timo honkela: from computational modeling of concepts to conceptual change
TRANSCRIPT
Timo Honkela: From Computational Modeling of Concepts to Conceptual Change. Conceptual Change – Digital Humanities Case Studies. 7 (-8) Dec 2015
Timo Honkela
7 Dec 2015
University of Helsinki
From computation modeling of concepts to
conceptual change
Conceptual Change – Digital Humanities Case Studies
Timo Honkela: From Computational Modeling of Concepts to Conceptual Change. Conceptual Change – Digital Humanities Case Studies. 7 (-8) Dec 2015
Agenda
● Computational modeling of concepts– Theory-driven versus data-driven
– Symbolic networks versus vector spaces
– Explicit versus implicit
● Conceptual changes– Among psychologists and education scientists
– Among historian
– Dynamical socio-cognitive historical processes as interplay between implicit and explicit as well as individual and shared
● Case stydies– Conceptual change in the advent of computers and AI
– Modeling subjective understanding
– Modeling community of language communities
Timo Honkela: From Computational Modeling of Concepts to Conceptual Change. Conceptual Change – Digital Humanities Case Studies. 7 (-8) Dec 2015
Computational modeling of concepts
● Theory-driven versus data-driven● Symbolic networks versus vector spaces● Explicit versus implicit
Timo Honkela: From Computational Modeling of Concepts to Conceptual Change. Conceptual Change – Digital Humanities Case Studies. 7 (-8) Dec 2015
Experience from the 1980s
● A large project Kielikone (“Language Machine”) aiming at developing a natural language database interface
● Example: “What is the turnover of ten largest forestry companies?”
● Rule- and logic-based processing of morphology, syntax and semantics (plus pragmatics)
● Conclusion: NLP (AI) is difficult● (Married to a historian)
Timo Honkela: From Computational Modeling of Concepts to Conceptual Change. Conceptual Change – Digital Humanities Case Studies. 7 (-8) Dec 2015
Classical example: A map of words (vector-space model) in Grimm fairy tales
Honkela, Pulkki & Kohonen 1995
Timo Honkela: From Computational Modeling of Concepts to Conceptual Change. Conceptual Change – Digital Humanities Case Studies. 7 (-8) Dec 2015
Research field classification (Theory driven)
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Timo Honkela: From Computational Modeling of Concepts to Conceptual Change. Conceptual Change – Digital Humanities Case Studies. 7 (-8) Dec 2015
Map of Finnish Science (Data driven)
Chemistry
Physics andengineering
Biosciences
Medicine
Culture and society
A fully automated process from terminology extraction (Likey) to semantic space construction (SOM) without any manually constructed resources.
Simulating processes of language emergence and communication 8
Timo Honkela: From Computational Modeling of Concepts to Conceptual Change. Conceptual Change – Digital Humanities Case Studies. 7 (-8) Dec 2015
Weaver on Shannon
● “Relative to the broad subject of communication, there seem to be problems at three levels. [...]
– LEVEL A. How accurately can the symbols of communication be transmitted? (The technical problem)
– LEVEL B. How precisely do the transmitted symbols convey the desired meaning? (The semantic problem)
– LEVEL C. How effectively does the received meaning affect conduct in the desired way? (The effectiveness problem)”
● “The semantic problems are concerned with the identity, or satisfactorily close approximation, in the interpretation of meaning by the receiver, as compared with the intended meaning of the sender.” (1949, p. 4)
Timo Honkela: From Computational Modeling of Concepts to Conceptual Change. Conceptual Change – Digital Humanities Case Studies. 7 (-8) Dec 2015
Michael Gavin, Helsinki 7 Dec 2015
Timo Honkela: From Computational Modeling of Concepts to Conceptual Change. Conceptual Change – Digital Humanities Case Studies. 7 (-8) Dec 2015
Peter de Bolla, Helsinki 7 Dec 2015
… Concepts are different things from
words ...
… concept is not a singular entity ...… autopoiesis …
… concepts as cultural entities …… patterns of lexical behaviours …
… probabilities of bindingsbetween tokens …
… density of conceptual form ...
Timo Honkela: From Computational Modeling of Concepts to Conceptual Change. Conceptual Change – Digital Humanities Case Studies. 7 (-8) Dec 2015
Conceptual changes
● Among psychologists and education scientists● Among historians● Dynamical socio-cognitive historical processes
as interplay between implicit and explicit as well as individual and shared
Timo Honkela: From Computational Modeling of Concepts to Conceptual Change. Conceptual Change – Digital Humanities Case Studies. 7 (-8) Dec 2015
A case stydy
Conceptual change in the advent of computers and artificial intelligence
http://www.computerhistory.org/timeline/1944/Colossus Harvard Mark 1
Timo Honkela: From Computational Modeling of Concepts to Conceptual Change. Conceptual Change – Digital Humanities Case Studies. 7 (-8) Dec 2015
Mechanical brain → Computer (Time)
Timo Honkela: From Computational Modeling of Concepts to Conceptual Change. Conceptual Change – Digital Humanities Case Studies. 7 (-8) Dec 2015
Mechanical brain ↔ Computer (Google)
Timo Honkela: From Computational Modeling of Concepts to Conceptual Change. Conceptual Change – Digital Humanities Case Studies. 7 (-8) Dec 2015
Instances of Mechanical brain (Time)
● 1935/03/18 748558 To have the public's first look at the biggest and keenest mechanical brain in the world, a total of 6.000 persons one day last week trooped down
● 1944/02/21 was acting even more so. In operation was a new Bell Telephone Laboratories mechanical brain which enables the instrument to put through long distance calls without human assistance. #
● 1944/02/21 has a numbered keyboard like an adding machine. The message goes to the mechanical brain, called a " marker, " which hunts out an available trunk line,
● 1945/08/13 princess in distress, an actress " telling all, " science's latest mechanical brain, and a snorting brontosaurus. Oldtime Goddard-admirers at the American Weekly say that his
● 1948/12/27 experience, like monstrous and precocious children racing through grammar school. One such mechanical brain, ripe with stored experience, might run a whole industry, replacing not only
● 1950/11/22 Atlantic edition, and immediately recognized the cover (Mark III, the mechanical brain) as the work of the same artist. # " Now I should like
Timo Honkela: From Computational Modeling of Concepts to Conceptual Change. Conceptual Change – Digital Humanities Case Studies. 7 (-8) Dec 2015
Time Magazine 18 March 1935
“To have the public's first look at the biggest and keenest
mechanical brain in the world, a total of 6.000 persons
one day last week trooped down into a basement of the
University of Pennsylvania's Moore School of Electrical
Engineering in Philadelphia. There they found a new
differential analyzer even more formidable than its name
—a maze of delicate mechanisms united in a 28-ft.
monster weighing three tons (see cut). They saw
innumerable gears mesh silently, shafting turn on jeweled
bearings, operators carefully adjust hand controls...”
Timo Honkela: From Computational Modeling of Concepts to Conceptual Change. Conceptual Change – Digital Humanities Case Studies. 7 (-8) Dec 2015
Instances of Mechanical brain (Time) ● 1953/11/23 they slammed to a halt, leaped out, and whirrilling like some great electronic brain, focused their
mechanical eye... Then, whoosh! - into the● 1954/01/18 message: Mi pyeryedayem mislyi posryedstvom ryech-yi. In a few seconds the mechanical " brain "
spewed out a translation from Russian to English: " We transmit thoughts by● 1954/04/05 of complexity, or are artificially arranged to be so, that the rigid mechanical brain can exhibit
superiority over the flexible human brain. "● 1954/11/15 the machine completely reversed its field. Commentator Charles Collingwood, who nursemaided the
mechanical brain both in 1952 and last week, says: " Suddenly Univac said the Republicans● 1954/01/25 Hour of Letdown, " a man enters a bar, plunks down a mechanical brain, and orders rye &; water for
two. After ingesting a couple of drinks● 1954/11/29 it amazing how the pollsters, observers and interpreters thought exactly like the marvelous
mechanical brain? A rather pertinent reminder that juggling statistics is not necessarily logical reasoning. Just● 1954/08/09 9:30 p.m., CBS). An old-fashioned detective pits his wits against a mechanical brain. # This Is Your
Life (Wed. 10 p.m., NBC).● 1955/09/19 a stream of electrons a sort of manmade lightning. A lathe with a mechanical brain, which computes
the correct cutting speed for each job. Its makers, Monarch● 1956/04/23 to stage 3, to the 300-mile level. While it coasts, its mechanical brain will be reading its numerous
instruments and telling little gas-jets how to turn it in● 1959/03/09 orders translated into number language. The tape is fed into the tool's mechanical brain, and without
further human guidance, the tool forthwith turns out the part that● 1981/11/02 at its heart lies a wondrous, and immensely profitable, link between the electronic brain and the
mechanical hand. It is a link that stretches from the designing room
Timo Honkela: From Computational Modeling of Concepts to Conceptual Change. Conceptual Change – Digital Humanities Case Studies. 7 (-8) Dec 2015
Analysis andsimulation of
socio-cognitive aspectsof linguistic and conceptual
behaviors–
More case studies
Timo Honkela: From Computational Modeling of Concepts to Conceptual Change. Conceptual Change – Digital Humanities Case Studies. 7 (-8) Dec 2015
Clifford Siskin, Helsinki 7 Dec 2015
Excellent!
WhyFodor?
Timo Honkela: From Computational Modeling of Concepts to Conceptual Change. Conceptual Change – Digital Humanities Case Studies. 7 (-8) Dec 2015
Modeling contextuality and subjectivity
● From shared static symbolic network representations
● To partially shared/overlapping dynamic patterns of subjective/intersubjective conceptual patterns and systems
Simulating processes of language emergence and communication 21
Timo Honkela: From Computational Modeling of Concepts to Conceptual Change. Conceptual Change – Digital Humanities Case Studies. 7 (-8) Dec 2015
Complex challenge: differentcontexts and cultures
“Shall I compare thee to a summer's day?”
? ?
Timo Honkela: From Computational Modeling of Concepts to Conceptual Change. Conceptual Change – Digital Humanities Case Studies. 7 (-8) Dec 2015
Förger & Honkela, 2013
WALKING
RUNNINGRUNNING
Consider how different languagesdivide the conceptual space
in different ways(cf. e.g. Melissa Bowerman et al.)
Extra-linguitic context: 600-dim. patterns of human movement
Timo Honkela: From Computational Modeling of Concepts to Conceptual Change. Conceptual Change – Digital Humanities Case Studies. 7 (-8) Dec 2015
Grounded IntersubjectiveConcept Analysis
● A method developed to model how langage is understood in context and with some degree of individuality
● Computational approaches often assume a shared epistemology; here we are interested in the differences in human interpretation
Timo Honkela: From Computational Modeling of Concepts to Conceptual Change. Conceptual Change – Digital Humanities Case Studies. 7 (-8) Dec 2015
GICA analysis of the word healthin State of the Union Addresses
Honkela et al. 2012
Simulating processes of language emergence and communication 25
Timo Honkela: From Computational Modeling of Concepts to Conceptual Change. Conceptual Change – Digital Humanities Case Studies. 7 (-8) Dec 2015
Language use and theoryformation as social phenomena
data collectionand generalization
theories language use
regularity,variation
regularity,variation
producing/creating
learning/observing
producing/creating
producing/creating
description andharmonization
Simulating processes of language emergence and communication 26
Timo Honkela: From Computational Modeling of Concepts to Conceptual Change. Conceptual Change – Digital Humanities Case Studies. 7 (-8) Dec 2015
Emergence of individual conceptual models anda coherent lexicon in a community of interacting
neural network agents
(Lindh-Knuutila, Lagus & Honkela, SAB'06)Related to e.g. Steels and Vogt on language games
Timo Honkela: From Computational Modeling of Concepts to Conceptual Change. Conceptual Change – Digital Humanities Case Studies. 7 (-8) Dec 2015
Let's reconsiderhistory of computers
and AI (statistical NLP)● Mechanical brain, …,
computer – Mental/cognitive
realization
– Social/linguistic realization
● ...● Self-organizing semantic
maps● Latent semantic analysis● Word category maps● …● Probabilistic topic models● Latent Dirichlet allocation