AI in culture and arts - Human-AI interaction (tech crash course)
📰 Announcements
15.01.2023 - Save the date! The tech crash course on AI in Culture and Arts is coming soon. First bloc will be on Tuesday, April 23rd and Thursday, April 25th, 2024.
Table of contents
- What is AICA?
- What is the crash course on Human-AI interaction ?
- Learning outcomes
- Prerequisites
- Course content
- Tutorials and teaching methods
- Evaluation and ECTS
- License
What is AICA?
The Digitization College “Artificial Intelligence in Culture and Arts” (AICA) aims to equip students at the University of Music and Performing Arts Munich (HMTM) and Hochschule München University of Applied Sciences (HM) with necessary skills to impact AI innovations in the creative and cultural industries.
What is the crash course on Human-AI interaction ?
Artificial intelligence (AI) is increasingly impacting the cultural and creative sectors. In particular, machine learning algorithms can now generate unprecedented synthetic media, transforming how we create, produce, and distribute art and culture. Students must develop a theoretical and practical understanding of machine learning to comprehend such transformative technology and foster the development of meaningful human-AI interactions. This course addresses this need and delves into interactive machine learning for the cultural and creative sectors. The course is intended for art, cultural management, design, and computer science students. After this course, students will master the theoretical and technological foundations of machine learning, be able to train and (critically) evaluate machine learning models, and deploy them in meaningful interactive systems. The course is structured in three 2-day blocks (6 days in total). Each block provides theoretical lectures and hands-on activities to develop interactive machine-learning systems for image, sound, and text-based applications in the creative and cultural sectors. Every teaching day starts with a lecture and discussion in the morning, followed by a hands-on session on the same topic in the afternoon.
The AICA tech crash course will be hosted at the Wavelab, in the Summer Semester, starting April 2024.
Learning outcomes
After successful participation in this course, students are able to:
- Understand the history and current state of AI: students will be able to explain the different waves of AI (symbolic, connectionist), precisely identify machine learning algorithms, and ex- plain their distinctive characteristics (dataset, optimization, loss, etc.).
- Trainand(critically)evaluateamachinelearningalgorithm:studentswillbeabletoexplain and apply the main steps of the development cycle of machine learning, from data collection, analysis, preprocessing, training, and evaluation. They will be able to critically examine a lear- ning curve and performance metrics to assess the performance of their machine-learning mo- dels. Furthermore, they will be able to critically discuss the limitations of their model from the content of their dataset and from the perspective of bias and fairness.
- Create interactive machine learning systems: students can design and implement interac- tive machine learning systems for image, sound, and text-based applications in the creative and cultural sectors. Three examples of interactive systems will be showcased in this course: a teachable image classifier, a gesture-to-sound synthesizer, and a tool for semantic and multi- modal exploration of museum archives. Students already familiar with programming and ma- chine learning will be able to dive deeper into the design and development of novel interactions with machine learning algorithms.
Prerequisites
The module is designed as an interdisciplinary venue that brings together a range of perspective. It is aimed at all students enrolled in a third-year Bachelor’s program at Hochschule München University of Applied Sciences (HM) or the Hochschule für Musik und Theater München (HMTM). Students in Master’s programs are also welcome. Students with prior computer science and machine learning knowledge will be assigned dedicated and more advanced activities to develop interactive ML systems using the open source Marcelle toolkit.
To apply, please refer to the Subscription section.
Course content
Structured over three 2-day blocks (6 days in total), the course addresses:
- Image: This introductory block focuses on image classification using machine learning. After a general introduction to AI’s history and current state, participants will explore the machine learning development cycle, engaging with dedicated interactive applications (made with Marcelle) and computational notebooks in Python. The hands-on session will focus on training and evaluating museum artifacts using open-access and open-source datasets (MAMe, Smithsonian Open Acces).
- Sound: The second block centers on musical applications. Students will be guided to create a regression model from physical gestures to sound using an open-source visual programming language for music and art (Pure Data). Participants will learn Pure Data basics and discover how to transform their smartphones into synthesizers. Students will also have the opportunity to tackle symbolic music generation using traditional programming (computational notebooks in Python).
- Text: Building on the first block, this third bloc explores the use of machine learning to “embed” and navigate cultural archives. Students will use multi-modal models that link images to textual descriptions to design interactive tools for exploring and retrieving artifacts in museum archives. The more advanced students will be able to train their own embedding models on personalized datasets in Python.
Tutorials and teaching methods
This website provides a range of tutorials and ressources, organized by topic and in increasing order of difficulty.
A typical day of teaching starts with a lecture on a topic, followed by a hands-on session where students can apply the concepts learned in the lecture. The hands-on sessions are based on the tutorials provided on this website.
Evaluation and ECTS
You will earn 2 ECTS for the validation of the course.
The evaluation will be based on:
- Attendance: you must attend at least 4/6 days of teaching. Attending the first day of the course is mandatory.
- Completion of in-class practical work: you must submit the completed practical work by the end of the course. If you do not finish during the in-class sessions, you will have to finish it at home.
License
The new teaching material (tutorials and code) created for the course is available under the Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0). Each tool and library demonstrated in the tutorials is subject to its own license.