Integrative biological simulation, neuropsychology, and AI safety
- Published
- Accepted
- Subject Areas
- Animal Behavior, Computational Biology, Entomology, Neuroscience, Zoology
- Keywords
- value alignment, artificial intelligence, biologically-inspired AI, AI safety, integrative biological simulation, human values, comparative neuroanatomy, neuropsychoanalysis, human-mimetic AI
- Copyright
- © 2019 Sarma et al.
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
- Cite this article
- 2019. Integrative biological simulation, neuropsychology, and AI safety. PeerJ Preprints 7:e27321v2 https://doi.org/10.7287/peerj.preprints.27321v2
Abstract
We describe a biologically-inspired research agenda with parallel tracks aimed at AI and AI safety. The bottom-up component consists of building a sequence of biophysically realistic simulations of simple organisms such as the nematode Caenorhabditis elegans, the fruit fly Drosophila melanogaster, and the zebrafish Danio rerio to serve as platforms for research into AI algorithms and system architectures. The top-down component consists of an approach to value alignment that grounds AI goal structures in neuropsychology, broadly considered. Our belief is that parallel pursuit of these tracks will inform the development of value-aligned AI systems that have been inspired by embodied organisms with sensorimotor integration. An important set of side benefits is that the research trajectories we describe here are grounded in long-standing intellectual traditions within existing research communities and funding structures. In addition, these research programs overlap with significant contemporary themes in the biological and psychological sciences such as data/model integration and reproducibility.
Author Comment
Substantive revisions based on editorial feedback.