Where would you drive Machine Learning and Artificial Intelligence (MLAI) for the next decade?

Many of the challenges facing our society require multidisciplinary solutions which are larger than a single human brain can solve. Machine learning provides the opportunity to solve science challenges using data-driven or model-driven science, increased data interpretation speeds, or increased speed of data analysis. Digital technologies will be one of the key drivers of new industries in coming decades, and to be ready for this change we need to have new approaches to understanding increasingly complex, large and interlinked data sets, and to ensure that these approaches are interpretable, scalable, ethical and trustable.

Machine learning and artificial intelligence (ML/AI) are capabilities that will transform economies and the basis of competition globally, unlock new societal and environmental value and accelerate scientific discovery.

The MLAI FSP worked across the whole of CSIRO on cross-disciplinary projects that apply ML/AI to solve fundamental problems about conceptual and data-driven research applications. The solutions, platforms and people trained through the MLAI FSP, created a new ongoing capability within CSIRO to address core research challenges for the benefit of Australia.

The MLAI FSP explored questions such as: how do we use machine learning to augment a scientist’s ability to generate and learn from scientific data? What is the best way to include domain constraints (such as physical laws) and design constraints (such as privacy and fairness) into machine learning models? Where can we exploit genomic information in plant and animal breeding? Why is deep learning so effective in extracting meaningful features? How can we provide explainable AI for decision-making to protect the great barrier reef? Solving these types of challenges open new vistas of scientific knowledge and positive impact.

The goals of the FSP were:

  • Science – an investment that will deliver lasting impact to areas of strategic interest across CSIRO by exploiting and advancing ML/AI research.
  • Technology – to deliver new ML/AI solutions to age-old problems, novel solutions, and platforms for emerging challenges in a data driven world.
  • People – the platform we create, and the people we train become a capability that fundamentally changes the way CSIRO undertakes core research challenges.

The MLAI FSP was designed to bring machine learning to CSIRO’s science. It accommodated areas of expertise defined through a consultative process, and was structured into “activities”, areas which could transform the science undertaken by CSIRO. The FSP was designed to consolidate the applications of ML into science across organisational boundaries.

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