Human knowledge models: Learning applied knowledge from the data
Artificial intelligence and machine learning have demonstrated remarkable results in science and applied work. However, present AI models, developed to be run on computers but used in human-driven applications, create a visible disconnect between AI forms of processing and human ways of discovering...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2022-01-01
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| Series: | PLoS ONE |
| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9584406/?tool=EBI |
| _version_ | 1798029147683946496 |
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| author | Egor Dudyrev Ilia Semenkov Sergei O. Kuznetsov Gleb Gusev Andrew Sharp Oleg S. Pianykh |
| author_facet | Egor Dudyrev Ilia Semenkov Sergei O. Kuznetsov Gleb Gusev Andrew Sharp Oleg S. Pianykh |
| author_sort | Egor Dudyrev |
| collection | DOAJ |
| description | Artificial intelligence and machine learning have demonstrated remarkable results in science and applied work. However, present AI models, developed to be run on computers but used in human-driven applications, create a visible disconnect between AI forms of processing and human ways of discovering and using knowledge. In this work, we introduce a new concept of “Human Knowledge Models” (HKMs), designed to reproduce human computational abilities. Departing from a vast body of cognitive research, we formalized the definition of HKMs into a new form of machine learning. Then, by training the models with human processing capabilities, we learned human-like knowledge, that humans can not only understand, but also compute, modify, and apply. We used several datasets from different applied fields to demonstrate the advantages of HKMs, including their high predictive power and resistance to noise and overfitting. Our results proved that HKMs can efficiently mine knowledge directly from the data and can compete with complex AI models in explaining the main data patterns. As a result, our study reveals the great potential of HKMs, particularly in the decision-making applications where “black box” models cannot be accepted. Moreover, this improves our understanding of how well human decision-making, modeled by HKMs, can approach the ideal solutions in real-life problems. |
| first_indexed | 2024-04-11T19:19:33Z |
| format | Article |
| id | doaj.art-062e35df2a8143c2a3ec50960905aa2c |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-04-11T19:19:33Z |
| publishDate | 2022-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-062e35df2a8143c2a3ec50960905aa2c2022-12-22T04:07:20ZengPublic Library of Science (PLoS)PLoS ONE1932-62032022-01-011710Human knowledge models: Learning applied knowledge from the dataEgor DudyrevIlia SemenkovSergei O. KuznetsovGleb GusevAndrew SharpOleg S. PianykhArtificial intelligence and machine learning have demonstrated remarkable results in science and applied work. However, present AI models, developed to be run on computers but used in human-driven applications, create a visible disconnect between AI forms of processing and human ways of discovering and using knowledge. In this work, we introduce a new concept of “Human Knowledge Models” (HKMs), designed to reproduce human computational abilities. Departing from a vast body of cognitive research, we formalized the definition of HKMs into a new form of machine learning. Then, by training the models with human processing capabilities, we learned human-like knowledge, that humans can not only understand, but also compute, modify, and apply. We used several datasets from different applied fields to demonstrate the advantages of HKMs, including their high predictive power and resistance to noise and overfitting. Our results proved that HKMs can efficiently mine knowledge directly from the data and can compete with complex AI models in explaining the main data patterns. As a result, our study reveals the great potential of HKMs, particularly in the decision-making applications where “black box” models cannot be accepted. Moreover, this improves our understanding of how well human decision-making, modeled by HKMs, can approach the ideal solutions in real-life problems.https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9584406/?tool=EBI |
| spellingShingle | Egor Dudyrev Ilia Semenkov Sergei O. Kuznetsov Gleb Gusev Andrew Sharp Oleg S. Pianykh Human knowledge models: Learning applied knowledge from the data PLoS ONE |
| title | Human knowledge models: Learning applied knowledge from the data |
| title_full | Human knowledge models: Learning applied knowledge from the data |
| title_fullStr | Human knowledge models: Learning applied knowledge from the data |
| title_full_unstemmed | Human knowledge models: Learning applied knowledge from the data |
| title_short | Human knowledge models: Learning applied knowledge from the data |
| title_sort | human knowledge models learning applied knowledge from the data |
| url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9584406/?tool=EBI |
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