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Breaking Down the Complex Language of AI Artificial intelligence is changing the world, and simultaneously inventing a whole new language to describe how it’s doing it. Spend five minutes reading about AI and you’ll run into LLMs, RAG, RLHF, and a dozen other terms that can make even very smart people in the tech world feel insecure. This glossary is our attempt to fix that. We update it regularly as the field evolves, so consider it a living document, much like the AI systems it describes. Artificial General Intelligence (AGI) Artificial general intelligence, or AGI, is a nebulous term. But it generally refers to AI that’s more capable than the average human at many, if not most, tasks. OpenAI CEO Sam Altman once described AGI as the “equivalent of a median human that you could hire as a co-worker.” Meanwhile, OpenAI’s charter defines AGI as “highly autonomous systems that outperform humans at most economically valuable work.” Google DeepMind’s understanding differs slightly from these two definitions; the lab views AGI as “AI that’s at least as capable as humans at most cognitive tasks.” Confused? Not to worry — so are experts at the forefront of AI research. AI Agent An AI agent refers to a tool that uses AI technologies to perform a series of tasks on your behalf — beyond what a more basic AI chatbot could do — such as filing expenses, booking tickets or a table at a restaurant, or even writing and maintaining code. However, as we’ve explained before, there are lots of moving pieces in this emergent space, so “AI agent” might mean different things to different people. Infrastructure is also still being built out to deliver on its envisaged capabilities. But the basic concept implies an autonomous system that may draw on multiple AI systems to carry out multistep tasks. API Endpoints Think of API endpoints as “buttons” on the back of a piece of software that other programs can press to make it do things. Developers use these interfaces to build integrations — for example, allowing one application to pull data from another, or enabling an AI agent to control third-party services directly without a human manually operating each interface. Most smart home devices and connected platforms have these hidden buttons available, even if ordinary users never see or interact with them. As AI agents grow more capable, they are increasingly able to find and use these endpoints on their own, opening up powerful — and sometimes unexpected — possibilities for automation. Chain-of-Thought Reasoning Given a simple question, a human brain can answer without even thinking too much about it — things like “which animal is taller, a giraffe or a cat?” But in many cases, you often need a pen and paper to come up with the right answer because there are intermediary steps. For instance, if a farmer has chickens and cows, and together they have 40 heads and 120 legs, you might need to write down a simple equation to come up with the answer (20 chickens and 20 cows). Coding Agent This is a more specific concept that an “AI agent,” which means a program that can take actions on its own, step by step, to complete a goal. A coding agent is a specialized version applied to software development. Rather than simply suggesting code for a human to review and paste in, a coding agent can write, test, and debug code autonomously, handling the kind of iterative, trial-and-error work that typically consumes a developer’s day. Compute Although somewhat of a multivalent term, compute generally refers to the vital computational power that allows AI models to operate. This type of processing fuels the AI industry, giving it the ability to train and deploy its powerful models. The term is often a shorthand for the kinds of hardware that provides the computational power — things like GPUs, CPUs, TPUs, and other forms of infrastructure that form the bedrock of the modern AI industry. Deep Learning A subset of self-improving machine learning in which AI algorithms are designed with a multi-layered, artificial neural network (ANN) structure. This allows them to make more complex correlations compared to simpler machine learning-based systems, such as linear models or decision trees.

The Lead At the Milken Institute Global Conference, leaders from across the AI supply chain gathered to discuss the current state and future of artificial intelligence. They touched on various challenges, including chip shortages, energy constraints, and the potential for new AI architectures. The Bottlenecks in AI Development The discussion highlighted several bottlenecks in AI development. Christophe Fouquet, CEO of ASML, noted that despite efforts to accelerate chip manufacturing, the market will likely remain supply-limited for the next two to five years. Francis deSouza, COO of Google Cloud, pointed out the immense demand for AI infrastructure, with Google Cloud's revenue growing 63% and its backlog nearly doubling to $460 billion. The Data and Energy Constraints Qasar Younis, co-founder and CEO of Applied Intuition, emphasized that the bottleneck for his company is not silicon but data gathered from the real world, which is essential for training physical AI models. The energy required to power AI infrastructure is also a significant concern. deSouza mentioned that Google is exploring data centers in space to address energy constraints, although this comes with its own set of challenges. New AI Architectures and Their Implications Eve Bodnia, founder of Logical Intelligence, discussed a different approach to AI, focusing on energy-based models (EBMs) that aim to understand the underlying rules of data, similar to human brain function. This approach could be particularly useful for applications requiring an understanding of physical rules, such as chip design and robotics. The Future of AI: Agents, Guardrails, and Trust Dmitry Shevelenko, chief business officer of Perplexity, talked about the evolution of its search product into a 'digital worker' called Perplexity Computer. This tool is designed to act as a staff that a knowledge worker can direct, raising questions about control and security. Shevelenko emphasized the importance of granularity in permissions and actions to ensure trust and security. The Geopolitical and Generational Impact The discussion also touched on the geopolitical implications of physical AI and its impact on national sovereignty. Younis noted that physical AI manifests in the real world in ways that governments can't ignore, leading to questions about safety, data collection, and control. Regarding the impact on the next generation, the panelists were optimistic, highlighting the potential for AI to help address significant problems and unleash new levels of creativity and opportunity.

Victory Highlights: Bow Echo’s Classic WinOn Saturday, 2 May 2026, Bow Echo captured the 2000 Guineas at Newmarket, delivering a decisive 2¾‑length victory. The triumph was steered by 20‑year‑old jockey Billy “the Kid” Loughnane and trained by 34‑year‑old George Boughey, marking a historic first Classic win for the young jockey and completing a Guineas double for the trainer. Bow Echo Secures 2000 Guineas in Dominant PerformanceThe race unfolded with Bow Echo positioned mid‑pack before launching a powerful run on the stands side. The horse, sent off at 9‑2, surged ahead in the final furlong, leaving joint‑favourites Gstaad and Distant Storm (both 3‑1) to finish second and third respectively. Winning margin: 2¾ lengthsFinal time: (official time not released at press time)Post‑race comments from Loughnane: “He’s an absolute star, a dream to ride.” Numbers Behind the Win: Odds, Ages, and MarginsThe statistical backdrop underscores the significance of the result: Trainer age: 34 – two years younger than Aidan O’Brien when he first won a Classic.Jockey age: 20 – the youngest Classic‑winning jockey since the early 2000s.Betting odds: 9‑2 for Bow Echo, compared with 3‑1 for the joint‑favourites.Margin of victory: 2¾ lengths, a clear superiority in a Classic field. What This Means for Young Trainer Boughey and Jockey LoughnaneThe win reshapes the narrative around emerging talent in British flat racing. Boughey’s Guineas double at 34 positions him as a potential long‑term rival to established powerhouses, while Loughnane’s confident ride cements his reputation as a future champion jockey. Potential impact on trainer‑jockey partnerships for the remainder of the season.Increased media and sponsor interest in both individuals.Boost to the market value and breeding prospects of Bow Echo. Looking Ahead: Bow Echo’s Target at Royal AscotFollowing the Guineas, connections have set their sights on the St James’s Palace Stakes at Royal Ascot next month. Bow Echo is currently an even‑money favourite with Paddy Power, while rival Gstaad offers 4‑1 odds to reverse the form. Key upcoming race: St James’s Palace Stakes (1 mile, Royal Ascot, June 2026).Strategic focus: Maintaining peak fitness and capitalising on the horse’s “extraordinary brain” as described by Boughey.Potential storyline: A bid for a Classic‑double season, echoing historic feats by legends such as Frankel.

The Rise of AI Interviews in UK Recruitment Nearly half (47%) of UK job seekers have had an AI interview, according to research from the hiring platform Greenhouse. In its survey of 2,950 active job seekers, including 1,132 UK-based workers, it found that 30% of UK candidates had walked away from a hiring process because it included an AI interview. These figures highlight a significant shift in recruitment practices as companies increasingly turn to artificial intelligence to streamline their hiring processes. The AI Interview Experience: Job Seekers' Perspectives Job seekers across the UK have shared their experiences with AI interviews, with many expressing frustration and dissatisfaction. The interviews typically involve candidates recording responses to pre-recorded questions, often with strict time limits. Thomas*, 21, a university student in northern England, described the experience as "frustrating," noting that "it feels strange talking into a camera, and it can be difficult to speak naturally. You can't see anyone other than yourself." The Human Element Missing in Digital Screening Many candidates emphasize the lack of human interaction as a significant drawback. Susannah*, 44, a scientist from Cambridge, found her AI interview "awkward and humiliating." She explained: "There's no human interaction. If you had an in-person interview, you'd be able to see how someone's reacting and that they're acknowledging what you say." This absence of real-time feedback and connection leaves many feeling that the process is impersonal and ineffective. AI Interviews and Accessibility Concerns The AI interview format presents particular challenges for certain groups. David*, 47, a marketing consultant with autism, described the experience as "completely horrible for the autistic brain." He explained: "I spoke in bullet points and keywords. The real me, who would take his time to understand the actual challenge and constraints of a project, would never deliver like that." This raises important questions about whether AI interviews create barriers for neurodiverse candidates. The Efficiency vs. Quality Dilemma in Modern Recruitment Companies increasingly turn to AI interviews to manage high volumes of applications. As Susannah noted: "There are just so many applications for these jobs that an HR department would not be able to go through them all." However, this efficiency comes at a cost. The technology often fails to capture the nuances of human communication and personality, potentially leading to missed opportunities for both candidates and employers. The Future of AI in Recruitment: Balancing Technology and Humanity As AI continues to transform recruitment, there's growing recognition that technology should augment rather than replace human judgment. Tom, a project manager from Scotland, observed: "I don't think the technology is ready for a full-blown interview yet – I guess maybe it depends on what sort of job you'll end up doing. But I think the human touch is probably a good thing, and I hope that lasts as long as possible." The future likely lies in hybrid approaches that leverage AI for initial screening while preserving human elements for critical evaluation stages.

The LeadMelbourne Storm coach Craig Bellamy has been diagnosed with an unspecified neurodegenerative disorder but will remain as coach in the immediate future, the club has confirmed. The 66-year-old recently underwent a series of medical tests, with the diagnosis coming 24 hours out from the Storm's NRL clash with the Dolphins in Brisbane.The Medical Diagnosis"Over recent weeks, in consultation with specialists, Craig has undergone a series of medical tests and has since been diagnosed with a form of neurodegenerative disorder," the club said in a statement on Thursday. "He is receiving the best possible medical treatment and has been advised by specialists that his diagnosis will not have an impact on his ability to coach the team in the immediate future."The Coaching LegacyBellamy has coached 614 NRL games, which puts him behind only Wayne Bennett and the retired Tim Sheens in the all-time coaching list. In February, he signed a new contract through until the end of the 2028 season, extending his remarkable tenure with the club. Bellamy has been at the helm of Melbourne Storm since 2003, making this his 24th season as coach.The Current SeasonThe Storm are enduring a difficult campaign, losing six successive matches for the first time since Bellamy took over as coach in 2003. A seventh straight loss in Brisbane would equal the all-time club record for consecutive defeats. The team's poor form stands in stark contrast to their usual dominance under Bellamy's leadership.The Club's ResponseMelbourne chair Matt Tripp expressed full confidence in Bellamy's ability to continue coaching at an elite level. "Despite our recent results, I firmly believe Craig is still coaching at an elite level and I have no doubt he is the right person to drive the club forward," Tripp said. "Craig has the full support of the board, players, coaches and staff to continue leading the club as he has done for the last 24 seasons."The Future OutlookBellamy was present at Storm training in Melbourne on Thursday, continuing his duties as coach. The club is also dealing with other health concerns, with players Eli Katoa (who underwent brain surgery last November) and Tui Kamikamica (sidelined after suffering a stroke and undergoing brain and heart surgery) both facing extended absences. The Storm organization remains committed to supporting Bellamy through his health challenges while maintaining their focus on improving on-field performance.

The Human Face of Rhyl’s Youth CrisisIn the cold February light of West Rhyl youth club, Sienna, 19, and Jake, 26, describe a town where the local garden known as “Crackhead Circle” has become a daily backdrop. Their stories illustrate how limited job prospects, unaffordable housing and lingering drug use shape the everyday reality for many 16‑ to 25‑year‑olds in this former seaside resort.Project Renew’s Year‑Long Crackdown on Gangs and DrugsLaunched by North Wales Police, Project Renew deploys patrols every 15 minutes around hotspots such as the public garden and the former Wilko store. The initiative, part of a broader effort to curb gang activity, also coordinates with youth workers and the newly formed neighbourhood board to target the root causes of antisocial behaviour.Police presence intensified across the town centre.Community outreach includes youth workshops and employment advice.Funding streams from the government’s Pride in Place programme support local infrastructure.Crime Statistics Show a 14% Drop, Yet Rates Remain HighIn January, North Wales Police reported a 14% year‑on‑year reduction in overall crime for Rhyl West. However, the ward still records a crime rate of 197 per 1,000 residents—about 2.5 times the Welsh average—and a violent‑crime rate of 88 per 1,000, more than double the national figure.What the Decline Means for Coastal Communities in WalesThe modest fall in offences coincides with several regeneration projects: completion of promenade construction, refurbishment of Queen’s Market, a new water‑park, and a modern cinema. These improvements aim to retain young people by creating local jobs and social spaces, addressing the “brain‑drain” that has long plagued coastal towns.According to Melanie Evans of Working Denbighshire, “Our issue in Rhyl is getting people into work. Many young people lack the basics.” The combination of policing, investment and community‑led planning could reshape the town’s socioeconomic profile.Can Revitalisation Efforts Sustain a Safer Future?Looking ahead, the key question is whether the current momentum can be maintained once the intensive police patrols ease. Continued success will likely depend on:Effective use of Pride in Place funds to create affordable housing.Long‑term job creation linked to tourism and new leisure facilities.Ongoing youth engagement programmes that give voice to local aspirations.If these elements align, Rhyl could become a model for other deprived coastal communities seeking to turn the tide for their young residents.

The Lawsuit Against OpenAI Families of seven victims of a mass shooting at a secondary school in British Columbia are suing OpenAI and the company’s CEO Sam Altman for negligence after it failed to alert authorities to the shooter’s troubling conversations with ChatGPT. The Event Details The lawsuits, filed on Wednesday in a federal court in San Francisco, allege that the violent intentions of the shooter, identified as 18-year-old Jesse Van Rootselaar, were well-known to OpenAI. Employees at the company flagged the shooter’s account eight months before the attack and determined that it posed “a credible and specific threat of gun violence against real people”, according to the lawsuit. The Data Analysis The school victims range in age from 12 to 13 and include a 39-year-old teaching assistant. One of the survivors, 12-year-old Maya Gebala, was shot in the head, neck and cheek. She has been in intensive care at Vancouver’s children’s hospital since the shooting and has received four brain operations. If she survives, she will likely have permanent disabilities, her attorneys said. The Impact Analysis The decision to not alert law enforcement led to the devastation of the rural community of Tumbler Ridge, the suit alleges, where on 10 February the shooter stormed the secondary school with a modified rifle and opened fire. They shot the first person they came across in a stairwell, and proceeded to the library, where they killed five others and injured 27 more. The shooter then killed themself. The Prediction The lawsuits are part of a groundswell of cases against AI companies over allegations that their chatbots are exacerbating mental health crises and provoking violent acts. In November, seven complaints were filed against OpenAI, blaming ChatGPT for acting as a “suicide coach”. Google was sued last month after its Gemini chatbot allegedly encouraged a 36-year-old man to stage a “catastrophic accident” and then kill himself.

The Podcast’s Core Question: Defining Food IntoleranceThe episode opens by distinguishing food intolerance from food allergy. While an allergy triggers an immune response that can be life‑threatening, intolerance typically involves digestive discomfort, headaches, or fatigue without the involvement of IgE antibodies.Symptoms, Self‑Assessment, and When to Seek Professional HelpBloating, gas, or abdominal pain after mealsChronic fatigue or brain fogSkin eruptions such as eczemaHeadaches or migraines linked to specific foodsThe hosts stress that recurring symptoms across multiple meals merit a medical review rather than sole reliance on anecdotal elimination diets.Market Numbers: The Rise of At‑Home Food Intolerance TestsAccording to industry data cited in the podcast, the global market for at‑home intolerance kits grew from £45 million in 2022 to £78 million in 2025, a compound annual growth rate of roughly 15 %. The surge is driven by consumer demand for personalised health insights and the perception of convenience.Public‑Health Implications of DIY TestingWhile the tests offer quick feedback, experts warn of false positives and the risk of unnecessary dietary restrictions. Misdiagnosis can lead to nutrient deficiencies, especially if individuals eliminate entire food groups without professional guidance.Looking Ahead: Regulation and the Promise of Precision NutritionThe podcast concludes with a look at upcoming EU regulations that will require clearer labelling of test accuracy, and the potential for AI‑driven platforms to integrate test results with medical records for truly personalised nutrition plans.

The Evolutionary Shift: Why Dogs' Brains Shrank 5,000 Years Ago For decades, the narrative of dog domestication has centered on the idea that our companions evolved smaller brains to fit a more docile, human-centric lifestyle. However, a comprehensive analysis of ancient and modern canine skulls suggests this biological downsizing was not an immediate consequence of domestication, but a gradual process that accelerated roughly 5,000 years ago. This revelation forces a re-evaluation of the timeline of the human-canine bond and the physiological changes that accompanied it. Decoding the Skulls: Methodology and Key Findings Researchers led by Dr Thomas Cucchi from the French National Centre for Scientific Research utilized advanced CT scanning technology to analyze the cranial structures of 22 prehistoric wolves and dogs spanning 35,000 to 5,000 years ago, alongside 59 modern wolves and 104 modern dogs. The Baseline: Modern dogs, village dogs, and dingoes possess brains that are 32% smaller than ancient and modern wolves. The Timeline: The most significant shrinkage occurred during the Late Neolithic period (approximately 5,000 to 4,500 years ago), where dogs had brains 46% smaller than wolves of the same era—comparable in size to modern pugs. The Anomaly: Contrary to expectations, 'protodogs' from 35,000 and 15,000 years ago did not exhibit smaller brains; one specimen actually had a relatively larger brain, implying an initial expansion in brain size during early domestication. The Paradox of Intelligence and Size A common misconception is that a smaller brain equates to lower intelligence. The study debunks this, highlighting that domestication did not make dogs 'dumber,' but rather rewired their neural architecture. As brain size decreases, researchers suggest the organ undergoes a process of reorganization. This may result in dogs being less trainable and more wary of environmental changes, yet highly specialized in reading human social cues and communicating with us. Rethinking the Domestication Timeline The findings challenge the 'domestication syndrome' theory, which posits that physical and behavioral changes happen simultaneously. Instead, the data suggests the relationship between humans and canines began loosely before evolving into a symbiotic bond. The significant brain reduction in the Late Neolithic period coincides with the rise of settled village life, leading experts to hypothesize that limited food resources may have favored smaller bodies and brains as an energy-efficient adaptation. Future Implications for Canine Evolution As selective breeding continues to shape modern breeds, the trend of brain size reduction appears to be accelerating. The study implies that the 'ideal' dog for early human coexistence may have been larger-brained, while the modern breeds we see today represent a later, more specialized evolutionary path driven by human selection for specific traits over general intelligence.