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We've got exciting news!

We are New Zealand data and environmental scientists using artificial intelligence to solve some of the biggest environmental issues facing New Zealand.

What's new? 

Check out the newly released TAIAO podcast series on Quantum Machine Learning featuring members of the internationally recognised team DelphiQ. 

Datasets and Notebooks

Click on the links to find or share notebooks and datasets.

Find a notebook
Share a notebook

Featured Projects

Find out more about the projects that the TAIAO team have worked on

Latest News

January newsletter

In our first newsletter for 2026, you’ll find:

  • Welcome back!

  • Highlights from the AAAI Workshop

  • What’s coming up

December newsletter

In this newsletter, you’ll find:

  • TAIAO wrap-up for 2025 

  • What's coming up for TAIAO in 2026

Winners of the World Bank Climate Risk Challenge 

A big congratulations to Léa Cassé and the DelphiQ team who were recently announced as the winners of the Global Industry Challenge – World Bank track at the Quantum World Congress 2025 for their innovative solution to improving insurance for communities exposed to flooding.

PRICAI & AIRA 2025

TAIAO is proud to have been a part of the AIRA & PRICAI conferences in Wellington. 

The team were excited to meet with others in the artificial intelligence research community to discuss the latest advances in this rapidly growing field and share the work that that we have been undertaking as part of the TAIAO project.

An initiative supported by the Ministry of Business, Innovation and Employment (MBIE), TAIAO is committed to promoting and facilitating the adoption of artificial intelligence (AI) and data science in New Zealand's environmental sector. 

Recognising that good data is essential to research, understand, and set policy for effective management of our natural environment, the team at TAIAO are working to develop new machine learning methods that are tailored to process environmental data gathered from our New Zealand context.

TAIAO uses AI models to analyse vast amounts of historical and real-time data, and identify patterns to predict future trends, such as extreme weather events. Through this mahi, we aim to break barriers by enhancing industry capability, and pioneer new machine learning methods to process vast amounts of data in real-time to meet today's pressing demands.

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