Core concepts, terminology, history and approaches that establish the foundations for understanding artificial intelligence.

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The mathematical, statistical and computational principles that underpin modern AI systems and methods.

How data is collected, prepared, governed and used to train, evaluate and improve AI systems.

Symbolic AI, knowledge representation, search, planning and reasoning techniques used to solve complex problems.

Methods that enable systems to learn patterns from data and make predictions, classifications and decisions.

Neural network architectures and deep learning techniques that power many advanced AI capabilities.

Technologies for understanding, generating and interacting through human language, text and speech.

Techniques that enable AI systems to interpret, analyse and generate information from images and video.

Foundation models and generative systems that create, transform and reason across text, images and other content.

AI systems that combine and reason across multiple forms of information such as text, images, audio and video.

How people communicate with, guide and collaborate effectively with AI systems through prompts and interfaces.

Approaches for connecting AI models to external knowledge, search, retrieval and enterprise information sources.

AI systems that can plan, use tools, take actions and coordinate tasks with increasing levels of autonomy.

The integration of AI with physical machines, sensors and control systems to enable autonomous behaviour.

How AI capabilities are designed, integrated and engineered into reliable applications and software systems.

The compute platforms, accelerators, infrastructure and hardware required to build and operate AI systems.

Practices for deploying, monitoring, managing and improving AI models throughout their operational lifecycle.

Methods for measuring AI capability, quality, reliability, performance and suitability for intended use.

How AI systems are protected against failure, misuse, attack, privacy risks and operational disruption.

Ethical principles and human considerations involved in designing, deploying and using AI responsibly.

Frameworks for governing AI, managing risk, establishing accountability and providing organisational assurance.

Legal and regulatory issues affecting AI, including liability, privacy, copyright and intellectual property.

How organisations evaluate AI opportunities, create business value and understand the economics of AI investment.

How organisations adopt AI, redesign operating models and prepare people, roles and processes for change.

How AI is being applied across industries and how emerging capabilities may reshape organisations and society.