This new video explains natural language processing: what it is, how it works, and what can it do for your organisation. Natural Language Processing (NLP) is a branch of Artificial Intelligence (AI) that focuses on giving computers the ability to understand human language, combining disciplines like linguistics, computer science, and engineering.
Companies need NLP because much of their crucial data, especially in industries like insurance, healthcare, and pharmaceuticals, exists as unstructured text in formats like PDFs, scanned documents, or audio, which computers struggle to process compared to clean numerical data.
The most effective way a business can get value out of NLP is by implementing it as part of a wider strategic and business-aware initiative, such as the development of a predictive risk model. This allows the company’s C-level to turn unstructured text documents into a quantifiable risk or cost estimate for the next quarter, delivering a phenomenal return on investment and a competitive advantage, especially in traditionally conservative industries. While lower-impact initiatives can save staffing costs (e.g., by triaging customer support), the highest impact comes from these larger strategic projects that provide predictive business insights.
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Thomas Wood presents the Clinical Trial Risk Tool before the November meeting of the Clinical AI Interest Group at Alan Turing Institute The Clinical AI Interest group is a community of health professionals from a broad range of backgrounds with an interest in Clinical AI, organised by the Alan Turing Institute.
Fast Data Science will appear at Ireland’s Expert Witness Conference on 20 May 2026 in Dublin On 20 May 2026, La Touche Training is running the Expert Witness Conference 2026, at the Radisson Blu Hotel, Golden Lane, Dublin 8, Ireland. This is a full-day event combining practical workshops and interactive sessions, aimed at expert witnesses and legal professionals who want to enhance their expertise. The agenda covers critical topics like recent developments in case law, guidance on report writing, and techniques for handling cross-examination.
Guest post by Alex Nikic In the past few years, Generative AI technology has advanced rapidly, and businesses are increasingly adopting it for a variety of tasks. While GenAI excels at tasks such as document summarisation, question answering, and content generation, it lacks the ability to provide reliable forecasts for future events. GenAI models are not designed for forecasting, and along with the tendancy to hallucinate information, the output of these models should not be trusted when planning key business decisions. For more details, a previous article on our blog explores in-depth the trade-offs of GenAI vs Traditional Machine Learning approaches.
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