Cargando…
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...
Autores principales: | , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9584406/ https://www.ncbi.nlm.nih.gov/pubmed/36264864 http://dx.doi.org/10.1371/journal.pone.0275814 |
_version_ | 1784813257447440384 |
---|---|
author | Dudyrev, Egor Semenkov, Ilia Kuznetsov, Sergei O. Gusev, Gleb Sharp, Andrew Pianykh, Oleg S. |
author_facet | Dudyrev, Egor Semenkov, Ilia Kuznetsov, Sergei O. Gusev, Gleb Sharp, Andrew Pianykh, Oleg S. |
author_sort | Dudyrev, Egor |
collection | PubMed |
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. |
format | Online Article Text |
id | pubmed-9584406 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-95844062022-10-21 Human knowledge models: Learning applied knowledge from the data Dudyrev, Egor Semenkov, Ilia Kuznetsov, Sergei O. Gusev, Gleb Sharp, Andrew Pianykh, Oleg S. PLoS One Research Article 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. Public Library of Science 2022-10-20 /pmc/articles/PMC9584406/ /pubmed/36264864 http://dx.doi.org/10.1371/journal.pone.0275814 Text en © 2022 Dudyrev et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Dudyrev, Egor Semenkov, Ilia Kuznetsov, Sergei O. Gusev, Gleb Sharp, Andrew Pianykh, Oleg S. Human knowledge models: Learning applied knowledge from the data |
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 |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9584406/ https://www.ncbi.nlm.nih.gov/pubmed/36264864 http://dx.doi.org/10.1371/journal.pone.0275814 |
work_keys_str_mv | AT dudyrevegor humanknowledgemodelslearningappliedknowledgefromthedata AT semenkovilia humanknowledgemodelslearningappliedknowledgefromthedata AT kuznetsovsergeio humanknowledgemodelslearningappliedknowledgefromthedata AT gusevgleb humanknowledgemodelslearningappliedknowledgefromthedata AT sharpandrew humanknowledgemodelslearningappliedknowledgefromthedata AT pianykholegs humanknowledgemodelslearningappliedknowledgefromthedata |