2016/03/27 by Kamil Rocki, Rocki, Kamil · 2 voices
Computer Science · #Anomaly Detection Techniques and Applications #Artificial intelligence #Computer science #Machine Learning and Algorithms #Neural Networks and Applications #cs.AI #cs.LG #cs.NE
paper · pdf · doi:10.48550/arxiv.1603.08262
published in arXiv (Cornell University) (Cornell University) · 10 pages, submitted to AGI-16. arXiv admin note: substantial text overlap with arXiv:1512.01926
arxiv created 2016/03/27 · openalex publication_date 2016/03/27 · arxiv updated 2016/03/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
There exists a theory of a single general-purpose learning algorithm which could explain the principles of its operation. This theory assumes that the brain has some initial rough architecture, a small library of simple innate circuits which are prewired at birth and proposes that all significant mental algorithms can be learned. Given current understanding and observations, this paper reviews and lists the ingredients of such an algorithm from both architectural and functional perspectives.