Improv Theater and Artificial Intelligence

Improvisational theater is an art form where unscripted theater is performed. Dialogue, characters, and actions are created on the spot. Errors made within an improvisational theater scene are encouraged, and can form an input to how the scene evolves. Ultimately this project focuses on the evolution and creation of artificial intelligence bots interacting with the world of improv theater. Chatbots Versus Improv Bots A chatbot is a software application used to conduct an online chat conversation via text or text-to-speech, in lieu of providing direct contact with a live human agent. There are many different types of chatbots ranging from a regular expression chatbot like Eliza, who was designed to imitate a therapist, a slot-response chatbot such as Amazon’s Alexa, who responds and acts on commands, or even neural nets like GPT-2 , BERT, or XLNet all of which are used for various elements of natural language processing and text classification tasks. The Artificial Improvisor is a form of artificial conversational agent, or chatbot, focused on open domain dialogue and collaborative narrative generation. Using state-of-the-art machine learning techniques, spanning from natural language processing and speech recognition, to reinforcement and deep learning, these improv bots provide a completely new and exciting asset to this technology that is different from these other types of chatbots. Below is an example of each type of chatbot listed in order from left to right

Chatbots as Unwitting Actors

Chatbots are popular for both task-oriented conversations and unstructured conversations with web users. Several different approaches to creating comedy and art exist across the field of computational creativity. Despite the popularity and ease of use of chatbots, there have not been any attempts by artists or comedians to use these systems for comedy performances. We present two initial attempts to do so from our comedy podcast and call for future work toward both designing chatbots for performance and for performing alongside chatbots

Improvisation for technically-oriented peoples

Teaching “soft” skills to technical people is just as important as learning “hard” skills. Improvisation techniques can also be used in teaching technical concepts such as cybersecurity, agile development, database design, programming concepts, and most importantly how to better one’s communication skills. In an age where rapid changes have become the norm, improvisation techniques can be used to help navigate the new challenges of the next generation careers, global interaction, and technologies. These techniques can easily be incorporated in other methodologies such as creative problem-solving and design thinking. There are clearly defined and flexible rules for improvising, which make it easier for technical persons to learn and use in their daily life and career.Enseñar las habilidades “blandas” a las personas técnicas es tan importante como aprender las habilidades “duras”. Las técnicas de improvisación también se pueden usar en la enseñanza de conceptos técnicos como la ciberseguridad, el desarrollo ágil, el diseño de bases de datos, los conceptos de programación y, lo más importante, cómo mejorar las habilidades de comunicación. En una era en la que los cambios rápidos se han convertido en la norma, las técnicas de improvisación pueden usarse para ayudar a navegar los nuevos desafíos de las carreras de la próxima generación, la interacción global y las tecnologías. Estas técnicas pueden incorporarse fácilmente en otras metodologías, como la resolución creativa de problemas y el pensamiento de diseño. Existen reglas claramente definidas y flexibles para la improvisación que facilitan que las personas técnicas aprendan y usen en su vida diaria y carrer

A Picasso of Perspectives on Formulaic Language

Peer reviewe

Deep Representation Learning for Open Vocabulary Electroencephalography-to-Text Decoding

Previous research has demonstrated the potential of using pre-trained language models for decoding open vocabulary Electroencephalography (EEG) signals captured through a non-invasive Brain-Computer Interface (BCI). However, the impact of embedding EEG signals in the context of language models and the effect of subjectivity, remain unexplored, leading to uncertainty about the best approach to enhance decoding performance. Additionally, current evaluation metrics used to assess decoding effectiveness are predominantly syntactic and do not provide insights into the comprehensibility of the decoded output for human understanding. We present an end-to-end deep learning framework for non-invasive brain recordings that brings modern representational learning approaches to neuroscience. Our proposal introduces the following innovations: 1) an end-to-end deep learning architecture for open vocabulary EEG decoding, incorporating a subject-dependent representation learning module for raw EEG encoding, a BART language model, and a GPT-4 sentence refinement module; 2) a more comprehensive sentence-level evaluation metric based on the BERTScore; 3) an ablation study that analyses the contributions of each module within our proposal, providing valuable insights for future research. We evaluate our approach on two publicly available datasets, ZuCo v1.0 and v2.0, comprising EEG recordings of 30 subjects engaged in natural reading tasks. Our model achieves a BLEU-1 score of 42.75%, a ROUGE-1-F of 33.28%, and a BERTScore-F of 53.86%, outperforming the previous state-of-the-art methods by 3.38%, 8.43%, and 6.31%, respectively.Comment: 12 pages, 5 figures, preprin

The Parthenon, September 15, 2014

The Parthenon, Marshall University’s student newspaper, is published by students Monday through Friday during the regular semester and weekly Thursday during the summer. The editorial staff is responsible for the news and the editorial content

Neighbouring Communities: Interaction, Lessons and Opportunities

Building, understanding and sharing software that works in creative spaces is increasingly popular and widespread, with many communities outside of academic research interested in pursuing questions highly relevant to Computational Creativity. We report here on several notable communities in the area: the Procedural Generation Jam, the National Novel Generating Month, the Twitterbot community and the #CreativeAI movement. By studying these communities, we benefit from different perspectives on building creative software, as well as how communities of like-minded people form, grow and sustain themselves. We reflect on these communities as sources of lessons for our field and opportunities for future growth and knowledge exchange, as well as raising awareness of sources of inspiration beyond academia