TCD is Hiring in Computational Literary Studies

The Trinity College Centre for Digital Humanities is recruiting a 2-year post-doctoral fellow with expertise in computational literary studies to join its growing and diverse team. The purpose of this post is to support the development of the LI4AI project, through the delivery of collaborative research in the field of computational literary studies (70%), and teaching backfill for the project PI (30%).

LI4AI is a proposal-stage research project that will use literary evidence to develop an evidence-based model upon which key competencies related to positive liberty – agency, autonomy, competency, transgression, creativity, trust and empathy – can be specified and made actionable within a software environment. As such, it will apply an urgently needed applied humanities approach to the challenge of AI development and regulation in light of the necessary pivot toward humane technology development and deployment.

LI4AI is led by Trinity College Dublin’s Professor Jennifer Edmond, Co-Director of the Trinity Centre for Digital Humanities. The appointee will be based in the Trinity Long Room Hub Arts and Humanities Research Institute and will work closely with Professor Edmond and the DH@TCD Team, also based in the Trinity Long Room Hub.

For more information, see the document below.

KPLEX Presented at DH 2019 Conference

Jörg Lehmann and Jennifer Edmond were very pleased to have been given a chance to present some learnings from the KPLEX project to an engaged audience at the DH 2019 conference on 12th July 2019.  The paper was entitled “Digital Humanities, Knowledge Complexity and the Six ‘Aporias’ of Digital Research,” and explored a number of the cultural clashes we found between the perspectives in our interviews.  While DH was never a planned audience for our results, the response today convinced us that there is still much to mine from our interviews and insights!

The slides from the presentation can be viewed here.

ACDH LECTURE 4.1- What can Big Data Research Learn from the Humanities?

csm_events_ACDH_Lecture_4.1_e156aa4aa6

Jennifer Edmond
Director of the Trinity College Dublin Centre for Digital Humanities and Principal Investigator on the KPLEX Project

One of the major terminological forces driving ICT development today is that of ‘big data.’ While the phrase may sound inclusive and integrative, in fact, ‘big data’ approaches are highly selective, excluding, as they do, any input that cannot be effectively structured, represented, or, indeed, digitised. Data of this messy, dirty sort is precisely the kind that humanities and cultural researchers deal with best, however.  In particular, knowledge creation and information management approaches from the humanities shed light on gaps such as: the manner in which data that are not digitised or shared become ‘hidden’ from aggregation systems; the fact that data are human created, and lack the objectivity often ascribed to the term; and the subtle ways in which data that are complex almost always become simplified before they can be aggregated. Humanities insight also exposes the problematic discursive strategies that big data research deploys, strategies that can be seen reflected not only in the research outputs of the field, but also in many of the urgent challenges our digitised society faces.

The lecture is available to view here: https://www.youtube.com/watch?v=E2vdFBo9wB4

A recipe for intimacy?

Mark Zuckerberg posted the following statement on his Facebook feed:
“Today we’re publishing research on how AI can deliver better language translations. With a new neural network, our AI research team was able to translate more accurately between languages, while also being nine times faster than current methods.

Getting better at translation is important to connect the world. We already perform over 2 billion translations in more than 45 languages on Facebook every day, but there’s still a lot more to do. You should be able to read posts or watch videos in any language, but so far the technology hasn’t been good enough.

Throughout human history, language has been a barrier to communication. It’s amazing we get to live in a time when technology can change that. Understanding someone’s language brings you closer to them, and I’m looking forward to making universal translation a reality. To help us get there faster, we’re sharing our work publicly so that all researchers can use it to build better translation tools.”

Key messages: taking time to understand people is for fools, and language is the problem.
When did language become a barrier to communication?  Would we not be hard pressed to communicate much at all without it?  Doesn’t machine translation have the potential to create as much distance as ‘understanding?’   Building intimacy (for this is what I take the phrase “brings you closer” to mean) is not about having a rough idea of what someone is saying, it is about understanding the nuance of every gesture, every reference and resonance.  Isn’t the joy of encountering a new culture tied up in the journey of discovery we make on the road to understanding?
I salute Facebook for making their research and software open, but a bit of humility in the face of the awesome and varied systems of signs and significations we humans have built could make this so much better news.

“Voluptuousness:” The Fourth “V” of Big Data?

Dr Jennifer Edmond, TCD

The KPLEX project is founded upon a recognition that definitions of the word ‘data’ tend to vary according to the perspective of the person using the word.  It is therefore useful, at least, to have a standard definition of ‘big’ data.

Big Data is commonly understood as meeting 3 criteria, each conveniently able to be described with a word beginning with the letter V: Volume, Velocity and Variety.

Volume is perhaps the easiest to understand, but a lot of data really means a lot.  Facebook, for example, stores more than 250 billion image .  ‘Nuf said.

Velocity is how fast the data grows.  That >250 billion images figure is estimated to be growing by a further 300-900 million per day (depending on what source you look at).  Yeah.

Variety refers to the different formats, structures etc. you have in any data set.

Now, from a humanities data point of view, these vectors are interesting.  Very few humanities data sets would be recognised as meeting criteria 1 or 2, though some (like the Shoah Foundation Video History Archive) come close.  But the comparatively low number of high volume digital cultural datasets is related to the question of velocity: the fact that so many of these information sources have existed for hundreds of years or longer in analogue formats means that recasting them as digital is a highly expensive process, and born digital data is only just proving its value for the humanist researcher.

But Variety?  Now you are talking.  If nothing else, we do have huge heterogeneity in the data, even before we consider the analogue as well as the digital forms.

Cultural data makes us consider another vector as well, however: if it must start with V, I will call it “voluptuousness.”  Cultural data can be steeped in meaning, referring to a massive further body of cultural information stored outside of the dataset itself.  This interconnectedness means that some data can be exceptionally dense, or exceptionally rich, without being large.  Think “to be or not to be;” think the Mona Lisa; think of a Bashō haiku. These are the ultimate big data, which, while tiny in terms of their footprint of 1s and 0s, sit at the centre of huge nets of referents, referents we can easily trace through the resonance of the words and images across people, cultures, and time.

Will the voluptuousness of data be the next big computational challenge?

Tim Hitchcock on ‘Big Data, Small Data and Meaning’

“I end up is feeling that in the rush to new tools and ‘Big Data’ Humanist scholars are forgetting what they spent much of the second half of the twentieth century discovering – that language and art, cultural construction, human experience, and representation are hugely complex – but can be made to yield remarkable insight through close analysis. In other words, while the Humanities and ‘Big Data’ absolutely need to have a conversation; the subject of that conversation needs to change, and to encompass close reading and small data. ”

http://historyonics.blogspot.ie/2014/11/big-data-small-data-and-meaning_9.html

KPLEX Kick-Off: 1 February 2017, Dublin Ireland

The KPLEX Project held its official kick-off meeting on 1 February 2017 in Dublin, Ireland.  The project team took this opportunity for some structured discussion and knowledge sharing on our 4 key themes and set out the plans for the work programme in the months ahead:

Toward a New Conceptualisation of Data,

Hidden Data and the Historical Record

Data, Knowledge Organisation and Epistemics

Culture and Representations of System Limitations

We are particularly grateful to our EU project officer, Pierre-Paul Sondag, who joined us in Dublin for this meeting.