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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.

Sunak’s Financial Literacy Initiative Stirs ControversyPrime Minister Rishi Sunak has called for a nationwide push to teach children how to handle money, insisting that the UK lags behind countries such as Germany. His broader vision ties financial literacy to an ambitious plan to keep maths in the classroom until the age of 18, sparking a heated debate among educators, former ministers and commentators.Proposed Extension of Maths to Age 18 and Its RationaleSunak’s proposal frames mathematics as the gateway to sound financial decisions. He argues that without a solid grounding in arithmetic, percentages and interest rates, young people cannot navigate inflation, assess risk or detect scams. The plan would make advanced maths a compulsory subject through the end of secondary education, effectively reshaping the national curriculum.Youth Unemployment and Education Gaps: The Numbers Behind the DebateApproximately 1 million 16‑24‑year‑olds are currently not in education, training or employment – roughly one in seven of them hold university degrees.This inactivity rate is double that of Ireland and three times higher than the Netherlands.Recent government measures aim to create 200,000 new apprenticeships, yet the overall transition support for school leavers remains weak.Why the Curriculum Push Could Reshape UK Education and EconomyThe emphasis on compulsory financial numeracy challenges the long‑standing “academic‑first” model of British schooling, which prioritises examinations over practical life skills. Critics warn that making advanced maths mandatory may marginalise students who would benefit more from broader competencies such as health literacy, civic engagement and basic budgeting. If adopted, the policy could influence employer expectations, apprenticeship uptake and long‑term economic productivity.What the Next Five Years May Hold for Financial Literacy in SchoolsShould the government follow through, we can expect a phased rollout of new curricula, teacher training programmes and assessment frameworks centred on real‑world financial scenarios. However, resistance from teachers’ unions and concerns over curriculum overload could delay implementation. In the medium term, successful integration may lower youth financial insecurity and improve labour‑market readiness, while failure could reinforce the gap between academic qualifications and employability.

UK Study Reveals Air Pollution's Impact on Children's Lung DevelopmentResearch shows that air pollution is slowing the lung growth of children in the UK. Scientists tracked the lung function of more than 5,000 people who were born in and around Bristol in the 1990s. Their health was assessed from birth onwards and their lungs were tested as they grew up, at eight and 15 years old and then as adults, aged 24, when their lung function should have reached its maximum.Longitudinal Study Tracks Lung Function From Birth to AdulthoodProf Ann Hansell, of the University of Leicester, who led the study, said: "Much of the evidence on health effects of air pollution relates to adults or pregnancy, but we think it's highly plausible it has impacts on growth and development of children. Those whose lungs didn't grow to maximum potential in childhood may be more vulnerable to the respiratory diseases of later life because they have a lower reserve."Dr Katie Eminson, also of the University of Leicester and a first author of the study, explained: "Lung function was measured using spirometry by trained technicians. Participants were asked to take a deep breath in, then blow out as hard and as fast as possible into a mouthpiece. A machine measured both the amount of air they can breathe out and the speed of that breath, providing an indication of how well their lungs are working."Pollution Exposure Linked to Reduced Lung CapacityThe researchers calculated the children's air pollution exposure in each trimester of pregnancy and then for each year of early childhood. This included particle pollution as well as nitrogen dioxide, a gas that comes mainly from diesel cars and fossil gas boilers.Hansell noted: "We spent literally years creating the particulate air pollution exposure estimates in pregnancy and early life, including sourcing road traffic data from Bristol city council that are not available in the national database."The researchers allowed for other factors that can affect children's health, including premature birth, breastfeeding, parental smoking and home conditions including damp.They found that breathing more air pollution during pregnancy, infancy and early childhood can slow lung development all the way up to early adulthood. The greatest impact was during adolescence, which is the time when lung growth accelerates.Health Implications Extend Beyond Respiratory SystemThose with reduced lung function face multiple health risks. "They are also more vulnerable to poorer health generally," Hansell explained. "For instance, low lung function in adults is associated with the same level of risk of heart disease as having high cholesterol. Research has also shown that people whose lung health has been affected by air pollution may be at greater risk of heart disease."An earlier study found that air pollution was reducing the growth of children's lungs in east London. There, the average nine-year-old's lungs were between 90 and 100 millilitres smaller than they should be—approximately the volume of two hen's eggs.Studies on children in Sweden showed that lung growth increased when air quality improved. Reductions in air pollution might have also allowed the Bristol children's lung growth to return closer to normal rates.Call for Action on Air Quality ProtectionEminson concluded: "While the effects in individual children are small and unlikely to have immediate clinical consequences, they shouldn't be dismissed. Because lung function tends to track from childhood into adulthood, small differences early in life may have implications for long-term health. This reinforces the importance of reducing exposures and protecting children's environments."

The Royal Legacy of Oak ApplesOnce a public holiday celebrating the restoration of the monarchy, Oak Apple Day (May 29th) marks a fascinating natural phenomenon where oak trees develop galls known as 'oak apples' through the lifecycle of the Biorhiza pallida gall wasp. These remarkable structures support diverse ecosystems and represent a unique intersection of natural history and royal heritage.The Biology Behind the Royal SymbolThe oak apples begin forming when an agamic, wingless female oak apple gall wasp emerges from a gall in the oak's roots. She climbs the tree and injects a cluster of eggs and venom into a leaf bud. The hatched grubs then produce substances that cause a tumor-like effect on the oak cells, forming the apple-like gall. Inside this protective structure, the larvae feed in individual chambers until they mature.A Microcosm of BiodiversityThe oak apple gall serves as a complex habitat supporting numerous species. Beyond the Biorhiza larvae, other gall wasps live commensally within the structure, including inquiline species and hyperparasitic wasps that feed on the primary inhabitants. Additionally, specialized communities of fungi and microbes thrive within the gall, creating a miniature ecosystem within the larger oak tree environment.From Royal Celebration to Natural RestorationWhile Oak Apple Day is no longer observed as a public holiday, the natural phenomenon it commemorates continues its annual cycle. The transformation from shiny, cherry-like galls to larger, browner, mottled structures represents the progression of the gall wasp lifecycle. As larvae mature and emerge as adults—some winged males flying off to find mates, others wingless females descending to lay eggs in rootlets—the cycle continues, connecting generations through this remarkable biological process.A New Celebration of Natural HeritageAs we move forward, perhaps Oak Apple Day can be reimagined not just as a celebration of royal restoration, but as a recognition of nature's resilience and the intricate connections between species. The ancient oak trees, older than Charles II himself, stand as living monuments to both historical events and natural processes. Their galls, once symbols of royal escape, now represent the complex, interdependent web of life that continues to thrive in the English countryside.

Researchers from Stanford, Oxford and UC San Diego have demonstrated a proof‑of‑concept wearable ultrasound patch that can monitor a baby’s heart rate and blood flow continuously, aiming to reduce false alarms and missed complications in pregnancy.A Patch That Turns Ultrasound Into a Wearable SensorThe device, dubbed UPatch, adheres to the abdomen and remains operational for hours, capturing real‑time images of the foetus and umbilical cord. Unlike intermittent hospital scans, the patch records a continuous stream of data, allowing clinicians to establish a personal baseline for each pregnancy and spot deviations instantly.Trial Results Show Near‑Parity With Conventional ScansIn a study published in Nature Biotechnology, the team evaluated the patch in two cohorts:62 pregnant participants – single‑time‑point blood‑flow measurements from UPatch matched those from standard handheld ultrasound.52 women – continuous monitoring revealed dynamic fluctuations in fetal blood flow that brief scans would miss.A pre‑eclamptic case where UPatch detected severe intra‑uterine growth restriction, prompting a timely caesarean delivery and preventing stillbirth.Lead author Tom Park highlighted that the technology captures transient changes without over‑diagnosing, addressing a key limitation of current intermittent methods.Potential Shift in Prenatal Care and Global HealthSenior author Prof Sheng Xu emphasized that continuous monitoring could become a routine part of prenatal visits, especially in low‑resource settings where access to skilled sonographers is limited. Dr Antoniya Georgieva noted the broader impact: reducing stillbirth rates, providing richer data for research, and enabling earlier interventions for conditions like pre‑eclampsia.Roadmap Toward a Fully Wireless Home‑Use SystemThe current prototype is tethered to external electronics for placement, but the team is already engineering a wireless version that patients could wear during daily activities and at home. Their long‑term vision is a seamless, battery‑efficient system that integrates with tele‑health platforms, delivering real‑time alerts to clinicians wherever the mother is.

The Art of Provocation T Venkanna's paintings are like a sucker-punch. His first institutional solo show at London's Studio Voltaire features an overbearing altarpiece, modified by two squat side panels, depicting an orgasmic thicket of desire. The artwork is a commentary on the disparity between puritanical religious doctrine and licentious reality. The Intersection of Myth and Reality Venkanna's work is a way to consider many things, including the myth of religions. He draws inspiration from ancient Indian temples, where people touch the breasts of sculptures, making them smooth and shiny over time. His paintings are not about shocking, but about showing things around us. The Impact of Upbringing Venkanna's childhood in a small town in south-central India, where his father was a Hindu priest, had a significant impact on his work. He grew up in a one-room home with five other family members, and village people would come to ask about rituals and expectations of appropriate behavior. However, Venkanna discovered that these rules had a convenient degree of flexibility. The Evolution of Style Venkanna's artistic journey began with painting and drawing naked figures at home. He was sent to train as a drawing teacher and later enrolled in a fine art college, where he learned printmaking, miniature painting, and how to make and work with tempera. His early work was met with criticism, but he has since gained recognition, including a gold medal and support from teachers who donated their spare materials. The Future of Art Venkanna's work continues to push boundaries, exploring themes of intimacy, isolation, consent, and violation. His paintings are a counterpoint to gender inequality and prejudice, foregrounding women's experiences and depicting them as satiating their sexual appetite. As Venkanna says, 'I don't want to shock. What I'm showing are things around us.' T Venkanna: Sculpture Garden is at Studio Voltaire, London, until 23 August

The Archers Actors on Their Flower Power Stage Show The actors from the popular rural radio drama The Archers are set to take the stage in a special production marking the show's 75th anniversary. Susie Riddell, who plays Tracy Horrobin, Sunny Ormonde, who plays Lilian Bellamy, and Tim Bentinck, who plays David Archer, sat down with The Guardian to discuss their excitement and nerves about performing live. Characters Come to Life The actors discussed how they bring their characters to life, even in a radio drama. Riddell mentioned that she has to be careful not to betray her true levels of excitement when speaking about the show. The actors also talked about the importance of using their imagination to create the world of Ambridge, where the show is set. From Radio to Stage The actors will be performing a specially written episode, set at Ambridge's annual flower and produce show. They discussed their concerns about adapting to a live stage performance, including what to wear and how to interact with the audience. Bentinck joked about trying to look cool on stage, while Riddell expressed her worry about people reacting to her "gurning" on stage. A Community of Fans The actors emphasized the strong connection they feel with the show's fans, who have been listening for decades. They discussed how the show's characters are very much character-led, and that the listeners have a sense of ownership over the characters. The actors are looking forward to performing in front of a live audience and interacting with fans.

The Strategic Shift in iOS 27Just ahead of Apple’s Worldwide Developers Conference (WWDC) in June, leaked renders reveal a significant overhaul of the iPhone's interface, driven by a new generation of AI capabilities. The most visible change is the integration of Apple’s AI upgrade directly into the user experience, moving beyond simple voice commands to a comprehensive, card-style interface.The Dynamic Island as the AI Command CenterThe iconic black pill-shaped area at the top of the screen, known as the Dynamic Island, is set to become the central hub for AI interactions. While users can still trigger Siri via a button press, the primary mode of interaction will shift to the Dynamic Island. This allows for quick voice queries and searches, mimicking current usage patterns while offering a richer visual output.Furthermore, Apple is capitalizing on muscle memory by integrating AI-powered search into the swipe-down gesture. This feature, powered by a rebuilt AI model using Google's Gemini technology, allows users to search, launch apps, send messages, and manage calendar events directly from the search card.Scale as Apple's Competitive AdvantageApple’s primary weapon in this AI race is its sheer scale. With a total install base of 2.5 billion devices, Apple has an unmatched runway to introduce AI to users who have not yet adopted standalone tools like ChatGPT. While ChatGPT boasts 900 million weekly active users, Apple’s ecosystem offers a frictionless entry point for millions of new users.A Hybrid Approach to AI DevelopmentApple’s strategy mirrors its successful partnership with Google for search: leveraging external technology to meet immediate user demand while simultaneously developing proprietary solutions. By utilizing Google's Gemini under the hood for cloud-based intelligence and investing in local AI models for on-device processing, Apple aims to maintain its privacy-first brand without the prohibitive costs of building a massive AI infrastructure from scratch.The Standalone Chatbot ChallengerIn addition to system-wide integration, Apple is developing a dedicated Siri app designed to compete directly with market leaders like ChatGPT and Claude. This standalone application will feature past chat history, document uploads, and photo analysis, providing a robust alternative for users seeking advanced AI assistance.

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.