Timo Honkela: From Computational Modeling of Concepts to Conceptual Change

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
timo.honkela@helsinki.fi
Conceptual Change – Digital Humanities Case Studies

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

Computational modeling of
concepts
● Theory-driven versus data-driven
● Symbolic networks versus vector spaces
● Explicit versus implicit

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)

Classical example: A map of words
(vector-space model) in Grimm fairy tales
Honkela, Pulkki & Kohonen 1995

Research field classification (Theory driven)
http://www.aka.fi/en/funding/how-to-apply/application-guidelines/research-field-classification/

Map of Finnish Science (Data driven)
Chemistry
Physics and
engineering
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
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)

Michael Gavin, Helsinki 7 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 bindings
between tokens …
… density of conceptual form ...

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

A case stydy
Conceptual change in the advent of
computers and artificial intelligence
http://www.computerhistory.org/timeline/1944/Colossus Harvard Mark 1

Mechanical brain → Computer (Time)

Mechanical brain ↔ Computer (Google)

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

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...”

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

Analysis and
simulation of
socio-cognitive aspects
of linguistic and conceptual
behaviors
–
More case studies

Clifford Siskin, Helsinki 7 Dec 2015
Excellent!
Why
Fodor?

Modeling contextuality and subjectivity
● From shared static symbolic network
representations
● To partially shared/overlapping dynamic
patterns of subjective/intersubjective
conceptual patterns and systems

Complex challenge: different
contexts and cultures
“Shall I compare thee
to a summer's day?”
? ?

Förger & Honkela, 2013
WALKING
RUNNINGRUNNING
Consider how different languages
divide the conceptual space
in different ways
(cf. e.g. Melissa Bowerman et al.)
Extra-linguitic context: 600-dim. patterns of human movement

Grounded Intersubjective
Concept 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

GICA analysis of the word health
in State of the Union Addresses
Honkela et al. 2012

Language use and theory
formation as social phenomena
data collection
and generalization
theories language
use
regularity,
variation
regularity,
variation
producing/
creating
learning/
observing
producing/
creating
producing/
creating
description and
harmonization

Emergence of individual conceptual models and
a 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

Let's reconsider
history 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

Thank you very much!

Timo Honkela: From Computational Modeling of Concepts to Conceptual Change

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