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.

Larry Niven is one of the biggest names in the history of science fiction, and it was a privilege to interview him via Zoom at his home in Los Angeles recently. His 1970 novel Ringworld is the latest pick for the New Scientist Book Club, but he has also written a whole space-fleet-load of novels and short stories over the years, including my favourite sci-fi of all time, A World Out of Time. At 87 years of age, he is very much still writing. I spoke to him about Ringworld, his start in sci-fi, his favourite work over the years, his current projects and whether he thinks humankind will ever leave this solar system. This is an edited version of our conversation.

Bora Chung's Red Sword is set on a disputed planet While there are no big names publishing new science fiction novels this May, there are some real gems nonetheless – including a big tip from me, Grace Chan's near-future Every Version of You. I want to press it into the hands of everyone I know. There are also two fascinating sci-fi-edged thrillers out this month, by Adam Oyebanji and Barnaby Martin, while Catherine Chidgey's creepy The Book of Guilt has intrigued me enough to make it my next read – if it's not ousted by Bora Chung's real history-inspired story of war on an alien planet, Red Sword, that is… Set in late-21st-century Australia, this novel (published in Australia in 2022 but out now more widely) follows Tao-Yi in a world where most people spend their lives in an immersive virtual reality called Gaia. Every morning, she climbs into a pod in her apartment to enter Gaia, where she works and socialises. In the real world, the unrelenting heat of the sun means there are no trees left and hardly any animals: this is a terrifying vision of the future.

Given the growing use of generative artificial intelligence as a tool for creating multilingual content and bypassing both machine and traditional translation methods, this study explores the ability of linguistically trained individuals to discern machine-generated output from human-written text (HT). After brief training sessions on the textual anomalies typically found in synthetic text (ST), twenty-three postgraduate translation students analysed excerpts of Italian prose and assigned likelihood scores to indicate whether they believed they were human-written or AI-generated (ChatGPT-4o). The results show that, on average, the students struggled to distinguish between HT and ST, with only two participants achieving notable accuracy. Closer analysis revealed that the students often identified the same textual anomalies in both HT and ST, although features such as low burstiness and self-contradiction were more frequently associated with ST. These findings suggest the need for improvements in the preparatory training. Moreover, the study raises questions about the necessity of editing synthetic text to make it sound more human-like and recommends further research to determine whether AI-generated text is already sufficiently natural-sounding not to require further refinement.

The new essay collection "Searches: Selfhood in the Digital Age," by Vauhini Vara, opens with a transcript. "If I paste some writing here, can we talk about it?" Her interlocutor, the large language model ChatGPT, responds, "Of course!" The chatbot asks what specific themes it should focus on. "Nothing in particular," Vara replies.