CascadiaPrime Cognition

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CascadiaPrime Cognition - AI Technology Stack

Artificial Intelligence will be supported by a diverse array of technologies that are now extant or will in the decades to come.

It is very easy to become narrowly focused on particular technologies, approaches and frames of reference.

The intent here is to stand back and look at the landscape as a whole recognizing that technological and conceptual surprise is possible and indeed probable. There is a need for a broad frame of reference - the OSI Model is one example of differing layers of abstraction. But there are other ways of slicing the cake.

The term technology stack used here will be the widest possible expression of the term and not the narrow expression of it often used in the information technology industry. It will therefore embrace all fields of knowledge that are supportive of advancing information technology including artificial intelligence and artificial general intelligence. Work on the technology stack is global.

Understanding the technology stack is important for legislators, executives and policy makers. Whether democracy survives the next few decades may well depend on that literacy. One aspect of the challenge is content moderation in the tech stack



  There is plenty of room at the Top: What will drive computer performance after Moore's law? Software, Algorithms, Hardware (June 5, 2020)
  Excellent @NCSC overview article: Security, complexity and Huawei; protecting the UK's telecoms networks (February 20, 2019)
  McKinsey: Artificial-intelligence hardware: New opportunities for semiconductor companies (See their nine layer technology stack model for artificial intelligence)(January 2019)
  David D. Clark: "Designing an Internet" | Talks at Google (January 3, 2019)
  Darpa Talk: DARPA ERI Summit 2018: The End of Moore's Law & Faster General Purpose Computing, & a New Golden Age (October 11, 2018)
  Darpa Talk & Panel: The Electronics Resurgence Initiative (ERI) with history (September 24, 2018)
  Computational Power and the Social Impact of Artificial Intelligence (March 24, 2018)
  Design News: The Battle of AI Processors Begins in 2018 (December 18, 2017)
  The Royal Society: Talks: Future directions of machine learning (October 2015) Furber, Hinton, Knowles, Hassabis AGI - 156min
  White House announces a grand challenge to develop transformational computing capabilities by combining innovations in multiple scientific disciplines (October 2015)
  ReBooting the IT Revolution - Semiconductor Industry Association Call to Action (PDF) (September 2015)

Governance and Regulation

  Navigating the Tech Stack: When, Where and How Should We Moderate Content? (October 28, 2019)
  The Role of Professional Norms in AI Governance: Some Observations and Outline of a Framework (April 25, 2019)
  Why A.I. Is a Threat to Democracy � and What We Can Do to Stop It (February 26, 2019)


  Auto bug repair system uses machine learning to fix 10 times as many errors as its predecessors


  See CascadiaPrime Cognition Mathematics
  Combinatorics and Probability

Neural Network Design

  Jeff Hinton on the criticality of Rectifier (Neural Networks)
  Rectifier (Neural Networks)


  Fueling the Gold Rush: The Greatest Public Datasets for�AI (February 11, 2017)


  Jim Keller: Moore's Law, Microprocessors, Abstractions, and First Principles | AI Podcast (February 5, 2020)
  Google Patent: System with wide operand architecture, and method (July 31, 2019)
  Towards artificial general intelligence with hybrid Tianjic chip architecture (July 31, 2019)
  The artificial intelligence computing stack. A look at why the U.S. and China are investing heavily in this new computing stack. (December 20, 2017)
  IEEE Spectrum: Motion-Planning Chip Speeds Robots (December 19, 2016)
  Wired: How AI Is Shaking Up the Chip Market (October 28, 2016)
  Chip Designer Simon Knowles, CTO XMOS, discusses what is required to create intelligent machines - discontinuities & challenges
  Photonic Switching
  Nature: Modern microprocessor built from complementary carbon nanotube transistors (August 29, 2019

Computation / Processing Paradigm

  Researchers demonstrate all-optical neural network for deep learning (August 29, 2019)
  Analog Computing
  Simulation studies of an All-Spin Artificial Neural Network: Emulating neural and synaptic functionalities through domain wall motion in ferromagnets (October 2015)
  See also CascadiaPrime Cognition Quantum Computing)
  See also CascadiaPrime Cognition Neuromorphic Computing


  1970s technology solution to internet 'capacity crunch' (February 29, 2016)
  Data movement as a central challenge for HPC (June 2015)

Long Term Information Storage

  Future Computing: DNA Hard Drives | Nick Goldman

Energy Production / Consumption

  The cost of TEPS

High Performance Computing Challenges - Exascale

  Defining Scalable OS Requirements for Exascale and Beyond (October 2015)
  Sadasivan Shankar - Exascale for Grand Challenge Problems in Science & Engineering (April 2015)
  Thomas Stirling - HPC in Phase Change for Exascale Computing (March 2014)

Big Data Standards

  National Institute of Standards and Technology Takes on Big Data

International Standards

  International AI Standards