Oscar-winning actress Cate Blanchett and former prime minister Baroness Theresa May are among the six public figures who will guest edit BBC Radio 4's Today programme over the Christmas period. Broadcaster Melvyn Bragg, historian and podcaster Tom Holland, inventor Sir James Dyson and Microsoft's head of artificial intelligence (AI) Mustafa Suleyman will also guest edit shows between 24 December and 31 December. For the past 22 years, the news programme has handed over the editorial reins to guest editors during the festive period. Owenna Griffiths, editor of Today, said: In a rapidly changing world, this year's guest editors will help bring illumination and understanding. She added: Every Christmas on Today, a new set of guest editors take up residence and bring with them a wonderful range of new stories, fresh ideas and, hopefully, a sprinkling of joy.

As dawn spread over San Antonio on September 9, 2021, almond-colored smoke began to fill the sky above the city's Far West Side. The plumes were whorling off the top of a 132-foot-tall cell tower that overshadows an office park just north of SeaWorld. At a hotel a mile away, a paramedic snapped a photo of the spectacle and posted it to the r/sanantonio subreddit. "Cell tower on fire around 1604 and Culebra," he wrote. In typical Reddit fashion, the comments section piled up with corny jokes. "Blazing 5G speeds," quipped one user. "I hope no one inhales those fumes, the Covid transmission via 5G will be a lot more potent that way," wrote another, in a swipe at the conspiracy theorists who claim that radiation from 5G towers caused the Covid-19 pandemic. The wisecracks went on: "Can you hear me now?" "Great, some hero trying to save us from 5G." That self-styled hero was actually lurking in the comments. As he followed the thread on his phone, Sean Aaron Smith delighted in the sheer volume of attention the tower fire was receiving, even if most of it dripped with sarcasm. A lean, tattooed--and until recently, entirely apolitical--27-year-old, Smith had come to view 5G as the linchpin of a globalist plot to zombify humanity. To resist that supposed scheme, he'd spent the past five months setting Texas cell towers ablaze. Smith's crude and quixotic campaign against 5G was precisely the sort of security threat that was fast becoming one of the US government's top concerns in 2021.

Recent studies on personas have improved the way Large Language Models (LLMs) interact with users. However, the effect of personas on domain-specific question-answering (QA) tasks remains a subject of debate. This study analyzes whether personas enhance specialized QA performance by introducing two types of persona: Profession-Based Personas (PBPs) (e.g., scientist), which directly relate to domain expertise, and Occupational Personality-Based Personas (OPBPs) (e.g., scientific person), which reflect cognitive tendencies rather than explicit expertise. Through empirical evaluations across multiple scientific domains, we demonstrate that while PBPs can slightly improve accuracy, OPBPs often degrade performance, even when semantically related to the task. Our findings suggest that persona relevance alone does not guarantee effective knowledge utilization and that they may impose cognitive constraints that hinder optimal knowledge application. Future research can explore how nuanced distinctions in persona representations guide LLMs, potentially contributing to reasoning and knowledge retrieval that more closely mirror human social conceptualization.

Article comprehension is an important challenge in natural language processing with many applications such as article generation or image-to-article retrieval. Prior work typically encodes all tokens in articles uniformly using pretrained language models. However, in many applications, such as understanding news stories, these articles are based on real-world events and may reference many named entities that are difficult to accurately recognize and predict by language models. To address this challenge, we propose an ENtity-aware article GeneratIoN and rEtrieval (ENGINE) framework, to explicitly incorporate named entities into language models. ENGINE has two main components: a named-entity extraction module to extract named entities from both metadata and embedded images associated with articles, and an entity-aware mechanism that enhances the model's ability to recognize and predict entity names. We conducted experiments on three public datasets: GoodNews, VisualNews, and WikiText, where our results demonstrate that our model can boost both article generation and article retrieval performance, with a 4-5 perplexity improvement in article generation and a 3-4% boost in recall@1 in article retrieval. We release our implementation at https://github.com/Zhongping-Zhang/ENGINE .

Kanerva's sparse distributed memory (SDM) is an associative-memo(cid:173) ry model based on the mathematical properties of high-dimensional binary address spaces. Holland's genetic algorithms are a search tech(cid:173) nique for high-dimensional spaces inspired by evolutionary processes of DNA. "Genetic Memory" is a hybrid of the above two systems, in which the memory uses a genetic algorithm to dynamically recon(cid:173) figure its physical storage locations to reflect correlations between the stored addresses and data. For example, when presented with raw weather station data, the Genetic Memory discovers specific fea(cid:173) tures in the weather data which correlate well with upcoming rain, and reconfigures the memory to utilize this information effectively. This architecture is designed to maximize the ability of the system to scale-up to handle real-world problems.

Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! Is your AI trustworthy or not? As the adoption of AI solutions increases across the board, consumers and regulators alike expect greater transparency over how these systems work. Today's organizations not only need to be able to identify how AI systems process data and make decisions to ensure they are ethical and bias-free, but they also need to measure the level of risk posed by these solutions.

Social influence prediction has permeated many domains, including marketing, behavior prediction, recommendation systems, and more. However, traditional methods of predicting social influence not only require domain expertise,they also rely on extracting user features, which can be very tedious. Additionally, graph convolutional networks (GCNs), which deals with graph data in non-Euclidean space, are not directly applicable to Euclidean space. To overcome these problems, we extended DeepInf such that it can predict the social influence of COVID-19 via the transition probability of the page rank domain. Furthermore, our implementation gives rise to a deep learning-based personalized propagation algorithm, called DeepPP. The resulting algorithm combines the personalized propagation of a neural prediction model with the approximate personalized propagation of a neural prediction model from page rank analysis. Four social networks from different domains as well as two COVID-19 datasets were used to demonstrate the efficiency and effectiveness of the proposed algorithm. Compared to other baseline methods, DeepPP provides more accurate social influence predictions. Further, experiments demonstrate that DeepPP can be applied to real-world prediction data for COVID-19.

Artificial intelligence (AI) and machine learning tools have become indispensable to fuel procurement and sourcing efforts at the Atlanta-based global beverage leader, according to Brett Fultz, director of global analysis, global procurement and supply chain at Coca-Cola. For any company that manufactures or sells goods, buying and sourcing are integral functions of supply chain management. Sourcing, an early stage of the buying process, is about identifying and assessing potential suppliers of goods or services, negotiating terms, and selecting vendors. Procurement, however, goes further, and is about getting supplies and payment from suppliers who compete for business by submitting bids and negotiating contracts. But challenges abound in a supply chain landscape full of constraints and risks - from issues related to the COVID-19 pandemic and the war in Ukraine to climate change.

The U.S. Air Force revealed recently that it had used artificial intelligence to aid targeting decisions for the first time. It turns out that this was not simply a test: AI is embedded in the Air Force's targeting operation, raising serious questions. Secretary of the Air Force Frank Kendall told the Air Force Association's Air, Space & Cyber Conference in National Harbor, Maryland on Sept. 20, that the Air Force had "deployed AI algorithms for the first time to a live operational kill chain." He did not give details of the strike, whether it was by a drone or piloted aircraft, and if there were civilian casualties. The "kill chain" is the entire province in which data gathered by various sensors is analyzed, targets selected and strikes planned and ordered and the results evaluated. AI takes some of the burden off human analysts, who spend thousands of hours searching through video footage trying to find, locate and positively identify targets.

On the factory floor, machine downtime is a disaster – production can slow to a crawl or a complete stop, wasting precious time and money. To dodge downtime, manufacturers typically plan rigorous maintenance regimes – fixing problems before they even occur. That's great for keeping production running at full pace, but costly in terms of having the right experts and spare parts to hand at all times. Preventative maintenance is labour intensive and expensive. Have faith in data – and artificial intelligence.