Academics discover black people'significantly more likely' to be identified when compared with other ethnic groups Essex police have paused the use of live facial recognition (LFR) technology after a study found cameras were significantly more likely to target black people than people of other ethnicities. The move to suspend use of the AI-enabled systems was revealed by the Information Commissioner's Office (ICO), which regulates the use of the technology deployed so far by at least 13 police forces in London, south and north Wales, Leicestershire, Northamptonshire, Hampshire, Bedfordshire, Suffolk, Greater Manchester, West Yorkshire, Surrey and Sussex. The ICO said Essex police had paused LFR deployments "after identifying potential accuracy and bias risks" and warned other forces to have mitigations in place. LFR systems are either mounted to fixed locations or deployed in vans. In January, the home secretary, Shabana Mahmood, announced the number of LFR vans would increase five-fold, with 50 available to every police force in England and Wales. Essex commissioned University of Cambridge academics to conduct a study, which involved 188 actors walking past cameras being actively deployed from marked police vans in Chelmsford.

Claude AI: Why are there so many internet outages? AI chatbot Claude going down is just one example of a recent IT outage. Anthropic's Claude chatbot recently had service troubles This week, AI chatbot Claude went down, leaving users unable to access the service via its maker Anthropic's website, but barely a week goes by without a similar incident at a technology giant, government website or hospital . One of the main vulnerabilities of the modern internet is the shift to cloud computing, meaning a huge range of websites and services now rely on just a handful of companies, such as Amazon and Microsoft. In the early days of the commercial internet in the 1990s, companies used to operate their own hardware and software, a bit like individual shops in a street.

Districting is a complex combinatorial problem that consists in partitioning a geographical area into small districts.

How do language models use information provided as context when generating a response?

This intervention significantly improves the performance of LLaMA models on the TruthfulQA benchmark.

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