If you are developing an application that needs to interpret free-text medical notes, you might be interested in getting the best possible performance by using OpenAI, Gemini, Claude, or another large language model. But to do that, you would need to send sensitive data, such as personal healthcare data, into the third party LLM. Is this allowed?
How can you use generative AI to find topics in a free text survey and identify the commonest mentioned topics? Imagine that you work for a market research company, and you’ve just run an online survey. You’ve received 10,000 free text responses from users in different languages. You want to quickly make a pie chart or bar chart showing common customer complaints, broken down by old customers, new customers, different locations, different spending patterns, and demographics.
When can lawyers, litigants in person, and expert witnesses use AI in court documents? In the last few years in the UK, the USA, Canada, Ireland and other jurisdictions, cases have been reported where submissions were made to a court where the author of a document used generative AI tools such as ChatGPT to create those documents. This has wasted court time, resulted in submissions being rejected or even resulted in changes to cost awards.
A person has recently returned from a camping trip and has a fever. Should a doctor diagnose flu or Lyme disease? Would this be any different if they had not mentioned their camping trip? Here’s how LLMs differ from human experts.
What is customer churn and why should I worry about it? Customer churn is when you have a number of customers in your company and a certain number of them are likely to end their relationship with your company in the near future. Customer churn can be harmful to a business because of the lost revenue and the wasted money on acquiring that customer, as well as the fact that a churned customer may have switched to a competitor, may be dissatisfied, and might leave negative reviews.
A generative AI-based legal advice tool for small businesses in distress We are pleased to announce the publication of our paper A generative AI-based legal advice tool for small businesses in distress, in collaboration with an interdisciplinary team based in the UK and Hungary.
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.
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.
After this ruling, will tech companies move all model training to data centres that they consider “copyright safe”? Will we see a new equivalent of a “tax haven” for training AI models on copyrighted content? An “AI haven”? This article is not legal advice.
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.
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