As 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.
In this newsletter, you’ll find more information about:
AI Hackathon 2026
Spotlight on Weka.ai
Latest episodes on the Quantum Machine Learning podcast
Aotearoa AI Hackathon 2026
TAIAO is proud to celebrate its fifth consecutive year as a key sponsor and partner of the Aotearoa AI Hackathon, held 3rd – 10th August this year.
Led by the AI Forum, the hackathon is a New Zealand wide festival where teams come together to learn, collaborate and build practical AI solutions to address the UN Sustainable Development Goals. Each location hosts a 48-hour hackathon with local winners announced at each venue.
In 2026, the event spanned nine venues across New Zealand. The University of Waikato location achieved a record 28 registrations, bringing together high schoolers, university students, and working professionals.
Participants presented a variety of impact-driven solutions, including:
an AI health coach targeting youth inactivity in Aotearoa
an AI system for managing and reducing household food waste
an AI-assisted medical transcription tool to bridge language barriers between healthcare staff and patients' families, and
a data-driven assessment platform to help council planners evaluate the impact of data centre developments.
Special congratulations to Wisetech, our Waikato Regional Winners, who presented an AI-enhanced Individual Placement and Support (IPS) system designed to help New Zealanders facing employment barriers secure meaningful work. This system streamlines case management by tracking potential employers and reducing administrative workloads for caseworkers. Wisetech have since qualified for the national final in September and we wish them the best of luck!
The growing popularity of AI coding agents significantly elevated the technical maturity of this year's submissions. Instead of presenting traditional wireframes, participants utilized these tools to deliver highly developed proof-of-concepts and functional prototypes.
Beyond hosting the venue, TAIAO continued its ongoing commitment to technical education by delivering specialized training in data science and machine learning. This year’s curriculum included utilizing Large Language Models (LLMs) for data science applications and deploying agentic systems for machine learning data analysis. Several TAIAO members also volunteered their expertise to mentor participants throughout the event.
Huge thank you to everyone who was involved!
Weka.ai Spotlight
For this month’s newsletter we wanted to highlight Weka.ai, one of TAIAO's exciting educational tools.
Weka.ai is a web-based machine learning platform launched by TAIAO to make machine learning accessible for everyone.
The platform has been designed to be easy to navigate with no complex setup or steep learning curves. Whether you're a beginner exploring data science or an expert building complex models, Weka.ai democratizes machine learning with its intuitive, web-based tool. Simply sign up with your email address and you are good to go!
Weka Web brings the power of Weka machine learning to your browser, allowing users to analyse data, preprocess datasets, run experiments with 200+ algorithms, and visualize results, all with no installation required. Weka Web provides a complete visual interface for machine learning workflows and is perfect for both beginners and experts who want a comprehensive ML toolkit without writing code.
For more information, listen to doctoral researcher Nilesh Verma talk about Weka.ai on one of our TAIAO Talks episodes here.
Stay tuned for next month for a spotlight on the team’s latest tool for AI Agents, TuiML.
New episodes released for the Quantum Machine Learning podcast
In case you’ve missed it, episodes 3 and 4 of the TAIAO Quantum Machine Learning podcast are out now!
In Episode 3, host Léa Cassé sits down with Sabari Ponnambalam to discuss what happens when spectral analysis moves from theory to real experiments. Together they examine the evidence behind quantum time-series models: how they were tested, what the experiments revealed, what remains uncertain, and whether frequency analysis can become a practical tool for monitoring environmental data streams.
In Episode 4, Léa and Sabari shift their focus to quantum optimization and ask a practical question: when does it actually become useful? Once a model produces forecasts, someone still has to make decisions. This episode is about understanding when quantum optimization deserves a place in a real decision-making pipeline, and when it doesn’t.
If you are interested in the practical applications of quantum time-series prediction and quantum optimization, take a listen: https://open.spotify.com/show/0J9aobImvRwjNX4knsU2Xu
Thanks for reading!
We’ll continue to keep you updated on exciting behind-the-scenes news each month.
