Generative AI

A type of neural network trained on vast corpora of text data, capable of generating human-like text, answering questions, summarising documents, and performing reasoning tasks. Large language models such as GPT and Claude represent significant R&D investment and are reshaping knowledge work, customer service, and content production across industries.

A field of artificial intelligence that enables machines to interpret and extract information from visual inputs such as images, video, and documents. Computer vision is applied in quality inspection, medical imaging, autonomous vehicles, and document processing.

Artificial intelligence systems designed to provide human-interpretable explanations of their decision-making processes and outputs. Explainability is increasingly required by regulators — particularly in financial services, healthcare, and criminal justice — and is a key differentiator for AI products seeking enterprise adoption in regulated industries.

Legal rights that grant the creator of original works exclusive control over their reproduction, distribution, and adaptation. In a business context, copyrights protect software code, written content, marketing materials, training programmes, and creative works as intangible assets.

The process of breaking text, code, or other sequential data into discrete units (tokens) that serve as the input and output elements for large language models. Tokenisation determines how a model processes language and directly affects inference costs, since API pricing for large language models is typically based on token count.

The branch of applied ethics concerned with the moral implications of designing, deploying, and using artificial intelligence systems. AI ethics addresses issues including fairness, transparency, privacy, accountability, and the societal impact of automation.

Systematic and repeatable errors in an AI system's outputs that create unfair outcomes for particular groups, typically arising from biased training data, flawed model design, or unrepresentative sampling. Algorithmic bias poses significant reputational, legal, and regulatory risks, and its identification and mitigation are core components of responsible AI governance.

The practice of designing and optimising input instructions (prompts) to elicit desired outputs from large language models and other generative AI systems. Effective prompt engineering can significantly improve AI output quality and consistency, and documented prompt libraries are emerging as a form of organisational knowledge capital.

Broadcasting Rights: Valuation for Media Companies

How to value broadcasting rights as contract-based intangible assets under IFRS 3. Covers sports broadcasting rights, content distribution agreements, spectrum licences, and the income approach for media rights valuation.