River of waste'visible for miles' dumped at mountain beauty spot A farmer says she is devastated by a disgusting river of fly-tipped waste dumped down the side of a mountain. Katie Davies, whose family has owned land on Bwlch Mountain in Treorchy for 90 years, said the clean up could cost thousands of pounds and could also harm her sheep which graze on the land. Travel blogger Nathan Dixon, who captured drone footage showing the scale of the discarded waste, said the mess could be seen from three to five miles away, adding that it sticks out like a sore thumb. Rhondda Cynon Taf council said it always took action to hold those responsible for fly-tipping to account, while Natural Resources Wales (NRW) said fly-tipping was a serious crime. Davies, who runs small family business Nantymoel farm which produces Welsh beef and lamb, said the mess keeps me up at night.

It's an interesting time to be a long-running science fantasy media property in the streaming TV age. Star Trek is in the grip of an existential crisis as it (wrongly) fears it's too old-aged to be relevant. Star Wars became a battlefield in the culture war and, to duck all future bad faith criticism, gave us The Rise of Skywalker. And then there's Doctor Who, which is somehow managing to plough a 62-year furrow and still fill it with original ideas. This week the Doctor and Belinda go up against a sentient cartoon holding the patrons of a 1950s cinema hostage.

It is sometimes assumed that Large Language Models (LLMs) know language, or for example that they know that Paris is the capital of France. But what -- if anything -- do LLMs actually know? In this paper, I argue that LLMs can acquire tacit knowledge as defined by Martin Davies (1990). Whereas Davies himself denies that neural networks can acquire tacit knowledge, I demonstrate that certain architectural features of LLMs satisfy the constraints of semantic description, syntactic structure, and causal systematicity. Thus, tacit knowledge may serve as a conceptual framework for describing, explaining, and intervening on LLMs and their behavior.

The start of any season of Doctor Who is important, doubly so when there's a new co-star to introduce. "The Robot Revolution" has to get us to fall in love with Belinda Chandra (Varada Sethu), ensnare new fans and keep existing ones hooked. Especially since it's the second of two series that Disney paid for, meaning it's got to do well enough to keep the money flowing. It's an awkward teenage date, with Alan clearly trying to win the heart of his beau by buying her one of those star adoption certificates. In 2025, Belinda is now a nurse at a busy London hospital where, in the background, the Doctor is searching for her.

This study aims at identifying and analyzing circumlocution categories and subcategories in the (SL) and their renditions into the (TL).It is based on criteria proposed for inclusion and exclusion of circumlocution.This study is concerned with the translation of literary texts, specifically short stories, from Arabic into English. It draws on four short stories selected from Arabic famous writers and their parallel translations into English. It hypothesizes that Arabic categories of circumlocution are applicable to English categories of metadiscourse, which include textual and interpersonal items. Nida's (1964) model is adopted in this study to judge the appropriateness in translation the study shows that the translators made serious decisions while opting for various techniques such as addition, subtraction and alteration. In this sense, it investigates whether the translators have successfully and appropriately managed to render the concept of Arabic circumlocution into English or not. The main problems that led to the inappropriate translations were also identified. This study concludes that there are lots of similarities between the categories of circumlocution in Arabic and the categories of metadiscourse in English. These similarities are clear when appropriate renditions are achieved.

Intel has created the world's largest neuromorphic computer, a device intended to mimic the operation of the human brain. The firm hopes that it will be able to run more sophisticated AI models than is possible on conventional computers, but experts say there are engineering hurdles to overcome before the device can compete with the state of the art, let alone exceed it. Expectations for neuromorphic computers are high because they are inherently different to traditional machines. While a regular computer uses its processor to carry out operations and stores data in separate memory, a neuromorphic device uses artificial neurons to both store and compute, just as our brains do. This removes the need to shuttle data back and forth between components, which can be a bottleneck for current computers.

Artificial Neural Networks (ANN) have gained large popularity thanks to their ability to learn using the well-known backpropagation algorithm. On the other hand, Spiking Neural Networks (SNNs), despite having wider abilities than ANNs, have always presented a challenge in the training phase. This paper shows a new method to perform supervised learning on SNNs, using Spike Timing Dependent Plasticity (STDP) and homeostasis, aiming at training the network to identify spatial patterns. The method is tested using the SpiNNaker digital architecture. A SNN is trained to recognise one or multiple patterns and performance metrics are extracted to measure the performance of the network. Some considerations are drawn from the results showing that, in the case of a single trained pattern, the network behaves as the ideal detector, with 100% accuracy in detecting the trained pattern. However, as the number of trained patterns on a single network increases, the accuracy of the identification is linked to the similarities between these patterns. This method of training an SNN to detect spatial patterns may be applied on pattern recognition in static images or traffic analysis in computer networks, where each network packet represents a spatial pattern. It will be stipulated that the homeostatic factor may enable the network to detect patterns with some degree of similarities, rather than only perfectly matching patterns.

Douglas Adams created the most famous ebook reader – The Hitchhiker's Guide to the Galaxy – almost 30 years before the first Kindle was released, but he didn't restrict his ideas to his science fiction. In the late 1990s, at least a decade before Amazon's e-reader first came on to the market in 2007, the author and humorist made a series of notes uncannily predicting the rise of electronic books. But Adams, who died in 2001, did not live to see his musings, spread over three A4 pages, become reality. He wrote: "Lots of resistance to the idea of ebooks from the public. Particularly all those people who 10 years ago said they couldn't see any point typing on a computer. "I believe this resistance will gradually disappear as the electronic book itself improves and becomes smaller, lighter, simpler, cheaper, in other words more like a book." Adams's notes are presented in their original handwritten form in a new book, 42: The Wildly Improbable Ideas of Douglas Adams. Featuring unseen material from Adams's personal archive, including notes, letters, speeches, fanmail and unused sections of his most famous work, The Hitchhiker's Guide, it has been put together by Kevin Jon Davies, who first met Adams in 1978 to interview him for a fanzine. Davies gained access to Adams's archived material held at St John's College, Cambridge to assemble a suitably eclectic insight into the writer's thoughts, processes and ideas. He describes Adams as "a man fascinated by technology" and both an "advocate for conservation and a forward thinking innovator". "His ideas in his writing, articles and speeches were often arguably ahead of their time," says Davies. "The three pages of notes which are Douglas's thoughts on the future possibilities of electronic books and publishing date from the late 1990s, and the musings are well ahead of Kindles and other ebooks.

Viz Subdural Hematoma (SDH), a new artificial intelligence (AI)-powered algorithm for diagnosing subdural hemorrhages, has garnered 510(k) clearance from the Food and Drug Administration (FDA), according to Viz.ai, the manufacturer of the module. Noting a rising incidence of subdural hematomas, Viz.ai said the SDH algorithm demonstrated sensitivity and specificity rates of 94 and 92 percent respectively in a multicenter trial of over 500 patients.1,2 "The algorithm is very sensitive and specific, significantly increasing the number of subdural hemorrhages detected and ensuring patients receive the necessary follow-up from this potentially life-threatening disease," maintained Jayme Strauss, the chief clinical officer at Viz.ai. The company emphasizes that Viz SDH is currently the only AI-powered platform specifically geared to identifying and differentiating between acute and chronic subdural hemorrhages. Jason Davies, M.D., Ph.D., said this is a key benefit given the different treatment pathways for these conditions.

One may forgive customer service technology users for laughing out loud at the suggestion of AI gaining sentience. They have a difficult time simply bringing their chatbots online to solve basic customer problems, let alone assigning them metaphysical characteristics. This comes as rogue ex-Google engineer Blake Lemoine said he believes that Google's LaMDA, a large language model in development, is sentient. While chatbot technology is evolving, the bots available in today's market are nowhere near sentient, according to a panel of contact center technology users and other experts. Much more work needs to be done to make customer service chatbots integrate with existing enterprise IT to automate even the simplest tasks.