The Mechanical Mind in History by Philip Husbands, Owen Holland and Michael Wheeler - ISBN 0262083779 - MIT Press 2008
Mechanistic view of the world including cognitive, cf MyBeliefs, and wanting to clarify it, especially against dualist arguments.
Draw a schema (using PmGraphViz or another solution) of the situation of the area in the studied domain before having read the book.
- Ideally I should methodically (cf my visual methodical reading poster)
- propose my own view before reading the book
- improve it based on the articles by adding references and arguments
- write my arguments against the views when they contradict but that I can demonstrate my rational is founded by being coherent and having matching data
- What do I expect from the book?
- Improve my human model on cognition to
- improve my own quality of thinking
- facilitate the embodiment of thinking in software thus providing better cognitive support as embedded tools designing internet websites/SaaS
- Clarify questions on
- Is our brain and it's resulting thinking process intrinsically, by nature, mechanistic (and thus potentially predictable)?
- Is it becoming so because of all our mechanical apparatus and thus becoming a self-fulling prophecy?
- How can propose experiments to determine if those are idealizations or realistic models?
- What are the pattern is this history of thinking, the evolution of "this mechanical mind", and thus what can potential futures can we expect?
- Consequently, how could one prepare for those potential futures?
- How did I find it?
- After doing some research on the history of computation (videos of Phun addition/subtraction then the Babbage engine cf cognitive tools evolution)
- What am I especially interested in and why?
- 7 From Mechanisms of Adaptation to Intelligence Amplifiers
- Discovering the paper on Asbhy Intelligence Amplification as opposed to independent artificial intelligence based on the concept of "neo-neo-cortex" (personal idea local work? during 05/2008)
- 11 The Mechanization of Art
- Work on Seedea and the management of ideas
- more generally based on an incremental and thus evolutionist understanding of today's situation thus through history
Note : page numbering refers to the electronic version, 11 pages more than the paper numbering (i.e. page 44 refers to page 33 on paper)
- Introduction: The Mechanical Mind Explore by Philip Husbands, Michael Wheeler, and Owen Holland
- explores the purpose of the book
- not GAI vs AI vs *AI
- but historical construction of the hardware and software computations including the needs and ideals shared by people working on those solutions
- a paragraph for each following chapter
- a short ironic ending on the speculative "church of NBIC" and the potential lessons to learn from our past
- Charles Babbage and the Emergence of Automated Reason by Seth Bullock
- a work and discovery within (and potentially thus thanks to) a context
- the necessity to automatize computation, especially logarithm, based on the pressure of intense automation of labor of the industrial revolution
- growing societal concerns regarding the automation of reasoning fostered by the field of natural theology (decades before Darwin's On the Origin of Species)
- published as Ninth Bridgewater Treatise as a response to critics regarding a mechanist view
- at that time Babbage was one of the very few to be able to carry out research thanks to computation models
- existing important debate over geological record's and the potential role of a deity as a mean to explain "the inexplicable"
- Babbage consequently proposed a "computation model" that would fit the data (thus providing the equivalent of running a simulation)
- the simulation would itself produce the discontinuity without the need of any intervention
- based on a stable and understandable general law or "program"
- he demonstrated its validity in print but also by carrying out the calculations
- Darwin was informed of this demonstrations by Charles Lyell (leading geologist at that time)
- providing Darwin with ‘‘an analogue for the origin of species by natural law without divine intervention’’
- by his experiment, Babbage propose that unexpected irregularities in a phenomenon do no reject the possibility of a model
- a current similar view is punctuated equilibrium by Gould/Eldredge
- nonlinearity once again does not reject the possibility of a model but ask to refine it
- Babbage's thinking process allowed him to rely on usage of his method to demonstrate difficult cases
- The Temple of Serapis from Lyell’s Principles of Geology being an example of such a a capability
- facilitate the usage of large amount of computations required to make a demonstration using tables
- distinguishing between two different usages
- provide a computational model as methodical thinking process
- automate computations to produce information
- already, the usage or precises tables is possible thanks to the division of mathematical labor, an early form of human-based distributed computing
- "strongly influenced by Baron Gaspard De Prony’s work on massive decimal tables in France from 1792"
- we can see that as early as 1792 the division of labor, including cognitive labor, has been envisioned and put into practice for efficiency and reducing costs (p41)
- Menebrea discussed to witch point this automation could go and what the limits would be and suggesting that only only basic cognition functions could be automatized
- Venn stated that only the treatment of prepared information could be automatized unlike gathering of data, formalize the process to compute them and interpret them could not
- Stanley Jevons (1835–1882) and Alfred Marshall (1842–1924) pursued to apply those recent techniques including the work of Venn and Boole to economics
- Jevons working so "that the logical consequences of known states of affairs could be generated efficiently"
- Marshall continuing his work but to go beyond "mere calculation" adding exploratory behavior
- proposing a form of evolutionary algorithm as early as 1860 (p44)
- raising the question of understandability and to witch extent such automatically produced result could hinder the learning mechanism of its users
- D’Arcy Thompson: A Grandfather of A-Life1 by Margaret A. Boden
- On Growth and Form published in 1917, a biological perspective as a starting point
- highly acclaimed but hardly practicable by the lack of computational tools, limited by then current technical feasibility
- Cavendish's fish demonstration try to simulate life by reproducing its mechanisms, not by looking at a living organism or learning from it as (bio)mimetics does
- A-Life is "seeking its logical-computational principles", it is an abstract discipline by nature
- searching for the "pure" mathematical aspect of nature and growth
- in 1787 Goethe suggest a "logic" of life derived from his work on plant metamorphosis (p58)
- but within a framework of "aesthetic", the beauty of efficiency based on needs
- thus being nearly opposed to analytical deconstruction of smaller parts but to quickly be eclipsed by Darwin's work
- Alan Turing’s Mind Machines by Donald Michie
- explores the principle of the halting problem (derived from Hilbert's work and the Entscheidungsproblem) and the universal turning machine
- displaying "intelligence" as an improvement to the initial program
- he expected that such behavior would become possible "As soon as one can provide a reasonably large memory capacity"
- combinatorial solutions did not seem to bother him
- concludes on the growing explanatory problem and the "seemingly irreducible opacity"
- What Did Alan Turing Mean by ‘‘Machine’’? by Andrew Hodges
- The Ratio Club: A Hub of British Cybernetics by Philip Husbands and Owen Holland
- starting now, page number comes from the paper edition
- From Mechanisms of Adaptation to Intelligence Amplifiers, The Philosophy of W. Ross Ashby by Peter M. Asaro
- Gordon Pask and His Maverick Machines by Jon Bird and Ezequiel A. Di Paolo
- Santiago Dreaming by Andy Beckett
- Steps Toward the Synthetic Method, Symbolic Information Processing and Self-Organizing Systems in Early Artificial Intelligence Modeling by Roberto Cordeschi
- The Mechanization of Art by Paul Brown
- The Robot Story: Why Robots Were Born and How They Grew Up by Jana Horáková and Jozef Kelemen
- God's Machines: Descartes on the Mechanization of Mind by Michael Wheeler
- Why Heideggerian AI Failed and How Fixing It Would Require Making It More Heideggerian by Hubert L. Dreyfus
- An Interview with John Maynard Smith
- An Interview with John Holland
- An Interview with Oliver Selfridge
- An Interview with Horace Barlow
- An Interview with Jack Cowan
Overall remarks and questions
So in the end, it was about X and was based on Y.
Point A, B and C are debatable because of e, f and j.
Draw a schema (using PmGraphViz or another solution) of the situation of the area in the studied domain after having read the book. Link it to the pre-reading model and align the two to help easy comparison.
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