Displaying 1 - 10 of 186 projects.
Tired of time series anomalies slipping through the cracks due to scarce labeled data? Look no further! Our groundbreaking research project [1,2], is set to transform the landscape of anomaly detection.
[1] https://arxiv.org/pdf/2308.09296
[2] https://arxiv.org/pdf/2211.05244
While large multimodal models (LMMs) have obtained strong performance on many multi-modal tasks, they may still hallucinate while generating text. Their performance on detecting salient features from visual data is also unclear. In this project, we develop a framework to generate faithful and salient text from mixed-modal data, which includes images and structured data.
This project is similar in flavour to the Conscious AI project but rather than come from a Philosophical/Neuroscience/Math/Theory angle, this project aims to build self-aware neural networks that are constructed in a way that is inspired by what we know about self-awareness circuits in the brain and the field of self-aware computing. The project will advanced state of the art AI for NLP or vision or both and embed self-awareness modules within these systems.
In collaboration with people from Monash materials engineering, neuroscience and biochemistry we are developing living AI networks where neurons in a dish are grown to form biological neural networks that can be trained to do machine learning and AI tasks in a similar way to artificial neural networks. In this project you will develop machine learning theory that is consistent with the learning that occurs within these biological neural networks, so that these networks can be leveraged for AI applications.
Creating efficient and beneficial user agent interaction is a challenging problem. Challenges include improving performance and trust and reducing over and under reliance. We investigate the development of Explainable AI Systems that can provide explanations of AI agent decisions to human users. Past work on plan explanations primarily focused on explaining the correctness and validity of plans.
As humans we have the ability, and even the necessity of distinguishing between orders that are ethical, safe and necessary and orders that may be harmful or unethical. Theoretically this ability should exist in robots. To coin Assimov’s second law “A robot must obey orders given by human beings except where such orders would conflict with the First Law '', the first law being not injuring or allowing any human to be injured.
Design efficient privacy-preserving method for different machine learning tasks, including training, inference and unlearning
This scholarship is open to Australian and New Zealand Citizens and Permanent Residents
The project is a partnership with Turning Point and will focus on the co-design and HCI elements of the larger program of work.
This scholarship is open to Australian and New Zealand Citizens and Permanent Residents
The project is a partnership with headspace National Youth Mental Health Foundation
We are seeking a highly motivated and innovative PhD student interested in exploring the opportunities for using AI to enhance personalisation of services and resource recommendations, ultimately optimising the overall user journey. This project will improve the care pathway experience for young people and families accessing mental health services through the headspace website.
What makes a machine conscious? This PhD would be at the intersection of Philosophy, AI and neuroscience. You would study the latest neuroscience based theories about how consciousness emerges in the brain, as well as the latest AI methods and examine what if any consciousness current AI methods might have and how we might define whether an AI is conscious based on what we know about consciousness in the brain. This wouldn't be a typical machine learning PhD, as many aspects can only be examined on a philosophical and theoretical level.