In many organisations, accurate forecasts are essential for making informed decisions for a variety of applications from inventory management to staffing optimization. Whatever forecasting model is used, changes in the underlying process can lead to inaccurate forecasts, which will be damaging to decision-making. At the same time, models are becoming increasingly complex and identifying change through direct modelling is problematic. We present a novel framework for online monitoring of forecasts to ensure they remain accurate. By utilizing sequential changepoint techniques on the forecast errors, our framework allows for the real-time identification of potential changes in the process caused by various external factors. We show theoretically that some common changes in the underlying process will manifest in the forecast errors and can be identified faster by identifying shifts in the forecast errors than within the original modelling framework. Moreover, we demonstrate the effectiveness of this framework on numerous forecasting approaches through simulations and show its effectiveness over alternative approaches. Finally, we present two concrete examples, one from Royal Mail parcel delivery volumes and one from NHS A\&E admissions relating to gallstones.

AI systems often exhibit political bias, influencing users' opinions and decision-making. While political neutrality-defined as the absence of bias-is often seen as an ideal solution for fairness and safety, this position paper argues that true political neutrality is neither feasible nor universally desirable due to its subjective nature and the biases inherent in AI training data, algorithms, and user interactions. However, inspired by Joseph Raz's philosophical insight that "neutrality [...] can be a matter of degree" (Raz, 1986), we argue that striving for some neutrality remains essential for promoting balanced AI interactions and mitigating user manipulation. Therefore, we use the term "approximation" of political neutrality to shift the focus from unattainable absolutes to achievable, practical proxies. We propose eight techniques for approximating neutrality across three levels of conceptualizing AI, examining their trade-offs and implementation strategies. In addition, we explore two concrete applications of these approximations to illustrate their practicality. Finally, we assess our framework on current large language models (LLMs) at the output level, providing a demonstration of how it can be evaluated. This work seeks to advance nuanced discussions of political neutrality in AI and promote the development of responsible, aligned language models.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. The former head of Britain's state-owned Post Office said Tuesday she will hand back a royal honor in response to mounting fury over a miscarriage of justice that saw hundreds of postmasters wrongfully accused of theft because of a faulty computer system. The British government is considering whether to offer a mass amnesty to more than 700 branch managers convicted of theft or fraud between 1999 and 2015, because Post Office computers wrongly showed that money was missing from their shops. The real culprit was a defective accounting system called Horizon, supplied by the Japanese technology firm Fujitsu.

Fine-tuning large language models (LLMs) for downstream tasks can greatly improve model quality, however serving many different fine-tuned LLMs concurrently for users in multi-tenant environments is challenging. Dedicating GPU memory for each model is prohibitively expensive and naively swapping large model weights in and out of GPU memory is slow. Our key insight is that fine-tuned models can be quickly swapped in and out of GPU memory by extracting and compressing the delta between each model and its pre-trained base model. We propose DeltaZip, an LLM serving system that efficiently serves multiple full-parameter fine-tuned models concurrently by aggressively compressing model deltas by a factor of $6\times$ to $8\times$ while maintaining high model quality. DeltaZip increases serving throughput by $1.5\times$ to $3\times$ and improves SLO attainment compared to a vanilla HuggingFace serving system.

For many islanders, delays to the postal service are an inescapable part of life. But that should no longer be the case for those living in Orkney, after it became the first place in Britain to have mail delivered by a drone. The new Royal Mail service will see post transported from the Kirkwall delivery office to the village of Stromness, where drones will then transfer items to posties on the islands of Hoy and Graemsay for their regular routes. Currently, mail arrives at Kirkwall Airport before being sent by plane or ferry to Orkney's 19 inhabited islands. But the challenging geography and weather conditions often result in delivery disruptions.

Royal Mail has begun using drones to deliver post in the Orkney islands, helping pave the way for drone deliveries to islands around the UK and on the mainland during emergencies. The service between the village of Stromness on Orkney's main island to the nearby islands of Hoy and Graemsay, using aircraft able to carry up to 6kg, is Royal Mail's first permanent drone delivery service. Using drones allows Royal Mail to provide a faster and more secure delivery service to islands such as the Orkneys, avoiding ferries or scheduled air services subject to weather cancellations, tides and timetables that do not suit the postal service. Royal Mail has been testing and evaluating drone services on Scottish islands for some time, as has the NHS, which has trialled their use for flying urgent medical samples from the small Hebridean islands of Coll and Tiree. Chris Paxton, the head of drone trials at Royal Mail, said these flights were far faster and more efficient, and helped cut carbon emissions.

Alex Brown, director of Skyports Drone Services, said: "By leveraging drone technology, we are revolutionising mail services in remote communities, providing more efficient and timely delivery, and helping to reduce the requirement for emissions-producing vehicles.

Matching addresses is a critical task for companies and post offices involved in the processing and delivery of packages. The ramifications of incorrectly delivering a package to the wrong recipient are numerous, ranging from harm to the company's reputation to economic and environmental costs. This research introduces a deep learning-based model designed to increase the efficiency of address matching for Portuguese addresses. The model comprises two parts: (i) a bi-encoder, which is fine-tuned to create meaningful embeddings of Portuguese postal addresses, utilized to retrieve the top 10 likely matches of the un-normalized target address from a normalized database, and (ii) a cross-encoder, which is fine-tuned to accurately rerank the 10 addresses obtained by the bi-encoder. The model has been tested on a real-case scenario of Portuguese addresses and exhibits a high degree of accuracy, exceeding 95% at the door level. When utilized with GPU computations, the inference speed is about 4.5 times quicker than other traditional approaches such as BM25. An implementation of this system in a real-world scenario would substantially increase the effectiveness of the distribution process. Such an implementation is currently under investigation.

This paper proposes a novel framework for accelerating support vector clustering. The proposed method first computes much smaller compressed data sets while preserving the key cluster properties of the original data sets based on a novel spectral data compression approach. Then, the resultant spectrally-compressed data sets are leveraged for the development of fast and high quality algorithm for support vector clustering. We conducted extensive experiments using real-world data sets and obtained very promising results. The proposed method allows us to achieve 100X and 115X speedups over the state of the art SVC method on the Pendigits and USPS data sets, respectively, while achieving even better clustering quality. To the best of our knowledge, this represents the first practical method for high-quality and fast SVC on large-scale real-world data sets

Campaigners, trade unions and MPs are calling for stricter oversight of the use of artificial intelligence in the workplace, amid growing concerns about its effect on staff rights. The Trades Union Congress (TUC) is holding a half-day conference on Tuesday to highlight the challenges of ensuring workers are treated fairly, as what it calls "management by algorithm" becomes increasingly prevalent. "Making work more rewarding, making it more satisfying, and crucially making it safer and fairer: these are all the possibilities that AI offers us," said Mary Towers, an employment lawyer who runs a TUC project on AI at work. "But what we're saying is, we're at a really important juncture, where the technology is developing so rapidly, and what we have to ask ourselves is, what direction do we want that to take, and how can we ensure that everyone's voice is heard?" The TUC has highlighted the growing use of employee surveillance. The Royal Mail chief executive, Simon Thompson, recently conceded some postal workers' movements were minutely tracked using handheld devices, with the data sused for performance management, for example.