Machine Learning
Definition
A subset of artificial intelligence in which algorithms improve their performance on a specific task through exposure to data, without being explicitly programmed for each scenario. Machine learning encompasses supervised learning, unsupervised learning, reinforcement learning, and deep learning techniques. As intangible assets, trained machine learning models represent substantial value — they encode patterns extracted from proprietary datasets and embody significant investment in data curation, feature engineering, model architecture design, and computational resources. In business applications, machine learning drives value through predictive analytics, automated decision-making, natural language processing, computer vision, and recommendation systems. The valuation of machine learning assets is complex because model performance depends on ongoing access to fresh data, computational infrastructure, and specialised talent — making these assets both highly valuable and highly perishable without continued investment.
Complementary Terms
Concepts that frequently appear alongside Machine Learning in practice.
A mathematical model trained on data to identify patterns and make predictions without being explicitly programmed for each task. Machine learning models underpin many AI-driven business applications, from demand forecasting to fraud detection, and their development costs are increasingly recognised as intangible assets under IAS 38 when they meet the identifiability and future economic benefit criteria.
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 machine learning technique that trains models across multiple decentralised devices or servers holding local data, without transferring the raw data to a central location. Federated learning addresses data privacy and sovereignty concerns by keeping sensitive data on-device while still enabling collaborative model improvement.
A machine learning technique where a model trained on one task is repurposed as the starting point for a different but related task, significantly reducing the data and compute required for training. Transfer learning accelerates AI development timelines and reduces costs, making AI adoption more accessible to SMEs.
A branch of artificial intelligence concerned with enabling computers to understand, interpret, and generate human language. NLP powers applications such as chatbots, sentiment analysis, document classification, and automated contract review.
The degradation in a machine learning model's predictive accuracy over time as the statistical properties of the input data diverge from the training data distribution. Model drift requires ongoing monitoring and periodic retraining to maintain performance, and is a key operational risk in production AI systems.
Related FAQ
How do you build trust in AI-driven products and recommendations?
AI products build trust by explaining how recommendations are generated, acknowledging limitations, and allowing users to override or contest outputs.
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