The Evolution of NLP: From Statistical Foundations to Generative AI Revolution

About the Session

This presentation traces Natural Language Processing's remarkable journey from basic statistical methods to today's sophisticated generative AI systems, backed by $91.9 billion in private investment (2022). We'll examine how early approaches like Bag of Words (78.2% classification accuracy) and TF-IDF (31.2% improvement in retrieval) laid crucial foundations before neural networks transformed the landscape. The RNN breakthrough dramatically reduced perplexity scores from 137+ to 23.7 on benchmark datasets, while the 2017 Transformer architecture achieved 41.8 BLEU scores on translation tasks with 600% faster training. BERT's performance across 11 NLP tasks (94.9% sentiment analysis accuracy) and GPT-3's 175B parameters demonstrated clear scaling advantages, with SuperGLUE performance jumping from 45.2% to 71.8%. Today's models process 2-5TB of training data versus 2019's 100-200GB, with computational requirements increasing 300,000-fold over a decade. Looking ahead, we'll explore how multimodal approaches are improving healthcare diagnostics by 35%, while efficiency techniques achieve 85% compression with minimal performance loss. Join us for essential insights into NLP's evolution and the technological developments shaping its future.

Speaker

Shahzeb Akhtar

Director of IP Strategy & Technology at United Lex