CascadiaPrime Cognition

A 21st Century Lens on Artificial Intelligence

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CascadiaPrime Cognition - A Short Introduction to Artifical Intelligence and Aiification

The perfect should not be an enemy of the good. This website evolves as the spirit, the energy and time permits. Keeping up with AI developments is like trying to drink through a firehose. Better to refine concurrently with adds than fail to share now.

AIification is the process of cognition by artificial intelligence (AI) with machine learning capabilities and is associated with changing over from previous cognitive sources. A precursor revolutionary concept was the "electrification" of our civilization. The broad meaning of Aiification is as it applies to an economic or institutional sector, a region, a national economy or the global economy.

AI takes many forms and some in the field do not like the term AI and prefer a wide variety of other terms. Others outside the field prefer euphemisms like "automation" or "digitization" to refer to AI and AIification. Machine learning, deep learning and AI are not synonymous. Artificial General Intelligence refers to the AI with broad capabilities and does not exist as yet. Current AI is "narrow AI" which correctly describes the narrow, limited special purpose capabilities of todays systems.

Public policy makers need an accelerated course in artificial intelligence. AI is advancing at a rate far beyond the adaptive capacity of most political systems. It will impact all aspects of society. It will impact jobs. It will impact some regions worse than others. It will impact power relationships at the sub-state, nation state and international levels. (It has national security and international stability implications of the first order). AI, climate change and the survival of demorcracy will be the great defining public policy issue of the next fifty years.

Satya Nadella, chief executive of Microsoft at the 2016 WEF in Davos has noted "There is an economic surplus that is going to be created as a result of this fourth industrial revolution"

"The question is how evenly will it be spread between countries, between people in different economic strata and also different parts of the economy."

What he could of said was there will be winners and losers - and the losers are going too be none to happy about it. The maintenance of social cohesion may be a challenge.

First it is important to appreciate that the kind of artificial intelligence involved here is "qualitatively different" than what flies the aircraft on your next flight - for the bulk of its flight. It is different than how the operating system on your computer works and most of its applications. It is different because unlike these programs it is not "written" by humans - in the traditional sense - having once been set up it learns on its own and begins to interact with the world. Machine learning is about to rewrite the rules of the civilization game. It involves computer coding for the initial setup but therafter it learns much as a child learns - by example and interaction. Human cognition is made up of a mosaic of discrete but related cognitive elements - some are more important than others but they likely number in the hundreds and perhaps thousands. Collectively these elements allow us to see, hear, feel, learn, plan and act. So it is with artificial cognition. These kinds of systems are rudimentary now but powerful. They have limited capabilities - for now.

Unlike human sharing of knowledge which occurs at great cost over years - the knowledge of these systems can be shared and replicated nearly instantaneously - like updates to your mobile operating system and APPS.

Policy development for AIification (the good and the bad implications) is complicated by the fact that AI is a global phenomena, can be developed by an individual, and complicated by the fact that global institution building is only just beginning to be effective and most polities can't really wrap their head around any kind of global management requirement for mankind.

Like any domain, there is what you know, and what you know that isn't so. And then there is always the issue of what you don't know, that you don't know. So where to start?

Wiki: "Thinking in abstractions is considered by anthropologists, archaeologists, and sociologists to be one of the key traits in modern human behaviour, which is believed to have developed between 50,000 and 100,000 years ago. Its development is likely to have been closely connected with the development of human language, which (whether spoken or written) appears to both involve and facilitate abstract thinking."

Wiki: "Abstraction in its main sense is a conceptual process where general rules and concepts are derived from the usage and classification of specific examples, literal ("real" or "concrete") signifiers, first principles, or other methods."

Deep learning networks are able to mathematically uncover "abstractions" hidden in "data"quantities beyond the human mind. For example

Cautionary note. Intelligence comes in many forms and guises and can be thought of as noted above as a modular concept. AI has only just begun to successfully replicate these "modules or "aspects" of intelligence.

While this site as a whole is a tool for an expanding appreciation of AI, CascadiaPrime Cognition has grown substantially and would involve hundreds of hours of your time to read, listen to and view what is referenced. Even a quick survey of the sections and the overall structure may prove daunting to those needing or wanting a "one pager".

This introduction then will be in the manner of a "one pager" of where to concentrate your energies. If you are a government or political leader with command of resources then at an early date you will want to establish a center of excellence in your administration designed keep you abreast of developments and how your administration might employ recent AI developments to advantage while developing social innovations to deal with the economic and social changes it will give rise to. This could be simply one person, full time, to start with given that assigned responsibility. Large national states will assign thousands but create focal points to coordinate action. Central banks and departments of finance will be early centers of excellence focusing on this subject.

Once you have absorbed a couple of the presentations below you will want to look at the Comparative AI Policy section of CascadiaPrime to see what other jurisdictions are doing.


Top Ten AI Policy Issues for government leaders

  Top Ten AI Policy Issues for public leaders (provisional)
Talks, Discussions and Papers for beginners and not so beginners

  WEF: A Framework for Developing a National (or Sub-national) Artificial Intelligence Strategy (October 4, 2019)
  MIT: What is machine learning?
  The State of AI Report - 2019 (June 2019)
  A Brief History of Artificial Intelligence including definitions: WHY DEEP LEARNING IS SUDDENLY CHANGING YOUR LIFE (September 28, 2017)
  Technology and the Economy What can machine learning do? Workforce implications Profound change is coming, but roles for humans remain Erik Brynjolfsson1 and Tom Mitchell (December 22, 2017)
  Talk: How Could Machines Learn as Efficiently as Animals and Humans? - Yann LeCun (December 13, 2017)
  Testimony by the Right Honourable Matt Hancock, UK Minister of State, Dept for Digital, Culture, Media & Sport before the House of Lords Artificial Intelligence Committee (December 12, 2017 )
  The One Hundred Year Study on Artificial Intelligence (September 6, 2016.)
  Pedro Domingos podcast interview on GIGAOM Voices in AI (December, 2017 )
  Podcasts: GIGAOM Voices in AI (December, 2017 )
   How AI will enhance propaganda, reprogram culture, and threaten democracy and what to do about it (September 27 2017 )
  McKinsey: Smartening up with Artificial Intelligence (A Primer)(April 2017)
  McKinsey: The Executive's Guide to AI (A Primer)(February 2018)
  PWC: Artificial Intelligence and Robotics 2017 Leveraging artificial intelligence and robotics for sustainable growth (March 2017)
  Microsoft: The animated guide to artificial intelligence (Explanimators: Episode 1)(May 1, 2017 )
  MIT News: Explained: Neural networks (April 14, 2016)
  DARPA's John Launchbury on the Three Waves of AI (February 14, 2016)
  Microsoft Research's Dr. Harry Shum on the future of AI at the 2017 Future Forum Annual Conference (January 15, 2016)
  NYT Magazine: AI through the lens of Google History (December 14, 2016)
  US Senate Subcommittee on Space, Science, & Competitiveness hearing on The Dawn of Artificial Intelligence (November 30, 2016)
  So What is Machine Learning? (March 2016)
  Martin Ford - What does the rise of AI imply for jobs and the economy? (January 2016)
  Pedro Domingos on Five Machine Learning Tribes
  Pedro Domingos: "The Master Algorithm" | Talks at Google (November 2015)
  Ruslan Salakhutdinov - Deep learning - Changing the playing field of artificial intelligence (CIFAR) (July 2015)
   Demis Hassabis - How Deep Learning Can Give Birth to General Artificial Intelligence (2015)
  Shane Legg - Defining AI - Google Deepmind (2009)
  Paths to Human-level AI | Murray Shanahan (November 2015)
  Miles Brundage - Modeling Progress in AI (December 2015)
  Google's Peter Norvig on the Technological Singularity (December 2015)

Recommended Books

  Andrew McAfee & Erik Brynjolfsson: Harnessing our Digital Future - Machine Platform Crowd
  Andrew McAfee & Erik Brynjolfsson: The Second Machine Age
  Pedro Domingos: The Master Algorithm

Recommended Top Ten AI Policy influencers to Follow on Twitter

  Erik Brynjolfsson - @erikbryn Economics of information and information technology. Professor @MIT. Director @MIT_IDE.
  Andrew McAfee - MIT scientist & Co-author of "The Second Machine Age" and "Machine | Platform | Crowd" Andrew McAfee @amcafee Andrew McAfee
  Eric Horvitz, Microsoft Researcher and long time computing industry leader@erichorvitz
  Miles Brundage, AI Policy Research Fellow, Future of Humanity Institute, Oxford@Miles_Brundage
  Susan Athey, the Economics of Technology Professor at Stanford Graduate School of Business @Susan_Athey