Bayes in the Age of Intelligent Machines. Griffiths, T. L., Zhu, J., Grant, E., & Thomas McCoy, R. Current Directions in Psychological Science, September, 2024. Publisher: SAGE Publications Inc![Bayes in the Age of Intelligent Machines [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex The success of methods based on artificial neural networks in creating intelligent machines seems like it might pose a challenge to explanations of human cognition in terms of Bayesian inference. We argue that this is not the case and that these systems in fact offer new opportunities for Bayesian modeling. Specifically, we argue that artificial neural networks and Bayesian models of cognition lie at different levels of analysis and are complementary modeling approaches, together offering a way to understand human cognition that spans these levels. We also argue that the same perspective can be applied to intelligent machines, in which a Bayesian approach may be uniquely valuable in understanding the behavior of large, opaque artificial neural networks that are trained on proprietary data.
@article{griffiths_bayes_2024,
title = {Bayes in the {Age} of {Intelligent} {Machines}},
issn = {0963-7214},
url = {https://doi.org/10.1177/09637214241262329},
doi = {10.1177/09637214241262329},
abstract = {The success of methods based on artificial neural networks in creating intelligent machines seems like it might pose a challenge to explanations of human cognition in terms of Bayesian inference. We argue that this is not the case and that these systems in fact offer new opportunities for Bayesian modeling. Specifically, we argue that artificial neural networks and Bayesian models of cognition lie at different levels of analysis and are complementary modeling approaches, together offering a way to understand human cognition that spans these levels. We also argue that the same perspective can be applied to intelligent machines, in which a Bayesian approach may be uniquely valuable in understanding the behavior of large, opaque artificial neural networks that are trained on proprietary data.},
urldate = {2024-10-03},
journal = {Current Directions in Psychological Science},
author = {Griffiths, Thomas L. and Zhu, Jian-Qiao and Grant, Erin and Thomas McCoy, R.},
month = sep,
year = {2024},
note = {Publisher: SAGE Publications Inc},
pages = {09637214241262329},
}
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