Nesta edição especial do Tópicos 2E1, o engenheiro e professor da Escola de Engenharia de Telecomunicações de Barcelona (Telecom BCN), Sergio Bermejo, apresenta um pequeno artigo (desta vez, disponível somente em inglês*) sobre a Teoria da Informação, seus principais conceitos e sua relação com a arte e a tecnologia.

TÓPICOS 2E1: Sergio Bermejo (Art, Technology and Information Theory)

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Sergio Bermejo (Barcelona, 1971) received the M.Sc. and Ph.D. degrees in telecommunications engineering from the Universitat Politècnica de Catalunya, BarcelonaTech (UPC), Barcelona, Spain, in 1996 and 2000, respectively. He joined the Department of Electronics Engineering (DEE) of UPC as a Researcher in 1996. Currently, he is an Associate Professor in the DEE and teaches at the School of Telecommunications Engineering of Barcelona (Telecom BCN). As a lecturer, he is focused primarily on information and communication technology systems. His research interests are machine learning and their application to e-learning, signal processing, smart sensors, and autonomous robotics.

1. What is Information Theory? 

Information theory (IT) is a mathematical approach for conceiving efficient ways of encoding and transferring data –also known as raw information, i.e. information without meaning– in the context of telecommunication engineering. The most successful information-theoretic model of communication [1] for dealing with these problems was proposed by Claude E. Shannon in the Fifties and consists of the following elements (Fig.1): 1) the source selects a desired message out of a set; 2) the transmitter changes it into an electrical signal which is sent with the inclusion of things not intended (i.e. noise) over the communication channel to the receiver; and 3) the receiver, i.e. a kind of inverse transmitter, tempts to recover the original message changing the transmitted signal back, and handles the message to 4) the destination. In this stochastic model, the problems to be studied are addressed to solve very specific technical matters such as the amount of raw information that has been transmitted, the capacity of the communication channel, the coding process that may be used to change a message into a signal and the effects of noise.

The starting point of such inquiries was to quantify raw information in the encoding process not to be individual messages but rather to represent the whole statistical structure of the information source, i.e. the complete ensemble of messages. For this purpose, entropy, an uncertainty-based measure for probabilistic schemas that reaches its minimum and maximum values for almost-sure and most uncertain cases respectively, was employed; then, a very predictable information source contains low (raw) information when measured with entropy and hence it is redundant and can be compressed. The second quantitative result obtained by the theory developed for this model was the channel capacity theorem, which states the limits of communication, i.e. the ultimate level of data transmission.

Additionally, the original model has had the most profound impact on philosophical analysis of information and other related fields since, besides, the incorporation of more dimensions can encompass different levels of analysis [2][3] in which semantic problems (concerned with identity and the interpretation of meaning) and effectiveness problems (related with the success with which the meaning sent leads to a desired behavior) can also be studied when considering information = data + meaning [4] (see Fig.1). In this sense, this model have provided a rigorous (strongly in the fifties and weakly now) constraint to any subsequent theoretical efforts concerning also the semantic and pragmatics aspects of information [5].

Fig.1

Fig.1. A general communication system (after [Shannon, 1948], [Floridi, 1994] and [Beynon-Davies, 2011]).

Areas related to information theoryCybernetics, Signal processing, Coding theory, Statistics, Artificial intelligence, Machine learning, Communication theory, Philosophy of information, Information technology


[1] Shannon, C. E. (1948). “A Mathematical Theory of Communication”, The Bell System Technical Journal, Vol. 27, pp. 379–423, 623–656.

[2] Weaver, W. (1949). “The Mathematics of Communication”, Scientific American, Vol. 181, pp. 11-15.

[3] Beynon-Davies, P.  (2011). Significance. Exploring the nature of information, systems and technology. New York: Palgrave Macmillan.

[4] Floridi, L. (2004). Information. In: Floridi, L. (Ed.), The Blackwell Guide to the Philosophy of Computing and Information, Malden: Blackwell Publishing.

[5] Ibid.

2. Information and Art

Although concepts of IT can be valuable to Art, the ‘establishing of such applications’ needs not a straightforward translation of terminology but a laborious testing and verification of the hypothesis under scrutiny, as already pointed out by Shannon himself [6]. For instance, the problem of effectiveness in the information-theoretic model of communication in fact could involve aesthetics considerations in the case of fine arts [7]; however, no serious attempts to establish rigorously the existence of such links have done. In addition, probabilistic models in IT for dealing with temporal structure in messages sent through a communication channel could be relevant in artistic areas such literature and music [8].

On the other hand, this communication model and its underlying theory provide, at worst, a comprehensive metaphor –if not paradigm– or, in other words, a suggestive, exploratory and heuristic path [9] to be studied in arts. In this line, several ideas derived from information theory and related fields have been explored, especially those based on the concept of entropy and the related notion of order and disorder in the search of structure in artworks. (It is worth noting that other approaches –maybe residual to IT but rooted in the inclusive and interdisciplinary field of information science [10]– have been also formulated based on the standard definition of information, i.e. data + meaning.)

Example 1: Music:

[Bruno Maderna]’s [Entropia I] (1964)

According to structuralism, a structure is a system of transformations that involve some fundamental constructive laws and is conserved or enriched by the interplay of their own transformations without the need of giving an external result out of its frontiers [see Piaget, J. (1968). Le Structuralisme. Paris: Presses Universitaries de France].

Example 2Graphics/painting:

[A. Michael Noll] (1966). “[Human or Machine]: A [Subjective Comparison] of Piet Mondrian’s ‘Composition with Lines’ and a [Computer–Generated] Picture”, The Psychological Record, 16, pp.1-10.

A state of equilibrium in an artwork, which implies the realization of a potential structural order, balances two opposing tendencies [11] (Fig.2): the anabolic tendency or the predisposition for articulating complex structural themes; and a counter-principle based on parsimoniousness in which economy and simplicity in structure is preferred. This equilibrium is reached increasing the entropy through tension reduction, i.e. the tendency toward simplicity, symmetry and regularity, and the catabolic effect, i.e. the destruction of shape fortuitously by the use of chance operations and indirectly by removing constraints [12].

Example 3: Music:

John Cage: Silence (1961), Music of Changes (1951)

Marcel Duchamp (1913): Erratum Musical

Example 4Cinema/literature/painting:

[what is][Brion Gysin]’s [cut-up technique]? (1966)

Brion Gysin -painting_for_dream

Example 5: Graphics:

Early digital art [Béla Julesz]’s [random dot stereograms]

Bela Julesz - Foundations of Cyclopean Perception

In addition, a set of heuristic methodologies based on the materiality of communication in the work of art can be listed for achieving a catabolic effect [13]: a) destroying the work by known, perceptive quantities (e.g. introducing distortions and noise in the transmissions channel); b) periodic destruction in time or space; c) partial or infinite clipping; d) (time) inversion; and e) the transposition of messages. These processes are actually related to combinatory (or permutation) art in which compositions were attempted to be built from a limited set of basic elements through a systematic exploration of combinatorial techniques; two notorious examples in music and painting are serialism and kinetic (and op) art respectively.

Fig.2

Fig.2. Processes in the creation of an artwork (after [Arnheim, 1971]).


[6] Shannon, C. E. (1956). The bandwagon. IRE Transactions on Information Theory, Vol. 2, p. 3.

[7] Shannon, C. E., Weaver, W. (1964). The Mathematical Theory of Communication. Urbana: The University of Illinois Press, p.5.

[8] Pierce, J. R. (1980). An Introduction to Information Theory: Symbols, Signals and Noise. Second and revised edition. New York: Dover, Chapter XIII.

[9] Moles, A. (1968). Information Theory and Esthetic Perception. Urbana: University of Illinois Press, p.x.

[10] Luenberger, D. G. (2006). Information Science. Princeton: Princeton University Press

[11] Arnheim, R. (1971). Entropy and Art. Berkeley: University of California Press.

[12] Ibid.

[13] Moles, A. (1968). Information Theory and Esthetic Perception. Urbana: University of Illinois Press, pp.201-3.

3. Art + Technology

After the end in contemporary art of conventional narratives [14], its hybridization with other (usually antagonist) fields such science and technology is one of the routes ‘under test’ that have coexisted with other more convectional approaches for more than five decades [15],[16],[17],[18]. Both science, which attempts to model natural phenomena, and technology, seen as applied science or as the science of the “artificial”, benefit art providing an augmented vision beyond its traditional aesthetic purpose when artists started working with non-art media, contexts and concepts [19]In principle, this mixture of fields would allow artists the use of cutting-edge technology to enquiry about different problematic issues in this techno-scientific era on areas such diverse as culture, sociology, and philosophy[20].

Example 6: Theater and Engineering:

[Experiments in Art and Technology (E.A.T.)]

[9 evenings (1966-67)]

Example 7: Photography:

Information Art: [Diagramming Microchips]([MOMA, 1990])

Diagramming Microchips - MOMA, 1990In particular, digital technologies as a new medium for arts [21] have become a dominant area in the intersection between arts and technology, which led to the emergence of new theoretical questions to be studied [22], categories [23], genres [24] and ways of distribution [25]. Also, some of the recent hybrid artworks in kinetics, robotics, artificial life, human-computer interfaces, algorithms and information visualization points out to new interesting directions which need further investigation [26]. But still, the footprints of the recent past can be seen through some of all these particular crossings: the prevailing exploration of noise [27], redundancy and uncertainty [28] in search for structural order –which  could be better illuminated within extensions of Shannon’s communication model and exploited under Arnheim’s aesthetic principles, i.e. the anabolic and catabolic tendencies–  seems to remain a constant, necessary prerequisite of art. However, in return, this artistic order should reflect, not inevitably the banality and confusion of our times, but still an ultimate mystery, a hidden, unrevealed knowledge, i.e. ‘a genuine, true, profound view of life’ [29].

Example 8: Digital art: glitch art:

[Michael Betancourt],[Kodak Moment (2013)]

Kodak_Moment_(2013)_sample_animation

 

Example 9: Installation:

Kasia Molga and Erik Overmeire,   Entropy (FILE, 2013)

 Symposiums and centers:

The Ars Electronica Center

artfutura, Festival of Digital Culture and Creativity

FACT, Foundation for Art and Creative Technology

FILE, Electronic Language International Festival

ISEA, International Symposium of Electronic Arts

V2_, Institute for the Unstable Media


[14] Danto, A. C. (1997). After The End of Art. Princeton: Princeton University Press, p.4.

[15] Kepes, G. (1956). New Landscape in Art & Science. Chicago: P. Theobald.

[16] Benthall, J. (1972). Science and Technology in Art Today. New York: Praeger.

[17] Wilson, S. (2002). Information Arts. Intersections of Art, Science, and Technology. Cambridge: MIT Press.

[18] Wilson, S. (2010). Art+Science Now. London: Thames & Hudson.

[19] Ibid., p.8.

[20] Ibid., preface.

[21] Paul, C. (2008). Digital Art, 2nd Edition, London: Thames & Hudson.

[22] Manovich, L. (2001). The Language of New Media. Cambridge: MIT Press.

[23] Dixos, S. (2007). Digital Performance. A History of New Media in Theater, Dance, Performance art, and installation. Cambridge: MIT Press.

[24] Hayles, N. K. (2008). Electronic Literature: New Horizons for the Literary. Notre Dame: University of Notre Dame Press.

[25] Chandler, A., Neumark, N. (2005). At a Distance: Precursors to Art and Activism on the Internet. Cambridge: MIT Press.

[26] Wilson, S. (2010). Art+Science Now. London: Thames & Hudson, p. 200.

[27] Nechvatal, J. (2011). Immersion Into Noise. University of Michigan Library, Ann Arbor: Open Humanities Press. Freely available online at http://hdl.handle.net/2027/spo.9618970.0001.001.

[28] Aarseth, E. J. (1997). Cybertext. Perspectives on Ergodic Literature. Baltimore, MA: The John Hopkins University Press.

[29] Arnheim, R. (1971). Entropy and Art. Berkeley: University of California Press.

*Não fizemos a tradução desta entrevista pois há termos técnicos muito específicos que se perderiam. Vale o esforço da leitura na língua inglesa. Qualquer dificuldade use o campo de comentários deste post que estaremos a disposição para ajudar nos esclarecimentos.

tópicos 2e1 is a column on contemporary art published monthly in the Ateliê Coletivo2e1′s Blog. Every month an international professional is invited to speak freely about three topics proposed by our editorial. Currently,tópicos 2e1 is coordinated by Monica Rizzolli.