Welcome!
Welcome to this searchable AI learning resource!
This handy space has a variety of introductory, intermediate and more advanced content suitable for anyone and everyone regardless of where you are in your AI learning journey. The AI space is vast and overwhelming at times, especially when just starting out.
This document has a search function if your on the hunt for something specific (in the navbar). If you are fresh to the AI space then head over to the 'Introduction' tab where you can find some introductory content to get things rolling.
About this Content
There is a broad range of content covered in this resource broken into separate sections that can be comfortably consumed on their own in a short sitting. This resource is focused around AI in health but is useful for general learning also. There are both technical and non-technical focused sections (they tend to be either or) with references to source material as well as many links to video based alternatives (where available) if reading isn't your thing.
We have also tried to include further reading, additional examples and links to more advanced material if you want to go further and learn more on your own.
If you've arrived here just for the basics and a general overview the 'Introduction' section should be enough to get started, although we do encourage everyone to try and work down through the sections in order to 'Ethics, Social License & Governance' at their own pace.
The remaining sections are the ones with a more technical focus, so unless your keen to learn about how machine learning works and like a healthy dose of stats don't feel like your missing out. The 'History of AI' being the exception to this, which is great for understanding the development of this not-so-new technology to better contextualize the present (we understand if history isn't your favorite though).
Remember learning never ends, especially with technology developing as rapidly as it does, don't feel like you need to learn it all at once. Come back as interest or situation demands and make use of this as a place to start!
Section Descriptions
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Introduction:
A primer and general overview to get you started in your AI learning journey that should cover all the main points you should be familiar with.
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Ethics, Social License & Governance:
Ethical considerations in the application of AI technologies, the preservation of privacy, consent, data sovereignty and governance practices.
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Risk Management:
Frameworks for considering and quantifying the risks of AI technologies.
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Machine Learning:
Different categories machine learning techniques, algorithms and related concepts.
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Deep Learning:
Neural networks, generative AI, computer vision and some key concepts related to their function.
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Modelling:
How data can be used to represent things in vector space, sampling, normalization and training models.
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Model Metrics:
Foundational statistical metrics used to evaluate model performance, useful when reading research papers related to the effectiveness of applied AI systems.
NAIAEAG
National Artificial Intelligence and Algorithm Expert Advisory Group
NAIAEAG advice all Te Whatu Ora employees and contractors to NOT:
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“Enter any personal, confidential or sensitive patient or organizational data into Large language models & Generative AI tools”
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“use large language models & generative AI tools for any clinical decisions, any personalized patient-related documentation or for personalised advice to patients”
The NAIAEAG are committed to continually updating their advice on the use of Large Language models and Generative AI tools.
To contact NAIAEAG there is a form available for TWO employees to fill out to tell the advisory group what they are using AI for or what they hope to use AI for.