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AIML Special Presentation Dr. Sebastian Sequoiah-Grayson

Dr. Sebastian Sequoiah-Grayson

Presentation by Dr. Sebastian Sequoiah-Grayson, a researcher with the University of New South Wales Sydney, on Formal Methods and Theoretical AI. The event delved into cutting-edge advancements and methodologies in AI research. 

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AIML Connect Fridays - Rising Sun Pictures

Alex Meddick, Thomas Rowntree, and Robert Beveridge from Rising Sun Pictures

Rising Sun Pictures (RSP) experts showcased how they are integrating machine learning workflows into special effects creation, ultimately empowering compositing artists with unprecedented creative control. This session was for anyone interested in the intersection of visual effects (VFX) and machine learning, providing insights into the cutting-edge techniques that are transforming the industry.

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Human performance in the lab and in complex environments

Humans actively gather visual information to guide interaction with their environment. Human vision is very efficient and highlights how a small amount of data can be used to drive inferences that can be acted on. Our group investigates the basic mechanisms underlying active vision. We also work with industry to translate these results for outcomes relevant to human-AI interaction in complex environments. In this talk, Prof Ma-Wyatt provides an overview of her work in these areas and highlight opportunities for collaboration. 

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AIML Incubator Presentation - Special Event

Zrinka Tokic from Thinclab

ThincLab is The University of Adelaide's business incubator supporting students, alumni, staff and startups. They offer coaching and networking programs to enable start-up enterprises to capitalise on their ideas with a variety of industry experts.

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South Australian Research and Development Institute (SARDI) visit

Dr Minh Nguyen during the SARDI visit

AIML hosted the South Australian Research and Development Institute (SARDI) to explore collaborative opportunities to use AI/ML for the benefit of primary industries in South Australia. SARDI's research is divided across 3 key areas: Aquatic & Livestock Sciences, Crop Sciences, and Food Sciences. 

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AIML Research Seminar: Real-time Prediction for Non-stationary Data Streams

This talk focused on one of the most challenging issues in non-stationary data streams, known as concept drift. Concept drift occurs when the pattern of data changes over time, making models trained on historical data less effective at predicting new data patterns. This inability to adapt compromises the performance of well-trained machine learning models. From the perspective of concept drift adaptation, this presentation will introduce three general strategies for updating learned models with newly arrived data, aimed at mitigating the impact of concept drift.

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AIML Special Presentation: Learning Spatial context-aware Global Visual Feature Representation for Instance Image Retrieval

Professor Lei Wang

In instance image retrieval, considering local spatial information within an image has proven effective to boost retrieval performance. It will be highly valuable to make ordinary global image representations spatial-context-aware because global representation based image retrieval is appealing thanks to its algorithmic simplicity, low memory cost, and being friendly to sophisticated data structures. This talk describes a novel feature learning framework proposed for instance image retrieval, which embeds local spatial context information into the learned global feature representations.

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AIML Connect Friday: AI Consciousness

Dr. Opie during his AIML talk June 2024

Jon spoke of the nature of consciousness and the prospects for conscious AI. An issue he raised is whether the ‘what-it-is-like’ of consciousness is a result of how brains process information or, by contrast, is a special material property of biological neural systems. If the former is true, we will have properly conscious AIs and robots in short order with all the ethical problems that raises.

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AIML Research Seminar: The ghost of forecasts past

Numerical Weather Prediction (NWP) is experiencing a turbulent upheaval. Machine learning (ML) promises to alter every aspect of the process, but integration of the new, fast ML-driven forecast systems is going slowly.

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AIML Special Presentation: Does a farmer want AI? Developing a Trusted AI Agronomist

Drs Sarah Hartman and Petra Kuhnert during their AIML presentation

A path to a productive, resilient, and sustainable agricultural future lies in the successful integration of disruptive AI-driven agritechnology into decision-making processes across the food supply chain. These advanced solutions are crucial for assessing trade-offs and risks in a world increasingly affected by climate volatility. However, these solutions cannot come without a deeper understanding of the underlying appetite for such solutions.

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