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AI on the Ground Seminar: Building responsible AI for healthcare: challenges and ensuring fairness
- Date: Fri, 29 Nov 2024, 10:30 am - 11:30 am
- Location: AIML
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.
AIML Research Seminar: A Couple of AI4Space Problems
- Date: Tue, 26 Nov 2024, 10:30 am - 11:15 am
- Location: AIML
Abstract: We’re living in an exciting new space era, where the cost of ‘going into space’ has dropped significantly, and the reasons for doing so have expanded dramatically. It’s not just about the numbers anymore—we’re tackling highly sophisticated tasks that once existed only in sci-fi novels. This surge in scale and complexity is why we turn to AI.
[Read more about AIML Research Seminar: A Couple of AI4Space Problems]
AIML Visits Roseworthy Campus
- Date: Fri, 22 Nov 2024, 10:00 am - 4:30 pm
- Location: Roseworthy Campus, ³ÉÈË´óƬ
AIML academics and professional staff visited the ³ÉÈË´óƬ'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 Special Presentation: Convergence and Asymptotic Optimality of the Heavy Ball Method and Its Relatives
- Date: Wed, 20 Nov 2024, 2:30 pm - 3:30 pm
- Location: AIML
Abstract: In this talk we first aim to shed light on the urban legend surrounding the ‘complexity lower bound’ 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 Special Presentation: Artificial Intelligence for Autonomous Scientific Exploration
- Date: Mon, 18 Nov 2024, 10:30 am - 11:30 am
- Location: AIML
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.
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