Artificial Intelligence and Machine Learning

Growing data volumes, increased data complexity and reduced data quality pose challenges for the marine science domain, but recent advances in machine learning offer new opportunities to address them.

CSIRO is home to a large applied AI capability, with more than one thousand researchers working on a diverse range of AI and data science projects.  CSIRO researchers are using AI to tackle challenges from bushfire management to boosting agricultural productivity, improving cybersecurity, and protecting our environments including the Great Barrier Reef.

Our research programs have invested approximately $2.9M in strategic funds over 2019-22 and planned in coming years, to grow its Machine Learning (ML) capability and business opportunities.  This future investment and capability uplift will be fostered as part of CSIRO Environment.

Machine Learning Collaboration

We have a vast collaboration network, both internal and external to CSIRO.

Machine Learning Project Work

Our machine learning work includes object detection and classification, anomaly detection, behaviour classification, and modelling and forecasting.

Recent Machine Learning Publications

Our work has been published in a range of journals and websites.

What is Machine Learning?

Artificial Intelligence (AI) refers to computational systems that mimic human intelligence and are capable of performing complex tasks. The main goal of AI is to develop autonomous systems to solve problems that previously required human intelligence, thereby automating complicated and time-consuming processes. Most AI systems simulate natural intelligence to solve complex problems.  

Machine learning (ML) is a subset of AI where intelligent algorithms are designed to automatically extract important and valuable patterns from data. ML is inherently data driven, requiring a model (typically a neural network) to be trained on a dataset using a learning process. Once trained, ML models are potentially capable of making highly accurate predictions at negligible computational expense. 

Key Contact

Ms Bec Gorton

Senior Research Engineer