The NSW government will be taking a "deliberate but cautious" approach when implementing new artificial intelligence technology in line with citizen trust around data and AI usage. NSW government chief data scientist and industry professor at UTS Dr Ian Oppermann told an Infosys and Trans-Tasman Business Circle event that trust is a "very big issue", with his work on the NSW AI assurance framework and a multitude of other government policies. "Ultimately, there are a whole lot of other elements around trust and demonstration of trustworthiness," Oppermann said. "We need to explore what happens when things go wrong. "We need to be very clear about what we will not do with data in order to help build confidence, that we're behaving appropriately and demonstrating trustworthiness.

Safer and more efficient services will be delivered for NSW residents using Artificial Intelligence (AI), with a new world-leading AI Assurance Framework to come into effect in March 2022. All agencies across the NSW Government can apply the Assurance Framework to ensure increasingly sophisticated AI systems are safe, effective and delivering on state outcomes, improving the lives of people in NSW and the resilience of communities and driving the economy. NSW Government's Chief Data Scientist Dr Ian Oppermann said the Framework would ensure Government services using AI were aligned to state outcomes, easy to access and use by customers as well as being personalised and secure. "AI creates a huge opportunity to improve Government services. We are already piloting the technology with eHealth NSW to help doctors to earlier identify sepsis in patients attending emergency departments," Dr Oppermann said.

Many Australian enterprises have spent years trying to justify their investments in data analytics models. On average, only half of the analytic models built by organisations will ever make it to production. Clearly, organisations that operationalise and monetise their artificial intelligence (AI) and analytics capabilities are more likely to succeed with their customer engagements. Tech execs gathered at a virtual roundtable recently to discuss the challenges they face when moving their AI and data analytics programs from an experiment inside their business to one that is a key part of their core operations. The conversation was sponsored by SAS.

Non-linear image reconstruction and signal analysis deal with complex inverse problems. To tackle such problems in a systematic way, I present information field theory (IFT) as a means of Bayesian, data based inference on spatially distributed signal fields. IFT is a statistical field theory, which permits the construction of optimal signal recovery algorithms even for non-linear and non-Gaussian signal inference problems. IFT algorithms exploit spatial correlations of the signal fields and benefit from techniques developed to investigate quantum and statistical field theories, such as Feynman diagrams, re-normalisation calculations, and thermodynamic potentials. The theory can be used in many areas, and applications in cosmology and numerics are presented.