'Pryce's Buoy' is an unfinished romance tale between two men. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. 'Pryce's Buoy' is a six-page draft clearly based on Turing's own experiences. Breakthroughs, discoveries, and DIY tips sent six days a week. By signing up, you confirm you are 16+, will receive newsletters and promotional content and agree to our Terms of Use and acknowledge the data practices in our Privacy Policy .

Image caption, Prof Sarah Dillon studied Alan Turing's short story, Pryce's Buoy A story written by World War Two codebreaker Alan Turing suggests he was a man with playful humour embracing his homosexuality, according to new analysis. The six-page hand-written short story, Pryce's Buoy, has been transcribed in full for the first time and studied by Sarah Dillon, professor of literature at the University of Cambridge. Dillon said the story offered fresh insights that challenge the view of Turing as an isolated, lonely genius. We've distilled a version of Turing that is a two-dimensional stereotype, she added. Turing played a crucial role in the Allies' victory over Nazi Germany in World War Two by helping to crack codes and deciphering the infamous Enigma machine at Bletchley Park.

Check your subscription status, update your details and more. Can you guess the origin of 7 tiny species on a fictional archipelago? Late last year, between one news event and another (honestly, who can remember?), Feedback was delighted to discover the Journal of Imaginary Research . This is an online magazine publishing "short works of fiction", but only if they take the form of "imaginary research abstracts".

Miniature people have been a staple of science fiction and fantasy going all the way back to Jonathan Swift's, and shrunken characters have taken the spotlight in everything from classic Hollywood movies like and to family-friendly blockbusters like and . References to these movies and others are strewn throughout the new Peacock limited series, but the drawn-out, 10-episode show isn't a particularly worthwhile addition to the sci-fi shrinking canon. Taking only the title and basic premise from Manuel Gonzales's 2014 short story, stars Elizabeth Banks as Lindy Littlejohn, a once-prominent author who now works as a university professor and has been overshadowed by her scientist husband Les (Matthew Macfadyen). Lindy, you see, feels metaphorically small in both her personal and professional lives, and is about to become literally small following an accident - or it? The most pressing problem for Lindy is that Les has yet to develop a stable antidote to his formula, and everything that he has attempted to return to its original size thus far has almost immediately exploded.

From Claire North's new novel to a 10th anniversary edition of a brilliant Adrian Tchaikovsky book, there's lots to watch out for in November's science fiction Claire North's Slow Gods follows a deep-space pilot We'll need to get our skates on if we're to keep up with all the new science fiction published in November. And I am creeped out by the idea at the heart of Grace Walker's . Everything feels frightening this month - perhaps the sci-fi world is still in Halloween mode. It sounds poignant, moving and beautiful, and without any supernatural scares. Emily H. Wilson is wild for this sci-fi novel: I've not heard our sci-fi columnist recommend a book so wholeheartedly in all the time she's written for us.

Evaluating the creative capabilities of large language models (LLMs) in complex tasks often requires human assessments that are difficult to scale. We introduce a novel, scalable methodology for evaluating LLM story generation by analyzing underlying social structures in narratives as signed character networks. To demonstrate its effectiveness, we conduct a large-scale comparative analysis using networks from over 1,200 stories, generated by four leading LLMs (GPT-4o, GPT-4o mini, Gemini 1.5 Pro, and Gemini 1.5 Flash) and a human-written corpus. Our findings, based on network properties like density, clustering, and signed edge weights, show that LLM-generated stories consistently exhibit a strong bias toward tightly-knit, positive relationships, which aligns with findings from prior research using human assessment. Our proposed approach provides a valuable tool for evaluating limitations and tendencies in the creative storytelling of current and future LLMs.

This study investigates how large language models, in particular LLaMA 3.2-3B, construct narratives about Black and white women in short stories generated in Portuguese. From 2100 texts, we applied computational methods to group semantically similar stories, allowing a selection for qualitative analysis. Three main discursive representations emerge: social overcoming, ancestral mythification and subjective self-realization. The analysis uncovers how grammatically coherent, seemingly neutral texts materialize a crystallized, colo-nially structured framing of the female body, reinforcing historical inequalities. The study proposes an integrated approach, that combines machine learning techniques with qualitative, manual discourse analysis.

With the advance of Artificial Intelligence (AI), Large Language Models (LLMs) have gained prominence and been applied in diverse contexts. As they evolve into more sophisticated versions, it is essential to assess whether they reproduce biases, such as discrimination and racialization, while maintaining hegemonic discourses. Current bias detection approaches rely mostly on quantitative, automated methods, which often overlook the nuanced ways in which biases emerge in natural language. This study proposes a qualitative, discursive framework to complement such methods. Through manual analysis of LLM-generated short stories featuring Black and white women, we investigate gender and racial biases. We contend that qualitative methods such as the one proposed here are fundamental to help both developers and users identify the precise ways in which biases manifest in LLM outputs, thus enabling better conditions to mitigate them. Results show that Black women are portrayed as tied to ancestry and resistance, while white women appear in self-discovery processes. These patterns reflect how language models replicate crystalized discursive representations, reinforcing essentialization and a sense of social immobility. When prompted to correct biases, models offered superficial revisions that maintained problematic meanings, revealing limitations in fostering inclusive narratives. Our results demonstrate the ideological functioning of algorithms and have significant implications for the ethical use and development of AI. The study reinforces the need for critical, interdisciplinary approaches to AI design and deployment, addressing how LLM-generated discourses reflect and perpetuate inequalities.

Alex Garland's 2015 film Ex Machina and Sierra Greer's Annie Bot (pictured below) follow a long tradition of female robots This year's Arthur C. Clarke award for the year's best science fiction novel was awarded last month to Sierra Greer's Annie Bot. Over the course of the novel, Annie, a sentient sex robot programmed to adore her selfish owner, gradually develops a sense of personhood – but she is hardly the first artificial woman to do so. Although the earliest fictional female robots were little more than wind-up toys, they have steadily gained substance until more recent artificial women, like Annie, have become as complex as their human counterparts. Artificial people are both ancient and ubiquitous. "Basically every culture around the world since recorded history has told stories about automatons," says Lisa Yaszek at the Georgia Institute of Technology.

Recent studies comparing AI-generated and human-authored literary texts have produced conflicting results: some suggest AI already surpasses human quality, while others argue it still falls short. We start from the hypothesis that such divergences can be largely explained by genuine differences in how readers interpret and value literature, rather than by an intrinsic quality of the texts evaluated. Using five public datasets (1,471 stories, 101 annotators including critics, students, and lay readers), we (i) extract 17 reference-less textual features (e.g., coherence, emotional variance, average sentence length...); (ii) model individual reader preferences, deriving feature importance vectors that reflect their textual priorities; and (iii) analyze these vectors in a shared "preference space". Reader vectors cluster into two profiles: 'surface-focused readers' (mainly non-experts), who prioritize readability and textual richness; and 'holistic readers' (mainly experts), who value thematic development, rhetorical variety, and sentiment dynamics. Our results quantitatively explain how measurements of literary quality are a function of how text features align with each reader's preferences. These findings advocate for reader-sensitive evaluation frameworks in the field of creative text generation.