Chihuahua or Muffin?
Elective - Chihuahua or Muffin?
The recent developments in the field of Artificial Intelligence and Machine Learning are opening up new possibilities but also presenting new challenges and ethical dilemmas.
AI systems are now capable of synthesize realistic portraits, psychedelic imagery, harmonic melodies or intriguing narratives. These systems are also used to make predictions based on data in wide ranging fields. They guess the words we are about to type, the songs or tv shows we might like or the content of a picture. But they are also used to determine whether we would commit a crime or pay back a loan, often perpetuating human prejudices.
In this elective, students will be introduced to Artificial Intelligence and Machine Learning through a series of lectures, presentations, hands-on workshops and discussions.
What is the current state of AI? How do these technologies work? How does a neural network learn from data? And most importantly, can these systems distinguish chihuahuas from muffins?
- Demystify and achieve a basic understanding of a series of terms in the domain of this course, including: AI, bots, machine learning, neural networks, singularity and computer vision.
- Have an overview of the possibilities that these techniques offer for artistic creation.
- Reflect on these current technological developments and their application to other fields connected to scientific and technological innovation.
- Develop a critical mindset questioning the impact of these developments in our society.
- Get familiar with a broad range of machine learning open source tools and frameworks that have been extensively used in recent years.
- Work on a project in which you inspired by the current developments of AI and Machine Learning.
- Free Format
- The project presentations will take place on Friday (January 17).
- You will have 5 minutes to present your project + 5 minutes to answer some questions.
AI and Machine Learning: Background history, philosophy & the Turing Test with Brigit Lichtenegger.
Theory Session on Neural Networks. Workshop Classification with Arjen Suijker.
Convolutional Neural Networks with Javier Lloret.
Ethics + Datasets with Javier Lloret. With invited guest speakers Florian Cramer & Lotte Klompenhouwer.
Generative Models and Reinforcement Learning with Javier Lloret + Screening of the short film “The year of the robot”, with an introduction from the director, Yves Gellie.
Natural Language Processing and Project proposal feedback with Brigit & Javier
Presentations with Brigit, Arjen & Javier.
Image recognition with Google Teachable Machine
Image recognition with YOLO with Darknet
Deep Dreams: Deep Dream with Darknet
Terminal basic tutorial: https://maker.pro/linux/tutorial/basic-linux-commands-for-beginners
Generating images using GANs DCGAN
Pix2pix: Online demo of Pix2pix
GauGAN: Online demo of GauGAN
Training a Neural Network how to play Pong: Pong
Generating text using a "Recurrent Neural Network" RNNs with Darknet
GPT-2: GPT-2 Language model
Cathy O'Neil - Weapons of Math Destruction (2016)
Florian Cramer - Crapularity Hermeneutics (2018)
Lev Manovich - AI Aesthetics (2018)
Safiya Umoja Noble - Algorithms of Oppression. How Search Engines Reinforce Racism (2018)
Hito Steyerl - A Sea of Data: Apophenia and Pattern (Mis-)Recognition (2016)
Kate Crawford and Trevor Paglen - Excavating AI. The Politics of Images in Machine Learning Training Sets (2019)
Geert Mul - Match of the Day (2004 - ongoing)
Marie Sester - Access (2003)
Obvious - Edmond de Belamy (2018) -> Painting generated with a GAN sold for 375000€
Helen Knowles - The trial of Superdepthunterbot (2016)
Oscar Sharp & Ross Goowin - Sunspring (2016)
Adam Harvey - CV Dazzle (2010 - 2017)
Adam Harvey - Hyperface (2017)
Constant Dullaart & Adam Harvey - Euronet (2017)
Scott Kelly & Ben Polkinghorne - Signs of the Times (2017)
Mario Klingemann - Memories of Passersby I (2018)
This human does not exist (2019)
Mschf - This foot does not exist (2019)
Andrew Bujalski - Computer Chess (2013) - Trailer
Greg Kohs - AlphaGo (2017) - Trailer
Image processing / Object recognition
DeepDream & Style Transfer
AI & Ethics
New AI can guess whether you're gay or straight from a photograph
OpenEth Computable Ethics
Why Self-Driving Cars Must Be Programmed to Kill
AI picks up racial and gender biases when learning from what humans write: There is no objectivity
Kate Crawford - The Trouble with Bias (2017)