Monday, February 15, 2016

The "Digital" in the Digital Humanities

Is a methodology an essential ingredient in a scientific discipline, so essential that it needs to be mentioned in the name? Digital Humanities is a commonly used name for a research activity where computers are used to support endeavours within humanities and social sciences. Similar combined terms are, for example, computational linguistics and bioinformatics. Some disciplines such as mathematics, statistics, computer science and logic in philosophy are already themselves methodologically oriented. Is the use of computers, at some time in the future, so commonplace and obvious in digital humanities that the qualifying part is left away? In which way is the qualification relevant? Considering good research in humanities, is it necessary to make a difference between approaches that make use of computers and those that do not?

As a starting point to the discussion one could state a claim that the objects of study and phenomena considered in humanities and social sciences are even much more complex than the ones of physical sciences and biological sciences. Human thinking, language and culture are dynamical phenomena, subject to continuous change. A theory may become invalid due to itself. Also in physics measurement influence the results but this effect is not as complex and unbredictable as in humanities. Many approaches in ”simpler” sciences are based on the concept of predictability. Scientist look for experimental settings that can be repeated even as a criteria for being scientific. As Von Foester has stated, such an attitude makes most of research irrelevant from the point of view of real world phenomena. A technical term that can be used here is non-stationarity. Unlike in physics, phenomena discussed in humanities are in constant flux, not only superficially but sometimes even concerning the basic framework. Peter Gärdenfors might explain this as introduction of new quality dimensions. In physics, new quality dimensions may be introduced to craft better theories of physics, but in human behaviours or social activities inherently new dimensions may emerge. These are not new explanatory models but new aspects of the phenomenon itself. What this means in practice is that one cannot compare two situations or contexts in a straightforward way. Due to such complexities, it has been necessary to focus on the use of qualitative methods and textually oriented mode of presentation unless taking a risk of reductionistism. For example, it seems that economics has suffered from such a problem by formulating closed-form equations with small numbers of variables, limited feedback methanisms and consideration of adaptive processes involved. Formalisation is not a guarantee of being scientific if the formalism is not on par with the complexity of the phenomenon under considetation.

Digital humanities is an interesting area because it includes a promise of approaching the complex phenomena in humanities in new ways, facilitated by the availability of large data collections and the latest developments in computer science. The use of the word ”digital” may be considered misleading. Having resources in digital form helps in sharing them, to involve a larger number of researchers than before that can reach the texts and data through networks. A more signiticant impact is, however, reachable through "Computational Humanities." This term can be used to characterize the activity in which humanities data is modeled using modern computer science methods such as statistical machine learning. The complexity of topics at hand motivate development of improved and novel methods that enable modeling data that is more complex than anything seen in physical or biological sciences. Future data sets and analysis processes could, for example, include all books and newspapers published and stored during the history of humankind. This would give a chance to study traditional questions in new ways as well as address wholly new perspectives in holistic manner. Methodological themes to address include non-stationarity, multilayered contextuality and multilevel simulation of large communities of cultural adaptive agents. It will be useful to continue the emerging practice and to bring togethe representatives of humanities and social sciences in one hand and data sciences in the other. The people with formal computing skills need to remember not to bring in reductionistic assuptions and the historians, economists, linguists and others may take a role in which they supervise the assumptions taken in the data driven modeling processes and an analytic role in interpreting the models and results. An essential factor that differs from the past is the chance to rely on emergent processes. They give rise to dynamical understanding that is not dependent on the limited capacity of coding knowledge in small pieces as a manual process. Human interpretation and analysis remains important but can be taken to high level of abstraction or to unforseen level of contextual detail. In this way, Digital Humanities can serve the humankind in its most burning challences and questions related, for example, to successful communication, organizing societies in a good way, solving crises in a peaceful manner, addressing climate change and other environmental issues, improving scientific communication to improve its results and their use, and protect and further refine human cultural heritage.

Tuesday, January 26, 2016

Modeling Meaning and Knowledge, Spring 2016

A series of mini-symposia entitled Modeling Meaning and Knowledge started on Monday, 25th of January. During the spring 2016, the topic is handled in a multidisciplinary fashion. In linguistics, philosophy of language, cognitive science, psychology, sociology, artificial intelligence, information systems design and some other scientific discipline or application areas, it has been of primary interest to study knowledge. What does it mean to know? How is knowledge acquired? How are knowledge and meaning related? How is prototypical meaning different from contextual meaning? What are the characteristics of explicit and implicit knowledge? Is there knowledge beyond language? What kind of approaches have been taken to model knowledge in computer science and artificial intelligence? Can computational modeling be used to test philosophical ideas related to knowledge and meaning? What is the relationship of these questions with digital humanities? Can large data and text collections, i.e. so called big data, be used to extract knowledge automatically? What kind of practical, ethical and societal consequences does the chosen approach have? The series addresses questions like this.

Timo Honkela gave a short introduction and discussed professor Terry Wingrad career from SHRDLU to the book on syntactic processing in NLP and all the way to the role in the advent of Google. Juha Himanka gave a talk "Fernando Flores reads Heidegger". He described how Flores had met Stafford Beer in the times of hopeful developments in Chile and how they were dramatically abrupted. Flores later collaboparted with Winograd and they authored an influential book together. Pirjo Kukkonen discussed dynamic semiotics providing a wide range of theoretical and practical views on the complexity of synbolic communication. Based on her long experience on the topic, Terttu Nevalainen presented in-depth views on linguistic variation.

Juha Himanka

The agenda for the spring is given below. The sessions are held in the Auditorium IV of the main building of the University of Helsinki.

  • Jan 25 (14.15-16.00):
    Complexity of meaning and knowledge dynamic semiotics, prof. Pirjo Kukkonen
    linguistic variation, prof. Terttu Nevalainen
    Tutorial: A story of syntax, semantics and pragmatics from Syntax I to phenomenology and Google; guest speaker Dr. Juha Himanka
  • Feb 1 (14.15-16.00):
    Knowledge representation
    networks of knowledge, prof. Eero Hyvönen (Aalto)
    spaces of knowledge, prof. Timo Honkela
    Tutorial: An introduction to artificial intelligence
  • Feb 8 (14.15-16.00):
    Conceptual change
    cognitive view, prof. Ismo Koponen
    historical view, prof. Mikko Tolonen
    Tutorial: Study of words and concepts – Qualitative and quantitative approaches
  • Feb 15 (14.15-16.00):
    Knowledge over language borders
    prof. Jörg Tiedemann
    prof. Liisa Tiittula
    Tutorial: An intellectual obituary of Melissa Bowerman
  • Feb 22 (14.15-16.00):
    Interactive session
    Tutorial: Independent component analysis of signals and texts
  • Feb 29 (14.15-16.00):
    From data to knowledge with machine learning
    prof. Tapani Raiko (Aalto)
    Tutorial: A history of machine learning and neural networks research
  • Mar 14 (14.15-16.00):
    Studying understanding and emotions through brain research
    prof. Mikko Sams (Aalto)
    prof. Arto Mustajoki
    Tutorial: An introduction to ambiguity and vagueness
  • Mar 21 (14.15-16.00):
    Meaning in art
    Automating literary creativity, prof. Hannu Toivonen
    Tutorial: Metaphors, analogies and conceptual blending
  • Apr 4 (14.15-16.00):
    Creating scientific knowledge as a social process
    Dr. Nina Janasik-Honkela
    Dr. Arho Toikka
    Tutorial: Kuhn’s Structure of Scientific Revolutions and Gärdenfors’ Conceptual Spaces
  • Apr 11 (14.15-16.00):
    Uncertain knowledge of the future
    Dr. Mikko Rask
    Tutorial: An introduction to futures studies
  • Apr 18 (14.15-16.00):
    Knowledge of society
    prof. Mika Pantzar
    Sakari Virkki (Competence Map Solutions)
    Tutorial: Modeling evolutionary and dynamical systems
  • Apr 25 (14.15-16.00):
    Legal and wellbeing knowledge
    Anna Ronkainen (TrademarkNow)
    Dr. Krista Lagus (TBC)
    Tutorial: Text mining of document collections and social media discussions
Small changes are possible.