Latest events

AIML Special Presentation: Artificial Intelligence for Autonomous Scientific Exploration

Miguel Balbin — Mon, 18 Nov 2024 10:48:21 +1030

David Wettergreen creates robots that explore and conducts field experiments in polar climates, deserts, underwater caverns, and volcanic craters. He has led numerous research projects including a decade of robotic investigation of microbial life in the Atacama Desert. His work in science autonomy enables robotic explorers to detect, classify, and evaluate geologic and biologic features to autonomously interpret and act upon their scientific observations. This work applies to space exploration and to applications in agriculture, forestry, ecology, and marine science.

AIML Special Presentation: Convergence and Asymptotic Optimality of the Heavy Ball Method and Its Relatives

Miguel Balbin — Thu, 21 Nov 2024 14:29:32 +1030

Abstract: In this talk we first aim to shed light on the urban legend surrounding the 鈥榗omplexity lower bound鈥� for the heavy ball algorithm. Second, we revisit the original heavy-ball algorithm proposed by Polyak and provide a conditions for it to be globally converging, and provide step-size rules. Then, we investigate the performance of a related algorithm dubbed the Accelerated Generalised Gradient Method and see how how it can be beneficial in the case of tracking the minimum of a time-varying function.

AIML Visits Roseworthy Campus

Miguel Balbin — Mon, 25 Nov 2024 09:54:15 +1030

AIML academics and professional staff visited 91自拍's Roseworthy Campus for an engaging day at the School of Animal & Veterinary Sciences. The event aimed to introduce AIML staff to the campus and foster meaningful discussions about the applications of machine learning in veterinary and animal sciences.

AIML Research Seminar: A Couple of AI4Space Problems

Miguel Balbin — Wed, 27 Nov 2024 14:27:12 +1030

Abstract: We鈥檙e living in an exciting new space era, where the cost of 鈥榞oing into space鈥� has dropped significantly, and the reasons for doing so have expanded dramatically. It鈥檚 not just about the numbers anymore鈥攚e鈥檙e tackling highly sophisticated tasks that once existed only in sci-fi novels. This surge in scale and complexity is why we turn to AI.

AI on the Ground Seminar: Building responsible AI for healthcare: challenges and ensuring fairness

Miguel Balbin — Mon, 02 Dec 2024 09:11:30 +1030

Abstract: The presentation covers the use of machine learning in healthcare, focusing on project deployment and key lessons learned. An outline of the Machine Learning Medical Directive project and the underlying research is given first, then the primary challenges that arose during deployment. Solutions to these challenges will be discussed, with special attention to fairness and ethical considerations in healthcare AI.