Chihuahua or Muffin?

Elective - Chihuahua or Muffin?

Tutors

Brigit Lichtenegger
Javier Lloret
Arjen Suijker

Special Guests

Florian Cramer
Lotte Klompenhouwer
Yves Gellie

Description

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?

Learning Goals

• 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.

Final assignment

• 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.

Planning

Week 1

Monday - 09:00 - 13:00

AI and Machine Learning: Background history, philosophy & the Turing Test with Brigit Lichtenegger.

Tuesday - 09:00 - 13:00

Theory Session on Neural Networks. Workshop Classification with Arjen Suijker.

Wednesday - 09:00 - 13:00

Convolutional Neural Networks with Javier Lloret.

Thursday - 09:00 - 13:00

Ethics + Datasets with Javier Lloret. With invited guest speakers Florian Cramer & Lotte Klompenhouwer.

Friday - 09:00 - 14:00

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.

Week 2

Monday - 09:00 - 13:00

Natural Language Processing and Project proposal feedback with Brigit & Javier 

Tuesday

Assignment time

Wednesday

Assignment time

Thursday

Assignment time

Friday

Presentations with Brigit, Arjen & Javier.

Tutorials

Generating images using GANs DCGAN
Generating text using a "Recurrent Neural Network" RNNs with Darknet
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

References

AI Painting sold for 375000 Euro
biggan
The trial of superdepthunterbot
neural network generated recipes
Machine Learning for Artists
coursera deep learning classes
Crapularity Hermeneutics
sunspring
Geert Mul - Match of the Day
Timo Arnall - Robot Readable World
progressive growing of GAN's
pix2pix
2 Minute Papers
quickdraw with google
ai experiments
what neural nets see
biggan
Algoliterary Bibliography
OpenEth Computable Ethics
Why Self-Driving Cars Must Be Programmed to Kill
Fooling Neural Networks in the Physical World with 3D Adversarial Objects
Deep Learning is not the AI future
Future of Life Institute
How Many Computers to Identify a Cat? 16,000
Speech Recognition and Deep Learning
NPU chips in intelligent phones
The FRIEND-Browser
Googlism
owow bots
New AI can guess whether you're gay or straight from a photograph
AI picks up racial and gender biases when learning from what humans write: There is no objectivity
mrpimpgoodgame.over
http://ssbkyh.com/works/cloud_face/