ArticleSmall science2021
Artificial Intelligence Goes Physical.
Article in Small science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
2 authors.
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Abstract
Exploiting the intrinsic nonlinearity in physical reservoirs, e.g., dopant-atom networks, provides a new approach toward highly efficient computing such as feature projection and classification. In a recent study by Chen et al., the computational capability of dopant-atom network was investigated and found to diminish as the signal-to-noise ratio (SNR) increased, indicating the existence of an optimal bias condition. Although high SNR is often pursued in signal processing, it shows that embracing noise in non-conventional computing systems may lead to a leap in computing capacity. This work showcased that material or device physics in different domains offer valuable substrates for complex computing functions and high energy efficiency.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.