Latest events

AIML Special Presentation: Accelerating Science with NVIDIA

Miguel Balbin — Thu, 03 Apr 2025 15:56:45 +1030

NVIDIA's advanced GPU technologies and AI platforms are revolutionising scientific computing across multiple domains. This talk will explore how NVIDIA's solutions are enabling unprecedented acceleration of research and discovery in fields like generative AI, digital twins, data science, climate modelling, robotics and drug discovery. Dr Johan Barthélemy will introduce the latest AI-powered tools such as RAPIDS, Modulus, (Bio)NeMo, Omniverse, Cosmos, and Groot, pushing the boundaries of what's possible. Real-world examples will demonstrate how researchers are leveraging those technologies to tackle complex problems orders of magnitude faster and more efficiently than traditional methods.

AIML Special Presentation: Introducing the Responsible AI Research (RAIR) Centre

Miguel Balbin — Wed, 09 Apr 2025 13:18:13 +0930

We're pleased to welcome Kate Klimeš, Program Manager for the newly established Responsible AI Research (RAIR) Centre, who will be presenting on the vision and goals of the new centre. The Centre is based at AIML in the Lot Fourteen business and technology precinct in Adelaide. RAIR represents a powerful collaboration between AIML’s world-class researchers in artificial intelligence and machine learning, scientific experts from CSIRO, and is supported by the South �Ӱ�ֱ��n Government.

AIML Special Presentation: How tough is your data? Applying structural graph theory to robust fitting and clustering

Miguel Balbin — Thu, 10 Apr 2025 11:13:15 +0930

Structural graph theory is making great advances in understanding what characteristics of graphs act to make those graphs difficult or easy for algorithms for a range of common problem types (maximum clique, graph cuts, etc.). Modern machine learning is largely driven by the success (or otherwise) of a proposed model/algorithm on standard test datasets. This involves a plethora of different measures and sometimes one might suspect that a dataset has “easy examples” and “hard examples” and that a statistic might be affected by the proportion of hard or easy samples. So a structural graph theory based understanding of AI problems should help both understand benchmark results and also to design algorithms according to the range of complexities of data a problem setting can “throw up”.