In this episode, we launch a new season of the Adapticx Podcast focused on the foundations of natural language processing—before transformers and large language models. We trace how early NLP systems represented language using simple statistical methods, how word embeddings introduced semantic meaning, and how sequence models attempted to capture context over time. This historical path explains why modern NLP works the way it does and why attention became such a decisive breakthrough.

This episode covers:

• Classical NLP approaches: bag-of-words, TF-IDF, and topic models • Why early systems struggled with meaning and context • The shift from word counts to word embeddings • How Word2Vec and GloVe introduced semantic representation • Early sequence models: RNNs, LSTMs, and GRUs • Why attention and transformers changed NLP permanently ...