14 research outputs found
DeepTingle
DeepTingle is a text prediction and classification system
trained on the collected works of the renowned fantastic
gay erotica author Chuck Tingle. Whereas the writing
assistance tools you use everyday (in the form of predictive
text, translation, grammar checking and so on)
are trained on generic, purportedly “neutral” datasets,
DeepTingle is trained on a very specific, internally consistent
but externally arguably eccentric dataset. This
allows us to foreground and confront the norms embedded
in data-driven creativity and productivity assistance
tools. As such tools effectively function as extensions
of our cognition into technology, it is important to identify
the norms they embed within themselves and, by
extension, us. DeepTingle is realized as a web application
based on LSTM networks and the GloVe word
embedding, implemented in JavaScript with Keras-JS.peer-reviewe
Controllable Neural Story Plot Generation via Reinforcement Learning
Language-modeling--based approaches to story plot generation attempt to
construct a plot by sampling from a language model (LM) to predict the next
character, word, or sentence to add to the story. LM techniques lack the
ability to receive guidance from the user to achieve a specific goal, resulting
in stories that don't have a clear sense of progression and lack coherence. We
present a reward-shaping technique that analyzes a story corpus and produces
intermediate rewards that are backpropagated into a pre-trained LM in order to
guide the model towards a given goal. Automated evaluations show our technique
can create a model that generates story plots which consistently achieve a
specified goal. Human-subject studies show that the generated stories have more
plausible event ordering than baseline plot generation techniques.Comment: Published in IJCAI 201
Is style reproduction a computational creativity task?
Is style reproduction a valid computational creativity task? Does producing output 'in the style of' an existing creator contribute to computational creativity research? Where is the creativity in imitation or replication of an existing style, and where does style reproduction fall into what has been criticised as `pastiche' rather than credible creative activity? This paper tackles these debates, which have been under-addressed in computational creativity literature. We review the presentation of past work in style reproduction, and consider the fit of such work into evolving definitions of computational creativity research. As part of this, we consider style reproduction itself as a creative task, both within and outside computational forms. We discuss various points of interest that emerge in the analysis, such as control in the creative process, intentionality and effort. Our work gives a more objective understanding of the level of creativity present in style generation, and specifically what value it brings to computational creativity research
Event Representations for Automated Story Generation with Deep Neural Nets
Automated story generation is the problem of automatically selecting a
sequence of events, actions, or words that can be told as a story. We seek to
develop a system that can generate stories by learning everything it needs to
know from textual story corpora. To date, recurrent neural networks that learn
language models at character, word, or sentence levels have had little success
generating coherent stories. We explore the question of event representations
that provide a mid-level of abstraction between words and sentences in order to
retain the semantic information of the original data while minimizing event
sparsity. We present a technique for preprocessing textual story data into
event sequences. We then present a technique for automated story generation
whereby we decompose the problem into the generation of successive events
(event2event) and the generation of natural language sentences from events
(event2sentence). We give empirical results comparing different event
representations and their effects on event successor generation and the
translation of events to natural language.Comment: Submitted to AAAI'1
Choose Your Weapon: Survival Strategies for Depressed AI Academics
Are you an AI researcher at an academic institution? Are you anxious you are
not coping with the current pace of AI advancements? Do you feel you have no
(or very limited) access to the computational and human resources required for
an AI research breakthrough? You are not alone; we feel the same way. A growing
number of AI academics can no longer find the means and resources to compete at
a global scale. This is a somewhat recent phenomenon, but an accelerating one,
with private actors investing enormous compute resources into cutting edge AI
research. Here, we discuss what you can do to stay competitive while remaining
an academic. We also briefly discuss what universities and the private sector
could do improve the situation, if they are so inclined. This is not an
exhaustive list of strategies, and you may not agree with all of them, but it
serves to start a discussion
Data-driven design : a case for maximalist game design
Maximalism in art refers to drawing on and combining
multiple different sources for art creation, embracing
the resulting collisions and heterogeneity. This paper
discusses the use of maximalism in game design
and particularly in data games, which are games that
are generated partly based on open data. Using Data
Adventures, a series of generators that create adventure
games from data sources such as Wikipedia and Open-
StreetMap, as a lens we explore several tradeoffs and
issues in maximalist game design. This includes the tension
between transformation and fidelity, between decorative
and functional content, and legal and ethical issues
resulting from this type of generativity. This paper
sketches out the design space of maximalist data-driven
games, a design space that is mostly unexplored.peer-reviewe
Data-driven Design: A Case for Maximalist Game Design
Maximalism in art refers to drawing on and combining multiple different
sources for art creation, embracing the resulting collisions and heterogeneity.
This paper discusses the use of maximalism in game design and particularly in
data games, which are games that are generated partly based on open data. Using
Data Adventures, a series of generators that create adventure games from data
sources such as Wikipedia and OpenStreetMap, as a lens we explore several
tradeoffs and issues in maximalist game design. This includes the tension
between transformation and fidelity, between decorative and functional content,
and legal and ethical issues resulting from this type of generativity. This
paper sketches out the design space of maximalist data-driven games, a design
space that is mostly unexplored.Comment: 9 pages, 2 Figures, Accepted in ICCC 201