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The Lead: A Provocative Debut Rozie Kelly's frank and feisty debut novel, Kingfisher, has been shortlisted for this year's Women's Prize for Fiction and begins with a case of lust at first sight. The unnamed narrator, a 35-year-old writer, becomes infatuated with a famous poet 17 years his senior, setting the stage for a complex exploration of desire, power dynamics, and unconventional relationships. The Novel's Premise: An Unconventional Attraction The novel introduces us to a "beautiful" 35-year-old writer in a complicated but loving relationship with the equally beautiful but somewhat boring Michael. The object of his sudden attraction is a renowned poet running a popular course at the same university. Despite barely knowing her, he experiences an intense desire "to be inside her," expressing surprise at his own reaction: "A woman! What was the world coming to?" The narrator's infatuation is initially fueled by the poet's success, wealth, and fame, though he also admits to wanting to subjugate her, "to push her down, to render her imperious intelligence stupid with the weight of my body." Thematic Exploration: Love, Lust, and Power Kelly shrewdly explores the different forms love and lust can take, complicated by shifting power dynamics. The relationship begins when the poet and narrator meet to discuss his (nonexistent) poetry collection, leading to an unexpected night together. As their relationship develops, particularly after the poet receives a terminal cancer diagnosis, they settle into the rhythms of a loving relationship. The narrative also explores the narrator's complicated relationship with his racist and homophobic mother, Hetty, confined to a care home, and his deteriorating relationship with Michael, who eventually leaves him for a younger partner. Narrative Structure: Stylistic Uncertainties Despite a confident start and intriguing premise, Kingfisher suffers from narrative inconsistencies. Interesting characters are established then forgotten, and narrative threads are never fully developed. The novel struggles with tonal shifts, moving from "bracing language and violent desires" to "bedside solicitude and quiet domesticity." A late-stage shift into gothic fantasy further destabilizes the narrative, with the ending dangerously approaching "it was all a dream" territory. Throughout, the narrator questions the nature of their relationship: "Who's using who here, do we think?" The answer, as presented in the novel, is that both characters are using each other, as "writers on the make; everyone is potential copy." Critical Assessment: Verve Without Fire Kingfisher possesses considerable verve and energy, demonstrating Kelly's willingness to take risks and embrace absurdity. The novel "crackles and sparks," but ultimately "never quite catches fire." While the book successfully challenges conventional narratives about love and desire, particularly through its exploration of non-traditional relationships and power dynamics, it fails to maintain its initial momentum. The narrative uncertainty and inconsistent tone prevent what could have been a groundbreaking exploration of contemporary relationships from achieving its full potential.

The Battle for Content Ownership: CNN Sues PerplexityUnited States news channel CNN has initiated a federal lawsuit against Perplexity in New York, alleging that the AI search engine provider is unlawfully distributing its copyrighted content. This legal action marks a significant escalation in the ongoing conflict between traditional media and the rapidly evolving generative AI sector.Allegations of Unlawful Content DistributionThe complaint, filed on Thursday, alleges that Perplexity unlawfully copied thousands of CNN stories, videos, and images to power its products. The lawsuit claims the company distributes "identical or substantially similar" content, effectively repurposing original reporting without permission. CNN is seeking an unspecified amount of monetary damages and a court order to block Perplexity from violating intellectual property rights.The High-Stakes Economics of AI DataThis legal battle centers on the valuation of data versus the protection of creative work. Perplexity, valued at tens of billions of dollars, has defended its practices by stating, "You can’t copyright facts." However, CNN argues that while facts may not be copyrightable, the specific reporting, curation, and presentation of news are protected by copyright law. The lawsuit emphasizes that Perplexity exploits the economic incentives that make original newsgathering possible.Shifting the Paradigm of AI TrainingThis case is not isolated; it is part of a broader industry trend. Since the launch of OpenAI’s ChatGPT in 2022, news publishers have faced existential threats regarding their content being scraped for training large language models. CNN's lawsuit joins a growing list of high-stakes cases brought against AI firms, including The New York Times, Reddit, and Dow Jones. Consequently, many news firms are now pivoting toward signing licensing deals and partnerships with Big Tech to ensure verified access and compensation.The Future of AI-News IntegrationThe outcome of this lawsuit will likely set a precedent for how AI companies handle copyrighted material. As legal challenges mount, the industry is moving away from "scraping" and toward "licensing." We can expect a future where AI search engines must pay for access to premium news content, fundamentally changing the revenue models of digital media.

The Failure of US Garbage Incinerators The nation's garbage incinerators are largely failing to eliminate Pfas 'forever chemicals' air pollution, and are putting people in largely low-income neighborhoods at risk, public health advocates and independent experts warn. The Industry's Misleading Claims A new industry trade group report alleges Minnesota's incinerators are reducing their forever chemical emissions by 99.6%. However, experts say the report is full of bad assumptions, incomplete data, and misleading language. The Health Risks of Pfas Pollution Pfas are a class of at least 16,000 compounds that have been linked to cancer, birth defects, decreased immunity, high cholesterol, kidney disease, and a range of other serious health problems. They are dubbed 'forever chemicals' because they do not naturally break down in the environment. The Impact on Low-Income Neighborhoods The incinerators are often located in low-income neighborhoods, putting vulnerable populations at risk. 'This trash becomes the problem of the poor and marginalized to deal with in their bodies,' said Nazir Khan, executive director of the Minnesota Environmental Justice Table. The Need for Stricter Regulations Experts say that stricter regulations are needed to address the issue of Pfas pollution. 'I'm not aware of any industrial-scale commercial incinerator that solves this problem,' said Michael Youhana, an attorney with the non-profit Earthjustice.

Leïla Slimani’s Madrid Residency: Finding Light in Goya’s DarknessFrench-Moroccan author Leïla Slimani is currently in Madrid, utilizing the Museo del Prado as a sanctuary for her next literary work. Her deep dive into Francisco Goya’s Black Paintings reveals a writer obsessed with the darkness of the human condition.The Residency and the Black PaintingsSlimani is participating in Writing the Prado, a residency inviting international authors to produce new work inspired by the museum. She is particularly drawn to Goya’s later works, which depict violence, fate, and societal disillusionment. Slimani explains that Goya painted the future, seeing things others did not, and that his bleak outlook resonates with her own preoccupations.Location: Museo del Prado, MadridResidency: Writing the PradoPrimary Inspiration: Goya’s Black Paintings (e.g., Saturn Devouring His Son)The Cultural Impact of Literary PrestigeWhile the article focuses on a residency, Slimani’s career trajectory highlights the immense cultural capital of literary recognition. Her success is not just personal but systemic.Award: First Moroccan woman to win the Prix Goncourt (2016) for Lullaby.Role: Appointed by President Emmanuel Macron as a representative for promoting French language and Francophone culture.Her presence in Madrid as a cultural ambassador demonstrates how high-profile authors bridge the gap between national identity and global literature.The Intersection of Trauma and Artistic ExpressionSlimani’s work is driven by a formative family trauma: the arrest and imprisonment of her father on financial charges. She describes her early impulse to write as driven by anger and a desire for revenge.“Literature is probably the best way to give justice back to people who are not understood or listened to,” she says. Her ability to transform personal pain into universal empathy—allowing readers to feel tenderness for characters they might reject in real life—defines her impact on modern literature.The Future of Cross-Cultural Literary InspirationSlimani is currently working on a new project inspired by the Prado, signaling a continued evolution in her style. Her upcoming work, I’ll Take the Fire, focuses on her family history, suggesting that her future writing will continue to explore the tension between nostalgia and the necessity of moving forward.As she navigates the complexities of being a French-Moroccan writer, Slimani’s journey suggests a future where literature will increasingly serve as a tool for deconstructing rigid cultural identities and embracing the contradictions of the human experience.

Global Study Quantifies Gender Attractiveness Gap Across AgesThe research team led by Dr Eugen Wassiliwizky at the Max Planck Institute for Empirical Aesthetics compiled the world’s largest dataset on facial attractiveness, drawing from 52 studies across 76 countries.Numbers Behind the Gap: 1.5 Million Ratings Reveal 60% Preference1.5 million attractiveness ratings17,000 distinct faces evaluated30,000 individual ratersAverage female face rated more attractive than 60% of male facesGap strongest in Western cultures, present across all sexual orientationsWhen participants rated themselves, the gender gap vanished, underscoring the role of external perception.Implications for Evolutionary Theory and Social PerceptionThe findings revive debate over Darwinian sexual selection. While Darwin noted male ornamentation in many species, he considered humans an exception where male competition dominated. This study suggests a universal bias toward rounder, more feminine facial structures, which may be linked to infant‑like features rather than purely cultural norms.Historical language—"the fairer sex", "le beau sexe"—reflects a long‑standing perception that the research now quantifies.Future Research Directions and Societal ShiftsAs the attractiveness gap diminishes after age 80, researchers hypothesize that facial structural differences shrink with age, reducing perceived bias. Ongoing work will explore:Neuro‑cognitive responses to facial roundness across agesCross‑cultural variations beyond the current datasetPotential impacts on age‑related social dynamics and media representationThe study, published in Proceedings of the Royal Society B, calls for cautious interpretation but highlights a robust, global pattern that challenges purely cultural explanations.

Why a Living AI Glossary Matters NowArtificial intelligence is reshaping every industry, but its rapid evolution has spawned a parallel explosion of terminology that can leave even seasoned technologists feeling insecure. TechCrunch’s new glossary aims to provide a single, regularly‑updated reference that translates the most common AI buzzwords into plain language.Key Definitions from AGI to RLHFThe article walks readers through a spectrum of concepts, including:Artificial General Intelligence (AGI) – AI that outperforms humans on most economically valuable tasks, as defined by OpenAI and Google DeepMind.AI Agent – An autonomous tool that can perform multi‑step tasks such as expense filing, ticket booking, or code maintenance.API Endpoints – “Buttons” that let software components interact, enabling agents to automate third‑party services.Chain‑of‑Thought Reasoning – A technique that breaks problems into intermediate steps to improve accuracy.Compute – The hardware (GPUs, CPUs, TPUs) that powers AI model training and inference.Deep Learning – Multi‑layered neural networks that learn features directly from data.Diffusion – The process behind many generative AI models that learns to reverse noise‑added data.Distillation – A teacher‑student method for creating smaller, faster models like GPT‑4 Turbo.Fine‑Tuning – Adding task‑specific data to a pre‑trained model to improve performance.GAN – Generative Adversarial Networks that pit a generator against a discriminator to produce realistic outputs.Hallucination – When models generate inaccurate or fabricated information.Inference – Running a trained model to make predictions, often accelerated by specialized hardware.LLM – Large Language Models that power assistants such as ChatGPT, Claude, Gemini, and Llama.Memory Cache (KV Caching) – An optimization that stores intermediate calculations to speed up inference.Open Source vs. Closed Source – The debate over publicly available model code (e.g., Meta’s Llama) versus proprietary systems (e.g., OpenAI’s GPT).Parallelization – Executing many calculations simultaneously, a cornerstone of modern AI hardware.RAMageddon – The current shortage of memory chips driven by AI data‑center demand.Recursive Self‑Improvement (RSI) – Models that can redesign themselves, a potential step toward singularity.Reinforcement Learning from Human Feedback (RLHF) – Training models with reward signals to improve helpfulness and safety.Tokens & Throughput – The basic units of text processing that determine cost and performance.Quantifying the AI Vocabulary ExplosionThe glossary covers more than 30 distinct terms, each accompanied by concise explanations and links to deeper resources. By cataloguing this breadth, the piece highlights how quickly the AI lexicon has expanded within just a few years of mainstream adoption.Implications for Developers, Investors, and the PublicUnderstanding this terminology is no longer optional. For developers, clear definitions accelerate product building and reduce miscommunication when integrating APIs or deploying agents. Investors gain a sharper lens for evaluating startup pitches that hinge on concepts like fine‑tuning or distillation. Meanwhile, the broader public can better assess claims about “AGI” or “hallucinations,” mitigating hype‑driven misinformation.Future of AI Terminology and Industry AdoptionTechCrunch positions the glossary as a “living document,” promising regular updates as new techniques (e.g., emerging diffusion variants or next‑gen RLHF methods) appear. As AI systems become more autonomous and specialized, the vocabulary will continue to evolve, making ongoing education essential for anyone interacting with the technology.

The Film’s Core Concept: Turning Internet Liminality into CinemaThe Guardian review details how *Backrooms* follows architect‑turned‑store‑owner Clark (played by Chiwetel Ejiofor) as he discovers a portal to an endless maze of fluorescent‑lit, drop‑ceiling rooms. The film expands the viral “backrooms” meme—originally a series of YouTube shorts made with Blender and After Effects—into a feature‑length narrative while retaining its minimalist visual language.Production Insight: A 20‑Year‑Old Director’s Low‑Budget MasteryDirector Kane Parsons, the youngest ever to helm an A24 feature, built the original series using free software, demonstrating how low‑cost tools can generate high‑impact horror aesthetics. The movie’s production emphasizes practical set design—repeating office‑style corridors, yellow lighting, and drop ceilings—to evoke the “junkspace” described by architects like Rem Koolhaas.Financial Snapshot: A24’s Continued Investment in Indie HorrorBudget details were not disclosed, but A24’s recent horror slate averages $5‑10 million per film.Box‑office expectations align with the studio’s strategy of modest budgets paired with strong niche appeal.Why It Matters: Architecture as a New Horror FrontierThe film taps into academic concepts such as Mark Augé’s “non‑places” and Juhani Pallasmaa’s idea of architecture as mental space, positioning the built environment itself as the antagonist. By visualising bureaucratic infinity, *Backrooms* expands horror beyond monsters to the sterile, endless corridors of modern capitalism.Looking Ahead: The Future of Liminal‑Space HorrorParsons’ success suggests a growing appetite for horror that interrogates everyday environments. Expect more studios to mine internet subcultures and architectural theory, blending low‑budget VFX with philosophical storytelling to attract both genre fans and critical audiences.

The Approval of the AmendmentMexico's lower house has approved a constitutional amendment to allow the nullification of elections in cases of foreign interference. The proposal passed the Chamber of Deputies with 307 votes in favour, 128 against, and one abstention.Defining Foreign InterferenceThe reform defines foreign interference as "illicit financing, propaganda, the systematic dissemination of disinformation, digital manipulation, and the intervention of foreign governments or agencies". It also covers acts of political, economic, diplomatic, or media pressure intended to influence public opinion.The Impact on ElectionsThe amendment, which is unlikely to affect the next federal elections in June 2027, still requires Senate approval to take effect. Electoral reforms must be enacted at least 90 days before the start of the election process in order to apply.Reactions from PoliticiansRicardo Monreal, the leader of the ruling Morena party in the lower house, defended the measure as a necessary safeguard of Mexico's democracy. Opposition lawmakers accused the governing party of overstating the threat to justify the reform.Concerns and CriticismsPresident Claudia Sheinbaum recognised previous instances of foreign funding for local candidates and organisations in Mexico. However, some politicians questioned how the new rules would be applied in practice, warning that the broad language of the amendment could create uncertainty.

The Rise of Recursive Self-Improvement in AIThe word "recursion" is the latest buzzword in AI circles. Two separate startups have taken on the name, and many more have started referencing recursive self-improvement (RSI) in their roadmaps. Like AGI before it, RSI has become a three-letter byword for a cataclysmic AI takeoff – even if there's still a little disagreement about what it exactly means.In basic terms, RSI refers to an AI system that can continuously upgrade itself. Once AI systems can manage the upgrade cycle better than humans, the process can become a closed loop, limited only by the compute power they can access, and humans are no longer necessary or even helpful.Scary or not, that's a vision that a lot of AI labs are eager to chase.Key Players Pursuing Recursive SystemsEarlier this month, well-known AI researcher Richard Socher launched the aptly named Recursive Superintelligence with RSI as an explicit goal. "Our main focus is to build truly recursive, self-improving superintelligence at scale," Socher told TechCrunch at launch, "which means that the entire process of ideation, implementation, and validation of research ideas would be automatic."A number of other prominent researchers are already chasing that same goal, hoping for a breakthrough that will make recursive self-improvement possible.One of the most prominent is Andrej Karpathy, a legendary figure from Tesla and OpenAI, who is using agent swarms to train LLMs on simple tasks for a project he calls Auto-Research. Karpathy has been unusually open about the project, tweeting about milestones regularly and making the building blocks available through a public GitHub repo. So far, the work has mostly been confined to making minor improvements on a GPT-2 scale model — as Karpathy noted in March, "It's not novel, ground-breaking 'research' (yet)" — but it's been enough to convince lots of other researchers to follow the RSI dream. And with Karpathy now working on pre-training at Anthropic, he will have plenty of opportunity to apply the idea at a larger scale.Adaption — founded by Cohere and Google alum Sara Hooker — recently launched a similar tool called AutoScientist in an effort to automate frontier training. Like Karpathy's auto-researchers, the system trains agents to make incremental improvements — but for Adaption, the goal is to make it easier to train a full-scale frontier model. If those same researchers start to push the frontier forward, the system could quickly spiral into something very much like RSI.Disarray founder Doris Xin drew more specific RSI interest when her self-trained machine learning agent took home 28 medals in a recent Kaggle competition, beating out many human-trained agents. As she sees it, the major challenge is reliability."I would argue, given infinite compute and infinite time horizon, we are already there," Xin told me. "I want to make an argument that this is not a creative endeavor, really. It's just a lot of meat-and-potatoes engineering."The Current State of Self-Improving AIThere's also plenty of evidence that the AI industry isn't very close to recursive systems in any meaningful way — and is still grappling with talking to a wary public about its progress. So Google CEO Sundar Pichai basically admitted in a recent podcast interview."It's a continuum, and we are all definitely making progress," Pichai said. "But in the way people describe RSI, that would represent a next level of acceleration and would have a lot of implications, but we aren't quite there yet."But the continuum includes an awful lot of self-improving AI systems.In January, one of Anthropic's lead programmers for Claude Code estimated that "close to 100%" of his team's code was written by the tool — a frank admission that Claude Code was literally writing itself.Just because engineers are using an AI tool doesn't mean the tool can replace them — but Anthropic seems to be getting close to replacing engineers too. In a recent survey tied to the Mythos preview, five out of 18 Anthropic engineers believed that, with harness improvements, this version of Mythos could soon substitute for an L4 engineer — a midlevel programmer who can take on involved projects without supervision.Still, there were some of the same weaknesses you might expect."Some of Claude's major reported weaknesses compared to an L4 include: self-managing week-long ambiguous tasks, understanding org priorities, taste, verification, instruction-following, and epistemics," the report reads.In other words, its weaknesses are everything involved with self-direction, which is the cornerstone for RSI. But sure, for everything else, Claude is ready to step right in.Expert Perspectives on RSI TimelinesJust like the AGI term before it, the AI industry also can't tell us how far away it is from showcasing a meaningful recursive system. When Georgetown's Center for Security and Emerging Technology assembled a group of experts to study RSI last year, the group found a major split in assessments — some expecting an imminent "superintelligence" style explosion while others expected slower progress and an eventual plateau. But all agreed that recursion made the future especially difficult to predict.Helen Toner, director of CSET and a former board member at OpenAI, told TechCrunch that simply using AI tools to do AI research isn't enough to qualify as RSI. "They're just using AI for as much as they can," Toner told TechCrunch. "And I think that is different from the classic definition of RSI, which is really that there are no humans needed."Toner pointed to a recent post by METR's Ajeya Cotra, which distinguishes different milestones on the path to the AI research takeover. One step, which Cotra calls "adequacy," would come when the system can still perform research after all humans are removed — even if the resulting research isn't as valuable or efficient. "Parity" comes when an AI-only system is as good at research as a human-only system. "Supremacy," the final stage, comes when an AI-only system outperforms a collaborative system between humans and AI.Ultimately, Cotra concludes that AI is very close to the adequacy threshold of being able to produce some work on its own — similar to the incremental changes made by Karpathy's Auto-Research system. "I wouldn't be totally shocked if you told me this milestone had already passed, and I expect it to happen in the next couple years," Cotra wrote.She was less clear on when parity will come, but once it does, she thinks it would "massively accelerate the pace of AI progress, leading to AI research supremacy within another year."The Challenges Ahead for Recursive AIWith so much of AI built on scaling laws, there's a strong tendency to think RSI will follow the same curve. Toner thinks that many of those pursuing AI research and development via RSI "think of it as a pretty smooth ladder, where you can just keep scaling up."But even if AI researchers are able to make incremental improvements like Karpathy's auto-researchers, there will be larger challenges in handing off the whole process of research. Toner put it in terms of the history of computing, which has seen human beings handing off more and more of the process while still directing things from the top."We went from machine languages to assembly language and compiled languages; you're getting further and further from the guts of the computer," Toner said. "But the human is still, in some intuitive sense, running the show."Moving beyond that paradigm will take significant challenges, both in engineering and alignment. But even with the massive investments happening, there's no infinite compute available — and the basic trade-off between human labor and machine intelligence will be hard to overcome.The Future of Recursive Self-ImprovementAs for a total recursive AI system of apocalyptic visions? The only thing researchers essentially agree on is that, like AGI, it's not here yet.