Monday 29th May at Accenture The Dock, 7 Hanover Quay
Speaker: Eda Bayram
Bio: Eda is a research scientist at Accenture Labs, Dublin. She obtained her PhD at Swiss Federal Institute of Technology, Lausanne (EPFL). Eda’s doctoral thesis was on representation learning for multi-relational data. She likes to research machine learning for graph-structured data and she is broadly interested in knowledge representation and reasoning for explainable AI.
Talk Title: Incorporating Literals for Knowledge Graph (KG) Completion Description: Link prediction, a crucial task in machine learning on KGs, involves inferring missing relationships between data entities. Apart from relationships, KGs can also incorporate literals attributed to entities, such as date of birth, gender, and education level for a person type of entity. This talk focuses on attribute prediction task and leveraging existing literals to enhance link prediction task in KGs.
Speaker: Aonghus McGovern
Bio: Aonghus is the Ethical AI Lead and Data Science Manager at Accenture’s Corporate Data and Analytics Office. Aonghus completed their PhD at the Knowledge and Data Engineering Group (KDEG) at Trinity College Dublin. Their research area was the application of structured representations of unstructured content in personalisation tasks using Natural Language Processing.
Talk Title: (The) Representation Matters
Description: The word ‘representation’ can mean a lot of things in Machine Learning. This talk will discuss the concepts of representation in data, model representations and the links between the two.
Speaker: Paul Walsh
Bio: Paul is an Analytics & AI Director with the New Products & Services team at Accenture’s Global Innovation Centre, The Dock. Paul holds a PhD in Computer Science from UCC and held a Professorship in Computer Science at MTU. His current role is leading and managing analytics, artificial intelligence and machine learning projects in The Dock.
Talk Title: Chains of Thought
Blurb: In an era dominated by transformative technologies, large language models have emerged as a fascinating breakthrough, revolutionizing the way we interact with information and generating novel possibilities for human creativity and problem-solving. This exciting talk explores the remarkable potential of large language models to unlock chains of thought, paving the way for unprecedented advancements in various domains.
MC for the evening: Stephen Redmond
Bio: Stephen is a Director, Data Science Innovation at Accenture’s Corporate Data & Analytics Office. Stephen holds an MSc in Data Analytics and is a Chartered IT Professional (CITP). He has authored numerous technical books and articles, including the bestselling Mastering QlikView.