Purpose
This page provides simplified definitions and establishes a common understanding of several Artificial Intelligence (AI) terms for professionals at all levels of familiarity with AI across NSW government.
There has yet to be a universally accepted technical or legal definition of AI. Authoritative sources such as the National Institute of Standards and Technology (NIST), the International Organization for Standardization (ISO), and Standards Australia have informed the contents of this page.
If you are seeking technically specific accurate definitions, we recommend you refer to international standards such as the ISO/IEC 22989:2022 Information Technology, Artificial Intelligence concepts and terminology.
With feedback and continuous support of industry partners and experts, we will evolve and improve these definitions over time.
What is AI?
AI is the ability of a computer system to perform tasks that would normally require human intelligence, such as learning, reasoning, and making decisions. AI encompasses various specialised domains that focus on different tasks. Examples include Machine Learning, which enables computers to learn from data; Computer Vision, allowing them to interpret visual information; and Natural Language Processing, for understanding and generating human language.
Specialised domains of Artificial Intelligence
Generative AI
Generative AI, often referred to as Gen AI, is an emerging field within AI that creates new content such as text, images, voice, video, and code by learning from data patterns. Notable examples include ChatGPT and Google's Bard.
Machine Learning (ML)
Machine learning (ML) is a subset of AI that allows computers to autonomously learn and improve without being explicitly programmed. ML algorithms are trained on data to make predictions or decisions.
Natural Language Processing (NLP)
Natural language processing (NLP) is a field of artificial intelligence (AI) that deals with the ability of computer systems to understand and generate human language. NLP algorithms are used to analyse text, comprehend, converse with users and perform tasks like language translation, sentiment analysis, and question answering.
Computer Vision (CV)
Computer Vision (CV) empowers computers to 'see' and comprehend the visual world, analysing images and videos like humans. CV algorithms analyse images and videos for tasks like object detection, face recognition, and self-driving cars.
Terms commonly used in AI
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Algorithm |
An algorithm is a set of instructions that guide a computer in performing specific tasks or solving problems. Algorithms can range from simple tasks like sending reminders to complex problem-solving, which is crucial in AI and ML. |
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Artificial general intelligence |
Artificial General Intelligence (AGI) or Strong AI is considered artificial intelligence's 'holy grail'. AGI represents a level of AI that possesses human-like intellect and the ability to perform any intellectual task that a human being can do. AGI could understand, learn, and adapt across a wide range of functions and domains, displaying general intelligence rather than being specialized for specific tasks or narrow domains. |
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Data science |
Data science is a multidisciplinary field that involves extracting insights and knowledge from data using various methods, including statistical analysis, data mining, machine learning, and data visualisation. |
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Data mining |
Data mining is a technique used to analyse large amounts of information to gain insights, spot trends, or uncover substantive patterns. Data mining can be used for tasks such as customer segmentation, fraud detection, and market research. |
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Deep learning |
Deep learning is a machine learning technique that uses interconnected layers of “neurons” to learn and understand patterns in data, especially in tasks like image recognition and speech synthesis. “Deep” refers to the fact that the circuits are typically organised into many layers, which means that computation paths from inputs to outputs have many steps. Deep learning is currently the most widely used approach for applications such as visual object recognition, machine translation, speech recognition, speech synthesis, and image synthesis. |
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Human-in-the-loop |
A human-in-the-loop (HITL) AI is a model that requires human interaction. A human plays an active and integral role in decision-making, monitoring, and control in an HITL system. They are part of the loop that produces outcomes, and their input or oversight is crucial to the system's functioning. |
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Human-on-the-loop |
Human-on-the-loop (HOTL) is an extension of HITL. A (HOTL) AI is a system where humans oversee an automated system and provide feedback but are not directly involved in decision-making. |
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Large Language Model (LLM) |
LLMs (large language models) are a subset of Gen AI model that specialises in generating human-like text. Unlike Generative AI, which encompasses a broad category of AI techniques and models designed to generate new content, such as text, images, audio, or video. |
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Multimodal Foundation Model (MfM) |
A type of large language model that can process and output multiple data types, including text, images, audio, and video. |
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Neural Networks |
Neural Networks are computer models inspired by the human brain's structure. These interconnected artificial neurons, organised in layers, learn from data to make predictions in machine learning, underpinning deep learning. |
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Reinforcement learning |
Reinforcement Learning (RL) is a machine learning approach where algorithms learn by taking actions to achieve a specific goal, guided by rewards or penalties. The AI agent continuously knows and improves its decision-making based on the feedback through these reward signals. |
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Supervised learning |
Supervised learning is sub-category of machine learning where algorithms learn from labelled data to make predictions or classifications, often with high accuracy. A real-world application includes classifying spam in a folder separate to your inbox. |
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Transformer |
A transformer is a powerful AI model for understanding and generating human language, widely used in tasks like translation and question answering. |
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Trustworthy and responsible AI |
Responsible AI systems are developed and used ethically, transparently, and accountable. It involves addressing issues such as bias, privacy, and security. |
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Unsupervised learning |
Unsupervised learning, named so because it doesn't rely on predefined labelled data. It’s a type of machine learning where algorithms group data objects based on similarities, without prior category specifications. |
Common applications of AI
Chatbot
Chatbots are conversational agents that interact with users, such as empathic patient robots or customer service chatbots. Chatbots understand human language through Natural Language Processing and can assist with various tasks.
Decision support system
A Decision Support System is an AI-powered tool that helps organisations analyse data and provide informed decision-making options, enhancing managerial capabilities. It analyses large amounts of data and presents an organisation with the best possible options available by bringing together data and knowledge from different areas and sources to provide users with information beyond the usual reports and summaries.
Expert system
An expert system is an intelligent computer program that emulates the knowledge and expertise of a human expert. They use information and reasoning to solve problems that are difficult, typically time intensive, and require significant human expertise for their solution, such as medicine, environment, finance, and law.
Recommendation system
A recommendation system is an AI system that suggests personalized content or products to users based on their preferences and behaviour, enhancing user experiences and engagement. These systems leverage machine learning, data analysis, and sometimes deep learning to make recommendations.
Robotics
Though robotics is not a subset of AI, it is a field that heavily relies on AI technologies to make robots more intelligent and capable of interacting with the physical world and performing tasks autonomously. Robotics is an interdisciplinary field that combines mechanical engineering, electronics, computer science, and AI to create and control physical machines (robots) capable of performing tasks autonomously or semi-autonomously.
Virtual assistants
Virtual assistants are computer programs that can interact with humans in a ‘natural’ way. Virtual assistants are a specific application of AI designed to assist users with various tasks and information retrieval through human-like interactions. Virtual assistants are used for tasks such as scheduling appointments, making reservations, and providing information. Differentiating itself from chatbots which are purely conversational agents.
The AI lifecycle
AI is a tool that supports the efficient solution of human problems. The following is a simplified lifecycle for using AI in your work and the kinds of considerations to make throughout.
Problem understanding and risk considerations:
Begin by clearly defining the problem you aim to solve. Understand the specific requirements and constraints of the problem domain and consider ethical implications, fairness, transparency, and privacy. Refer to AI Assurance Framework for further guidance. More info here.
Data acquisition and pre-processing:
Identify and gather relevant data for your AI solution. Pre-process and clean the data to ensure its quality and suitability for modelling. Always comply with applicable legislative requirements, laws, and ethics. More info here.
ML lifecycle stages:
Apply the ML lifecycle stages, including data pre-processing, feature engineering, model selection, training, evaluation, and deployment.
Additional AI techniques:
Consider incorporating additional AI techniques beyond traditional machine learning, depending on the requirements of your solution. You may include natural language processing (NLP), computer vision, knowledge representation, inference, and reasoning methods.
Integration and system design:
Integrate the ML models or AI components into a more extensive system or application architecture, considering scalability, performance, and compatibility with existing infrastructure.
User experience and interaction:
Design the user interface and interaction components to facilitate seamless user interaction and engagement with the AI solution. You may design chatbots, voice interfaces, or visualisations.
Continuous monitoring and improvement:
Implement mechanisms to continuously monitor the AI solution's performance, gather user feedback, and iteratively improve the system based on new data or changing requirements.
More AI resources