Difference between revisions of "Chihuahua or Muffin?"
(Created page with "== Elective - Chihuahua or Muffin? (2018)== 9 days == Tutors == Jeroen Bouweriks<br> Brigit Lichtenegger<br> Arjen Suijker<br> Javi Lloret<br> == Special Guests == Florian...") |
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− | == Elective - Chihuahua or Muffin? | + | == Elective - Chihuahua or Muffin? == |
− | + | 10 days | |
== Tutors == | == Tutors == | ||
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Brigit Lichtenegger<br> | Brigit Lichtenegger<br> | ||
+ | Javier Lloret<br> | ||
Arjen Suijker<br> | Arjen Suijker<br> | ||
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== Special Guests == | == Special Guests == | ||
Florian Cramer<br> | Florian Cramer<br> | ||
− | + | Gaspard Bos<br> | |
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== Description == | == Description == | ||
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=== Week 1 === | === Week 1 === | ||
Monday<br> | Monday<br> | ||
− | Kickoff with Brigit Lichtenegger & | + | Kickoff with Brigit Lichtenegger & Javier Lloret. Workshop Wekinator, and a selected Short Screening |
Tuesday<br> | Tuesday<br> | ||
− | AI and Machine Learning - Background history, philosophy, Turing Test and | + | AI and Machine Learning - Background history, philosophy, Turing Test and a chatbots with Javier Lloret |
Wednesday<br> | Wednesday<br> | ||
− | Theory Session on Neural Networks. Workshop | + | Theory Session on Neural Networks. Workshop Classification with Arjen Suijker |
[http://interactionstation.wdka.hro.nl/mediawiki/images/b/b4/Machine_Learning_%26_Neural_Nets.pdf workshop content] | [http://interactionstation.wdka.hro.nl/mediawiki/images/b/b4/Machine_Learning_%26_Neural_Nets.pdf workshop content] | ||
Thursday<br> | Thursday<br> | ||
− | + | Image Generation using DCGans, Jolo and Deepdream With Brigit and Javier | |
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Friday<br> | Friday<br> | ||
− | + | Lecture and discussion with Florian Cramer | |
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=== Week 2 === | === Week 2 === | ||
Monday<br> | Monday<br> | ||
− | + | Presentation Gaspard Bos. Session to prepare final presentations with Brigit. | |
Tuesday<br> | Tuesday<br> | ||
− | + | Propose ideas for final presentation with Brigit | |
Wednesday<br> | Wednesday<br> | ||
− | + | Assignment time | |
Thursday<br> | Thursday<br> | ||
− | + | Assignment time | |
+ | |||
+ | Friday<br> | ||
+ | Presentations with Brigit and Javier | ||
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== Tutorials == | == Tutorials == | ||
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Generating text using a "Recurrent Neural Network" [[RNN]]<br> | Generating text using a "Recurrent Neural Network" [[RNN]]<br> | ||
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== References == | == References == |
Revision as of 06:21, 6 January 2019
Elective - Chihuahua or Muffin?
10 days
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 artists and theorists Geert Mul and Florian Cramer.
Planning
Week 1
Monday
Kickoff with Brigit Lichtenegger & Javier Lloret. Workshop Wekinator, and a selected Short Screening
Tuesday
AI and Machine Learning - Background history, philosophy, Turing Test and a chatbots with Javier Lloret
Wednesday
Theory Session on Neural Networks. Workshop Classification with Arjen Suijker workshop content
Thursday
Image Generation using DCGans, Jolo and Deepdream 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 a "Deep Convolutional Generative Adversarial Network" DCGAN
Generating text using a "Recurrent Neural Network" RNN
References
Crapularity Hermeneutics
Geert Mul - Match of the Day
Timo Arnall - Robot Readable World
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