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Multiscale computation and dynamic attention in biological and artificial intelligence
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Badman, Ryan Paul, Hills, Thomas Trenholm and Akaishi, Rei (2020) Multiscale computation and dynamic attention in biological and artificial intelligence. Brain sciences, 10 (6). 396. doi:10.3390/brainsci10060396
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Official URL: http://dx.doi.org/10.3390/brainsci10060396
Abstract
Biological and artificial intelligence (AI) are often defined by their capacity to achieve a hierarchy of short-term and long-term goals that require incorporating information over time and space at both local and global scales. More advanced forms of this capacity involve the adaptive modulation of integration across scales, which resolve computational inefficiency and explore-exploit dilemmas at the same time. Research in neuroscience and AI have both made progress towards understanding architectures that achieve this. Insight into biological computations come from phenomena such as decision inertia, habit formation, information search, risky choices and foraging. Across these domains, the brain is equipped with mechanisms (such as the dorsal anterior cingulate and dorsolateral prefrontal cortex) that can represent and modulate across scales, both with top-down control processes and by local to global consolidation as information progresses from sensory to prefrontal areas. Paralleling these biological architectures, progress in AI is marked by innovations in dynamic multiscale modulation, moving from recurrent and convolutional neural networks—with fixed scalings—to attention, transformers, dynamic convolutions, and consciousness priors—which modulate scale to input and increase scale breadth. The use and development of these multiscale innovations in robotic agents, game AI, and natural language processing (NLP) are pushing the boundaries of AI achievements. By juxtaposing biological and artificial intelligence, the present work underscores the critical importance of multiscale processing to general intelligence, as well as highlighting innovations and differences between the future of biological and artificial intelligence.
| Item Type: | Journal Article | ||||||||||||
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| Subjects: | B Philosophy. Psychology. Religion > BF Psychology Q Science > Q Science (General) Q Science > QP Physiology |
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| Divisions: | Faculty of Science > Psychology | ||||||||||||
| Library of Congress Subject Headings (LCSH): | Artificial intelligence, Decision making -- Data processing, Computational neuroscience, Prefrontal cortex | ||||||||||||
| Journal or Publication Title: | Brain sciences | ||||||||||||
| Publisher: | MDPI | ||||||||||||
| ISSN: | 2076-3425 | ||||||||||||
| Official Date: | 20 June 2020 | ||||||||||||
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| Date of first compliant deposit: | 26 June 2020 | ||||||||||||
| Volume: | 10 | ||||||||||||
| Number: | 6 | ||||||||||||
| Article Number: | 396 | ||||||||||||
| DOI: | 10.3390/brainsci10060396 | ||||||||||||
| Status: | Peer Reviewed | ||||||||||||
| Publication Status: | Published | ||||||||||||
| Access rights to Published version: | Restricted or Subscription Access | ||||||||||||
| RIOXX Funder/Project Grant: |
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