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Ten years after ImageNet: a 360° perspective on artificial intelligence
It is 10 years since neural networks made their spectacular comeback. Prompted by this anniversary, we take a holistic perspective on artificial intelligence (AI). Supervised learning for cognitive tasks is effectively solved—provided we have enough high-quality labelled data. However, deep neural n...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Royal Society
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10049745/ https://www.ncbi.nlm.nih.gov/pubmed/36998769 http://dx.doi.org/10.1098/rsos.221414 |
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author | Chawla, Sanjay Nakov, Preslav Ali, Ahmed Hall, Wendy Khalil, Issa Ma, Xiaosong Taha Sencar, Husrev Weber, Ingmar Wooldridge, Michael Yu, Ting |
author_facet | Chawla, Sanjay Nakov, Preslav Ali, Ahmed Hall, Wendy Khalil, Issa Ma, Xiaosong Taha Sencar, Husrev Weber, Ingmar Wooldridge, Michael Yu, Ting |
author_sort | Chawla, Sanjay |
collection | PubMed |
description | It is 10 years since neural networks made their spectacular comeback. Prompted by this anniversary, we take a holistic perspective on artificial intelligence (AI). Supervised learning for cognitive tasks is effectively solved—provided we have enough high-quality labelled data. However, deep neural network models are not easily interpretable, and thus the debate between blackbox and whitebox modelling has come to the fore. The rise of attention networks, self-supervised learning, generative modelling and graph neural networks has widened the application space of AI. Deep learning has also propelled the return of reinforcement learning as a core building block of autonomous decision-making systems. The possible harms made possible by new AI technologies have raised socio-technical issues such as transparency, fairness and accountability. The dominance of AI by Big Tech who control talent, computing resources, and most importantly, data may lead to an extreme AI divide. Despite the recent dramatic and unexpected success in AI-driven conversational agents, progress in much-heralded flagship projects like self-driving vehicles remains elusive. Care must be taken to moderate the rhetoric surrounding the field and align engineering progress with scientific principles. |
format | Online Article Text |
id | pubmed-10049745 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | The Royal Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-100497452023-03-29 Ten years after ImageNet: a 360° perspective on artificial intelligence Chawla, Sanjay Nakov, Preslav Ali, Ahmed Hall, Wendy Khalil, Issa Ma, Xiaosong Taha Sencar, Husrev Weber, Ingmar Wooldridge, Michael Yu, Ting R Soc Open Sci Computer Science and Artificial Intelligence It is 10 years since neural networks made their spectacular comeback. Prompted by this anniversary, we take a holistic perspective on artificial intelligence (AI). Supervised learning for cognitive tasks is effectively solved—provided we have enough high-quality labelled data. However, deep neural network models are not easily interpretable, and thus the debate between blackbox and whitebox modelling has come to the fore. The rise of attention networks, self-supervised learning, generative modelling and graph neural networks has widened the application space of AI. Deep learning has also propelled the return of reinforcement learning as a core building block of autonomous decision-making systems. The possible harms made possible by new AI technologies have raised socio-technical issues such as transparency, fairness and accountability. The dominance of AI by Big Tech who control talent, computing resources, and most importantly, data may lead to an extreme AI divide. Despite the recent dramatic and unexpected success in AI-driven conversational agents, progress in much-heralded flagship projects like self-driving vehicles remains elusive. Care must be taken to moderate the rhetoric surrounding the field and align engineering progress with scientific principles. The Royal Society 2023-03-29 /pmc/articles/PMC10049745/ /pubmed/36998769 http://dx.doi.org/10.1098/rsos.221414 Text en © 2023 The Authors. https://creativecommons.org/licenses/by/4.0/Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, provided the original author and source are credited. |
spellingShingle | Computer Science and Artificial Intelligence Chawla, Sanjay Nakov, Preslav Ali, Ahmed Hall, Wendy Khalil, Issa Ma, Xiaosong Taha Sencar, Husrev Weber, Ingmar Wooldridge, Michael Yu, Ting Ten years after ImageNet: a 360° perspective on artificial intelligence |
title | Ten years after ImageNet: a 360° perspective on artificial intelligence |
title_full | Ten years after ImageNet: a 360° perspective on artificial intelligence |
title_fullStr | Ten years after ImageNet: a 360° perspective on artificial intelligence |
title_full_unstemmed | Ten years after ImageNet: a 360° perspective on artificial intelligence |
title_short | Ten years after ImageNet: a 360° perspective on artificial intelligence |
title_sort | ten years after imagenet: a 360° perspective on artificial intelligence |
topic | Computer Science and Artificial Intelligence |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10049745/ https://www.ncbi.nlm.nih.gov/pubmed/36998769 http://dx.doi.org/10.1098/rsos.221414 |
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