Difference between revisions of "Chihuahua or Muffin?"

Latest revision as of 12:12, 20 January 2023

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

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

Tuesday

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

Wednesday

Convolutional Neural Networks with Javier Lloret.

Thursday

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

Friday

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

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

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

Inspiration

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

Other resources

@@ Line 22: / Line 22: @@
 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  ==
+== 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.
@@ Line 88: / Line 88: @@
 [https://strelka.com/en/press/books/lev-manovich-ai-aesthetics Lev Manovich - AI Aesthetics (2018)]<br>
 [https://nyupress.org/9781479837243/algorithms-of-oppression/ Safiya Umoja Noble - Algorithms of Oppression. How Search Engines Reinforce Racism (2018)]<br>
+[https://www.e-flux.com/journal/72/60480/a-sea-of-data-apophenia-and-pattern-mis-recognition/ Hito Steyerl - A Sea of Data: Apophenia and Pattern (Mis-)Recognition (2016)]<br>
+[https://www.excavating.ai/ Kate Crawford and Trevor Paglen - Excavating AI. The Politics of Images in Machine Learning Training Sets  (2019)]<br>
 == Inspiration ==
 [http://geertmul.nl/projects/match_of_the_day/ Geert Mul - Match of the Day (2004 - ongoing)]<br>
-[https://nos.nl/artikel/2256421-schilderij-geveild-van-min-g-max-d-ex-log-d-x-ez-log-1-d-g-z.html AI Painting sold for 375000 Euro]<br>
+[http://www.sester.net/access/ Marie Sester - Access (2003)]<br>
+[https://nos.nl/artikel/2256421-schilderij-geveild-van-min-g-max-d-ex-log-d-x-ez-log-1-d-g-z.html Obvious - Edmond de Belamy (2018) -> Painting generated with a GAN sold for 375000€]<br>
 [http://www.helenknowles.com/index.php/work/the_trial_of_superdebthunterbot Helen Knowles - The trial of Superdepthunterbot (2016)]<br>
-[https://www.youtube.com/watch?v=LY7x2Ihqjmc  Oscar Sharp + Ross Goowin - Sunspring (2016)]<br>
+[https://www.youtube.com/watch?v=LY7x2Ihqjmc  Oscar Sharp & Ross Goowin - Sunspring (2016)]<br>
+[https://cvdazzle.com/  Adam Harvey - CV Dazzle (2010 - 2017)]<br>
+[https://ahprojects.com/hyperface/  Adam Harvey - Hyperface (2017)]<br>
+[http://imagenet.xyz/euronet/  Constant Dullaart & Adam Harvey - Euronet (2017)]<br>
+[http://scottandbenorbenandscott.com/#/signs-of-the-times/  Scott Kelly & Ben Polkinghorne  - Signs of the Times (2017)]<br>
+[https://vimeo.com/298000366  Mario Klingemann  - Memories of Passersby I  (2018)]<br>
+[https://www.thispersondoesnotexist.com/ This human does not exist (2019)]<br>
+[https://thisfootdoesnotexist.com/  Mschf  - This foot does not exist (2019)]<br>
+[https://www.youtube.com/watch?v=NuGT_L13bQ8 Andrew Bujalski - Computer Chess (2013) - Trailer]<br>
+[https://www.youtube.com/watch?v=8tq1C8spV_g Greg Kohs  - AlphaGo (2017) - Trailer]<br>
 == Other resources ==
@@ Line 103: / Line 115: @@
 [http://www.labsix.org/physical-objects-that-fool-neural-nets/ Fooling Neural Networks in the Physical World with 3D Adversarial Objects]<br>
 [http://ssbkyh.com/works/cloud_face/ Cloud Face]<br>
-[http://www.sester.net/access/ Marie Sester - Access]<br>
 [https://github.com/CMU-Perceptual-Computing-Lab/openpose OpenPose]<br>
 [http://densepose.org/ DensePose]<br>
+=== DeepDream & Style Transfer ===
+[https://www.garysnotebook.com/20190826_1 DeepDream explained]<br>
+[https://www.vice.com/en_us/article/53dwg3/heres-how-google-deep-dream-generates-those-trippy-images DeepDream: How does it work]<br>
+[https://towardsdatascience.com/a-brief-introduction-to-neural-style-transfer-d05d0403901d Neural Style Transfer explained]
+=== Reinforcement Learning ===
+[https://www.youtube.com/watch?v=gn4nRCC9TwQ DeepMind - Producing flexible behaviours in simulated environments (2017)]<br>
+[https://www.youtube.com/watch?v=kopoLzvh5jY DeepMind - Multi-Agent Hide and Seek (2019)]<br>
+[https://www.youtube.com/user/BostonDynamics Boston Dynamics]<br>
 === GANs ===
@@ Line 111: / Line 132: @@
 [https://tfhub.dev/s?q=biggan biggan implementations]<br>
 [https://github.com/tkarras/progressive_growing_of_gans progressive growing of GAN's]<br>
+[https://github.com/NVlabs/stylegan2 StyleGAN2]<br>
 === Conditional GANs ===
@@ Line 125: / Line 147: @@
 [https://www.technologyreview.com/s/542626/why-self-driving-cars-must-be-programmed-to-kill/ Why Self-Driving Cars Must Be Programmed to Kill]<br>
 [https://www.theverge.com/2017/4/13/15287678/machine-learning-language-processing-artificial-intelligence-race-gender-bias AI picks up racial and gender biases when learning from what humans write: There is no objectivity]<br>
+[https://www.youtube.com/watch?v=fMym_BKWQzk Kate Crawford - The Trouble with Bias (2017)]<br>
 === Natural Language Processing + Speech Recognition  ===
@@ Line 134: / Line 157: @@
 === Online Courses ===
 [http://ml4a.github.io/classes/itp-F18/?fbclid=IwAR0NxhgFYyqb-FbKAdwdGMvxPxyoGzEkOoNOp7Sv6jFjBHFw11ulNPxQ0AY#syllabus Machine Learning for Artists]<br>
-[https://www.coursera.org/specializations/deep-learning coursera deep learning classes]<br>
+[https://www.coursera.org/specializations/deep-learning coursera deep learning classes]
 === Misc ===
-[http://www.elasticspace.com/2012/02/robot-readable-world Timo Arnall - Robot Readable World]<br>
 [https://www.youtube.com/channel/UCbfYPyITQ-7l4upoX8nvctg 2 Minute Papers]<br>
 [https://experiments.withgoogle.com/collection/ai ai experiments]<br>
@@ Line 147: / Line 168: @@
 [http://www.thechurchofgoogle.org/frequently-asked-questions/ Googlism]<br>
 [https://www.instagram.com/mrpimpgoodgame.over/ mrpimpgoodgame.over]<br>
+[[Category:2019]]

Difference between revisions of "Chihuahua or Muffin?"

Latest revision as of 12:12, 20 January 2023

Contents

Elective - Chihuahua or Muffin?

Tutors

Special Guests

Description

Learning goals

Final assignment

Planning

Week 1

Week 2

Tutorials

Recommended readings

Inspiration

Other resources

Image processing / Object recognition

DeepDream & Style Transfer

Reinforcement Learning

GANs

Conditional GANs

AI & Ethics

Natural Language Processing + Speech Recognition

Online Courses

Misc

Navigation menu

Search