This project delves into the evolving landscape of Artificial Intelligence (AI) research by analyzing a collection of recent academic papers. The power of the Bidirectional Encoder Representations from Transformers (BERT) model has been leveraged to perform unsupervised topic modeling on a diverse collection of AI research papers.
- Utilize BERT to extract meaningful topics from a corpus of AI research papers.
- Generate interpretable representations of the identified topics.
- Gain insights into the current direction and potential future of AI development.
- Bayesian
- Reinforcement Learning
- Neural Networks
- Speech Recognition
- Clustering