Difference between revisions of "Chihuahua or Muffin? (2018)"
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[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> | ||
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Latest revision as of 09:59, 21 November 2022
Elective - Chihuahua or Muffin?
9 days
Tutors
Jeroen Bouweriks
Brigit Lichtenegger
Arjen Suijker
Javi Lloret
Special Guests
Florian Cramer
Geert Mul
Maaike Harbers
Boris Smeenk
Arthur Boer
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 & Jeroen Bouweriks
Tuesday
AI and Machine Learning - Background history, philosophy, Turing Test and ChatBots with Brigit Lichtenegger
Wednesday
Theory Session on the inner workings of Neural Networks. Workshop supervised machine learning with Arjen Suijker workshop content
Thursday
Arthur de Boer and Boris Smeenk present their work, and part 1 of their Deep Learning workshop.
Workshop Wekinator with Javier Lloret
Friday
Part 2 and results of the workshop with Boris and Arthur
Presentation, lecture and discussion with Geert Mul en Florian Cramer
Week 2
Monday
Presentation and Workshop on Value Sensitive Design with Maaike Harbers
Tuesday
Progressing on the Research with Jeroen Bouweriks
Wednesday
Finish up research with Javier Lloret
Thursday
Presentations on Research with Brigit and Jeroen
Movie Program (may change)
Tuesday
Ex Machina
Wednesday
Her
Thursday
Monday
Animatrix
Tuesday
Chappy
Wednesday
Terminator
Tutorials
Generating images using a "Deep Convolutional Generative Adversarial Network" DCGAN
Generating text using a "Recurrent Neural Network" RNN
Epoch Workshop Results
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
[Category:AI]][Category:Machine Learning]][Category:Classes]]