Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Comprehension as compression

210 views

Published on

A talk on understanding, information theory, and big data. University of Melbourne, HPS series, 16 October 2019

Published in: Education
  • DOWNLOAD THIS BOOKS INTO AVAILABLE FORMAT (2019 Update) ......................................................................................................................... ......................................................................................................................... Download Full PDF EBOOK here { https://soo.gd/irt2 } ......................................................................................................................... Download Full EPUB Ebook here { https://soo.gd/irt2 } ......................................................................................................................... Download Full doc Ebook here { https://soo.gd/irt2 } ......................................................................................................................... Download PDF EBOOK here { https://soo.gd/irt2 } ......................................................................................................................... Download EPUB Ebook here { https://soo.gd/irt2 } ......................................................................................................................... Download doc Ebook here { https://soo.gd/irt2 } ......................................................................................................................... ......................................................................................................................... ................................................................................................................................... eBook is an electronic version of a traditional print book THIS can be read by using a personal computer or by using an eBook reader. (An eBook reader can be a software application for use on a computer such as Microsoft's free Reader application, or a book-sized computer THIS is used solely as a reading device such as Nuvomedia's Rocket eBook.) Users can purchase an eBook on diskette or CD, but the most popular method of getting an eBook is to purchase a downloadable file of the eBook (or other reading material) from a Web site (such as Barnes and Noble) to be read from the user's computer or reading device. Generally, an eBook can be downloaded in five minutes or less ......................................................................................................................... .............. Browse by Genre Available eBooks .............................................................................................................................. Art, Biography, Business, Chick Lit, Children's, Christian, Classics, Comics, Contemporary, Cookbooks, Manga, Memoir, Music, Mystery, Non Fiction, Paranormal, Philosophy, Poetry, Psychology, Religion, Romance, Science, Science Fiction, Self Help, Suspense, Spirituality, Sports, Thriller, Travel, Young Adult, Crime, Ebooks, Fantasy, Fiction, Graphic Novels, Historical Fiction, History, Horror, Humor And Comedy, ......................................................................................................................... ......................................................................................................................... .....BEST SELLER FOR EBOOK RECOMMEND............................................................. ......................................................................................................................... Blowout: Corrupted Democracy, Rogue State Russia, and the Richest, Most Destructive Industry on Earth,-- The Ride of a Lifetime: Lessons Learned from 15 Years as CEO of the Walt Disney Company,-- Call Sign Chaos: Learning to Lead,-- StrengthsFinder 2.0,-- Stillness Is the Key,-- She Said: Breaking the Sexual Harassment Story THIS Helped Ignite a Movement,-- Atomic Habits: An Easy & Proven Way to Build Good Habits & Break Bad Ones,-- Everything Is Figureoutable,-- What It Takes: Lessons in the Pursuit of Excellence,-- Rich Dad Poor Dad: What the Rich Teach Their Kids About Money THIS the Poor and Middle Class Do Not!,-- The Total Money Makeover: Classic Edition: A Proven Plan for Financial Fitness,-- Shut Up and Listen!: Hard Business Truths THIS Will Help You Succeed, ......................................................................................................................... .........................................................................................................................
       Reply 
    Are you sure you want to  Yes  No
    Your message goes here
  • DOWNLOAD THIS BOOKS INTO AVAILABLE FORMAT (2019 Update) ......................................................................................................................... ......................................................................................................................... Download Full PDF EBOOK here { https://soo.gd/irt2 } ......................................................................................................................... Download Full EPUB Ebook here { https://soo.gd/irt2 } ......................................................................................................................... Download Full doc Ebook here { https://soo.gd/irt2 } ......................................................................................................................... Download PDF EBOOK here { https://soo.gd/irt2 } ......................................................................................................................... Download EPUB Ebook here { https://soo.gd/irt2 } ......................................................................................................................... Download doc Ebook here { https://soo.gd/irt2 } ......................................................................................................................... ......................................................................................................................... ................................................................................................................................... eBook is an electronic version of a traditional print book THIS can be read by using a personal computer or by using an eBook reader. (An eBook reader can be a software application for use on a computer such as Microsoft's free Reader application, or a book-sized computer THIS is used solely as a reading device such as Nuvomedia's Rocket eBook.) Users can purchase an eBook on diskette or CD, but the most popular method of getting an eBook is to purchase a downloadable file of the eBook (or other reading material) from a Web site (such as Barnes and Noble) to be read from the user's computer or reading device. Generally, an eBook can be downloaded in five minutes or less ......................................................................................................................... .............. Browse by Genre Available eBooks .............................................................................................................................. Art, Biography, Business, Chick Lit, Children's, Christian, Classics, Comics, Contemporary, Cookbooks, Manga, Memoir, Music, Mystery, Non Fiction, Paranormal, Philosophy, Poetry, Psychology, Religion, Romance, Science, Science Fiction, Self Help, Suspense, Spirituality, Sports, Thriller, Travel, Young Adult, Crime, Ebooks, Fantasy, Fiction, Graphic Novels, Historical Fiction, History, Horror, Humor And Comedy, ......................................................................................................................... ......................................................................................................................... .....BEST SELLER FOR EBOOK RECOMMEND............................................................. ......................................................................................................................... Blowout: Corrupted Democracy, Rogue State Russia, and the Richest, Most Destructive Industry on Earth,-- The Ride of a Lifetime: Lessons Learned from 15 Years as CEO of the Walt Disney Company,-- Call Sign Chaos: Learning to Lead,-- StrengthsFinder 2.0,-- Stillness Is the Key,-- She Said: Breaking the Sexual Harassment Story THIS Helped Ignite a Movement,-- Atomic Habits: An Easy & Proven Way to Build Good Habits & Break Bad Ones,-- Everything Is Figureoutable,-- What It Takes: Lessons in the Pursuit of Excellence,-- Rich Dad Poor Dad: What the Rich Teach Their Kids About Money THIS the Poor and Middle Class Do Not!,-- The Total Money Makeover: Classic Edition: A Proven Plan for Financial Fitness,-- Shut Up and Listen!: Hard Business Truths THIS Will Help You Succeed, ......................................................................................................................... .........................................................................................................................
       Reply 
    Are you sure you want to  Yes  No
    Your message goes here
  • DOWNLOAD THIS BOOKS INTO AVAILABLE FORMAT (2019 Update) ......................................................................................................................... ......................................................................................................................... Download Full PDF EBOOK here { https://soo.gd/irt2 } ......................................................................................................................... Download Full EPUB Ebook here { https://soo.gd/irt2 } ......................................................................................................................... Download Full doc Ebook here { https://soo.gd/irt2 } ......................................................................................................................... Download PDF EBOOK here { https://soo.gd/irt2 } ......................................................................................................................... Download EPUB Ebook here { https://soo.gd/irt2 } ......................................................................................................................... Download doc Ebook here { https://soo.gd/irt2 } ......................................................................................................................... ......................................................................................................................... ................................................................................................................................... eBook is an electronic version of a traditional print book THIS can be read by using a personal computer or by using an eBook reader. (An eBook reader can be a software application for use on a computer such as Microsoft's free Reader application, or a book-sized computer THIS is used solely as a reading device such as Nuvomedia's Rocket eBook.) Users can purchase an eBook on diskette or CD, but the most popular method of getting an eBook is to purchase a downloadable file of the eBook (or other reading material) from a Web site (such as Barnes and Noble) to be read from the user's computer or reading device. Generally, an eBook can be downloaded in five minutes or less ......................................................................................................................... .............. Browse by Genre Available eBooks .............................................................................................................................. Art, Biography, Business, Chick Lit, Children's, Christian, Classics, Comics, Contemporary, Cookbooks, Manga, Memoir, Music, Mystery, Non Fiction, Paranormal, Philosophy, Poetry, Psychology, Religion, Romance, Science, Science Fiction, Self Help, Suspense, Spirituality, Sports, Thriller, Travel, Young Adult, Crime, Ebooks, Fantasy, Fiction, Graphic Novels, Historical Fiction, History, Horror, Humor And Comedy, ......................................................................................................................... ......................................................................................................................... .....BEST SELLER FOR EBOOK RECOMMEND............................................................. ......................................................................................................................... Blowout: Corrupted Democracy, Rogue State Russia, and the Richest, Most Destructive Industry on Earth,-- The Ride of a Lifetime: Lessons Learned from 15 Years as CEO of the Walt Disney Company,-- Call Sign Chaos: Learning to Lead,-- StrengthsFinder 2.0,-- Stillness Is the Key,-- She Said: Breaking the Sexual Harassment Story THIS Helped Ignite a Movement,-- Atomic Habits: An Easy & Proven Way to Build Good Habits & Break Bad Ones,-- Everything Is Figureoutable,-- What It Takes: Lessons in the Pursuit of Excellence,-- Rich Dad Poor Dad: What the Rich Teach Their Kids About Money THIS the Poor and Middle Class Do Not!,-- The Total Money Makeover: Classic Edition: A Proven Plan for Financial Fitness,-- Shut Up and Listen!: Hard Business Truths THIS Will Help You Succeed, ......................................................................................................................... .........................................................................................................................
       Reply 
    Are you sure you want to  Yes  No
    Your message goes here
  • DOWNLOAD THIS BOOKS INTO AVAILABLE FORMAT (Unlimited) ......................................................................................................................... ......................................................................................................................... Download Full PDF EBOOK here { https://soo.gd/qURD } ......................................................................................................................... Download Full EPUB Ebook here { https://soo.gd/qURD } ......................................................................................................................... Download Full doc Ebook here { https://soo.gd/qURD } ......................................................................................................................... Download PDF EBOOK here { https://soo.gd/qURD } ......................................................................................................................... Download EPUB Ebook here { https://soo.gd/qURD } ......................................................................................................................... Download doc Ebook here { https://soo.gd/qURD } ......................................................................................................................... ......................................................................................................................... ................................................................................................................................... eBook is an electronic version of a traditional print book THIS can be read by using a personal computer or by using an eBook reader. (An eBook reader can be a software application for use on a computer such as Microsoft's free Reader application, or a book-sized computer THIS is used solely as a reading device such as Nuvomedia's Rocket eBook.) Users can purchase an eBook on diskette or CD, but the most popular method of getting an eBook is to purchase a downloadable file of the eBook (or other reading material) from a Web site (such as Barnes and Noble) to be read from the user's computer or reading device. Generally, an eBook can be downloaded in five minutes or less ......................................................................................................................... .............. Browse by Genre Available eBooks .............................................................................................................................. Art, Biography, Business, Chick Lit, Children's, Christian, Classics, Comics, Contemporary, Cookbooks, Manga, Memoir, Music, Mystery, Non Fiction, Paranormal, Philosophy, Poetry, Psychology, Religion, Romance, Science, Science Fiction, Self Help, Suspense, Spirituality, Sports, Thriller, Travel, Young Adult, Crime, Ebooks, Fantasy, Fiction, Graphic Novels, Historical Fiction, History, Horror, Humor And Comedy, ......................................................................................................................... ......................................................................................................................... .....BEST SELLER FOR EBOOK RECOMMEND............................................................. ......................................................................................................................... Blowout: Corrupted Democracy, Rogue State Russia, and the Richest, Most Destructive Industry on Earth,-- The Ride of a Lifetime: Lessons Learned from 15 Years as CEO of the Walt Disney Company,-- Call Sign Chaos: Learning to Lead,-- StrengthsFinder 2.0,-- Stillness Is the Key,-- She Said: Breaking the Sexual Harassment Story THIS Helped Ignite a Movement,-- Atomic Habits: An Easy & Proven Way to Build Good Habits & Break Bad Ones,-- Everything Is Figureoutable,-- What It Takes: Lessons in the Pursuit of Excellence,-- Rich Dad Poor Dad: What the Rich Teach Their Kids About Money THIS the Poor and Middle Class Do Not!,-- The Total Money Makeover: Classic Edition: A Proven Plan for Financial Fitness,-- Shut Up and Listen!: Hard Business Truths THIS Will Help You Succeed, ......................................................................................................................... .........................................................................................................................
       Reply 
    Are you sure you want to  Yes  No
    Your message goes here
  • DOWNLOAD THIS BOOKS INTO AVAILABLE FORMAT (2019 Update) ......................................................................................................................... ......................................................................................................................... Download Full PDF EBOOK here { https://soo.gd/irt2 } ......................................................................................................................... Download Full EPUB Ebook here { https://soo.gd/irt2 } ......................................................................................................................... Download Full doc Ebook here { https://soo.gd/irt2 } ......................................................................................................................... Download PDF EBOOK here { https://soo.gd/irt2 } ......................................................................................................................... Download EPUB Ebook here { https://soo.gd/irt2 } ......................................................................................................................... Download doc Ebook here { https://soo.gd/irt2 } ......................................................................................................................... ......................................................................................................................... ................................................................................................................................... eBook is an electronic version of a traditional print book THIS can be read by using a personal computer or by using an eBook reader. (An eBook reader can be a software application for use on a computer such as Microsoft's free Reader application, or a book-sized computer THIS is used solely as a reading device such as Nuvomedia's Rocket eBook.) Users can purchase an eBook on diskette or CD, but the most popular method of getting an eBook is to purchase a downloadable file of the eBook (or other reading material) from a Web site (such as Barnes and Noble) to be read from the user's computer or reading device. Generally, an eBook can be downloaded in five minutes or less ......................................................................................................................... .............. Browse by Genre Available eBooks .............................................................................................................................. Art, Biography, Business, Chick Lit, Children's, Christian, Classics, Comics, Contemporary, Cookbooks, Manga, Memoir, Music, Mystery, Non Fiction, Paranormal, Philosophy, Poetry, Psychology, Religion, Romance, Science, Science Fiction, Self Help, Suspense, Spirituality, Sports, Thriller, Travel, Young Adult, Crime, Ebooks, Fantasy, Fiction, Graphic Novels, Historical Fiction, History, Horror, Humor And Comedy, ......................................................................................................................... ......................................................................................................................... .....BEST SELLER FOR EBOOK RECOMMEND............................................................. ......................................................................................................................... Blowout: Corrupted Democracy, Rogue State Russia, and the Richest, Most Destructive Industry on Earth,-- The Ride of a Lifetime: Lessons Learned from 15 Years as CEO of the Walt Disney Company,-- Call Sign Chaos: Learning to Lead,-- StrengthsFinder 2.0,-- Stillness Is the Key,-- She Said: Breaking the Sexual Harassment Story THIS Helped Ignite a Movement,-- Atomic Habits: An Easy & Proven Way to Build Good Habits & Break Bad Ones,-- Everything Is Figureoutable,-- What It Takes: Lessons in the Pursuit of Excellence,-- Rich Dad Poor Dad: What the Rich Teach Their Kids About Money THIS the Poor and Middle Class Do Not!,-- The Total Money Makeover: Classic Edition: A Proven Plan for Financial Fitness,-- Shut Up and Listen!: Hard Business Truths THIS Will Help You Succeed, ......................................................................................................................... .........................................................................................................................
       Reply 
    Are you sure you want to  Yes  No
    Your message goes here
  • Be the first to like this

Comprehension as compression

  1. 1. Comprehension and Compression Scientific Understanding, Pattern Recognition, and Kolmogorov Complexity John S. Wilkins School of Historical and Philosophical Studies
  2. 2. John S Wilkins john@wilkins.id.au 1. Understanding in music, business, and science maybe 2 Outline
  3. 3. John S Wilkins john@wilkins.id.au 1. Understanding in music, business, and science maybe 2. Traditional accounts and recent work 2 Outline
  4. 4. John S Wilkins john@wilkins.id.au 1. Understanding in music, business, and science maybe 2. Traditional accounts and recent work 3. The mechanics of understanding 2 Outline
  5. 5. John S Wilkins john@wilkins.id.au 1. Understanding in music, business, and science maybe 2. Traditional accounts and recent work 3. The mechanics of understanding 4. Kolmogorov complexity and compression 2 Outline
  6. 6. John S Wilkins john@wilkins.id.au 1. Understanding in music, business, and science maybe 2. Traditional accounts and recent work 3. The mechanics of understanding 4. Kolmogorov complexity and compression 5. The subjects that understand 2 Outline
  7. 7. John S Wilkins john@wilkins.id.au 1. Understanding in music, business, and science maybe 2. Traditional accounts and recent work 3. The mechanics of understanding 4. Kolmogorov complexity and compression 5. The subjects that understand 6. Handwaving 2 Outline
  8. 8. John S Wilkins john@wilkins.id.au 1. Understanding in music, business, and science maybe 2. Traditional accounts and recent work 3. The mechanics of understanding 4. Kolmogorov complexity and compression 5. The subjects that understand 6. Handwaving 7. Phylogenies 2 Outline
  9. 9. John S Wilkins john@wilkins.id.au 1. Understanding in music, business, and science maybe 2. Traditional accounts and recent work 3. The mechanics of understanding 4. Kolmogorov complexity and compression 5. The subjects that understand 6. Handwaving 7. Phylogenies 8. Conclusions [tentative] 2 Outline
  10. 10. John S Wilkins john@wilkins.id.au “The accompanying diagram will aid us in understanding this rather perplexing subject.” [Darwin, Origin, chapter 4] 3 Understanding perplexing subjects
  11. 11. John S Wilkins john@wilkins.id.au 4 Biology and the big data problem
  12. 12. John S Wilkins john@wilkins.id.au 4 Biology and the big data problem “It’s human DNA!”
  13. 13. John S Wilkins john@wilkins.id.au “But besides this practical concern, there is a second basic motivation for the scientific quest, namely, man's insatiable intellectual curiosity, his deep concern to know the world he lives in, and to explain, and thus to understand, the unending flow of phenomena it presents to him.” [Carl Hempel 1962] From the data up
  14. 14. John S Wilkins john@wilkins.id.au “But besides this practical concern, there is a second basic motivation for the scientific quest, namely, man's insatiable intellectual curiosity, his deep concern to know the world he lives in, and to explain, and thus to understand, the unending flow of phenomena it presents to him.” [Carl Hempel 1962] “Information is not knowledge. Knowledge is not wisdom. Wisdom is not truth.” [Zappa 1979] From the data up
  15. 15. John S Wilkins john@wilkins.id.au “But besides this practical concern, there is a second basic motivation for the scientific quest, namely, man's insatiable intellectual curiosity, his deep concern to know the world he lives in, and to explain, and thus to understand, the unending flow of phenomena it presents to him.” [Carl Hempel 1962] “Information is not knowledge. Knowledge is not wisdom. Wisdom is not truth.” [Zappa 1979] “Data is not information, information is not knowledge, knowledge is not wisdom, wisdom is not truth.” [Robert Royar 1994] Hempel, Carl G. "Explanation in Science and History," in Frontiers of Science and Philosophy, ed. R.C. Colodny, 1962, pp. 9- 19. Pittsburgh: The University of Pittsburgh Press. Royar, Robert. “New Horizons, Clouded Vistas.” Computers and Composition 11, no. 2 (January 1, 1994): 93–105. Zappa, Frank. “Packard Goose”. 1979. Joe’s Garage: Acts I, II & III. FZ Records. From the data up
  16. 16. John S Wilkins john@wilkins.id.au The missing level in the DIKW pyramid
  17. 17. John S Wilkins john@wilkins.id.au The missing level in the DIKW pyramid Understanding?
  18. 18. John S Wilkins john@wilkins.id.au The missing level in the DIKW pyramid Understanding? If we approach this from the machine learning perspective, we might get a better idea of human scientific understanding
  19. 19. John S Wilkins john@wilkins.id.au Since Aristotle, understanding has been seen as based on causal explanation (cf. de Regt 2004, Baumberger et al. 2017 for summaries) 7 Traditional accounts of understanding
  20. 20. John S Wilkins john@wilkins.id.au Since Aristotle, understanding has been seen as based on causal explanation (cf. de Regt 2004, Baumberger et al. 2017 for summaries) Hempel and the logical empiricists rejected subjective elements such as feelings of confidence or enlightenment 7 Traditional accounts of understanding
  21. 21. John S Wilkins john@wilkins.id.au Since Aristotle, understanding has been seen as based on causal explanation (cf. de Regt 2004, Baumberger et al. 2017 for summaries) Hempel and the logical empiricists rejected subjective elements such as feelings of confidence or enlightenment • “Empathic insight and subjective understanding provide no warrant of objective validity, no basis for the systematic prediction or explanation of phenomena…” [Hempel 1965, 163] 7 Traditional accounts of understanding
  22. 22. John S Wilkins john@wilkins.id.au Since Aristotle, understanding has been seen as based on causal explanation (cf. de Regt 2004, Baumberger et al. 2017 for summaries) Hempel and the logical empiricists rejected subjective elements such as feelings of confidence or enlightenment • “Empathic insight and subjective understanding provide no warrant of objective validity, no basis for the systematic prediction or explanation of phenomena…” [Hempel 1965, 163] The objectivist approach requires ultimately some pragmatist notion: 7 Traditional accounts of understanding
  23. 23. John S Wilkins john@wilkins.id.au Since Aristotle, understanding has been seen as based on causal explanation (cf. de Regt 2004, Baumberger et al. 2017 for summaries) Hempel and the logical empiricists rejected subjective elements such as feelings of confidence or enlightenment • “Empathic insight and subjective understanding provide no warrant of objective validity, no basis for the systematic prediction or explanation of phenomena…” [Hempel 1965, 163] The objectivist approach requires ultimately some pragmatist notion: • Of prediction (Trout 2002, Wittgenstein 1953 §152) 7 Traditional accounts of understanding
  24. 24. John S Wilkins john@wilkins.id.au Since Aristotle, understanding has been seen as based on causal explanation (cf. de Regt 2004, Baumberger et al. 2017 for summaries) Hempel and the logical empiricists rejected subjective elements such as feelings of confidence or enlightenment • “Empathic insight and subjective understanding provide no warrant of objective validity, no basis for the systematic prediction or explanation of phenomena…” [Hempel 1965, 163] The objectivist approach requires ultimately some pragmatist notion: • Of prediction (Trout 2002, Wittgenstein 1953 §152) • Of explanation (van Fraassen 1980) 7 Traditional accounts of understanding
  25. 25. John S Wilkins john@wilkins.id.au Since Aristotle, understanding has been seen as based on causal explanation (cf. de Regt 2004, Baumberger et al. 2017 for summaries) Hempel and the logical empiricists rejected subjective elements such as feelings of confidence or enlightenment • “Empathic insight and subjective understanding provide no warrant of objective validity, no basis for the systematic prediction or explanation of phenomena…” [Hempel 1965, 163] The objectivist approach requires ultimately some pragmatist notion: • Of prediction (Trout 2002, Wittgenstein 1953 §152) • Of explanation (van Fraassen 1980) The utility of causal accounts ultimately constitutes understanding. 7 Traditional accounts of understanding
  26. 26. John S Wilkins john@wilkins.id.au Since Aristotle, understanding has been seen as based on causal explanation (cf. de Regt 2004, Baumberger et al. 2017 for summaries) Hempel and the logical empiricists rejected subjective elements such as feelings of confidence or enlightenment • “Empathic insight and subjective understanding provide no warrant of objective validity, no basis for the systematic prediction or explanation of phenomena…” [Hempel 1965, 163] The objectivist approach requires ultimately some pragmatist notion: • Of prediction (Trout 2002, Wittgenstein 1953 §152) • Of explanation (van Fraassen 1980) The utility of causal accounts ultimately constitutes understanding. Subjectivist or phenomenological accounts of understanding are merely psychologistic on this approach. 7 Traditional accounts of understanding Van Fraassen, Bas C. The ScienQfic Image. Oxford: Clarendon Press, 1980. Hempel, Carl G. Aspects of ScienQfic ExplanaQon, and Other Essays in the Philosophy of Science. New York: The Free Press, 1965. Regt, Henk W. de. “Discussion Note: Making Sense of Understanding.” Philosophy of Science 71, no. 1 (January 1, 2004): 98–109. Trout, J. D. “Scienffic Explanafon and the Sense of Understanding.” Philosophy of Science 69, no. 2 (June 1, 2002): 212–33. Baumberger, Christoph, Claus Beisbart, and Georg Brun. “What Is Understanding? An Overview of Recent Debates in Epistemology and Philosophy of Science.” In Explaining Understanding: New PerspecQves from Epistemolgy and Philosophy of Science, edited by Stephen Grimm Christoph Baumberger and Sabine Ammon, 1–34. Routledge, 2017.
  27. 27. John S Wilkins john@wilkins.id.au Henk de Regt and his collaborators have developed an account of understanding as: 8 Recent work on understanding
  28. 28. John S Wilkins john@wilkins.id.au Henk de Regt and his collaborators have developed an account of understanding as: • Intelligibility 8 Recent work on understanding
  29. 29. John S Wilkins john@wilkins.id.au Henk de Regt and his collaborators have developed an account of understanding as: • Intelligibility • Empirical adequacy 8 Recent work on understanding
  30. 30. John S Wilkins john@wilkins.id.au Henk de Regt and his collaborators have developed an account of understanding as: • Intelligibility • Empirical adequacy • Consistency 8 Recent work on understanding
  31. 31. John S Wilkins john@wilkins.id.au Henk de Regt and his collaborators have developed an account of understanding as: • Intelligibility • Empirical adequacy • Consistency These are contextual features of disciplinary or professional understanding, without reference to subjects 8 Recent work on understanding De Regt, Henk W. Understanding ScienQfic Understanding. Oxford Studies in Philosophy of Science. Oxford University Press, 2017. De Regt, Henk W., Sabine Leonelli, and Kai Eigner. ScienQfic Understanding: Philosophical PerspecQves. Piksburgh: University of Piksburgh Press, 2009.
  32. 32. John S Wilkins john@wilkins.id.au Henk de Regt and his collaborators have developed an account of understanding as: • Intelligibility • Empirical adequacy • Consistency These are contextual features of disciplinary or professional understanding, without reference to subjects 8 Recent work on understanding De Regt, Henk W. Understanding ScienQfic Understanding. Oxford Studies in Philosophy of Science. Oxford University Press, 2017. De Regt, Henk W., Sabine Leonelli, and Kai Eigner. ScienQfic Understanding: Philosophical PerspecQves. Piksburgh: University of Piksburgh Press, 2009.
  33. 33. John S Wilkins john@wilkins.id.au 9 De Regt’s Criteria
  34. 34. John S Wilkins john@wilkins.id.au 1.Intelligibility: the value that scientists attribute to the cluster of qualities of a theory (in one or more of its representations) that facilitate the use of the theory: A scientific theory T (in one or more of its representations) is intelligible for scientists (in context C) if they can recognize qualitatively characteristic consequences of T without performing exact calculations. 9 De Regt’s Criteria
  35. 35. John S Wilkins john@wilkins.id.au 1.Intelligibility: the value that scientists attribute to the cluster of qualities of a theory (in one or more of its representations) that facilitate the use of the theory: A scientific theory T (in one or more of its representations) is intelligible for scientists (in context C) if they can recognize qualitatively characteristic consequences of T without performing exact calculations. 2.Empirical adequacy Nuff said (for now; I’ll get back to this) 9 De Regt’s Criteria
  36. 36. John S Wilkins john@wilkins.id.au 1.Intelligibility: the value that scientists attribute to the cluster of qualities of a theory (in one or more of its representations) that facilitate the use of the theory: A scientific theory T (in one or more of its representations) is intelligible for scientists (in context C) if they can recognize qualitatively characteristic consequences of T without performing exact calculations. 2.Empirical adequacy Nuff said (for now; I’ll get back to this) 3.Consistency: A phenomenon P is understood scientifically if and only if there is an explanation of P that is based on an intelligible theory T and conforms to the basic epistemic values of empirical adequacy and internal consistency. 9 De Regt’s Criteria
  37. 37. John S Wilkins john@wilkins.id.au 1.Intelligibility: the value that scientists attribute to the cluster of qualities of a theory (in one or more of its representations) that facilitate the use of the theory: A scientific theory T (in one or more of its representations) is intelligible for scientists (in context C) if they can recognize qualitatively characteristic consequences of T without performing exact calculations. 2.Empirical adequacy Nuff said (for now; I’ll get back to this) 3.Consistency: A phenomenon P is understood scientifically if and only if there is an explanation of P that is based on an intelligible theory T and conforms to the basic epistemic values of empirical adequacy and internal consistency. Note the passive nature here. It’s as if scientists don’t do very much in understanding 9 De Regt’s Criteria
  38. 38. John S Wilkins john@wilkins.id.au The mechanism of understanding is not generally considered in epistemology… 10 Formal mechanics
  39. 39. John S Wilkins john@wilkins.id.au The mechanism of understanding is not generally considered in epistemology… • because we do not have the requisite neurobiological or neuropsychological knowledge yet (we do not understand understanding); 10 Formal mechanics
  40. 40. John S Wilkins john@wilkins.id.au The mechanism of understanding is not generally considered in epistemology… • because we do not have the requisite neurobiological or neuropsychological knowledge yet (we do not understand understanding); • but also because there are objections to subjectivism in psychological approaches (with which I concur). 10 Formal mechanics
  41. 41. John S Wilkins john@wilkins.id.au The mechanism of understanding is not generally considered in epistemology… • because we do not have the requisite neurobiological or neuropsychological knowledge yet (we do not understand understanding); • but also because there are objections to subjectivism in psychological approaches (with which I concur). How to approach this? 10 Formal mechanics
  42. 42. John S Wilkins john@wilkins.id.au The mechanism of understanding is not generally considered in epistemology… • because we do not have the requisite neurobiological or neuropsychological knowledge yet (we do not understand understanding); • but also because there are objections to subjectivism in psychological approaches (with which I concur). How to approach this? One domain in which such matters are dealt with mechanically (i.e., mechanisms are sought) is machine learning (ML, a subset of AI). 10 Formal mechanics
  43. 43. John S Wilkins john@wilkins.id.au The mechanism of understanding is not generally considered in epistemology… • because we do not have the requisite neurobiological or neuropsychological knowledge yet (we do not understand understanding); • but also because there are objections to subjectivism in psychological approaches (with which I concur). How to approach this? One domain in which such matters are dealt with mechanically (i.e., mechanisms are sought) is machine learning (ML, a subset of AI). • The mechanisms can be opaque “black boxes” in their operation (deep learning) 10 Formal mechanics
  44. 44. John S Wilkins john@wilkins.id.au The mechanism of understanding is not generally considered in epistemology… • because we do not have the requisite neurobiological or neuropsychological knowledge yet (we do not understand understanding); • but also because there are objections to subjectivism in psychological approaches (with which I concur). How to approach this? One domain in which such matters are dealt with mechanically (i.e., mechanisms are sought) is machine learning (ML, a subset of AI). • The mechanisms can be opaque “black boxes” in their operation (deep learning) • There is no subjectivity required (but there is a “subject”; that is, a learning system) 10 Formal mechanics
  45. 45. John S Wilkins john@wilkins.id.au The mechanism of understanding is not generally considered in epistemology… • because we do not have the requisite neurobiological or neuropsychological knowledge yet (we do not understand understanding); • but also because there are objections to subjectivism in psychological approaches (with which I concur). How to approach this? One domain in which such matters are dealt with mechanically (i.e., mechanisms are sought) is machine learning (ML, a subset of AI). • The mechanisms can be opaque “black boxes” in their operation (deep learning) • There is no subjectivity required (but there is a “subject”; that is, a learning system) I will attempt to generalise ML and algorithmic information theoretic tools to apply to this problem of understanding within knowing systems 10 Formal mechanics
  46. 46. John S Wilkins john@wilkins.id.au It may help if we avail ourselves of an old distinction in physics between two kinds of description: 11 Kinematic versus dynamic
  47. 47. John S Wilkins john@wilkins.id.au It may help if we avail ourselves of an old distinction in physics between two kinds of description: • Kinematics: description of motions without consideration of forces 11 Kinematic versus dynamic
  48. 48. John S Wilkins john@wilkins.id.au It may help if we avail ourselves of an old distinction in physics between two kinds of description: • Kinematics: description of motions without consideration of forces • Dynamics: the relations between forces and motion* 11 Kinematic versus dynamic *Dunamis [δύναμις] is an ordinary Greek word for possibility or capability. Depending on context, it could be translated "potency", "potential", "capacity", "ability", "power", "capability", "strength", "possibility", "force" [Wikipedia]
  49. 49. John S Wilkins john@wilkins.id.au It may help if we avail ourselves of an old distinction in physics between two kinds of description: • Kinematics: description of motions without consideration of forces • Dynamics: the relations between forces and motion* In brief: between phenomenal accounts and causal explanations. 11 Kinematic versus dynamic *Dunamis [δύναμις] is an ordinary Greek word for possibility or capability. Depending on context, it could be translated "potency", "potential", "capacity", "ability", "power", "capability", "strength", "possibility", "force" [Wikipedia]
  50. 50. John S Wilkins john@wilkins.id.au It may help if we avail ourselves of an old distinction in physics between two kinds of description: • Kinematics: description of motions without consideration of forces • Dynamics: the relations between forces and motion* In brief: between phenomenal accounts and causal explanations. We can explore the problem of understanding in knowing systems via the shift from kinematic accounts (what the behaviour of the system that understands is) to dynamic explanations (what forces the behaviour of the understanding system) 11 Kinematic versus dynamic *Dunamis [δύναμις] is an ordinary Greek word for possibility or capability. Depending on context, it could be translated "potency", "potential", "capacity", "ability", "power", "capability", "strength", "possibility", "force" [Wikipedia]
  51. 51. John S Wilkins john@wilkins.id.au It may help if we avail ourselves of an old distinction in physics between two kinds of description: • Kinematics: description of motions without consideration of forces • Dynamics: the relations between forces and motion* In brief: between phenomenal accounts and causal explanations. We can explore the problem of understanding in knowing systems via the shift from kinematic accounts (what the behaviour of the system that understands is) to dynamic explanations (what forces the behaviour of the understanding system) • Of systems that are the subject of understanding 11 Kinematic versus dynamic *Dunamis [δύναμις] is an ordinary Greek word for possibility or capability. Depending on context, it could be translated "potency", "potential", "capacity", "ability", "power", "capability", "strength", "possibility", "force" [Wikipedia]
  52. 52. John S Wilkins john@wilkins.id.au It may help if we avail ourselves of an old distinction in physics between two kinds of description: • Kinematics: description of motions without consideration of forces • Dynamics: the relations between forces and motion* In brief: between phenomenal accounts and causal explanations. We can explore the problem of understanding in knowing systems via the shift from kinematic accounts (what the behaviour of the system that understands is) to dynamic explanations (what forces the behaviour of the understanding system) • Of systems that are the subject of understanding • Of systems that understand 11 Kinematic versus dynamic *Dunamis [δύναμις] is an ordinary Greek word for possibility or capability. Depending on context, it could be translated "potency", "potential", "capacity", "ability", "power", "capability", "strength", "possibility", "force" [Wikipedia]
  53. 53. John S Wilkins john@wilkins.id.au It may help if we avail ourselves of an old distinction in physics between two kinds of description: • Kinematics: description of motions without consideration of forces • Dynamics: the relations between forces and motion* In brief: between phenomenal accounts and causal explanations. We can explore the problem of understanding in knowing systems via the shift from kinematic accounts (what the behaviour of the system that understands is) to dynamic explanations (what forces the behaviour of the understanding system) • Of systems that are the subject of understanding • Of systems that understand These coincide: as we move from kinematic descriptions to dynamic explanations of knowing systems, we also move from considering knowledge to considering understanding 11 Kinematic versus dynamic *Dunamis [δύναμις] is an ordinary Greek word for possibility or capability. Depending on context, it could be translated "potency", "potential", "capacity", "ability", "power", "capability", "strength", "possibility", "force" [Wikipedia]
  54. 54. John S Wilkins john@wilkins.id.au Knowing systems have a limit to their working memory under realistic assumptions 12 Pattern recognition
  55. 55. John S Wilkins john@wilkins.id.au Knowing systems have a limit to their working memory under realistic assumptions Knowing systems also have to deal with noisy data and uncertainty 12 Pattern recognition
  56. 56. John S Wilkins john@wilkins.id.au Knowing systems have a limit to their working memory under realistic assumptions Knowing systems also have to deal with noisy data and uncertainty Once the scale of the data set exceeds the capacity of the system’s cognitive limits [working memory, processing power] pattern recognition must be out-sourced 12 Pattern recognition
  57. 57. John S Wilkins john@wilkins.id.au Knowing systems have a limit to their working memory under realistic assumptions Knowing systems also have to deal with noisy data and uncertainty Once the scale of the data set exceeds the capacity of the system’s cognitive limits [working memory, processing power] pattern recognition must be out-sourced • E.g., regression and other statistical analyses 12 Pattern recognition
  58. 58. John S Wilkins john@wilkins.id.au Knowing systems have a limit to their working memory under realistic assumptions Knowing systems also have to deal with noisy data and uncertainty Once the scale of the data set exceeds the capacity of the system’s cognitive limits [working memory, processing power] pattern recognition must be out-sourced • E.g., regression and other statistical analyses • Machine Learning classifications (ANNs) 12 Pattern recognition https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0069191
  59. 59. John S Wilkins john@wilkins.id.au Knowing systems have a limit to their working memory under realistic assumptions Knowing systems also have to deal with noisy data and uncertainty Once the scale of the data set exceeds the capacity of the system’s cognitive limits [working memory, processing power] pattern recognition must be out-sourced • E.g., regression and other statistical analyses • Machine Learning classifications (ANNs) • Distance analyses (Hamming, Manhattan) 12 Pattern recognition
  60. 60. John S Wilkins john@wilkins.id.au Knowing systems have a limit to their working memory under realistic assumptions Knowing systems also have to deal with noisy data and uncertainty Once the scale of the data set exceeds the capacity of the system’s cognitive limits [working memory, processing power] pattern recognition must be out-sourced • E.g., regression and other statistical analyses • Machine Learning classifications (ANNs) • Distance analyses (Hamming, Manhattan) Less memory and cognitive resources are required to retrieve and interpret patterns 12 Pattern recognition
  61. 61. John S Wilkins john@wilkins.id.au Knowing systems have a limit to their working memory under realistic assumptions Knowing systems also have to deal with noisy data and uncertainty Once the scale of the data set exceeds the capacity of the system’s cognitive limits [working memory, processing power] pattern recognition must be out-sourced • E.g., regression and other statistical analyses • Machine Learning classifications (ANNs) • Distance analyses (Hamming, Manhattan) Less memory and cognitive resources are required to retrieve and interpret patterns A pattern therefore has information the data does not 12 Pattern recognition
  62. 62. John S Wilkins john@wilkins.id.au Knowing systems have a limit to their working memory under realistic assumptions Knowing systems also have to deal with noisy data and uncertainty Once the scale of the data set exceeds the capacity of the system’s cognitive limits [working memory, processing power] pattern recognition must be out-sourced • E.g., regression and other statistical analyses • Machine Learning classifications (ANNs) • Distance analyses (Hamming, Manhattan) Less memory and cognitive resources are required to retrieve and interpret patterns A pattern therefore has information the data does not [Here, information is a property of the state of a knowing system] 12 Pattern recognition
  63. 63. John S Wilkins john@wilkins.id.au As any ML system is a Turing machine, it follows that, even when we do not know it, there is an algorithm and data set A that generates the output pattern P [where P is the phenomenon] 13 Machine learning and understanding
  64. 64. John S Wilkins john@wilkins.id.au As any ML system is a Turing machine, it follows that, even when we do not know it, there is an algorithm and data set A that generates the output pattern P [where P is the phenomenon] The information content of P is therefore the complexity of A 13 Machine learning and understanding
  65. 65. John S Wilkins john@wilkins.id.au As any ML system is a Turing machine, it follows that, even when we do not know it, there is an algorithm and data set A that generates the output pattern P [where P is the phenomenon] The information content of P is therefore the complexity of A • If P is lossy, then its information content is lower in complexity than the data set 13 Machine learning and understanding
  66. 66. John S Wilkins john@wilkins.id.au As any ML system is a Turing machine, it follows that, even when we do not know it, there is an algorithm and data set A that generates the output pattern P [where P is the phenomenon] The information content of P is therefore the complexity of A • If P is lossy, then its information content is lower in complexity than the data set • If P is lossless, then the complexity of A equals the complexity of P [defn] 13 Machine learning and understanding
  67. 67. John S Wilkins john@wilkins.id.au As any ML system is a Turing machine, it follows that, even when we do not know it, there is an algorithm and data set A that generates the output pattern P [where P is the phenomenon] The information content of P is therefore the complexity of A • If P is lossy, then its information content is lower in complexity than the data set • If P is lossless, then the complexity of A equals the complexity of P [defn] So A is a compression algorithm*. By analogy, when we generate a pattern (such as a regression curve) from a large or noisy data set, we are compressing the data to generate information, and from that information we can fairly say we know the domain the data set samples or represents. 13 Machine learning and understanding * The worst compression is, of course, the data set itself plus a “print” command
  68. 68. John S Wilkins john@wilkins.id.au As any ML system is a Turing machine, it follows that, even when we do not know it, there is an algorithm and data set A that generates the output pattern P [where P is the phenomenon] The information content of P is therefore the complexity of A • If P is lossy, then its information content is lower in complexity than the data set • If P is lossless, then the complexity of A equals the complexity of P [defn] So A is a compression algorithm*. By analogy, when we generate a pattern (such as a regression curve) from a large or noisy data set, we are compressing the data to generate information, and from that information we can fairly say we know the domain the data set samples or represents. The complexity of P is specified in Algorithmic Information Theory (AIT) as the length (in bits) of the shortest program that generates P. 13 Machine learning and understanding * The worst compression is, of course, the data set itself plus a “print” command
  69. 69. John S Wilkins john@wilkins.id.au AIT is based on the work of Andrey Kolmogorov (1903–1987) and Ray Solomonov (1926–2009) 14 Kolmogorov compressed
  70. 70. John S Wilkins john@wilkins.id.au AIT is based on the work of Andrey Kolmogorov (1903–1987) and Ray Solomonov (1926–2009) Kolmogorov Complexity (K) is the length of the shortest program in a language L that generates a string w 14 Kolmogorov compressed
  71. 71. John S Wilkins john@wilkins.id.au AIT is based on the work of Andrey Kolmogorov (1903–1987) and Ray Solomonov (1926–2009) Kolmogorov Complexity (K) is the length of the shortest program in a language L that generates a string w • The most complex string in L is a fully random string 14 Kolmogorov compressed
  72. 72. John S Wilkins john@wilkins.id.au AIT is based on the work of Andrey Kolmogorov (1903–1987) and Ray Solomonov (1926–2009) Kolmogorov Complexity (K) is the length of the shortest program in a language L that generates a string w • The most complex string in L is a fully random string • A fully random string is incompressible 14 Kolmogorov compressed
  73. 73. John S Wilkins john@wilkins.id.au AIT is based on the work of Andrey Kolmogorov (1903–1987) and Ray Solomonov (1926–2009) Kolmogorov Complexity (K) is the length of the shortest program in a language L that generates a string w • The most complex string in L is a fully random string • A fully random string is incompressible • The working memory of a ML system is thus the largest random string that it can hold in memory while applying functions to it 14 Kolmogorov compressed
  74. 74. John S Wilkins john@wilkins.id.au AIT is based on the work of Andrey Kolmogorov (1903–1987) and Ray Solomonov (1926–2009) Kolmogorov Complexity (K) is the length of the shortest program in a language L that generates a string w • The most complex string in L is a fully random string • A fully random string is incompressible • The working memory of a ML system is thus the largest random string that it can hold in memory while applying functions to it Thus K gives the complexity of a string (of digits, or other structured data) 14 Kolmogorov compressed
  75. 75. John S Wilkins john@wilkins.id.au AIT is based on the work of Andrey Kolmogorov (1903–1987) and Ray Solomonov (1926–2009) Kolmogorov Complexity (K) is the length of the shortest program in a language L that generates a string w • The most complex string in L is a fully random string • A fully random string is incompressible • The working memory of a ML system is thus the largest random string that it can hold in memory while applying functions to it Thus K gives the complexity of a string (of digits, or other structured data) If we conceive of ourselves as knowing systems analogous to ML systems, the information in data is thus the K “program” in our cognitive processes 14 Kolmogorov compressed Li, Ming, and Paul Vitányi. An IntroducQon to Kolmogorov Complexity and Its ApplicaQons. 4th ed. Texts in Computer Science. Springer Internafonal Publishing, 2019. Grünwald, Peter, and Paul M. B. Vitányi. “Shannon Informafon and Kolmogorov Complexity.” CoRR cs.IT/0410002 (2004). Wallace, C. S., and D. L. Dowe. “Minimum Message Length and Kolmogorov Complexity.” The Computer Journal 42, no. 4 (January 1, 1999): 270–83.
  76. 76. John S Wilkins john@wilkins.id.au Note that while using complexity metrics is based on the subject – the ML or the knowing system – it is not subjective, as the experience felt by the knower is not relevant for understanding the information 15 Reducing the complexity of data
  77. 77. John S Wilkins john@wilkins.id.au Note that while using complexity metrics is based on the subject – the ML or the knowing system – it is not subjective, as the experience felt by the knower is not relevant for understanding the information [Nor is this an objectivist account, as it does not depend upon a truth operator] 15 Reducing the complexity of data
  78. 78. John S Wilkins john@wilkins.id.au Note that while using complexity metrics is based on the subject – the ML or the knowing system – it is not subjective, as the experience felt by the knower is not relevant for understanding the information [Nor is this an objectivist account, as it does not depend upon a truth operator] Complexity of the information is inversely related to the informativeness for a limited knowing system 15 Reducing the complexity of data
  79. 79. John S Wilkins john@wilkins.id.au Note that while using complexity metrics is based on the subject – the ML or the knowing system – it is not subjective, as the experience felt by the knower is not relevant for understanding the information [Nor is this an objectivist account, as it does not depend upon a truth operator] Complexity of the information is inversely related to the informativeness for a limited knowing system • Put another way, the tractability of inferences increases as complexity reduces 15 Reducing the complexity of data
  80. 80. John S Wilkins john@wilkins.id.au Note that while using complexity metrics is based on the subject – the ML or the knowing system – it is not subjective, as the experience felt by the knower is not relevant for understanding the information [Nor is this an objectivist account, as it does not depend upon a truth operator] Complexity of the information is inversely related to the informativeness for a limited knowing system • Put another way, the tractability of inferences increases as complexity reduces [Inversely, tractability reduces as data complexity increases, unless it is compressible] 15 Reducing the complexity of data
  81. 81. John S Wilkins john@wilkins.id.au Note that while using complexity metrics is based on the subject – the ML or the knowing system – it is not subjective, as the experience felt by the knower is not relevant for understanding the information [Nor is this an objectivist account, as it does not depend upon a truth operator] Complexity of the information is inversely related to the informativeness for a limited knowing system • Put another way, the tractability of inferences increases as complexity reduces [Inversely, tractability reduces as data complexity increases, unless it is compressible] • Compression of data to information is a lossy process; the structure/pattern in the data is simplified through regression, etc. 15 Reducing the complexity of data
  82. 82. John S Wilkins john@wilkins.id.au Note that while using complexity metrics is based on the subject – the ML or the knowing system – it is not subjective, as the experience felt by the knower is not relevant for understanding the information [Nor is this an objectivist account, as it does not depend upon a truth operator] Complexity of the information is inversely related to the informativeness for a limited knowing system • Put another way, the tractability of inferences increases as complexity reduces [Inversely, tractability reduces as data complexity increases, unless it is compressible] • Compression of data to information is a lossy process; the structure/pattern in the data is simplified through regression, etc. • The function of this information is to set the expectations of the knowing system [but that’s another talk; contrastive explanation is why] 15 Reducing the complexity of data
  83. 83. John S Wilkins john@wilkins.id.au Note that while using complexity metrics is based on the subject – the ML or the knowing system – it is not subjective, as the experience felt by the knower is not relevant for understanding the information [Nor is this an objectivist account, as it does not depend upon a truth operator] Complexity of the information is inversely related to the informativeness for a limited knowing system • Put another way, the tractability of inferences increases as complexity reduces [Inversely, tractability reduces as data complexity increases, unless it is compressible] • Compression of data to information is a lossy process; the structure/pattern in the data is simplified through regression, etc. • The function of this information is to set the expectations of the knowing system [but that’s another talk; contrastive explanation is why] • In short: the information derived from the data sets the prior probabilities for future data, highlighting anomalies and points of interest 15 Reducing the complexity of data
  84. 84. John S Wilkins john@wilkins.id.au 16 Compression and generalisation “Solomonoff held that all inference problems can be cast in the form of extrapolation from an ordered sequence of binary symbols.” [Li and Vintányi 2019,63]
  85. 85. John S Wilkins john@wilkins.id.au 16 Compression and generalisation Measurement compresses to “Solomonoff held that all inference problems can be cast in the form of extrapolation from an ordered sequence of binary symbols.” [Li and Vintányi 2019,63]
  86. 86. John S Wilkins john@wilkins.id.au Analysis compresses to 16 Compression and generalisation Measurement compresses to “Solomonoff held that all inference problems can be cast in the form of extrapolation from an ordered sequence of binary symbols.” [Li and Vintányi 2019,63]
  87. 87. John S Wilkins john@wilkins.id.au Analysis compresses to 16 Compression and generalisation Measurement compresses to Kinematic rules y = f(x) applies to “Solomonoff held that all inference problems can be cast in the form of extrapolation from an ordered sequence of binary symbols.” [Li and Vintányi 2019,63]
  88. 88. John S Wilkins john@wilkins.id.au Analysis compresses to Dynamic or causal models y = f(x) 16 Compression and generalisation Measurement compresses to Kinematic rules y = f(x) applies to “Solomonoff held that all inference problems can be cast in the form of extrapolation from an ordered sequence of binary symbols.” [Li and Vintányi 2019,63]
  89. 89. John S Wilkins john@wilkins.id.au Analysis compresses to Dynamic or causal models y = f(x) 16 Compression and generalisation Measurement compresses to Kinematic rules y = f(x) applies to Data Information KnowledgeUnderstanding “Solomonoff held that all inference problems can be cast in the form of extrapolation from an ordered sequence of binary symbols.” [Li and Vintányi 2019,63]
  90. 90. John S Wilkins john@wilkins.id.au Analysis compresses to Dynamic or causal models y = f(x) 16 Compression and generalisation Measurement compresses to Kinematic rules y = f(x) applies to Data Information KnowledgeUnderstanding “Solomonoff held that all inference problems can be cast in the form of extrapolation from an ordered sequence of binary symbols.” [Li and Vintányi 2019,63]
  91. 91. John S Wilkins john@wilkins.id.au Analysis compresses to Dynamic or causal models y = f(x) 16 Compression and generalisation Measurement compresses to Kinematic rules y = f(x) applies to Data Information KnowledgeUnderstanding Understanding “Solomonoff held that all inference problems can be cast in the form of extrapolation from an ordered sequence of binary symbols.” [Li and Vintányi 2019,63]
  92. 92. John S Wilkins john@wilkins.id.au Analysis compresses to Dynamic or causal models y = f(x) 16 Compression and generalisation Measurement compresses to Kinematic rules y = f(x) applies to Data Information KnowledgeUnderstanding Understanding What counts as wisdom is left to you to decide “Solomonoff held that all inference problems can be cast in the form of extrapolation from an ordered sequence of binary symbols.” [Li and Vintányi 2019,63]
  93. 93. John S Wilkins john@wilkins.id.au So, how might this analogy work in the arena of science? Scientists seek to understand the universe, of course, as Hempel said. 17 Subjects understanding
  94. 94. John S Wilkins john@wilkins.id.au So, how might this analogy work in the arena of science? Scientists seek to understand the universe, of course, as Hempel said. • But what does “scientists” refer to? What are the knowing systems, or subjects, in science? 17 Subjects understanding
  95. 95. John S Wilkins john@wilkins.id.au So, how might this analogy work in the arena of science? Scientists seek to understand the universe, of course, as Hempel said. • But what does “scientists” refer to? What are the knowing systems, or subjects, in science? • particular scientists 17 Subjects understanding
  96. 96. John S Wilkins john@wilkins.id.au So, how might this analogy work in the arena of science? Scientists seek to understand the universe, of course, as Hempel said. • But what does “scientists” refer to? What are the knowing systems, or subjects, in science? • particular scientists • disciplines and sub disciplines 17 Subjects understanding
  97. 97. John S Wilkins john@wilkins.id.au So, how might this analogy work in the arena of science? Scientists seek to understand the universe, of course, as Hempel said. • But what does “scientists” refer to? What are the knowing systems, or subjects, in science? • particular scientists • disciplines and sub disciplines • research groups 17 Subjects understanding
  98. 98. John S Wilkins john@wilkins.id.au So, how might this analogy work in the arena of science? Scientists seek to understand the universe, of course, as Hempel said. • But what does “scientists” refer to? What are the knowing systems, or subjects, in science? • particular scientists • disciplines and sub disciplines • research groups I would take this approach to apply equally well to both individual and collective knowing systems/subjects. 17 Subjects understanding
  99. 99. John S Wilkins john@wilkins.id.au So, how might this analogy work in the arena of science? Scientists seek to understand the universe, of course, as Hempel said. • But what does “scientists” refer to? What are the knowing systems, or subjects, in science? • particular scientists • disciplines and sub disciplines • research groups I would take this approach to apply equally well to both individual and collective knowing systems/subjects. We can say that Professor X understands some phenomenon, or that Discipline D understands some phenomenon independently (individuals can fail to understand what the discipline does, and vice versa) 17 Subjects understanding
  100. 100. John S Wilkins john@wilkins.id.au So, how might this analogy work in the arena of science? Scientists seek to understand the universe, of course, as Hempel said. • But what does “scientists” refer to? What are the knowing systems, or subjects, in science? • particular scientists • disciplines and sub disciplines • research groups I would take this approach to apply equally well to both individual and collective knowing systems/subjects. We can say that Professor X understands some phenomenon, or that Discipline D understands some phenomenon independently (individuals can fail to understand what the discipline does, and vice versa) [Does this mean we have actual Turing machines in our heads? No, it’s an abstract way to consider the problem (just as neurones are not artificial neurones or v.v.)] 17 Subjects understanding
  101. 101. John S Wilkins john@wilkins.id.au I do not have time here to give more than one proper case study; so I will handwave at some: 18 Insert case studies here
  102. 102. John S Wilkins john@wilkins.id.au I do not have time here to give more than one proper case study; so I will handwave at some: • Gas laws (Boyles’ Law, Charles’ Law, Gay-Lussac’s Law, Avogadro’s Law, etc.) leading to the Ideal Gas Law, to van der Waals’ equation 18 Insert case studies here
  103. 103. John S Wilkins john@wilkins.id.au I do not have time here to give more than one proper case study; so I will handwave at some: • Gas laws (Boyles’ Law, Charles’ Law, Gay-Lussac’s Law, Avogadro’s Law, etc.) leading to the Ideal Gas Law, to van der Waals’ equation • Periodic table construction by empirical lab work, followed first by valency, then electron shell, then quantum mechanical causal accounts 18 Insert case studies here
  104. 104. John S Wilkins john@wilkins.id.au I do not have time here to give more than one proper case study; so I will handwave at some: • Gas laws (Boyles’ Law, Charles’ Law, Gay-Lussac’s Law, Avogadro’s Law, etc.) leading to the Ideal Gas Law, to van der Waals’ equation • Periodic table construction by empirical lab work, followed first by valency, then electron shell, then quantum mechanical causal accounts • Mendelian genetics (phenomenal) through to population genetics (kinematic), through to molecular genetics (dynamic) 18 Insert case studies here
  105. 105. John S Wilkins john@wilkins.id.au I do not have time here to give more than one proper case study; so I will handwave at some: • Gas laws (Boyles’ Law, Charles’ Law, Gay-Lussac’s Law, Avogadro’s Law, etc.) leading to the Ideal Gas Law, to van der Waals’ equation • Periodic table construction by empirical lab work, followed first by valency, then electron shell, then quantum mechanical causal accounts • Mendelian genetics (phenomenal) through to population genetics (kinematic), through to molecular genetics (dynamic) • Arguably: observational to theoretical ecology, through to ecological thermodynamics and “ecological orbits” (Haynie 2001, Ginsburg and Colyvan 2004) 18 Insert case studies here
  106. 106. John S Wilkins john@wilkins.id.au I do not have time here to give more than one proper case study; so I will handwave at some: • Gas laws (Boyles’ Law, Charles’ Law, Gay-Lussac’s Law, Avogadro’s Law, etc.) leading to the Ideal Gas Law, to van der Waals’ equation • Periodic table construction by empirical lab work, followed first by valency, then electron shell, then quantum mechanical causal accounts • Mendelian genetics (phenomenal) through to population genetics (kinematic), through to molecular genetics (dynamic) • Arguably: observational to theoretical ecology, through to ecological thermodynamics and “ecological orbits” (Haynie 2001, Ginsburg and Colyvan 2004) • Tectonic drift theory (Oreskes and le Grand 2003) 18 Insert case studies here Ginzburg, Lev R., and Mark Colyvan. Ecological Orbits : How Planets Move and PopulaQons Grow. Oxford; New York: Oxford University Press, 2004. Haynie, Donald T. Biological Thermodynamics. Cambridge ; New York: Cambridge University Press, 2001. Oreskes, Naomi, and Homer E. LeGrand. Plate Tectonics: An Insider’s History of the Modern Theory of the Earth. Boulder, CO.: Westview Press, 2003.
  107. 107. John S Wilkins john@wilkins.id.au • Phylogenies used to be done qualitatively/ subjectively 19 Case: Phylogenies From Stirton, R. A. 1940. Phylogeny of North American Equidae. Bull. Dept. Geol. Sci., Univ. California 25(4): 165-198.
  108. 108. John S Wilkins john@wilkins.id.au • Phylogenies used to be done qualitatively/ subjectively • In cases like the palaeontology of the horse, only morphological-skeletal characters were available 19 Case: Phylogenies From Stirton, R. A. 1940. Phylogeny of North American Equidae. Bull. Dept. Geol. Sci., Univ. California 25(4): 165-198.
  109. 109. John S Wilkins john@wilkins.id.au • Phylogenies used to be done qualitatively/ subjectively • In cases like the palaeontology of the horse, only morphological-skeletal characters were available • Consequently, the diagrams relied upon the apprehension of small data sets combined with the “expertise” of the taxonomist 19 Case: Phylogenies From Stirton, R. A. 1940. Phylogeny of North American Equidae. Bull. Dept. Geol. Sci., Univ. California 25(4): 165-198.
  110. 110. John S Wilkins john@wilkins.id.au • Phylogenies used to be done qualitatively/ subjectively • In cases like the palaeontology of the horse, only morphological-skeletal characters were available • Consequently, the diagrams relied upon the apprehension of small data sets combined with the “expertise” of the taxonomist • As phylogenetics became mathematised with “numerical” taxonomy (computer-based, “total evidence”), it was supposed to become more objective 19 Case: Phylogenies From Stirton, R. A. 1940. Phylogeny of North American Equidae. Bull. Dept. Geol. Sci., Univ. California 25(4): 165-198.
  111. 111. John S Wilkins john@wilkins.id.au • Phylogenies used to be done qualitatively/ subjectively • In cases like the palaeontology of the horse, only morphological-skeletal characters were available • Consequently, the diagrams relied upon the apprehension of small data sets combined with the “expertise” of the taxonomist • As phylogenetics became mathematised with “numerical” taxonomy (computer-based, “total evidence”), it was supposed to become more objective • It wasn’t (choice of principal components skewed the results unpredictably) 19 Case: Phylogenies From Stirton, R. A. 1940. Phylogeny of North American Equidae. Bull. Dept. Geol. Sci., Univ. California 25(4): 165-198.
  112. 112. John S Wilkins john@wilkins.id.au • Phylogenies used to be done qualitatively/ subjectively • In cases like the palaeontology of the horse, only morphological-skeletal characters were available • Consequently, the diagrams relied upon the apprehension of small data sets combined with the “expertise” of the taxonomist • As phylogenetics became mathematised with “numerical” taxonomy (computer-based, “total evidence”), it was supposed to become more objective • It wasn’t (choice of principal components skewed the results unpredictably) • Enter molecular data 19 Case: Phylogenies From Stirton, R. A. 1940. Phylogeny of North American Equidae. Bull. Dept. Geol. Sci., Univ. California 25(4): 165-198.
  113. 113. John S Wilkins john@wilkins.id.au • At first, DNA-DNA hybridisation studies 20 Molecular phylogenies Sibley, Charles G., and Jon E. Ahlquist. “Phylogeny and Classification of Birds Based on the Data of DNA-DNA Hybridization.” Current Ornithology. Boston, MA: Springer US, 1983.
  114. 114. John S Wilkins john@wilkins.id.au • At first, DNA-DNA hybridisation studies • E.g., Sibley and Ahlquist’s taxonomy of birds in 1980s 20 Molecular phylogenies Sibley, Charles G., and Jon E. Ahlquist. “Phylogeny and Classification of Birds Based on the Data of DNA-DNA Hybridization.” Current Ornithology. Boston, MA: Springer US, 1983.
  115. 115. John S Wilkins john@wilkins.id.au • At first, DNA-DNA hybridisation studies • E.g., Sibley and Ahlquist’s taxonomy of birds in 1980s • Based on percentage of similarity of total DNA [T50H]; a single number representing billions of bases 20 Molecular phylogenies Sibley, Charles G., and Jon E. Ahlquist. “Phylogeny and Classification of Birds Based on the Data of DNA-DNA Hybridization.” Current Ornithology. Boston, MA: Springer US, 1983.
  116. 116. John S Wilkins john@wilkins.id.au • At first, DNA-DNA hybridisation studies • E.g., Sibley and Ahlquist’s taxonomy of birds in 1980s • Based on percentage of similarity of total DNA [T50H]; a single number representing billions of bases • “With few exceptions the DNA comparisons “see” the same clusters of species that are seen by the human eye.” 20 Molecular phylogenies Sibley, Charles G., and Jon E. Ahlquist. “Phylogeny and Classification of Birds Based on the Data of DNA-DNA Hybridization.” Current Ornithology. Boston, MA: Springer US, 1983.
  117. 117. John S Wilkins john@wilkins.id.au • Full genomic sequences 21 Sequence data
  118. 118. John S Wilkins john@wilkins.id.au • Full genomic sequences • Molecular clocks 21 Sequence data
  119. 119. John S Wilkins john@wilkins.id.au • Full genomic sequences • Molecular clocks • Problems 21 Sequence data
  120. 120. John S Wilkins john@wilkins.id.au • Full genomic sequences • Molecular clocks • Problems • Paralogy and homology 21 Sequence data
  121. 121. John S Wilkins john@wilkins.id.au • Full genomic sequences • Molecular clocks • Problems • Paralogy and homology • Statistical probabilities of sequences 21 Sequence data
  122. 122. John S Wilkins john@wilkins.id.au • Full genomic sequences • Molecular clocks • Problems • Paralogy and homology • Statistical probabilities of sequences • Hybridisation and later transfer 21 Sequence data
  123. 123. John S Wilkins john@wilkins.id.au • Full genomic sequences • Molecular clocks • Problems • Paralogy and homology • Statistical probabilities of sequences • Hybridisation and later transfer • What do the diagrams summarise? 21 Sequence data
  124. 124. John S Wilkins john@wilkins.id.au • A cladogram is a binary tree [B-Tree in computing] 22 Phylogenies are compressed data Images: https://commons.wikimedia.org/wiki/File:Cladogram_stomiid.svg http://fishesofaustralia.net.au/home/family/208
  125. 125. John S Wilkins john@wilkins.id.au • A cladogram is a binary tree [B-Tree in computing] • Its topology represents the analysed structure in the very large data set 22 Phylogenies are compressed data Images: https://commons.wikimedia.org/wiki/File:Cladogram_stomiid.svg http://fishesofaustralia.net.au/home/family/208
  126. 126. John S Wilkins john@wilkins.id.au • A cladogram is a binary tree [B-Tree in computing] • Its topology represents the analysed structure in the very large data set • Contrary to Hollywood/TV, a biologist cannot eyeball a DNA sequence and identify the species 22 Phylogenies are compressed data Images: https://commons.wikimedia.org/wiki/File:Cladogram_stomiid.svg http://fishesofaustralia.net.au/home/family/208
  127. 127. John S Wilkins john@wilkins.id.au • A cladogram is a binary tree [B-Tree in computing] • Its topology represents the analysed structure in the very large data set • Contrary to Hollywood/TV, a biologist cannot eyeball a DNA sequence and identify the species • In fact, they cannot even identify alignments 22 Phylogenies are compressed data Images: https://commons.wikimedia.org/wiki/File:Cladogram_stomiid.svg http://fishesofaustralia.net.au/home/family/208
  128. 128. John S Wilkins john@wilkins.id.au • A cladogram is a binary tree [B-Tree in computing] • Its topology represents the analysed structure in the very large data set • Contrary to Hollywood/TV, a biologist cannot eyeball a DNA sequence and identify the species • In fact, they cannot even identify alignments • The bigger the data set, the more compression of the data is required to be able to understand the relationships 22 Phylogenies are compressed data Images: https://commons.wikimedia.org/wiki/File:Cladogram_stomiid.svg http://fishesofaustralia.net.au/home/family/208
  129. 129. John S Wilkins john@wilkins.id.au • A cladogram is a binary tree [B-Tree in computing] • Its topology represents the analysed structure in the very large data set • Contrary to Hollywood/TV, a biologist cannot eyeball a DNA sequence and identify the species • In fact, they cannot even identify alignments • The bigger the data set, the more compression of the data is required to be able to understand the relationships • This is due to the fact that humans, and epistemic groups of humans, have limitations on the working memory, cognitive power, and information transmission channels, that they can bring to a problem set, like any ML system 22 Phylogenies are compressed data Images: https://commons.wikimedia.org/wiki/File:Cladogram_stomiid.svg http://fishesofaustralia.net.au/home/family/208
  130. 130. John S Wilkins john@wilkins.id.au The more complete a large data set, the more compression is required to comprehend the data structure 23 Big data is not always better
  131. 131. John S Wilkins john@wilkins.id.au The more complete a large data set, the more compression is required to comprehend the data structure A total census, for example, is reduced to trends, thresholds, averages, etc. Economic activity is reduced to Gross Domestic Products, etc. 23 Big data is not always better
  132. 132. John S Wilkins john@wilkins.id.au The more complete a large data set, the more compression is required to comprehend the data structure A total census, for example, is reduced to trends, thresholds, averages, etc. Economic activity is reduced to Gross Domestic Products, etc. In light of Big Data, even empirical adequacy becomes a statistical property, as error creeping into measurement is magnified 23 Big data is not always better
  133. 133. John S Wilkins john@wilkins.id.au The more complete a large data set, the more compression is required to comprehend the data structure A total census, for example, is reduced to trends, thresholds, averages, etc. Economic activity is reduced to Gross Domestic Products, etc. In light of Big Data, even empirical adequacy becomes a statistical property, as error creeping into measurement is magnified So, to understand, compress. 23 Big data is not always better
  134. 134. John S Wilkins john@wilkins.id.au The more complete a large data set, the more compression is required to comprehend the data structure A total census, for example, is reduced to trends, thresholds, averages, etc. Economic activity is reduced to Gross Domestic Products, etc. In light of Big Data, even empirical adequacy becomes a statistical property, as error creeping into measurement is magnified So, to understand, compress. Mind, compression is not, ipso facto, comprehension. 23 Big data is not always better
  135. 135. John S Wilkins john@wilkins.id.au Science is, in my view, an attempt to express the greatest amount of empirical consequences in the fewest terms. Understanding thus sets and constitutes: 24 Conclusions
  136. 136. John S Wilkins john@wilkins.id.au Science is, in my view, an attempt to express the greatest amount of empirical consequences in the fewest terms. Understanding thus sets and constitutes: • Empirical adequacy (van Fraassen) 24 Conclusions
  137. 137. John S Wilkins john@wilkins.id.au Science is, in my view, an attempt to express the greatest amount of empirical consequences in the fewest terms. Understanding thus sets and constitutes: • Empirical adequacy (van Fraassen) • Generalisation/-ability 24 Conclusions
  138. 138. John S Wilkins john@wilkins.id.au Science is, in my view, an attempt to express the greatest amount of empirical consequences in the fewest terms. Understanding thus sets and constitutes: • Empirical adequacy (van Fraassen) • Generalisation/-ability • Causal explanation 24 Conclusions
  139. 139. John S Wilkins john@wilkins.id.au Science is, in my view, an attempt to express the greatest amount of empirical consequences in the fewest terms. Understanding thus sets and constitutes: • Empirical adequacy (van Fraassen) • Generalisation/-ability • Causal explanation which are pretty much de Regt’s three criteria restated. 24 Conclusions
  140. 140. John S Wilkins john@wilkins.id.au Science is, in my view, an attempt to express the greatest amount of empirical consequences in the fewest terms. Understanding thus sets and constitutes: • Empirical adequacy (van Fraassen) • Generalisation/-ability • Causal explanation which are pretty much de Regt’s three criteria restated. The initial epistemic activities of measurement, analysis and kinematic generalisation all involve compression so that we may comprehend the things needing explanation 24 Conclusions
  141. 141. John S Wilkins john@wilkins.id.au Science is, in my view, an attempt to express the greatest amount of empirical consequences in the fewest terms. Understanding thus sets and constitutes: • Empirical adequacy (van Fraassen) • Generalisation/-ability • Causal explanation which are pretty much de Regt’s three criteria restated. The initial epistemic activities of measurement, analysis and kinematic generalisation all involve compression so that we may comprehend the things needing explanation • Compression of data is a kind of pattern matching, on the basis of which ampliative inferences rest. 24 Conclusions
  142. 142. John S Wilkins john@wilkins.id.au Science is, in my view, an attempt to express the greatest amount of empirical consequences in the fewest terms. Understanding thus sets and constitutes: • Empirical adequacy (van Fraassen) • Generalisation/-ability • Causal explanation which are pretty much de Regt’s three criteria restated. The initial epistemic activities of measurement, analysis and kinematic generalisation all involve compression so that we may comprehend the things needing explanation • Compression of data is a kind of pattern matching, on the basis of which ampliative inferences rest. • Big data is not, in its own right, a good thing, so there! 24 Conclusions
  143. 143. John S Wilkins john@wilkins.id.au 25 Acknowledgements
  144. 144. John S Wilkins john@wilkins.id.au 25 For asking the question: • Adam Ford For discussion: • The late John Collier • Malte Ebach Acknowledgements
  145. 145. John S Wilkins john@wilkins.id.au 25 For asking the question: • Adam Ford For discussion: • The late John Collier • Malte Ebach • Ward Wheeler • Marcus Hutter Acknowledgements
  146. 146. John S Wilkins john@wilkins.id.au 25 For asking the question: • Adam Ford For discussion: • The late John Collier • Malte Ebach • Ward Wheeler • Marcus Hutter For funding: • ISHPSSSB Oslo conference organisers For ear nibbles: Clio, a muse Thank you Acknowledgements

×