BusPatrol plans to scan the license plates of all vehicles the buses drive past, and then let law enforcement search that data. The plan would essentially turn school buses into roaming surveillance vehicles.

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How I turned 100 messy pdfs into structured insights by building a deterministic loop around agents The post Stop Using LLMs Like Giant Problem Solvers appeared first on Towards Data Science.

Pope Leo XIV attends the presentation of his first encyclical, Magnifica Humanitas, at the Vatican on May 25, 2026. AP Photo/Alessandra Tarantino With the release of his encyclical letter Magnifica Humanitas on May 25, 2026, Pope Leo XIV has signaled that he wants the church to respond to artificial intelligence much as a predecessor, Pope Leo XIII, responded to upheavals during the Industrial Revolution over a century ago. Since the first act of his papacy – choosing his name – the current pope has repeatedly invoked the earlier Leo’s 1891 encyclical Rerum Novarum. That document, which waded into the political and economic debates of the time, denounced the excesses of the Gilded Age and pointed toward a more just social order. Now, Leo XIV has used his first major statement to the world to present a new Rerum Novarum for the age of AI. Rerum Novarum was more than just a theological text. It helped reshape economic policy around the rights of workers, serving as a spiritual foundation for European social democracy and the 1930s New Deal programs that still undergird economic life for working Americans today. It also spurred a movement of entrepreneurs to transform the economic system from within. Understanding its influence is key to seeing the potential of Leo XIV’s encyclical. From guilds to cooperatives in the industrial era In his time, Leo XIII rejected both unfettered capitalism and revolutionary socialism. He invoked the medieval guilds, in which craftspeople self-organized, and asserted the rights of industrial workers to organize as well. This was a radical statement at a time when unions often faced violent suppression from employers and police. But in contrast to communist agitators, he didn’t want to do away with private property. He argued that to bring out the best in human beings, as creatures made in the image of God, governments should “induce as many as possible of the people to become owners.” Pope Leo XIII. Francesco De Federicis (1853–1908) via Wikimedia Commons This was more of a vision than a detailed plan, but Catholics in many countries started trying to figure out what the vision meant in practice. The English writers G.K. Chesterton and Hilaire Belloc, for instance, tried to systematize his vision in a movement they called “distributism,” which proposed policies for land redistribution and a revival of guilds. In the United States, economist and Catholic priest John A. Ryan argued in favor of cooperatives – businesses that could be co-owned by workers, consumers or small-business owners. Ryan went on to be an important adviser for the New Deal in the United States, which used cooperatives as a powerful tool for economic development through farmer co-ops, rural electric associations and the credit union system. The spirit of Rerum Novarum continued to spread. Starting in the 1950s, the largest network of worker cooperatives in the world, the Mondragon Corporation in Spain’s Basque region, was founded by a Catholic priest. It was a direct result of Leo XIII’s encyclical. My own career has been in its shadow. As a media scholar and a Roman Catholic – and an advocate for efforts to build cooperative tech platforms – I sometimes think of my own work as applying Rerum Novarum to the online economy. With Magnifica Humanitas, the pope appears to be making a similar argument for the age of artificial intelligence. A tale of 2 cities Once again, society is going through an economic upheaval: New technologies are changing the nature of work, political systems are under strain, and wealth inequality is staggering. In Magnifica Humanitas, Leo XIV argues that an intervention akin to Rerum Novarum is needed. Copies of Leo XIV’s first encyclical, Magnifica Humanitas, were distributed at the Vatican on May 25, 2026. AP Photo/Alessandra Tarantino The guiding metaphor of Magnifica Humanitas is the choice between two biblical scenes: the Tower of Babel and the rebuilding of Jerusalem under the prophet Nehemiah. The first is a story about the dream of a city that sets out to erect a single building as high as the heavens. Babel, as Leo XIV writes in the encyclical, is a city “built on pride and the claim to self-sufficiency.” In the biblical account, the project collapses as the world’s common language is scattered into many diverse ones. The pope contrasts this with the story of the Hebrew prophet Nehemiah, who lived in the fifth century B.C.E., when Jews were returning from exile to a ruined Jerusalem. Nehemiah organized the city’s rebuilding through a collaborative process based on shared responsibility. While united in prayer, the city’s various families and professions could each put their distinctive marks on their work. The current AI industry, he argues, is in danger of becoming a new Tower of Babel. Just a few companies control this powerful technology supposedly poised to transform work, politics and society for everyone. He warns that many AI leaders are enthused by ideologies that propose to trade human limits for the godlike powers of machines. Some are even cheerfully embracing a world where human labor is no longer central to the economy. Leo also fears that human choice is becoming more removed from the execution of war. In the face of all this, the encyclical calls on people everywhere to adopt “the pressing duty to remain profoundly human” – to be neither “spectators” nor “commentators” but to take an active role by participating in what he calls “the construction sites of history.” Some already are. Construction sites for a different kind of AI A few large AI companies dominate the technology and decide how people can use it, but alternative models are beginning to emerge. greenbutterfly/iStock/Getty Images Plus It is easy to see the emerging AI industry in Babel-like terms – a few massive tech companies build the models and provide access to them on their terms. But other paths are still possible. My colleagues and I have been documenting cases that could be the germ of a different kind of AI industry – one more aligned with what the pope is calling for. Just as during the Industrial Revolution, a more just future begins with workers resisting against the abuses of the present. From Hollywood to Nairobi, workers have been fighting for dignity as AI changes their professions. Magnifica Humanitas stresses the importance of decent jobs to a healthy society, and workers’ demands can help identify what the future of work should look like. Other approaches begin among AI developers themselves. In Switzerland, a collaboration between government and academia has produced Apertus, a foundational model based on fully documented designs and data sources – a far cry from the opaque and at times illegal practices of leading AI companies. Some of Apertus’ developers have created a consumer cooperative, enabling users to co-own their interface with the model. Cooperative ownership like this allows users to tune AI experiences more intentionally toward their needs. The large U.S. farmer co-op Land O’Lakes, for example, has created AI-enabled tools that provide analysis and guidance for its members based on the data that they collectively co-own. The more nascent Transkribus in Europe is co-owned by research institutions that collectively train their AI software to transcribe texts for historical research. These kinds of systems follow Leo XIV’s call to “manage data as a common or shared good.” It is telling that even among leading AI companies such as OpenAI and Anthropic, the founders attempted to build unusual corporate governance structures to insulate their products from profit motives. Governments could encourage more appropriate ownership designs or outright require them for high-risk industries like AI. If Rerum Novarum is any guide, the impact of Magnifica Humanitas will depend on the creative entrepreneurship and policy experiments to put it into practice – and this work has already begun. Nathan Schneider and his lab receive financial support from various funders, most recently the Siegel Family Endowment. He chairs the board of Metagov, an organization whose work informed this article, and he has interacted with several of the entities discussed here.

ClickUp cut 22% of its staff, with CEO Zeb Evans framing the layoffs as part of an AI restructuring focused on 100x output. The post ClickUp Cuts 22% of Staff as CEO Pushes AI-First ‘100x Org’ Model appeared first on TechRepublic.

The interior is spectacular; the exterior looks better in person than on screen.

Mauro Orru / Wall Street Journal: Spain says it is blocking Polymarket and Kalshi as a precautionary measure while it probes possible gambling law violations over the next three to four months  —  The government said companies seeking to provide clients with gambling services need a license

AI can detect financial fraud more efficiently than previous technology did, but it also flags legitimate transactions that it shouldn't. CardMapr.nl on Unsplash, CC BY Imagine you’re at the supermarket checkout. Your cart is full. The line behind you is long. You tap your card. Declined. You try again. Declined. You haven’t overspent. You haven’t done anything suspicious. But somewhere inside your bank’s computer systems, a machine made a decision about you in less time than it takes to blink – and it made a mistake. What just happened? And why does it keep happening to people who haven’t done anything wrong? This isn’t a rare glitch, but something that happens to millions of people every day. And most of us have no idea why it happens or what we can do about it. The answer lies inside a fraud detection system powered by AI. As a data science teaching professor and former financial-services data scientist, I understand how this system works and can explain why it sometimes fails the very customers it’s meant to protect. Just as important, I can help you find out what you need to know and what you can do if you or your loved ones are unfairly flagged. A decision in milliseconds When you tap your card, a signal travels to your bank’s fraud detection system in the time it takes to blink. The transaction processing at your checkout is fully automated, operating within AI systems that handle millions of payments simultaneously, and computes a risk score based on dozens of features extracted from that single moment. Those features might include the transaction amount relative to your recent spending average; the type of merchant; your geographic location; the time of day; the device used for online purchases; and how this purchase compares to your historical patterns. Once those factors are plugged in, an algorithm scores your purchase in real time. A model trained on millions of past transactions then assigns each combination of features a probability on how likely it is that this transaction would be fraudulent. If that probability crosses a threshold, the transaction is blocked or flagged for review. The whole process takes less than 200 milliseconds. ‘99% accurate’ still fails millions of people What sets this technology apart is speed. Financial institutions process millions of transactions every day, which is far greater than any human team can effectively monitor. Banks also have fraud analysts, but their work happens at a different layer entirely – reviewing patterns, investigating cases, and handling disputes that the automated system escalates to them. To their credit, these new systems are usually accurate at catching fraud. Banks lose far less money due to card fraud today than they did before machine learning – one of the foundational technologies that power today’s AI systems – became standard. Still, the word “accurate” conceals a problem. Consider the numbers. The Federal Trade Commission reported that Americans lost more than $12.5 billion to fraud in 2024 – a 25% increase from the year before. As banks process more transactions than ever, fraudsters are keeping pace, too. And here is the part that is especially worth noting: According to Stripe, one of the world’s largest payment processors, “false declines” (legitimate transactions wrongly rejected) are a structural problem across the entire industry, and industry research consistently suggests they cost the financial system more than actual fraud does. These errors aren’t random. They cluster around people and situations that the algorithm wasn’t properly trained to expect. Buying gas in a city you’ve never visited or making a large rent payment for the first time aren’t inherently suspicious. But to a machine trained on past patterns, they can look that way. There’s something even more troubling. These algorithms learn from historical data, which is almost always imbalanced. Because fraudulent transactions are rare on a per-transaction basis, the model has seen relatively few examples of what fraud looks like across every type of customer. What does this mean? Research has found that customers in lower-income areas and communities of color face higher rates of erroneous declines. When a model hasn’t seen enough transactions from a particular group of people or in a given situation, it has less data to build an accurate baseline for them. So when something slightly unusual happens, it flags it. Not out of intent, but out of unfamiliarity. The model isn’t necessarily explicitly discriminating against anyone. But its outputs can still produce what researchers call disparate impact – unequal harm, distributed unequally. As researchers at MIT explain in their book “Fairness and Machine Learning,” this is a known limitation. A model trained on incomplete representation will perform less reliably for the groups it saw least. The fix isn’t to blame the algorithm, but to train it on better, more representative data, and to test its error rates across different customer groups before deployment. When machine learning declines a payment, you’re faced with a black box that isn’t designed for human interpretation. Vitaly Gariev on Unsplash, CC BY Why you don’t have the right to an explanation What makes these cases worse is the lack of any information. When a loan officer denies your mortgage application, the law requires a written explanation. But when an algorithm declines your debit card, you get “flagged by our system” message. If you’re lucky enough to connect with a human representative, they can’t tell you much more. This gap isn’t an accident. Most high-performing fraud models are black boxes. Their internal logic isn’t designed for human interpretation. A bank may genuinely be unable to articulate plainly why your transaction was stopped. That’s not because it’s hiding something, but because the model itself doesn’t produce a reason. It produces a number. In response, some financial institutions are moving toward tools that make their algorithms more transparent. Known in the industry as “explainable AI,” these systems are designed to surface the most influential factors behind a given decision – flagging, for instance, that a transaction was blocked because of an unusual location combined with an atypically large amount. It’s a meaningful step toward accountability. However, these adoptions are uneven, and the explanations that do exist are rarely surfaced to customers. Meanwhile, those pressures haven’t yet translated into a consistent, enforceable right to a meaningful explanation when your card gets declined. Challenging a decision made by AI can be enormously difficult, and most of us don’t even know we have the right to try. For most people, the path of least resistance is simply to move on, switch to another card, take their business elsewhere or say nothing. Research suggests a quarter of consumers who experience a false decline never return to that merchant at all. Some people go further and close the account entirely. That instinct is understandable. However, it carries a hidden cost. A declined transaction won’t appear on your credit report, but closing the card can. Shutting down an account reduces your available credit and can shorten your credit history, which can directly affect your credit score. What you can actually do right now? You have more power here than the banks would like you to think. Call your bank immediately: A fraud flag is probabilistic, not final. A bank representative can override a declined transaction in real time. The model made a guess, but a human can correct it. Do not wait. Set alerts if you’re planning to make unusual purchases: Most banks allow you to notify them of upcoming travel, large purchases, or changes in your spending pattern. This doesn’t override the model, but it gives it new information to work with, which can prevent the flag from triggering in the first place. Know your rights: Under the Fair Credit Billing Act, you can dispute erroneous transaction blocks and request an explanation. If you believe you’ve been systematically and unfairly blocked, the Consumer Financial Protection Bureau accepts consumer complaints. Ask your bank what appeal processes are available: Increasingly, banks are building more customer-facing appeal services. Visa reported 106 million disputes globally in 2025, a 35% rise since 2019, and has called dispute management a “strategic priority.” Improper declines are expensive for payment companies and financial institutions, too, through customer service costs, lost revenue and eroded trust. The bigger picture The algorithm that blocked your payment isn’t all-knowing or neutral. It’s a machine making a statistical guess about you, based on data that was probably never perfectly fair to begin with. As AI spreads further into our daily lives, the question of who controls these decisions, and whether we can challenge them, becomes ever more urgent. The technology will keep expanding into new realms. The rules, and our own financial fluency, need to keep up. Pragati Awasthi does not work for, consult, own shares in or receive funding from any company or organization that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.

Jess Weatherbed / The Verge: Spotify launches a library of over 650 narrated long-form magazine articles in English for Premium users; free users can buy articles “individually for $1.99”  —  More than 650 long-form articles are available starting today as part of Spotify's audiobook library.

Michael J. de la Merced / New York Times: OpenRouter raised $113M led by CapitalG, a source says at a $1.3B valuation, and now processes 25T tokens across 400+ models weekly, up from 5T six months ago  —  An investment arm of Alphabet is backing OpenRouter, which helps companies choose among hundreds of models for different software tasks.

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Julie Bort / TechCrunch: Atlanta-based e-commerce logistics company Stord raised a $250M Series F led by Strike at a $3B valuation, up from $1.5B after a $200M Series E in May 2025  —  E-commerce logistics company Stord has raised a $250 million round at a $3 billion valuation, it announced Tuesday.

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. A reality check on the AI jobs hysteria Despite the growing hysteria over AI’s threat to white-collar jobs, there’s still scant evidence that the technology has had a large-scale impact on…

Carmen Reinicke / Bloomberg: SEC filing: Quantinuum is seeking to raise $1.05B in its US IPO, marketing ~21M shares for $45 to $50 each, giving it a $12.7B valuation at the top of the range  —  Quantinuum Inc., a quantum computing company backed by Honeywell International Inc., is seeking to raise $1.05 billion …

In a <a href="https://machinelearningmastery.

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How shifting the operational focus from isolated data products to systemic domain architecture resolves technical bottlenecks and optimizes platform investment. The post The Domain Shift: Moving Data Governance from Product Triage to Infrastructure Investment appeared first on Towards Data Science.

The co-founder and former editor-in-chief passed away five years ago in November. As Dark Reading enters its third decade, we pause to celebrate and honor Wilson's instrumental role in building and elevating the media site.

David Jeans / Reuters: Sources: SpaceX successfully pressured the Pentagon to raise Starlink fees for LUCAS kamikaze drones amid increasing tensions over Starlink's pricing  —  As U.S. kamikaze drones guided by Elon Musk's Starlink network began to make visible gains in the war against Iran, senior SpaceX officials reached …