Jack Bodine
Artifacts are tangible pieces of evidence demonstrating the skills and knowledge I’ve acquired from various learning experiences. Links labeled “currently unavailable” indicate either that I haven’t gotten around to publishing it or that the project contains exam/coursework which must remain private for furture iterations of the course. However, if you email me, I’ll try to expedite it.
April 2024
Kierkegaard Essay
Søren Kierkegaard - Subjectivity, Irony and the Crisis of Modernity
Outside of computer science I have a deep affinity for philosophy. And while there is unfortunately no room in my MSc schedule to take any philosophy courses, I enrolled in the online course “Søren Kierkegaard - Subjectivity, Irony and the Crisis of Modernity” offered by my university.
The course covered Kierkegaard’s life, from his personal affairs to his time at the university, and included an analysis of his works. For the course conclusion, I wrote a paper examining how Kierkegaard used Socrates as a model in his writing and explored the implications of Kierkegaard’s ideas in the present day.
This course was personally rewarding, especially as I discovered that one of Kierkegaard’s former residence is just a block from where I live. Additionally, his old desk is right outside my office at the Royal Danish Library, and his journals are on display just downstairs. Seeing these artifacts daily gives me a connection to his work and legacy.
Philosophy
May 2022
Drunk Philosophers
Artificial Neural Networks and Deep Learning
Drunk Philosophers bridges the gap between ancient philosophical discourse and modern computational techniques using generative neural networks to recreate conversations between historical philosophers. The objective was to bring the insights of great thinkers like Aristotle, Hume, Kant, and Nietzsche into contemporary debates, leveraging the power of AI to generate new dialogues and potentially uncover new insights.
We used a comprehensive dataset from Kaggle containing over 300,000 sentences from 51 philosophical texts across 10 major schools of philosophy. After pre-processing the data, we trained Long Short-Term Memory (LSTM) networks for each philosopher to generate text segments, which were then refined using GPT-3 for coherent conversations. Despite initial challenges, our philosopher-bots eventually produced text that not only reflected the style and themes of each philosopher but also engaged in somewhat coherent debates. This outcome demonstrates that the nuances of each philosopher’s writing style can be effectively captured by neural networks, opening new avenues for exploring and understanding philosophical ideas.
Philosophy, Machine Learning, Natural Language Processing