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.