and fears of a revolution.
However, AI is based on trial and error learning. As recently proven in a Science article (Jeong et al., 2022), humans do not learn by trial and error.
In this article, we examine the difference between human learning and trial and error learning quantitatively. The progress of trial and error learning is given by learning curves derived from a random walk. Though real human learning is far from being understood, the progress of human
learning is given in human learning curves derived much earlier than 2022, which are in accordance with the new findings of Jeong et al. (2022).
This allows a quantitative analysis of how AI differs from human learning. The greatest risk of AI is that one mixes it up with human intelligence. |
| Forthcoming: |
No |
| Language: |
English |
| Uncontrolled Keywords: |
Artificial Intelligence, AI, Random Walk, Pavlov’s Dog, Learning Curves,
Human Thinking |
| Link eMedia: |
Download |
| Citation: |
Grabinski, Michael and Klinkova, Galiya
(2024)
Artificial Intelligence Differs Strikingly from Human Thinking Due to Quantitative Reasons.
Theoretical Economics Letters, 14 (3).
pp. 1095-1110.
ISSN 2162-2086
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