Cognitive science in the era of artificial intelligence: A roadmap for reverse-engineering the infant language-learner
•Key theoretical puzzles of infant language development are still unsolved.•A roadmap for reverse engineering infant language learning using AI is proposed.•AI algorithms should use realistic input (little or no supervision, raw data).•Realistic input should be obtained by large-scale recording in e...
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| Vydáno v: | Cognition Ročník 173; s. 43 - 59 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Netherlands
Elsevier B.V
01.04.2018
Elsevier Science Ltd Elsevier |
| Témata: | |
| ISSN: | 0010-0277, 1873-7838, 1873-7838 |
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| Abstract | •Key theoretical puzzles of infant language development are still unsolved.•A roadmap for reverse engineering infant language learning using AI is proposed.•AI algorithms should use realistic input (little or no supervision, raw data).•Realistic input should be obtained by large-scale recording in ecological environments.•Machine/Human comparison should be run on a benchmark of psycholinguistic tests.
Spectacular progress in the information processing sciences (machine learning, wearable sensors) promises to revolutionize the study of cognitive development. Here, we analyse the conditions under which ’reverse engineering’ language development, i.e., building an effective system that mimics infant’s achievements, can contribute to our scientific understanding of early language development. We argue that, on the computational side, it is important to move from toy problems to the full complexity of the learning situation, and take as input as faithful reconstructions of the sensory signals available to infants as possible. On the data side, accessible but privacy-preserving repositories of home data have to be setup. On the psycholinguistic side, specific tests have to be constructed to benchmark humans and machines at different linguistic levels. We discuss the feasibility of this approach and present an overview of current results. |
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| AbstractList | Spectacular progress in the information processing sciences (machine learning, wearable sensors) promises to revolutionize the study of cognitive development. Here, we analyse the conditions under which 'reverse engineering' language development, i.e., building an effective system that mimics infant's achievements, can contribute to our scientific understanding of early language development. We argue that, on the computational side, it is important to move from toy problems to the full complexity of the learning situation, and take as input as faithful reconstructions of the sensory signals available to infants as possible. On the data side, accessible but privacy-preserving repositories of home data have to be setup. On the psycholinguistic side, specific tests have to be constructed to benchmark humans and machines at different linguistic levels. We discuss the feasibility of this approach and present an overview of current results.Spectacular progress in the information processing sciences (machine learning, wearable sensors) promises to revolutionize the study of cognitive development. Here, we analyse the conditions under which 'reverse engineering' language development, i.e., building an effective system that mimics infant's achievements, can contribute to our scientific understanding of early language development. We argue that, on the computational side, it is important to move from toy problems to the full complexity of the learning situation, and take as input as faithful reconstructions of the sensory signals available to infants as possible. On the data side, accessible but privacy-preserving repositories of home data have to be setup. On the psycholinguistic side, specific tests have to be constructed to benchmark humans and machines at different linguistic levels. We discuss the feasibility of this approach and present an overview of current results. Spectacular progress in the information processing sciences (machine learning, wearable sensors) promises to revolutionize the study of cognitive development. Here, we analyse the conditions under which ’reverse engineering’ language development, i.e., building an effective system that mimics infant’s achievements, can contribute to our scientific understanding of early language development. We argue that, on the computational side, it is important to move from toy problems to the full complexity of the learning situation, and take as input as faithful reconstructions of the sensory signals available to infants as possible. On the data side, accessible but privacy-preserving repositories of home data have to be setup. On the psycholinguistic side, specific tests have to be constructed to benchmark humans and machines at different linguistic levels. We discuss the feasibility of this approach and present an overview of current results. Spectacular progress in the information processing sciences (machine learning, wearable sensors) promises to revolutionize the study of cognitive development. Here, we analyse the conditions under which ’reverse engineering’ language development, i.e., building an effective system thatmimics infant’s achievements, can contribute to our scientific understanding of early language development. We argue that, on the computational side, it is important to move from toy problems to the full complexity of the learning situation, and take as input as faithful reconstructions of the sensorysignals available to infants as possible. On the data side, accessible but privacy-preserving repositories of home data have to be setup. On the psycholinguistic side, specific tests have to be constructed to benchmark humans and machines at different linguistic levels. We discuss the feasibility of this approach and present an overview of current results. •Key theoretical puzzles of infant language development are still unsolved.•A roadmap for reverse engineering infant language learning using AI is proposed.•AI algorithms should use realistic input (little or no supervision, raw data).•Realistic input should be obtained by large-scale recording in ecological environments.•Machine/Human comparison should be run on a benchmark of psycholinguistic tests. Spectacular progress in the information processing sciences (machine learning, wearable sensors) promises to revolutionize the study of cognitive development. Here, we analyse the conditions under which ’reverse engineering’ language development, i.e., building an effective system that mimics infant’s achievements, can contribute to our scientific understanding of early language development. We argue that, on the computational side, it is important to move from toy problems to the full complexity of the learning situation, and take as input as faithful reconstructions of the sensory signals available to infants as possible. On the data side, accessible but privacy-preserving repositories of home data have to be setup. On the psycholinguistic side, specific tests have to be constructed to benchmark humans and machines at different linguistic levels. We discuss the feasibility of this approach and present an overview of current results. |
| Author | Dupoux, Emmanuel |
| Author_xml | – sequence: 1 givenname: Emmanuel orcidid: 0000-0002-7814-2952 surname: Dupoux fullname: Dupoux, Emmanuel email: emmanuel.dupoux@ens.fr organization: EHESS, ENS, PSL Research University, CNRS, INRIA, France |
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| Keywords | Infant development Computational modeling Early language acquisition psycholinguistics Machine learning Speech Artificial intelligence Language bootstrapping Corpus analysis early language acquisi-tion corpus analysis language bootstrapping speech compu-tational modeling machine learning infant development |
| Language | English |
| License | Copyright © 2017 Elsevier B.V. All rights reserved. Distributed under a Creative Commons Attribution 4.0 International License: http://creativecommons.org/licenses/by/4.0 |
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| Snippet | •Key theoretical puzzles of infant language development are still unsolved.•A roadmap for reverse engineering infant language learning using AI is proposed.•AI... Spectacular progress in the information processing sciences (machine learning, wearable sensors) promises to revolutionize the study of cognitive development.... |
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| SubjectTerms | Artificial Intelligence Building engineers Cognition Cognition & reasoning Cognitive ability Cognitive development Cognitive science Computational mathematics Computational modeling Computer applications Computer Science Corpus analysis Early language acquisition Engineering Feasibility Infant development Infants Information processing Language Language acquisition Language bootstrapping Learning algorithms Linguistics Machine learning Machinery Privacy Psycholinguistics Speech |
| Title | Cognitive science in the era of artificial intelligence: A roadmap for reverse-engineering the infant language-learner |
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