Natural Language Processing (NLP) has witnessed a paradigm shift in recent years, driven by the advent of Generative AI models. This seminar offers a comprehensive exploration of the evolutionary trajectory of language technologies, spanning from the rudimentary Bag-of-Words model to the sophisticated Long Short-Term Memory (LSTM) networks and culminating in the transformative power of Transformers and Generative Pre-trained Transformers (GPT) based Large Language Models (LLMs).
The seminar will provide a rigorous overview of these state-of-the-art models, encompassing their underlying architectural innovations, training methodologies, and their profound implications for NLP tasks. The focal point of the seminar is the Transformer model – the model that has reshaped the entire NLP landscape, rendering remarkable achievements in machine translation, sentiment analysis, and text summarization, among other tasks. The speaker will delve deeply into the architecture of the Transformer model and dissect what makes them so powerful and adaptable to a wide array of NLP tasks. Furthermore, the seminar will conclude with a discussion of Large Language Models based on the generated pre-trained Transformer (GPT) models, elucidating their remarkable capacity to understand language and generate human-like text, thereby enabling transformative applications across diverse domains, from conversational AI and content creation to information retrieval, healthcare, and beyond.
Quick details about the event:
Date: 14 March 2024
Time: 12:00 pm – 01:00 pm
Venue: Khalifa University – L01011
Dr. Ruchit Agrawal
Bio:
Dr Ruchit Agrawal is an Assistant Professor and Head of Computer Science Outreach at the University of Birmingham Dubai. Before this, he was a Postdoctoral Research Scientist at the University of Oxford in the Computational Health Informatics group. He received his PhD in Computer Science with the prestigious Marie-Curie scholarship from the Queen Mary University of London. Dr. Agrawal is an expert in machine learning and natural language processing and has published several research papers in leading journals and reputed international conferences in his field. His broad research interests include Large Language Models, Multimodal Deep Learning, Natural Language Processing and Clinical Machine Learning.