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

[email protected]

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)

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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


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 bindingsbetween 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 andsimulation of

socio-cognitive aspectsof linguistic and conceptual

behaviors–

More case studies


Clifford Siskin, Helsinki 7 Dec 2015

Excellent!

WhyFodor?


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: differentcontexts and cultures

“Shall I compare thee to a summer's day?”

? ?


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


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


GICA analysis of the word healthin State of the Union Addresses

Honkela et al. 2012



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



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


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


Thank you very much!