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MAT594SP: Aesthetics and Politics of Artificial Intelligence

Wednesdays and Fridays, 2-4, Elings 2003



The class starts from a basic hypothesis, put forward by Philip Agre in the late 1990s: "AI is philosophy underneath". Given the rapid development of the field since 2012, does this hypothesis hold? When we talk about artificial intelligence today, we talk about highly specialized machine learning models. Unlike in the 1990s, the primary function of these models is not the mechanization of reason but the mechanization of perception, most prominently the mechanization of vision. As a consequence, the tasks that many machine learning models operate on are aesthetic tasks, ranging from the classification of images in regard to their content and form to the generation of completely new images.

At the same time, the technical opacity of many machine learning models makes it inherently difficult to properly evaluate their results. This is complicated even further whenever a model is deployed as a product and opacity becomes a desirable property. In fact, the interpretability of machine learning models — their ability to generate and/or facilitate explanations of their results — has not only become an independent field of research within computer science but has also grown into an increasingly important legal challenge. Hence, the once speculative phenomenological question "how does the machine perceive the world" suddenly becomes a real-world problem.

Contemporary machine learning models thus raise a set of issues that are completely independent of the ones raised by the possibility of a future general artificial intelligence. Most prominently, they are real-life socio-technical systems that have politics. Adapting Agre's hypothesis: AI is aesthetics and politics underneath.

Participants in the class meet twice weekly to investigate this peculiar nexus of aesthetics and politics in contemporary machine learning through equal parts of critical reading and technical reviews (of technical papers and code examples).


📖 = article/book/blog post; 🎓 = talk; 💻 = source code close reading; 📼 = video in class

Class GitHub repository:

4/4: Introduction

4/6: Artificial Intelligence as a Philosophical Project

4/11: History of Deep Learning

4/13: Limits of Deep Learning

4/18: Deep Dreaming I

4/20: Deep Dreaming II

4/25: Feature Visualization I

4/27: Feature Visualization II

  • 🎓 3pm: Mhaskar, Hrushikesh, A New Look at Machine Learning as Function Approximation (Webb Hall 1100)

5/2: Feature Visualization III

5/4: Interpretability I

5/9: GANs

5/16: Word Embeddings

5/18: Reinforcement Learning

  • 🎓 2pm: Luo, Rodger and Green, Sam, Visualization for Deep Reinforcement Learning (Elings 2003)

5/23: RNNs

5/25: FAT

5/30: Interpretability II

Further Resources

For a list of further resources, see:

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