Difference between revisions of "Chihuahua or Muffin?"
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== Elective - Chihuahua or Muffin? == | == Elective - Chihuahua or Muffin? == | ||
| − | + | ||
| + | [[File:Chihuahua.jpeg]] | ||
== Tutors == | == Tutors == | ||
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In this elective, students will be introduced to Artificial Intelligence and Machine Learning through a series of lectures, presentations, hands-on workshops and discussions. | 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, and where is it going? How does a machine learn? And why must self-driving cars be programmed to kill? Invited guest lecturers will include media artist and theorist Florian Cramer, and artist and entrepreneur Gaspard Bos. | What is the current state of AI, and where is it going? How does a machine learn? And why must self-driving cars be programmed to kill? Invited guest lecturers will include media artist and theorist Florian Cramer, and artist and entrepreneur Gaspard Bos. | ||
| + | |||
| + | == Goals == | ||
| + | The main goals of this elective for students to: | ||
| + | * 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. | ||
| + | * Speculate about potential utopian and dystopian future scenarios that AI might lead us to. | ||
| + | * Get familiar with a broad range of machine learning open source tools and frameworks that have been extensively used in recent years. | ||
| + | |||
| + | == Assignment == | ||
| + | Prepare a performance that displays a future speculative scenario inspired by the current developments of AI and Machine Learning. | ||
== Planning == | == Planning == | ||
| + | 10 days | ||
=== Week 1 === | === Week 1 === | ||
Monday<br> | Monday<br> | ||
| − | Kickoff with Brigit Lichtenegger & Javier Lloret. Workshop Wekinator | + | Kickoff with Brigit Lichtenegger & Javier Lloret. Workshop Computer Vision + Wekinator with Javier. |
Tuesday<br> | Tuesday<br> | ||
| − | AI and Machine Learning - Background history, philosophy, Turing Test and a chatbots with Javier | + | AI and Machine Learning - Background history, philosophy, Turing Test and a chatbots with Brigit & Javier |
| + | [http://interactionstation.wdka.hro.nl/diy/downloads/AI.key Brigit's Presentation] | ||
Wednesday<br> | Wednesday<br> | ||
| Line 34: | Line 49: | ||
Thursday<br> | Thursday<br> | ||
| − | Image Generation | + | Image Recognition & Generation using YOLO, Deepdream & DCGans with Brigit and Javier |
Friday<br> | Friday<br> | ||
| Line 56: | Line 71: | ||
== Tutorials == | == Tutorials == | ||
| − | Generating images using | + | Generating images using GANs [[DCGAN]]<br> |
| − | Generating text using a "Recurrent Neural Network" [[ | + | Generating text using a "Recurrent Neural Network" [[RNNs with Darknet]]<br> |
| − | + | Image recognition with [[YOLO | YOLO with Darknet]]<br> | |
| + | Deep Dreams: [[Deep Dream with Darknet | Deep Dream with Darknet]]<br> | ||
| + | Terminal basic tutorial: https://maker.pro/linux/tutorial/basic-linux-commands-for-beginners | ||
== References == | == References == | ||
| Line 65: | Line 82: | ||
[http://www.helenknowles.com/index.php/work/the_trial_of_superdebthunterbot The trial of superdepthunterbot]<br> | [http://www.helenknowles.com/index.php/work/the_trial_of_superdebthunterbot The trial of superdepthunterbot]<br> | ||
[http://aiweirdness.com/post/163878889437/try-these-neural-network-generated-recipes-at-your neural network generated recipes]<br> | [http://aiweirdness.com/post/163878889437/try-these-neural-network-generated-recipes-at-your neural network generated recipes]<br> | ||
| + | [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> | ||
[http://cramer.pleintekst.nl/essays/crapularity_hermeneutics/ Crapularity Hermeneutics]<br> | [http://cramer.pleintekst.nl/essays/crapularity_hermeneutics/ Crapularity Hermeneutics]<br> | ||
| + | [https://www.youtube.com/watch?v=LY7x2Ihqjmc sunspring]<br> | ||
[http://geertmul.nl/projects/match_of_the_day/ Geert Mul - Match of the Day]<br> | [http://geertmul.nl/projects/match_of_the_day/ Geert Mul - Match of the Day]<br> | ||
[http://www.elasticspace.com/2012/02/robot-readable-world Timo Arnall - Robot Readable World]<br> | [http://www.elasticspace.com/2012/02/robot-readable-world Timo Arnall - Robot Readable World]<br> | ||
| + | [https://github.com/tkarras/progressive_growing_of_gans progressive growing of GAN's]<br> | ||
| + | [https://phillipi.github.io/pix2pix/ pix2pix]<br> | ||
| + | [https://www.youtube.com/channel/UCbfYPyITQ-7l4upoX8nvctg 2 Minute Papers]<br> | ||
| + | [https://quickdraw.withgoogle.com/# quickdraw with google]<br> | ||
| + | [https://experiments.withgoogle.com/collection/ai ai experiments]<br> | ||
| + | [https://experiments.withgoogle.com/what-neural-nets-see what neural nets see]<br> | ||
| + | [https://tfhub.dev/s?q=biggan biggan]<br> | ||
[http://www.algolit.net/index.php/Algoliterary_Bibliography Algoliterary Bibliography]<br> | [http://www.algolit.net/index.php/Algoliterary_Bibliography Algoliterary Bibliography]<br> | ||
[https://openeth.com/ OpenEth Computable Ethics]<br> | [https://openeth.com/ OpenEth Computable Ethics]<br> | ||
| Line 83: | Line 110: | ||
[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.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.instagram.com/mrpimpgoodgame.over/ mrpimpgoodgame.over]<br> | [https://www.instagram.com/mrpimpgoodgame.over/ mrpimpgoodgame.over]<br> | ||
| + | http://ssbkyh.com/works/cloud_face/<br> | ||
Revision as of 17:06, 10 January 2019
Elective - Chihuahua or Muffin?
Tutors
Brigit Lichtenegger
Javier Lloret
Arjen Suijker
Special Guests
Florian Cramer
Gaspard Bos
Description
In 2014, a machine passed the Turing Test for the first time since it was developed by Alan Turing in 1950.
The test was based in the question “Can a computer trick a human into thinking it’s actually a fellow human?”.
Even more recently, self-driving cars started driving completely autonomously without a safety driver.
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.
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, and where is it going? How does a machine learn? And why must self-driving cars be programmed to kill? Invited guest lecturers will include media artist and theorist Florian Cramer, and artist and entrepreneur Gaspard Bos.
Goals
The main goals of this elective for students to:
- 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.
- Speculate about potential utopian and dystopian future scenarios that AI might lead us to.
- Get familiar with a broad range of machine learning open source tools and frameworks that have been extensively used in recent years.
Assignment
Prepare a performance that displays a future speculative scenario inspired by the current developments of AI and Machine Learning.
Planning
10 days
Week 1
Monday
Kickoff with Brigit Lichtenegger & Javier Lloret. Workshop Computer Vision + Wekinator with Javier.
Tuesday
AI and Machine Learning - Background history, philosophy, Turing Test and a chatbots with Brigit & Javier Brigit's Presentation
Wednesday
Theory Session on Neural Networks. Workshop Classification with Arjen Suijker workshop content
Thursday
Image Recognition & Generation using YOLO, Deepdream & DCGans with Brigit and Javier
Friday
Lecture and discussion with Florian Cramer
Week 2
Monday
Presentation Gaspard Bos. Session to prepare final presentations with Brigit.
Tuesday
Propose ideas for final presentation with Brigit
Wednesday
Assignment time
Thursday
Assignment time
Friday
Presentations with Brigit and 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/