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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.

The Lead: Vertu's AI-Powered Foldable Targets Executive Market Luxury smartphone brand Vertu has unveiled the Alphafold, a foldable phone powered by an AI agent designed specifically for executives managing business operations on the move. The device represents Vertu's latest attempt to reinvent itself for the AI era, combining luxury materials with enterprise-focused AI capabilities to target the high-end business market. The Event Details: Luxury Meets AI: The Alphafold's Enterprise Capabilities The Alphafold features Hermes Agent, built on the open-source Hermes project by Nous Research, which can connect to enterprise systems like ERP and CRM. The AI agent coordinates tasks such as approvals, scheduling, sales tracking, travel planning, and operational reporting through natural-language prompts. The device can route requests across multiple AI models including OpenAI's GPT, Anthropic's Claude, Google's Gemini, and selected open-source models, while integrating with more than 80 apps and dozens of native phone functions for cross-platform workflows. Vertu has emphasized the device's privacy-focused architecture featuring a proprietary A5 security chip designed to isolate authentication keys, biometric credentials, and sensitive enterprise information from the main operating system. The company states that commercially sensitive data can be processed locally on the device, while prompts sent to external AI models are redacted or tokenized before leaving the phone. The Data Analysis: Premium Pricing Strategy in the Smartphone Market The Alphafold starts at $6,880 for the calfskin version, with higher-end models featuring bespoke finishes including alligator leather, 18K gold, and natural diamond accents. Vertu's highest-end standard model is currently priced at $46,800, with further customization options available. This pricing strategy positions Vertu firmly in the ultra-premium segment of the smartphone market. While foldable smartphones remain a niche segment globally—with IDC data showing approximately 20 million units shipped in 2025, accounting for less than 2% of total smartphone shipments—Vertu is betting that the combination of luxury materials and AI capabilities will justify its premium pricing. The average price of foldable smartphones was about $1,300 last year, roughly three times the price of non-foldable smartphones. The Impact Analysis: How AI is Transforming Executive Productivity Vertu CEO Molly Ma highlighted that existing AI features on smartphones from major manufacturers remain focused largely on consumer tools such as image editing and voice assistance, leaving room for more advanced AI-agent workflows tied to enterprise systems. The Alphafold aims to address this gap by providing executives with a device that can seamlessly integrate with their business operations and workflows. The device's larger foldable display (8.05-inch inner screen and 6.53-inch outer screen) is better suited for multitasking and productivity-oriented experiences, according to Kiranjeet Kaur, associate research director for mobile phones research at IDC. However, she noted that enterprise AI adoption on smartphones still lags behind computers, with most enterprise smartphone decisions continuing to be driven by ecosystem integration and device management support rather than AI capabilities. The Prediction: The Future of Luxury AI-Powered Mobile Devices The Alphafold represents Vertu's significant step forward from its previous AI-focused device, Agent Q, with Ma noting that AI-agent technology has matured rapidly over the past year, with improvements in memory, automation, and app integration. While the company has not yet undergone third-party security audits for the device, it has confirmed that independent audits and certification remain on its security roadmap. As the first 115-unit batch of Vertu's Alphafold begins shipping across major markets including the U.S., the device will serve as a test case for whether there's a market for luxury smartphones with enterprise AI capabilities. If successful, Vertu's approach could inspire other manufacturers to develop similar devices targeting the executive market, potentially accelerating the integration of AI agents into mobile workflows.

Google’s AI Overview Stumbles on Simple Letter Counting Google’s newly rolled‑out AI Overview feature in Search incorrectly counted letters in everyday words – claiming there are two “P”s in “Google”, one “r” in “poop”, and even misspelling “journalism”. The blunders highlight a long‑standing weakness of large language models (LLMs) when it comes to exact spelling. The Miscounted Letters Behind the New Search AI “Google” – AI said 2 Ps (actual: 0) “poop” – AI said 1 r (actual: 0) “journalism” – AI said 2 d’s (actual: 0) U.S. President’s last name – AI reported 1 P but rendered “t‑r‑p‑u‑m” Quantifying the Miscounts: Numbers Behind the Errors Beyond the anecdotal examples, the AI also produced a faulty definition for the word “disregard”, responding with “Understood. Let me know whenever you have a new prompt or question!” This illustrates that token‑based encoding can produce nonsensical outputs even when the input is a single word. Implications for Search Trust and AI Adoption Google’s AI‑driven overhaul aims to make generative responses the centerpiece of its 29‑year‑old search product. Repeated factual and spelling errors risk eroding user confidence, especially after earlier AI Overviews cited satirical sources and gave absurd advice such as “eat rocks”. Trust in AI‑generated answers remains a critical hurdle. What’s Next for Google’s Generative Search? Google told TechCrunch it is “working to fix this particular issue” and will likely refine its tokenizer and post‑processing pipelines. Industry observers expect incremental improvements rather than a complete architectural shift, meaning users may continue to see occasional glitches while the broader AI‑search strategy matures.

The Corporate AI MirageUK communications executives are reporting a surge in demand from non-tech companies to be rebranded as artificial intelligence specialists. Public relations professionals describe this trend as a desperate attempt to capitalize on the current technology buzz, often stretching the truth to secure media coverage for brands that have little genuine connection to the sector.The Mechanics of 'AI Washing'The phenomenon, often termed 'AI washing,' involves companies retrofitting the 'AI' label onto existing products or services that rely on basic automation rather than advanced generative intelligence. This rebranding effort has led to bizarre applications of the technology, such as AI-powered basketball hoops and lasers designed to protect women on underground platforms.AllBirds recently 'pivoted' to acquiring AI graphics processing units.Genetics companies are hyping AI-powered blood tests.Property firms are marketing handheld scanners that generate floor plans as AI tools.The PR Backlash and Market FatigueThe saturation of the market is causing significant friction within the PR industry. Account directors report that roughly 50% of the AI-related pitches they send out are unwanted, as journalists and executives become numb to the language. This fatigue is compounded by the skepticism surrounding claims of 'AI-driven' products that are merely better automation.Even high-profile corporate figures are under scrutiny. The chief executive of Standard Chartered recently apologized for describing workers displaced by AI as 'lower-value human capital,' highlighting the tension between corporate efficiency strategies and public perception.Future Outlook: From Hype to SubstanceWhile stock market investors have largely shrugged off recent jitters over the AI boom, the long-term viability of 'AI washing' is questionable. As the industry matures, the gap between genuine AI integration and superficial rebranding will likely widen, forcing companies to either innovate or face further reputational damage.