16 posts tagged with "Artificial intelligence"

There is a tendency to discuss Māori technology futures at very large scale: sovereign clouds, Māori foundation models, national infrastructure and major programmes. Those ambitions may have value, but meaningful capability can also begin with much smaller and more practical steps.

The hardware and software threshold for local AI has fallen rapidly. Organisations can now experiment with open-weight models, local document retrieval and offline inference without building a research laboratory. The important opportunity is not to chase every new model. It is to deliberately build enough technical capability that Māori organisations can make their own choices about architecture, data and use.

AI governance often ends with a rating such as low, medium or high risk. Those labels can be useful, but only after somebody understands what the system actually does. Rating “AI” in the abstract is much less useful than examining the information, the architecture, the authority and the consequence of a particular use.

A more practical Māori AI assessment can begin with four dimensions. They do not replace tikanga, organisational policy or legal obligations. They help make the technical system visible enough for those things to be applied proportionately.

GIS is unusually well suited to careful AI experimentation. Geospatial work already teaches several habits that transfer directly into responsible AI: source matters, scale matters, coordinate systems matter, metadata matters, and precision does not automatically mean certainty. A map is an interpretation of evidence rather than the whenua itself, and a confident AI answer should be treated with the same discipline.

For Māori GIS, this creates a practical opportunity. AI can help people deal with very large collections of text, metadata, reports and technical material while GIS practitioners retain the familiar responsibility to check sources, preserve provenance and understand where interpretation begins.

Microsoft Copilot will be the first substantial organisational encounter with generative AI for many people in Aotearoa. There are understandable reasons for that. Microsoft 365 is already deeply embedded across government and business, identity and permissions already exist, documents are already in SharePoint and OneDrive, and security teams already understand the platform.

There is nothing inherently wrong with using Copilot. The danger is subtler: we may allow one product to define what we think AI is. If Māori organisations want real choice over future AI systems, product literacy needs to grow into broader technical capability.

Local AI deserves far more attention in Māori technology discussions. It also deserves more careful discussion. Running a model locally can solve a very specific and important problem: information does not necessarily have to be transmitted to an external AI provider for processing. That is a substantial advantage, but it is not the same as solving Māori data sovereignty.

The distinction matters because Māori data sovereignty is not simply about geography. Keeping information in Aotearoa, on an iwi server or even on a disconnected laptop can strengthen control, but the deeper question remains who has authority over the information, who may use it, for what purpose, and with what consequences.

One of the least useful questions we can ask about a new technology is simply, “Is AI safe?” AI is not one system. A public chatbot, Microsoft Copilot, a commercial AI API, a local language model, a local document-search system and an entirely Māori-controlled AI environment may all contain a large language model somewhere in the architecture, but beyond that they can be profoundly different.

If Māori data sovereignty is fundamentally concerned with authority and control, those differences matter. The simplest way to understand them is to stop looking at the AI brand and follow the information. Where does it start, where does it travel, who processes it, what is retained, and who can change the rules later?

Artificial intelligence is often explained with one sentence that causes more confusion than it resolves: “AI learns from your data.” Sometimes that is true. Sometimes it is not. Sometimes your information is used only for the few seconds needed to answer a question. Sometimes a service stores it. Sometimes a provider may later use selected conversations to improve future models. Sometimes an organisation can contractually prevent that. Sometimes the entire process happens on a local computer and no document content leaves the machine at all.

The problem is that several different technical processes are being collapsed into one idea. If Māori organisations, GIS teams, researchers and data-governance practitioners are going to make sensible decisions about AI, those processes need to be separated.

This article explains the main stages in plain English, with mapping examples throughout. It is not an argument for or against AI. It is an argument for knowing which part of the system is actually doing what.

The previous articles in this series establish several uncomfortable but useful facts. Public Māori material has already entered web-scale AI training corpora. Māori-related information is likely far more extensive than the visible te reo Māori subset because large quantities of Māori history, planning, legal and environmental material are written in English. At the same time, models can remain weak in Māori language and context because the global training corpus is overwhelmingly larger than the Māori material within it.

We also know that entering a document into ChatGPT, Claude, Gemini, Copilot, DeepSeek or Qwen today is a separate issue from historic foundation-model training. Current prompts may be used only for inference, may be retained for a period, may be excluded from model training under enterprise terms, or may be eligible for later model improvement depending on the product and settings. Local AI can change the data path again by keeping current documents and inference on infrastructure controlled by the organisation.

For Māori GIS, this is not an abstract debate. Mapping is where information becomes spatially explicit. AI can make hidden relationships easier to discover, and GIS can then turn those findings into points, lines, polygons and maps that appear much more certain than the underlying evidence.

The practical challenge is therefore not to decide whether AI is good or bad for Māori mapping. It is to decide which parts of the AI architecture should be used for which kaupapa, with what information, and under whose authority.

When people ask whether ChatGPT, Claude, Gemini, Llama, Qwen or DeepSeek has been trained on Māori material, the honest answer is more complicated than yes or no. The developers of current frontier models do not publish complete lists of every webpage, PDF, book, image or other item used during training. That means we cannot point to a current model and prove that it learned from a particular Waitangi Tribunal report or iwi website unless the developer has disclosed it.

But that does not mean we know nothing. We can inspect some of the large public web corpora that have fed the modern AI ecosystem. When we do that, Māori material is plainly there.

The most useful evidence comes from mC4, the multilingual version of Google’s C4 corpus built from Common Crawl. The publicly accessible Māori-language subset contains roughly 101,000 training records labelled mi. Those records include genuine Māori-language and Māori-related pages from Te Ara, Dictionary of New Zealand Biography material, Māori media, arts organisations and at least one iwi website. They also include classification errors and rubbish, so the number must not be misrepresented as 101,000 clean Māori documents. What it does prove is that Māori online material has already been swept into web-scale corpora used by the AI research community.

Southland is becoming part of the global AI infrastructure story in a very physical way. Datagrid New Zealand has received resource consent for a hyperscale data centre at Makarewa, north of Invercargill, promoted by the developer as New Zealand’s first “AI Factory”. Southland District Council describes six data halls across three modules and up to 240 MW of IT capacity for AI training, data processing and storage. Datagrid describes a 280 MW hyperscale campus, together with a high-capacity international fibre connection through the Tasman Ring Network.

Those are extraordinary numbers by New Zealand standards. The project is important. It could create substantial new computing capacity in Aotearoa and give New Zealand a larger role in global artificial intelligence, high-performance computing and cloud infrastructure.

But an AI data centre in Southland is easy to misunderstand. The most important distinction is this: a data centre used to train an AI model is a compute location. It does not tell us where the training data came from, who has authority over those data, or whether the resulting model represents a New Zealand or Māori worldview.

A model could be trained in Makarewa using data gathered from servers and websites around the world. The GPUs would be in Southland. The training corpus could still be overwhelmingly international.