These robots can clean, exercise - and care for your elderly parents. Would you trust them to? Hidden away in a lab in north-west London three black metal robotic hands move eerily on an engineering work bench. We're not trying to build Terminator, jokes Rich Walker, director of Shadow Robot, the firm that made them. Bespectacled, with long hair and a beard and moustache, he seems more like a latter-day hippy than a tech whizz, and he is clearly proud as he shows me around his firm.

This paper presents an interdisciplinary PhD project using a humanoid robot to encourage interactive activities for people with dementia living in two aged care facilities. The aim of the project was to develop software and use technologies to achieve successful robot-led engagement with older people with dementia. This paper outlines the qualitative findings from the project's feasibility stage. The researcher's observations, the participants' attitudes and the feedback from carers are presented and discussed.

The adoption of large cloud-based models for inference has been hampered by concerns about the privacy leakage of end-user data. One method to mitigate this leakage is to add local differentially private noise to queries before sending them to the cloud, but this degrades utility as a side effect. Our key insight is that knowledge available in the noisy labels returned from performing inference on noisy inputs can be aggregated and used to recover the correct labels. We implement this insight in LDPKiT, which stands for Local Differentially-Private and Utility-Preserving Inference via Knowledge Transfer. LDPKiT uses the noisy labels returned from querying a set of noised inputs to train a local model (noise^2), which is then used to perform inference on the original set of inputs. Our experiments on CIFAR-10, Fashion-MNIST, SVHN, and CARER NLP datasets demonstrate that LDPKiT can improve utility without compromising privacy. For instance, on CIFAR-10, compared to a standard $\epsilon$-LDP scheme with $\epsilon=15$, which provides a weak privacy guarantee, LDPKiT can achieve nearly the same accuracy (within 1% drop) with $\epsilon=7$, offering an enhanced privacy guarantee. Moreover, the benefits of using LDPKiT increase at higher, more privacy-protective noise levels. For Fashion-MNIST and CARER, LDPKiT's accuracy on the sensitive dataset with $\epsilon=7$ not only exceeds the average accuracy of the standard $\epsilon$-LDP scheme with $\epsilon=7$ by roughly 20% and 9% but also outperforms the standard $\epsilon$-LDP scheme with $\epsilon=15$, a scenario with less noise and minimal privacy protection. We also perform Zest distance measurements to demonstrate that the type of distillation performed by LDPKiT is different from a model extraction attack.

Britain's hard-pressed carers need all the help they can get. But that should not include using unregulated AI bots, according to researchers who say the AI revolution in social care needs a hard ethical edge. A pilot study by academics at the University of Oxford found some care providers had been using generative AI chatbots such as ChatGPT and Bard to create care plans for people receiving care. That presents a potential risk to patient confidentiality, according to Dr Caroline Green, an early career research fellow at the Institute for Ethics in AI at Oxford, who surveyed care organisations for the study. "If you put any type of personal data into [a generative AI chatbot], that data is used to train the language model," Green said.

Artificial intelligence is to be used to check if carers are likely to quit their jobs. Any issues employees have had with pay, punctuality, or problems with their manager will be among the data fed into the algorithm. The technology is meant to combat the staffing crisis in social care by giving bosses an early chance to persuade workers to stay. Private healthcare company Cera claims its AI could prevent around 50,000 staff leaving every year. The firm said it has been shown to detect carers who are at risk of quitting three times more accurately than human managers can.

Care patients could see at home visits replaced by a call from an AI-powered voice assistant in a new British trial. Dubbed'Siri for care', a human-like virtual assistant will ring patients once a week to ask a list of automated questions. An algorithm will then analyse the answers and alert carers if there are any deteriorations in health so they can arrange a doctor's visit. Similar trials in Europe have reduced A&E visits by 55 per cent, according to the tech company behind it. The new technology will be tested out on patients in domiciliary care for those who are living independently but who rely on helpers to visit them regularly.

Left: The display that carers will see in the Milbotix app. Inventor Dr Zeke Steer quit his job and took a PhD at Bristol Robotics Laboratory so he could find a way to help people like his great-grandmother, who became anxious and aggressive because of her dementia. Milbotix's smart socks track heart rate, sweat levels and motion to give insights on the wearer's wellbeing – most importantly how anxious the person is feeling. They look and feel like normal socks, do not need charging, are machine washable and provide a steady stream of data to carers, who can easily see their patient's metrics on an app. Current alternatives to Milbotix's product are worn on wrist straps, which can stigmatise or even cause more stress.

A figure sits alone on stage, dressed in comfy jumper and trousers, one leg crossed over the other. He slowly moves his hands and turns his head. But this sole performer in Uncanny Valley, by theatre company Rimini Protokoll, is not human. It is a lifelike animatronic model of the German writer Thomas Melle. The show's director, Stefan Kaegi, had seen animatronics used in museums, where he found there was not sufficient time for what he calls the "empathy mechanism" to kick in. But he wondered what would happen if the robot became a performer, "someone with whom we start to identify".

The NIHR has awarded £2.5 million for new research led by the University of Birmingham that will use artificial intelligence (AI) to produce computer programmes and tools that will help doctors improve the choice of drugs in patients with clusters of multiple long-term conditions. Called the OPTIMAL study (OPTIMising therapies, discovering therapeutic targets and AI assisted clinical management for patients Living with complex multimorbidity), the research aims to understand how different combinations of long-term conditions and the medicines taken for these diseases interact over time to worsen or improve a patient's health. The study will be led by Dr Thomas Jackson and Professor Krish Nirantharakumar at the University of Birmingham and carried out in collaboration with the University of Manchester, University Hospitals Birmingham NHS Foundation Trust, NHS Greater Glasgow & Clyde, University of St Andrews,and the Medicines and Healthcare Products Regulatory Agency. An estimated 14 million people in England are living with two or more long-term conditions, with two-thirds of adults aged over 65 expected to be living with multiple long-term conditions by 2035. Dr Thomas Jackson, Associate Professor in Geriatric Medicine at the University of Birmingham, said: "Currently when people have multiple long-term conditions, we treat each disease separately. This means we prescribe a different drug for each condition, which may not help people with complex multimorbidity which is a term we use when patients have four or more long-term health problem. "A drug for one disease can make another disease worse or better, however, presently we do not have information on the effect of one drug on a second disease.

Kellye Franklin recalls the devastation when her now 81-year-old father, a loyal air force veteran, tried to make his own breakfast one morning. Seven boxes of open cereal on the living room floor with milk poured directly into every one of them. He would later be diagnosed with moderate to severe dementia. Yet Franklin, 39, who is her dad's only child and his primary caregiver, does not worry about that repeating now. In late 2019, she had motion sensors that are connected to an artificial intelligence (AI) system installed in the two-floor townhome she and her dad share in Inglewood, in Los Angeles county.