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The 2026 World Cup promises a global celebration. Many Arab fans may find themselves locked out.

I recently calibrated a recovery-rate model that had only two weak features. Its point accuracy was almost nothing — R² basically zero. I expected its uncertainty estimates to be junk too. They weren't: the 90% conformal prediction intervals covered ~89% of held-out outcomes. Valid, just wide . That surprised me enough to nail it down, because it contradicts a belief a lot of us carry around: "my model isn't accurate, so I can't trust its uncertainty." For split conformal prediction, that's backwards. Here's the precise statement, a runnable demo, and the one caveat that actually bites. Coverage is a property of the procedure, not the model Split conformal prediction gives a distribution-free, finite-sample marginal coverage guarantee : P( Y ∈ Ĉ(X) ) ≥ 1 − α and it holds for any point model, as long as the calibration and test data are exchangeable. The model is a black box. You fit it however you like, then on a held-out calibration set you take the (1−α) quantile of the absolute residuals, and that quantile becomes the half-width of your intervals. Nowhere does that construction require the model to be good. A bad model just has large residuals, so the calibration quantile is large, so the intervals are wide — wide enough to still cover at the stated rate. Accuracy doesn't buy you validity ; it buys you efficiency (narrower intervals at the same coverage). The demo (numbers are reproducible, seed fixed) Same dataset and target, three models from strong to useless, target coverage 90%: model R² marginal coverage mean interval width gradient boosting 0.741 0.895 5.39 weak linear (1 noisy feature) 0.061 0.905 10.39 predict-the-mean −0.000 0.907 10.83 All three land at ~90% coverage. The only thing that changes is width: the good model's intervals are half as wide . That's the whole story in one table — validity is constant, efficiency tracks accuracy. import numpy as np from sklearn.linear_model import LinearRegression from sklearn.ensemble import GradientBoostingReg

Elsewhere, beyond-classical quantum hardware, plus classical computing fires back.

Trump administration officials tell WIRED that if Anthropic wants to rerelease Fable 5, it will need to ensure the model's guardrails can't be circumvented. Security experts say that can't be done.

Hello and welcome to Regulator, an email for Verge subscribers about technology, politics, and what happens when science crashes headlong into self-interest. Not a subscriber? Sign up here today! Got the scoop on a petty feud that's going to somehow fundamentally reshape the entire field of frontier AI development? Send 'em over to tina.nguyen+tips@theverge.com. Back […]

You type "k8s deployment troubleshooting" into your documentation search. The top result is a page about Kubernetes architecture that never mentions the word "troubleshooting." It is exactly what you need. BM25 would have missed it entirely. This is the promise of vector search: finding documents by meaning, not just matching words. In 2025 and 2026, vector search has moved from niche ML engineering to a core Elasticsearch capability. If you are building search for AI applications - RAG pipelines, semantic Q&A, recommendation systems - understanding how Elasticsearch handles vectors is no longer optional. I have spent the past year building RAG pipelines at Cloudera, and I have learned that vector search is powerful but easy to misuse. This post covers what works, what does not, and how to implement it in production. Why Vector Search Matters (And When It Does Not) BM25, which we covered in a previous post, is brilliant at matching exact terms. But it is fundamentally lexical. It does not understand that: "k8s" and "kubernetes" are the same thing "docker container" and "containerization" are related concepts "out of memory error" and "heap exhaustion" describe the same problem Vector search solves this by converting text into high-dimensional numerical vectors (embeddings) where semantically similar content lives close together in vector space. A query for "k8s deployment troubleshooting" gets embedded into a vector, and Elasticsearch finds the nearest document vectors - even if they do not share a single keyword. But vector search is not a replacement for BM25. It is a complement. BM25 is faster, requires no ML infrastructure, and excels at exact-term matching. Vector search is slower, requires embedding models, and shines at conceptual similarity. The best search systems in 2026 use both. How Elasticsearch Stores and Indexes Vectors Elasticsearch introduced the dense_vector field type in version 7.x and has dramatically improved it through 8.x and into 2026. Here

From defections and protests to moments of national pride, the 2026 World Cup arrives amid decades of tension between identity and the state.

Transferring genes across species doesn't just happen in microbes.

This article presents a straightforward approach to automatically and efficiently tune hyperparameters for machine learning models using Optuna as the optimisation framework. We explore how to use both Optuna’s native storage options and InterSystems IRIS as a database backend to track the progress of hyperparameter searches. We also show how MLflow can be used to monitor experiments and manage models through its tracking and model registry UI. This article is based on this Kaggle Notebook , which you can run and directly edit yourself. When training ML models, the choice of hyperparameters can strongly influence performance. They are not the only factor, but they can significantly affect both convergence and generalisation. Tuning hyperparameters manually takes a lot of effort. This is especially true because hyperparameters interact with each other, so tuning them independently is usually not enough. For example, higher regularisation may require a lower learning rate for more stable optimization. A more complex model may require stronger regularization to avoid overfitting, but at the same time, a very small learning rate on a complex model can make learning too slow. Optuna is an MIT-licensed open source library, which allows commercial use, that automates hyperparameter search for ML models developed with the most popular frameworks such as scikit-learn, PyTorch, TensorFlow, and LightGBM. It works by defining a search space and an objective metric to either minimize or maximize. Optuna then explores the search space efficiently to find well-performing configurations. Here we use Optuna to tune a LightGBM model on a dummy dataset and show how to scale the search using shared database storage. We will also use MLflow for experiment tracking and model registry, and IRIS DB as a possible Optuna storage backend for concurrent studies. We will use the California Housing dataset, commonly used in ML examples, to populate IRIS tables and run the tuning workflow. Note:

There's a line usually pinned on the Roman philosopher Seneca: luck is what happens when preparation meets opportunity. People put it all over social media and like most things on social media, it gets repeated so often that it stops meaning anything. So let me try to make it mean something again, with a math equation and a football match that happened recently at the latest FIFA World Cup 2026. The equation nobody writes down We talk about luck like it's a single mysterious force, either you have it or you don't. But it's not one thing. It's two things multiplied together: Luck = Preparation × Opportunities Look at what that multiplication does. If your preparation is zero, it doesn't matter how many opportunities show up, zero times anything is still zero. And if you're the most prepared person alive but you never put yourself in front of a single opportunity, same result. Zero. The lucky people aren't the ones who got more luck handed to them. They're the ones who kept both numbers high. They got good and they kept showing up to the table where things happen. Hold that thought. Let's go to Texas. Japan, the Netherlands, and the 88th minute On June 14th, 2026, Japan played the Netherlands in their World Cup group opener in Arlington, Texas. On paper it was a mismatch in the most literal, physical sense. The Netherlands are tall . Van Dijk, Van de Ven, the whole spine of that team is built like a row of wardrobes. Japan are one of the shorter sides in world football, quick, technical, but not the people you'd bet on to win a header. If you were designing a contest specifically to humiliate the Japanese, you'd make it about jumping. And for most of the night, the script ran exactly as the bodies predicted. The Dutch dominated the run of play, around 60% possession, more passes, more touches in the box, the better expected goals. Van Dijk, a defender, rose for a cross and headed the Netherlands ahead. Later Summerville restored their lead. The Oranje even won the aer

In a bid to dismiss a lawsuit over xAI’s polluting gas turbines, the Justice Department claimed the company is integral to military operations—including the Iran War.

You scan one to pay at a sari-sari store, pull up a restaurant menu, or board a flight. The QR code has quietly become one of the most universal pieces of interface design on the planet. But it was never meant for any of that. The QR code was invented in 1994 to solve a very specific problem on a Japanese car factory floor, and the engineering decisions made under that constraint are exactly why it later conquered the world. A barcode problem on the assembly line In the early 1990s, Toyota's manufacturing arm had a data problem. Tracking thousands of distinct components through production meant scanning barcodes, and barcodes are stingy: a standard one-dimensional barcode holds roughly 20 characters. Workers were ending up with parts plastered in ten or more barcodes just to encode enough information, and each one had to be scanned separately. It was slow, and on an assembly line, slow is expensive. Masahiro Hara, an engineer at Denso Wave, a Toyota subsidiary, took on the challenge of designing something better. He wanted a code that could hold far more data, be read much faster, and tolerate the dirt, smudges, and odd angles of a real factory rather than a clean lab. Designing for speed and any angle The breakthrough was going two-dimensional. By encoding data in a grid of black and white squares rather than a single row of lines, Hara's team could pack in thousands of characters instead of a few dozen. The name they chose, QR for "Quick Response," was a direct promise about scanning speed. The most recognizable feature of a QR code, the three large squares in its corners, solves the hardest part of the problem: letting a scanner instantly find the code and work out its orientation no matter how the part is turned. Hara's team analyzed printed material to find a black-and-white sequence that almost never occurs naturally in text and images, and settled on a ratio of 1:1:3:1:1 for those corner markers. Because that pattern is so rare in everyday print, a scanner ca

Anthropic was already navigating one dispute with the government in its standoff with the Pentagon, and then came an order on June 12th to block off foreign access to its most recently released AI models, Fable 5 and Mythos 5. When they launched on June 9th, Anthropic said “Fable 5’s capabilities exceed those of any […]

For months, Big Tech's Washington lobbyists have chased after the holy grail of pro-AI legislation: preemption. This would be a comprehensive federal law, passed in Congress and signed by the president, applying one set of AI rules across the entire country and overriding the legally messy state-by-state approach to regulation. For months, lobbyists have run […]

From the day it was announced, on June 16th, 2025, the Trump phone sounded ridiculous. The T1 Phone 8002 (gold version), as it was officially called, was a combination of contradictory specs, product images that were clearly not photographs of a real phone, and the worrying requirement of a $100 deposit to secure a preorder […]

Both kratom and one of its active components, 7-OH, have opioid-like effects and are widely available across the US. As health secretary RFK Jr. aims to get 7-OH banned, proponents of both are fighting.

The federal government is planning to let a rule regulating federal data center operations sunset in September with no replacement.

The UK is the latest country to follow Australia in implementing a total social media ban for children under 16, Prime Minister Keir Starmer has announced. The ban, which could take effect from early next year, will be joined by wider measures that will also prevent children from talking to strangers in online games, livestreaming, […]

Three days into November, a disapproval cascade pulled 40% of active SKUs from Shopping and Performance Max simultaneously — on day one of a promotional window we'd spent six weeks building. No feed changes on our side triggered it. Here's the part most guides miss: Google's automated review threshold for certain policy categories (health claims, price accuracy, before/after imagery) tightens as platform ad volume increases heading into Q4. I've watched this happen across accounts running ₩50M–₩120M/month in combined Google spend, three years in a row, with zero feed-side changes preceding it. Same feed that sailed through August catches 15–20% disapprovals on recheck in September. The products didn't change. The enforcement did. When it hits during a live window, fix order matters more than fix speed. Price mismatches go first — not because they're the most dramatic, but because they cascade silently. One bestseller disapproved during a flash sale means Performance Max quietly reallocates budget to lower-performing products. By the time ROAS visibly drops, you've lost 48 hours of peak traffic. The specific failure mode I've seen twice on Cafe24 with direct API feeds: a site-wide price update propagates to the feed before the landing page CDN cache clears. Google crawls the feed, sees the new price, crawls the landing page, sees the old cached price. Mismatch. Disapproval. Fixing it is one line — force a manual fetch and verify sale_price_effective_date formatting — but finding it at 2am during a live sale is a different problem. Prohibited content disapprovals are deprioritized by most teams because they're rare. That's exactly wrong. A single escalation during Black Friday week can trigger account-level review, not just product suspension. Pull the SKU yourself within the hour if you can't fix the content immediately. Suspending your own SKU is recoverable. A suspended account during peak is not. GTIN and identifier issues — despite getting the most attention in s

Plug a sensor into a switch, wire up a building full of cameras, or rack a server, and you are using Ethernet. It is the most widely deployed wired networking standard on earth, the quiet backbone under offices, factories, and data centers. And it is named after a scientific idea that turned out to be completely wrong. The name was not an accident or a marketing afterthought. It was a deliberate engineering choice, and the reasoning behind it explains why Ethernet outlived nearly every rival and still underpins industrial IoT half a century later. A memo, a laser printer, and a dead theory The date Ethernet enthusiasts celebrate is May 22, 1973. On that day, a young engineer named Robert Metcalfe, working at Xerox's legendary Palo Alto Research Center (PARC), circulated a memo describing how to connect the Alto - one of the first personal computers - to a new device PARC had built: the laser printer. The problem was getting many machines to share one wire without their messages colliding into noise. Metcalfe needed a name for the shared medium that carried the signals. He reached back into nineteenth-century physics and borrowed the term luminiferous ether . For generations, physicists had assumed that light, being a wave, needed something to wave through - just as sound needs air. They called that invisible, all-pervading substance the ether, and they believed it filled the entire universe as a silent carrier of electromagnetic waves. The trouble is that the ether does not exist. The famous Michelson-Morley experiment of 1887 failed to detect it, and Einstein's special relativity in 1905 made it unnecessary altogether. By the time Metcalfe wrote his memo, the luminiferous ether had been a discredited idea for decades. He used it anyway, and on purpose. Why a debunked idea made for brilliant engineering Metcalfe later explained the choice plainly: "We called it Ethernet because the ether could be coax, twisted pair, radio, optical fibers, power line, whatever you wa