Martin O'Reilly
Martin O'Reilly is Director of Research Engineering at the Alan Turing Institute, where he leads a team of software, data and infrastructure engineers who work across the Turing's research portfolio to bridge the gap between research and practice - from AI for weather prediction to AI-assisted air-traffic control. Prior to Turing, Martin spent several years developing software, data standards and engineering practices in the education sector before going back to school to build robots and try and understand the brain by modelling it.
Session
Large language models (LLMs) have taken the world by storm since the public launch of ChatGPT 3 in November 2022, sparking a huge number of LLM-powered tools, products and start-ups. Since then LLMs have gained reasoning and tool use capabilities, and have been integrated into more autonomous agentic workflows, leading to significant increases in their usefulness for software engineering work. However, despite being readily accessible to us all, these models and their agentic wrappers remain black boxes to many of us using them in our daily work.
Martin will demysitify LLMs by providing an intuitive understanding of how they build upon key prior advances to successfully cross the "uncanny valley" of text generation and achieve almost flawless fluency. He will explain what makes these models "foundational", illustrating how this "one weird trick" of next word prediction results in models that can be easily fine-tuned for conversation, coding and reasoning, and we'll take a peek under the hood of how LLMs have been extended to integrate private data sets, call external tools and support more autonomous agentic workflows.
This talk won't make you an expert on deep neural networks, transformers, fine-tuning or agentic workflows, but it will give you a peek behind the curtain of how these seemingly magical models work and hopefully give you enough intuitive understanding to explain them to friends and family.