Dating apps, booze and clubbing - Jane Austen's Emma comes into the 21st Century And your pushy best friend is trying to sort out your love life. It's Jane Austen's Emma, but not as you know it. For the uninitiated, the 1815 novel follows the charmed life of our protagonist in Regency England as she busies herself interfering in her friends' relationships (or matchmaking, depending on your point of view). In Ava Pickett's fresh adaptation, being staged at London's Rose Theatre, Emma Woodhouse still has all the trademark traits of our beloved original heroine - she's clever, quick-witted, meddling, haughty and occasionally cruel. But instead of navigating society balls and dowries, Pickett's modern Emma is poking her nose into her friends' online dating profiles, having returned home after failing her exams at Oxford University.

Large Language Models (LLMs) have demonstrated remarkable proficiency in generating fluent text. However, they often encounter the challenge of generating inaccurate or hallucinated content. This issue is common in both non-retrieval-based generation and retrieval-augmented generation approaches, and existing post-hoc rectification methods may not address the accumulated hallucination errors that may be caused by the "snowballing" issue, especially in reasoning tasks. To tackle these challenges, we introduce a novel approach called Real-time Verification and Rectification (Ever). Instead of waiting until the end of the generation process to rectify hallucinations, Ever employs a real-time, step-wise generation and hallucination rectification strategy. The primary objective is to detect and rectify hallucinations as they occur during the text generation process. When compared to both retrieval-based and non-retrieval-based baselines, Ever demonstrates a significant improvement in generating trustworthy and factually accurate text across a diverse range of tasks, including short-form QA, biography generation, and multi-hop reasoning.

While your body is lying in bed, your mind may be strolling around the manicured gardens of a manor house or the gritty streets of Victorian London. But now you can see some of the most iconic homes in literature with your own eyes, thanks an artificial intelligence (AI). These include Pemberley House, Mr Darcy's lavish estate in'Pride and Prejudice', and the residence of the world's most famous detective, Sherlock Holmes. Book lovers at Hammonds Furniture used the text-to-image software Midjourney to bring fictional homes to life in celebration of World Book Day 2023 - but how many of them can you guess? Jay Gatsby's mansion in'The Great Gatsby' (pictured) is described as a'colossal affair by any standard' and an'imitation of some Hôtel de Ville in Normandy' Daisy Buchanan's estate in'The Great Gatsby' (pictured) is described as a'cheerful red-and-white Georgian Colonial mansion', as well as'elaborate', 'bright' and'rosy-coloured' The above two houses are depictions of those from'The Great Gatsby', a novel set in 1922 that follows the life of mysterious millionaire Jay Gatsby.

Computers can now mimic everything from literary characters to Nirvana songs.

Irina Raicu is the director of the Internet Ethics program (@IEthics) at the Markkula Center for Applied Ethics. It is a truth universally acknowledged--or at least a belief shared by many artificial intelligence and machine learning researchers--that, given a vast database and sophisticated modeling, an algorithm will be able to predict the behavior of individual human beings. Jane Austen might seem like the wrong authority to turn to in order to dispute this. But she does have some relevant insights on the topic. You might remember that in her novel Pride and Prejudice Austen features a heroine named Elizabeth who has a lot of interactions with a character named Mr. Darcy, whom she eventually marries.

Computer pioneer and codebreaker Alan Turing will feature on the new design of the Bank of England's £50 note. He is celebrated for his code-cracking work that proved vital to the Allies in World War Two. The £50 note will be the last of the Bank of England collection to switch from paper to polymer when it enters circulation by the end of 2021. The note was once described as the "currency of corrupt elites" and is the least used in daily transactions. However, there are still 344 million £50 notes in circulation, with a combined value of £17.2bn, according to the Bank of England's banknote circulation figures.

Computer pioneer and codebreaker Alan Turing will feature on the new design of the Bank of England's £50 note. He is celebrated for his code-cracking work that proved vital to the Allies in World War Two. The £50 note will be the last of the Bank of England collection to switch from paper to polymer when it enters circulation by the end of 2021. The note was once described as the "currency of corrupt elites" and is the least used in daily transactions. However, there are still 344 million £50 notes in circulation, with a combined value of £17.2bn, according to the Bank of England's banknote circulation figures.

Computer pioneer and codebreaker Alan Turing will feature on the new design of the Bank of England's £50 note. He is celebrated for his code-cracking work that proved vital to the Allies in World War Two. The £50 note will be the last of the Bank of England collection to switch from paper to polymer when it enters circulation by the end of 2021. The note was once described as the "currency of corrupt elites" and is the least used in daily transactions. However, there are still 344 million £50 notes in circulation, with a combined value of £17.2bn, according to the Bank of England's banknote circulation figures.

Cross-referencing, which links passages of text to other related passages, can be a valuable study aid for facilitating comprehension of a text. However, cross-referencing requires first, a comprehensive thematic knowledge of the entire corpus, and second, a focused search through the corpus specifically to find such useful connections. Due to this, cross-reference resources are prohibitively expensive and exist only for the most well-studied texts (e.g. religious texts). We develop a topic-based system for automatically producing candidate cross-references which can be easily verified by human annotators. Our system utilizes fine-grained topic modeling with thousands of highly nuanced and specific topics to identify verse pairs which are topically related. We demonstrate that our system can be cost effective compared to having annotators acquire the expertise necessary to produce cross-reference resources unaided.

"It is a truth universally acknowledged that Jane Austen has been filling women with unrealistic expectation of men since 1813. Zoe Bunsen was one such woman. Twenty-eight and single, she'd never encountered a man as compelling as Mr. Darcy…" BUILDING MR. DARCY by Ashlinn Craven had me smiling from the onset. As a Jane Austen fan, I loved the idea of creating an AI (think Siri) who is the 2-D equivalent of a modern Mr. Darcy (well, mostly 2-D).