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Multiscale Computation and Dynamic Attention in Biological and Artificial Intelligence

1
Center for Brain Science, RIKEN, Saitama 351-0198, Japan
2
Department of Psychology, University of Warwick, Coventry CV4 7AL, UK
*
Authors to whom correspondence should be addressed.
Brain Sci. 2020, 10(6), 396; https://doi.org/10.3390/brainsci10060396
Received: 4 March 2020 / Revised: 23 May 2020 / Accepted: 17 June 2020 / Published: 20 June 2020
(This article belongs to the Special Issue Environmental Neuroscience)
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. View Full-Text
Keywords: artificial intelligence (AI); decision making, attention; multiscale computation; environmental neuroscience; prefrontal cortex; exploration-exploitation; information search artificial intelligence (AI); decision making, attention; multiscale computation; environmental neuroscience; prefrontal cortex; exploration-exploitation; information search
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MDPI and ACS Style

Badman, R.P.; Hills, T.T.; Akaishi, R. Multiscale Computation and Dynamic Attention in Biological and Artificial Intelligence. Brain Sci. 2020, 10, 396. https://doi.org/10.3390/brainsci10060396

AMA Style

Badman RP, Hills TT, Akaishi R. Multiscale Computation and Dynamic Attention in Biological and Artificial Intelligence. Brain Sciences. 2020; 10(6):396. https://doi.org/10.3390/brainsci10060396

Chicago/Turabian Style

Badman, Ryan P.; Hills, Thomas T.; Akaishi, Rei. 2020. "Multiscale Computation and Dynamic Attention in Biological and Artificial Intelligence" Brain Sci. 10, no. 6: 396. https://doi.org/10.3390/brainsci10060396

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