An entrepreneur has told how she was left feeling "humiliated" after being turned away from London Tech Week, an annual corporate event, because she was with her baby daughter. Davina Schonle was prevented from entering the event on Monday after travelling for three hours with her eight-month-old and had to cancel meetings with potential suppliers to her tech startup. Schonle told TheBusinessDesk.com that as she went to the entrance with her daughter in her pram: "I was asked if I was a VIP. I was then told I wasn't allowed in with a baby. I went to get my badge, but was then taken over to the organisers from Informa, who told me they weren't insured. But they asked again if I was a VIP or speaker, and later another lady came over and twisted my badge around to see, clearly checking to see if I was a VIP."

Artificial intelligence (AI) has long promised to improve energy management in smart grids by enhancing situational awareness and supporting more effective decision-making. While traditional machine learning has demonstrated notable results in forecasting and optimization, it often struggles with generalization, situational awareness, and heterogeneous data integration. Recent advances in foundation models such as Transformer architecture and Large Language Models (LLMs) have demonstrated improved capabilities in modelling complex temporal and contextual relationships, as well as in multi-modal data fusion which is essential for most AI applications in the energy sector. In this review we synthesize the rapid expanding field of AI applications in the energy domain focusing on Transformers and LLMs. We examine the architectural foundations, domain-specific adaptations and practical implementations of transformer models across various forecasting and grid management tasks. We then explore the emerging role of LLMs in the field: adaptation and fine tuning for the energy sector, the type of tasks they are suited for, and the new challenges they introduce. Along the way, we highlight practical implementations, innovations, and areas where the research frontier is rapidly expanding. These recent developments reviewed underscore a broader trend: Generative AI (GenAI) is beginning to augment decision-making not only in high-level planning but also in day-to-day operations, from forecasting and grid balancing to workforce training and asset onboarding. Building on these developments, we introduce the concept of the Agentic Digital Twin, a next-generation model that integrates LLMs to bring autonomy, proactivity, and social interaction into digital twin-based energy management systems.

Transitional care may play a vital role for the sustainability of Europe future healthcare system, offering solutions for relocating patient care from hospital to home therefore addressing the growing demand for medical care as the population is ageing. However, to be effective, it is essential to integrate innovative Information and Communications Technology technologies to ensure that patients with comorbidities experience a smooth and coordinated transition from hospitals or care centers to home, thereby reducing the risk of rehospitalization. In this paper, we present an overview of the integration of Internet of Things, artificial intelligence, and digital assistance technologies with traditional care pathways to address the challenges and needs of healthcare systems in Europe. We identify the current gaps in transitional care and define the technology mapping to enhance the care pathways, aiming to improve patient outcomes, safety, and quality of life avoiding hospital readmissions. Finally, we define the trial setup and evaluation methodology needed to provide clinical evidence that supports the positive impact of technology integration on patient care and discuss the potential effects on the healthcare system.

In May, a multibillion-dollar UK-based multinational called Informa announced in a trading update that it had signed a deal with Microsoft involving "access to advanced learning content and data, and a partnership to explore AI expert applications". Informa is the parent company of Taylor & Francis, which publishes a wide range of academic and technical books and journals, so the data in question may include the content of these books and journals. According to reports published last week, the authors of the content do not appear to have been asked or even informed about the deal. What's more, they say they had no opportunity to opt out of the deal, and will not see any money from it. Academics are only the latest of several groups of what we might call content creators to take umbrage at having their work ingested by the generative AI models currently racing to hoover up the products of human culture.

Objective: Information retrieval (IR, also known as search) systems are ubiquitous in modern times. How does the emergence of generative artificial intelligence (AI), based on large language models (LLMs), fit into the IR process? Process: This perspective explores the use of generative AI in the context of the motivations, considerations, and outcomes of the IR process with a focus on the academic use of such systems. Conclusions: There are many information needs, from simple to complex, that motivate use of IR. Users of such systems, particularly academics, have concerns for authoritativeness, timeliness, and contextualization of search. While LLMs may provide functionality that aids the IR process, the continued need for search systems, and research into their improvement, remains essential.

Recent work in machine learning for healthcare has raised concerns about patient privacy and algorithmic fairness. For example, previous work has shown that patient self-reported race can be predicted from medical data that does not explicitly contain racial information. However, the extent of data identification is unknown, and we lack ways to develop models whose outcomes are minimally affected by such information. Here we systematically investigated the ability of time-series electronic health record data to predict patient static information. We found that not only the raw time-series data, but also learned representations from machine learning models, can be trained to predict a variety of static information with area under the receiver operating characteristic curve as high as 0.851 for biological sex, 0.869 for binarized age and 0.810 for self-reported race. Such high predictive performance can be extended to a wide range of comorbidity factors and exists even when the model was trained for different tasks, using different cohorts, using different model architectures and databases. Given the privacy and fairness concerns these findings pose, we develop a variational autoencoder-based approach that learns a structured latent space to disentangle patient-sensitive attributes from time-series data. Our work thoroughly investigates the ability of machine learning models to encode patient static information from time-series electronic health records and introduces a general approach to protect patient-sensitive attribute information for downstream tasks.

Total technology industry artificial intelligence deals worth $9.6bn were announced globally for February 2022, with the $2.58bn private equity deal with Informa being the sector's biggest investment, according to GlobalData's deals database. Copy and paste the image source into your website to display the chart. The value marked an increase of 44.2% over the previous month of $6.63bn and a rise of 8.3% when compared with the last 12-month average of $8.83bn. In terms of number of artificial intelligence deals, the sector saw a drop of 7.28% with 280 deals in February 2022 when compared to the last 12-month average of 302 deals. In value terms, North America led the activity with artificial intelligence deals worth $4.15bn in February 2022.