Computational · Paper · 2023

A Natural AI Based on The Science of Computational Physics, Biology and Neuroscience: Policy and Societal Significance

John Clippinger, Bert de Vries, Beth Noveck, Chris Fields, Cory Slater, Daniel Ari Friedman, David A. Silbersweig, Francesco Lapenta, Holly Grimm, Jeff Emmett, Joshua Shane, Karl Friston, Martin Nkafu Nkemnkia, Matthew Brown, Matthew Pirkowski, Michael Levin, Michael Zargham, Nguyen Anh Tuan, Krishnashree Achuthan, Thomas Patterson, Scott L. David, Thomas Kehler, Virginia Bleu Knight, Yasuhide Nakayama

Zenodo

Catalog Row135
Citation KeyFriedman2023NaturalAIBasedScience135
Paper FolderAvailable
DOI10.5281/zenodo.10360056
Platform availability

Overview

Extracted from the local paper documentation when available.

Letter on: "A Natural AI Based on The Science of Computational Physics, Biology and Neuroscience: Policy and Societal Significance". v1 released on December 12, 2023.

Active InferenceAINaturalPolicy

Use Notes

Concise findings and methods pulled from README/SKILL documentation.

Findings / Concepts
  • Letter on: "A Natural AI Based on The Science of Computational Physics, Biology and Neuroscience: Policy and Societal Significance". v1 released on December 12, 2023.
Methods / Techniques
  • Software pipeline design
  • Data-driven analysis

Citation

Plain-text citation for quick reuse.

Friedman, Daniel Ari. 2023. A Natural AI Based on The Science of Computational Physics, Biology and Neuroscience: Policy and Societal Significance. Zenodo. DOI: 10.5281/zenodo.10360056. URL: https://doi.org/10.5281/zenodo.10360056.

Primary source Documentation Full Text BibTeX

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