Latest news with #disruption
BBC News
9 hours ago
- BBC News
London to Brighton rail line closed by 'severe disruption'
Rail services between London and Brighton in East Sussex are being severely disrupted, with the Gatwick Express Govia Thameslink said no services will be running between Brighton, Preston Park, Haywards Heath and Three Bridges for the rest of trains can run to or from Southampton, while some services in Surrey are also being operator said this was due to "a number of incidents". Anyone travelling to Brighton will have to use services via Lewes or Horsham, adding at least an hour to their between Brighton and Cambridge have also been suspended.A fault with signalling at Wivelsfield means drivers are having to be talked through the signals.A faulty signal at Salfords in Surrey means delays to services between Redhill and East Croydon, while another signal fault is affecting trains running between Purley and Gatwick Airport.
BBC News
11 hours ago
- Politics
- BBC News
Latest HS2 delay 'extends the agony' says Northants villages
People living near the route of the HS2 rail project said the latest delay to the scheme "extends the agony" for Secretary of State for Transport, Heidi Alexander, told MPs on Wednesday that there was "no route" to meeting the 2033 deadline, although she did not suggest a new said there had been a "litany of failure" around the work has been taking place in villages in south Northamptonshire since 2019. The only remaining section of the HS2 project, between London and Birmingham, was originally due to be up and running by 2026, but the deadline got pushed back to 2031 and then to latest announcement means villagers on the route in south Northamptonshire now know they will be facing disruption from the work for at least another nine years. The village of Thorpe Mandeville has been living with disruption caused by work on an access road, a site compound and a viaduct for some time Brown from the Thorpe Mandeville Parish Council said there were more headaches to come with HS2 waiting permission to close a route into the village that is currently controlled by traffic said: "It would make it a peaceful village, but for village commuting, it would make getting north damn near impossible." As a former parish councillor in Lower Boddington, Peter Deeley has had dealings with HS2 since the idea first surfaced. He said: "We've had nothing else but problems in regards to noise pollution, we've had inability to get access, we have the situation of air pollution - I cleaned my car yesterday, it now looks as if half the Sahara's desert is on it." Not far down the road is Greatworth Hall, where Stephen Adkins' family have been tending the land since construction of HS2 is taking place just yards from his said: "The delays are unbelievable and, personally, it just extends the agony."He said that, if anyone asked him for advice on living near a national infrastructure project, his "advice now would be just get out if you can because it has been miserable". The Conservative MP for south Northamptonshire, Sarah Bool, said: "I fully understand the frustrations many feel. "I continue to work closely with affected communities in Radstone, Greatworth and across south Northamptonshire to hold HS2 Ltd to account — pushing for better mitigation, timely communication and proper treatment of those whose lives and land have been upended."The BBC has contacted HS2 for a statement. Follow Northamptonshire news on BBC Sounds, Facebook, Instagram and X.
Harvard Business Review
17 hours ago
- Business
- Harvard Business Review
What Gets Measured, AI Will Automate
AI doesn't need a sci-fi upgrade to upend the economy—current models, and the cheaper, more capable versions already in the pipeline, are set to disrupt nearly every corner of the labor market. Their surprising performance across text, image, and video threatens to upend how work is done across the creative ranks of writers, designers, photographers, architects, animators, and brand advertisers, as well as the spreadsheet crowd of financial analysts, consultants, accountants, and tax preparers. Not even the credentialed bastions of law, medicine, or academia are safe: AI can sift through oceans of content and serve up bespoke advice or coursework at a fraction of today's cost—and with quality that's closing in fast. There are major questions about how much more powerful AI tools might become—and how soon. Anthropic's Dario Amodei and OpenAI's Sam Altman claim artificial general intelligence (AGI) could be only a year or two away. Meta's Yann LeCun is more skeptical, arguing that current models lack grounded physical understanding, durable memory, coherent reasoning, and strategic foresight, and Apple just published new research claiming that today's models perform only within the limits of their training data. Yet even if progress stopped tomorrow, the disruption is already underway. To navigate this new landscape, leaders need to understand—and plan for—how automation will affect their businesses. That requires understanding which tasks and responsibilities are most likely to come under pressure and charting a course to move the enterprise up the intelligence value chain before time runs out. What Is Not at Risk of Automation? Academic researchers and practitioners have extensively debated which jobs and tasks are most vulnerable to automation. Some threats are obvious: self-driving vehicles may soon be in a position to displace millions of ride-sharing, bus, and truck drivers. Meanwhile, language translation, swaths of creative writing, design, and even everyday coding are being handed off to AI. In February, Anthropic shared revealing user stats: although the chat format naturally steers people toward human augmentation, about 43% of interactions already represented some form of automation, in which users ask the AI to perform a task directly as opposed to helping them iterate and think it through. That share will keep climbing as modular AI agents enter the workforce, trading data and coordinating tasks through protocols like MCP. Environments that are extensively measured or codified—whether through laws, tax codes, compliance protocols, or streams of sensor data—face the greatest near-term risk of being handed over to machines. AI research pioneers Ajay Agrawal, Joshua Gans, and Avi Goldfarb argued in 2018 that as AI advances, the last bastion of human advantage will be judgment—the ability to weigh options and make decisions under uncertainty. Yet that insight hands us an impossible homework assignment: pinning down exactly what qualifies as judgment at any given moment. Tasks that demand human judgment today—choosing a medical treatment, reviewing a legal contract, scripting a film that nails the zeitgeist—could soon pass to AI as models tap richer data and greater compute. Nor can we assume people will always prefer a human therapist, counselor, or mediator, according to recent research. An AI counterpart can operate around the clock, at a fraction of the cost, and—aside from a handful of human superstars—may offer more consistent quality. So, how can we separate the tasks AI will automate next from those that will require new breakthroughs in AI technology to do so? To answer that, we must go back to first principles and revisit where it all began. From Lab Contest to Industrial Revolution Back in the mid-2000s, computer scientist Fei-Fei Li saw that the field of computer vision, which is focused on enabling computers to 'see' and interpret images, was dealing with a bottleneck: algorithms were pixel-starved, ingesting too little visual data to reach human performance. Her solution was refreshingly brute-force: she built ImageNet—a vast, meticulously labeled image trove assembled with help from Amazon Mechanical Turk. But her true stroke of genius came in 2010, when she bolted a global leaderboard onto the dataset—transforming image recognition into a gladiatorial contest for researchers. For two years, the annual leaderboard inched forward. Then, in 2012, Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton blew the competition away. Using two off-the-shelf NVIDIA GTX 580 graphics cards, the trio from Toronto was able to train a breakthrough convolutional neural network in just a few days—a groundbreaking approach that proved you could bend computer-vision history on a grad student budget. That moment ended the decades-long AI winter, put neural nets at the center of progress, and revealed the playbook the field still runs on. First, gather relevant data—roughly 14 million labeled images in the ImageNet case. Next, rely on metrics to quantify and drive progress. Last, flood a model with data and GPU muscle until it teaches itself, a formula that has carried AI from categorizing objects to writing fluent prose and, most recently, to reasoning, planning, and wielding external tools in today's emerging 'thinking' systems. Data, Reward, Compute The framework that propelled the image recognition breakthrough is far more general than most realize. It can be invoked whenever we can a) define the task environment and assemble its data—be it a corpus of text, a repository of images and video, logged driving miles, or streams from a robot's sensors; b) specify a target reward, explicit ('did the model predict the next word?') or implicit (inferred from observing human behavior); and c) provide the computational power to let the system iterate. Stack those three ingredients and you get a general-purpose automation engine. Two data trends now accelerate the flywheel. First, models can mint limitless synthetic examples—for instance, generating virtual 'driving miles' that cover every oddball scenario, rather that relying on data from real world drivers. And second, AI is increasingly fielded across a variety of devices and sensors—on phones, in cars, and elsewhere—as a low-cost surveyor, capturing and quantifying real-world signals that were once too expensive or impractical to measure. If you can shoehorn a phenomenon into numbers, AI will learn it and reproduce it back at scale—and the tech keeps slashing the cost of that conversion, so measurement gets cheaper, faster, and quietly woven into everything we touch. More things become countable, the circle resets, and the model comes back for seconds. That means that any job that can be measured can, in theory, be automated. Measurement Too Cheap to Meter Economist Zvi Griliches's landmark 1957 study of hybrid corn adoption gives us a sharp lens on what comes next. Farmers first planted the pricey seed only on their best acres—where the yield jump easily covered the extra cost and learning curve of using a new product. As hybrids improved and word spread, even thin-margin fields soon cleared the benefit-cost bar. With AI, the investment into measuring things follows the same payoff curve. When turning reality into data is expensive, companies tend to only invest in the headline cases—credit-card fraud, algorithmic market-making, jet-engine prognostics. But AI now slashes the cost of precise measurement, making continuous, fine-grained sensing the default. Lightweight models run beside the sensors, trimming bandwidth and latency, while synthetic data fills gaps when the real world is slow or awkward to capture. Each extra decimal place quickly pays for itself: tiny error cuts multiplied across millions of AI-driven decisions add up fast. As precise measurement gets cheaper, ever-slimmer benefit streams pencil out, and tasks once too minor to monitor slide into the automation net. Not only may we soon have intelligence too cheap to meter, we'll also be measuring ever more of the world to expand—and continuously upgrade—what that intelligence can reach. We already live in the era of 'artificial-metrics intelligence,' where anything we can quantify is swiftly queued for automation. Thriving Despite Unknown Unknowns Humans are evolutionary generalists, selected to navigate half-drawn maps. We don't merely survive unknown unknowns—we thrive on them, and that resilience is our defining edge. Over countless generations we fine-tuned our vocal cords and social brains until language emerged—opening the door to cumulative knowledge, abstract reasoning, and symbolic thought. From there we pushed beyond our biological limits, forging tools that stretched our senses, expanded our memory, and multiplied our abilities. But the cornerstone of our advantage is our highly plastic, densely wired prefrontal cortex. This neural command center lets us spin endless 'what-ifs,' rehearse counterfactual futures, and pivot strategy the instant conditions shift. Short of a true singularity, even quantum machines will struggle to match our talent for open-ended, cross-domain counterfactual planning. As AI accelerates progress, it creates fresh unknown unknowns, so our maps keep being redrawn. Meanwhile, it routinizes the predictable—much as mechanized farming lifted us from subsistence—freeing more of our counterfactual brainpower for higher-level problems. AI will also struggle in domains where measurement verges on the impossible—witness the decade-long, globe-spanning effort the Event Horizon Telescope needed to capture a single black-hole image, and the still-unsolved challenges of probing extreme-scale physics, Earth's deep mantle and abyssal oceans, or live cellular interactions inside the human brain. It will also lag where measurement is throttled by privacy, ethics, or regulation; where society requires transparent reasoning—at least until model interpretability catches up; and where people simply prefer a human touch. Yet, as with hybrid corn adoption, future generations will keep revisiting the cost-benefit calculus for each of these—and may reach conclusions very different from ours. But one crucial carve-out in what can be measured may prove decisive: tasks that defy quantification because their outcome odds are fundamentally unknowable—the realm of Knightian uncertainty, where you can't assign any probabilities because the risks themselves are undefined. Scaling a startup, allocating capital or talent into highly uncertain ventures, containing a novel pathogen, setting central bank policy during a financial regime shift, drafting AI ethics, inventing a new artistic medium, igniting a fashion trend, or creating a new genre-bending blockbuster—all sit in zones where probabilities vanish. Some creative acts and discoveries amount to little more than clever recombinations of the familiar, but the truly ambitious hinge on our singular ability to envision genuinely new and complex counterfactual worlds. The list is fluid—tasks drop off the moment they become measurable, and new ones surface just as quickly. Each shift forces painful economic and social adjustments, squeezing more work into a superstar economy that concentrates outsized rewards at the peaks of creativity, talent, and capital. Yet AI offers a paradoxical gift: by democratizing education and serving as everyone's personal copilot, it hands more people than ever the tools to reach those peaks. Jobs themselves will keep evolving, and any breakthrough that turns the unknown into the countable will scale and be imitated at meme speed. For leaders steering their organizations through this turbulent transition, what lies beyond the spreadsheet? It's everything that won't fit in a cell: the skills that refuse to be tallied, the open-ended problems with no reliable precedent, the intangibles—trust, taste, and the subtle dimensions of quality and experience—and the conviction to press ahead even when every metric says 'wait.' Manage only what you can measure, and you surrender the most valuable ground to rivals who cultivate what can't be counted. Amar Bose, the sound and electrical engineer who founded the Bose Corporation, proved the point: while others worshipped spec-sheet numbers, he zeroed in on how music sounded to people in real rooms—a quality no existing metric could catch—and in doing so, he rewrote the rules of the audio industry. Directionally, the prescription is simple. Back wildcard bets with fuzzy ROI, reward teams that reframe problems and lean into the unknown, and rotate talent through roles that confront uncertainty across R&D, new markets, and complex customer, partner, and policy interactions. Carve out slack time and engineer cross-team collisions to spark serendipity and idea recombination. Treat those pockets of planned ambiguity not as liabilities, but as strategic assets. Only leaders who pay attention to what is measurable—and, more crucially, to what stubbornly isn't—will be ready when the next shift arrives.
National Post
2 days ago
- Automotive
- National Post
C3 Solutions Releases Insightful White Paper on Today's Automotive Supply Chain Challenges
Article content MONTREAL — C3 Solutions, a leading provider of yard management and dock scheduling software, has released a timely new white paper examining the ongoing disruptions impacting automotive supply chains in North America. Titled 'Driving Through Disruption ' the paper addresses critical challenges industry leaders face in a time marked by uncertainty and rapid change. Article content By asking the right questions, this white paper challenges conventional thinking and invites decision-makers to rethink their supply chain strategies. It dives into the evolving realities automotive manufacturers, suppliers, and logistics providers are confronting; from geopolitical shifts and trade policy shocks to production bottlenecks and cost pressures. The report avoids overused industry cliches and instead offers a thought-provoking look at what resilience really requires in 2025. Article content 'For years, the automotive supply chain has been a marvel of just-in-time precision and Article content cross-border collaboration. But in today's environment, the rules have changed, and they're still changing,' Article content said Frank Couture, Automotive Account Manager for C3 Solutions. Article content Acknowledging that ongoing disruptions are now common, C3 Solutions urges a shift from reactive responses to proactive strategies. Key themes discussed include automation, inventory visibility, supplier diversification, and smarter logistics management, all presented to encourage awareness and dialogue without overwhelming readers with technical details. Article content Couture added Article content . 'We're here to encourage reflection. Leaders across the automotive supply chain need to take a step back, reframe their assumptions, and prepare to operate in a state of ongoing volatility.' Article content 'Driving Through Disruption' offers a preview of the conversations many executives are already having behind closed doors, but in a format designed to provoke broader industry engagement. It also hints at how innovative technologies, like Ai-Driven dock and yard management systems, can give businesses the edge they need to stay agile when timelines shift, and priorities change overnight. Article content The full white paper is now available for download on C3's website here. C3 invites supply chain professionals and logistics strategists to explore the insights within and join the conversation on building a more resilient future. About C3 Solutions In 2025, C3 Solutions proudly celebrates 25 years of empowering businesses with innovative supply chain and logistics solutions. As a world leader in yard management and dock scheduling software, C3 Solutions has partnered with organizations across 15 countries to optimize their logistics operations with secure, reliable, and innovative solutions. With a quarter-century of proven expertise and a commitment to continuous innovation, C3 Solutions remains dedicated to helping its customers achieve operational excellence. Article content Article content Article content Article content Article content
CNN
2 days ago
- CNN
Watch: Indonesia volcano spews ash into the sky
Indonesia's Mount Lewotobi Laki Laki volcano erupted on Tuesday, spewing ash high into the sky over the island of Flores. The national volcanology agency has raised the country's highest alert and dozens of flights were disrupted or canceled as a result of the eruption.
