CascadiaPrime Cognition - AGI Stewpot

The multidisciplinary character of Artificial Intelligence suggests that insights are likely to come from the synthesis of ideas from multiple domains. Juxtaposition then can be an ally. Why deep learning works is still not understood.

This Section of CascadiaPrime is a holding place for random articles that have not yet been processed and organized over the years.

2020   A guided tour of the first five layers of InceptionV1, taxonomized into “neuron groups.” (April 1, 2020 )      Is COBOL holding you hostage with Math? ( fixedpoint vs floatingpoint ) (July 28, 2018 )    2015 - 2016    "Overcoming the Static Learning Bottleneck -- the Need for Adaptive Neural Learning" (October 4, 2016)      Marta Garnelo, Kai Arulkumaran, Murray Shanahan - Towards Deep Symbolic Reinforcement Learning (September 20, 2016)      Talk by Max Tegmark at the Center for Brains, Minds and Machines (CBMM) Connections between physics and deep learning (August 29, 2016)      The Extraordinary Link Between Deep Neural Networks and the Nature of the Universe (September 9, 2016)      Tomaso Poggio - Massachusetts Institute of Technology - M-theory - Why deeplearning networks work      George Lakoff: How Brains Think: The Embodiment Hypothesis      Danko Nikolic: Practopoiesis Tells Us Machine Learning Isn't Enough      Alex Nugent, Introduction to AHaH Computing (Neuromorphic) and self-organizing emergence (April 2015)      Gashler, Kindle & Smith: A Minimal Architecture for General Cognition (August 2015)      Quanta Magazine: Complex Systems: The New Laws of Explosive Networks (July 2015)      The Giant Component and Explosive Percolation (February 2015)      Solution of the explosive percolation quest. II. Infinite-order transition produced by initial distributions of clusters (January 2015)      Chimera-like states in modular neural networks (October 2015)      Chimera states: coexistence of coherence and incoherence in networks of coupled oscillators (January 2015)      Complex modular structure of large-scale brain networks (June 2009)       The inherent difficulty of decisions, levels of thinking, and stopping rules by which to convert thought into action. (October 2015)      How the mind switches among different ways of thinking about a sequence (October 2015)      Memory Networks (Revised November 2015)    Top