The changes may be less perceptible than in smartphones, tablets, or wearables, but chargers have also been quietly reinvented over the last decade. At one time a bulky mix of tangled cables and connectors, slow to perform and prone to overheating, they’re now smaller, safer, and faster, thanks to a slew of technological advances. These…

In finance departments that have long been defined by precision and control, AI has arrived less as a neatly managed upgrade than as a quiet insurgency. Employees are already using it while leadership races to impose structure, governance, and strategy after the fact. The result is a paradox: one of the most tightly regulated functions…

The apparently insatiable demand for AI compute has data center entrepreneurs looking to the stars. There's a key problem: There aren't enough rockets to put data centers in orbit around Earth, and they're too expensive.

Sam Tobin / Reuters: In a two-week UK High Court trial, Shein accuses Temu of “industrial scale” copyright infringement of its photos; Temu says Shein is suing to stifle competition  —  Online fast-fashion platform Shein accused Temu of copyright infringement “on an industrial scale” …

Carmen Reinicke / Bloomberg: SEC filing: Cerebras upsizes its IPO to 30M shares at $150-$160 each, up from 28M shares at $115-$125, aiming to raise up to $4.8B at an up to $34.4B valuation  —  Cerebras Systems Inc. increased the size of its initial public offering, now seeking to raise as much as $4.8 billion …

Table of Contents Building and Training a Kimi-K2 Model Using DeepSeek-V3 Components Kimi-K2 vs DeepSeek-V3: Key Architecture Differences in LLM Design Mixture of Experts Scaling in Kimi-K2: Model Size, Sparsity, and Efficiency Attention Head Optimization in Kimi-K2 for Efficient Long-Context… The post Building and Training a Kimi-K2 Model Using DeepSeek-V3 Components appeared first on PyImageSearch.

A few commissions, contracts or cancellations can dramatically change an artist's annual earnings. Hyoung Chang/The Denver Post via Getty Images Though artificial intelligence is making it easier than ever to produce images, music and text, the technology is also making it harder for the people who have traditionally produced this work to earn a living. A photographer who once was commissioned to make art for an advertising campaign is now competing with graphics produced by the AI image generator Midjourney. A novelist who used to make money on the side as a technical writer is seeing that work be replaced by a series of prompts in ChatGPT. The extent to which AI will upend creative work remains unsettled. But that uncertainty has made guaranteeing income for creatives a more viable policy idea. In fact, creatives in New York recently participated in the largest basic income program for artists in U.S. history, the Guaranteed Income for Artists initiative. Spearheaded by Creatives Rebuild New York and primarily funded by the Andrew W. Mellon Foundation, the program gave 2,400 artists across New York state US$1,000 a month beginning in June 2022. There were no work requirements and no restrictions on how the money could be spent. The program sought to improve the financial stability of artists and encourage the public to see them as workers who deserve a stable income and social support. As researchers who study artists, cultural work and public policy, we evaluated this program to see whether it achieved its stated goals. Our main finding was simple: Artists did not stop working. Instead, they changed the kind of work they did. Cash buys time Artists often make choices that look strange in standard economic models, which typically assume workers will prioritize higher wages while balancing work against leisure time. Artists, on the other hand, may stay in poorly paid, unstable arts work, even when other work pays more. Economists have long described this as a “work-preference” model. Put plainly, they argue that artists get value from the work itself, not just from the paycheck. The guaranteed-income program, which was geared toward low-income artists, offered a rare chance to see how a financial cushion would influence the kind of work they focused on, along with their overall earnings. The program selected artists through a weighted lottery. It adopted an expansive definition of “artist.” Anyone engaged in artistic, cultural or community-centered creative practices – such as musicians, storytellers or muralists – was eligible to apply. However, it excluded commercial workers like wedding photographers or food caterers. Our analysis, which is forthcoming in the Journal of Cultural Economics, compared artists who received payments with applicants who hadn’t been selected. For purposes of the study, artists broke down their work time into “artistic/cultural practice(s),” “other arts work” and “non-arts work.” The work didn’t necessarily have to involve a paycheck or stipend; it could simply mean time spent on a personal artistic pursuit. However, it’s safe to assume that “non-arts work” usually involved some sort of side job to earn extra money. The results lined up almost exactly with what the work-preference model predicts. Artists who received the payments spent about 3.9 more hours per week on arts work than comparable artists who did not receive the payments. They also spent about 2.4 fewer hours per week on non-arts work. Opponents to basic income programs often argue that recipients will become less motivated to do any work whatsoever. That isn’t what happened, though. The money helped artists move time out of work they were doing mainly to survive, and into the creative work they preferred. Earnings told a messier story The earnings results were more complicated. Artists receiving the monthly payments earned significantly less from non-arts work. That makes sense, given that many of them switched away from non-arts work. But total earnings from all work also fell by about $11,600 a year on average, close to the $12,000 annual value of the cash payments. But we cannot confidently say that the basic income program reduced total earnings by that amount. That’s because artists’ incomes are so volatile: A few commissions, contracts, sales or cancellations can dramatically change what artists earn in a given year. Income varied widely among both the artists who received monthly payments and the applicants who hadn’t been selected, which made it hard to see the precise cause and effect of the program on total earnings. An artist in Brooklyn’s Bushwick neighborhood participates in Bushwick Open Studios, an annual event when hundreds of local artists open their workspaces to the public. Andrew Lichtenstein/Corbis via Getty Images The program may have given artists enough financial room to stop chasing some non-arts income, but it did not change their overall income from where it had been. That is a very different policy effect than “more cash equals more income.” It is closer to “more cash equals more control over time.” A lesson beyond the arts The findings do not mean that guaranteed income is the right policy for everyone. Artists are unique. Many have strong reasons to keep doing creative work even when it pays poorly. The study also took place after the COVID-19 pandemic, while the arts and entertainment sector was still recovering. And Creatives Rebuild New York’s guaranteed-income program was a temporary, one-time opportunity. A longer-term follow-up could show whether these shifts lasted. Did artists keep making more art after the payments ended? Did the extra time they spent on their own artistic pursuits lead to new work, new income or more stable careers? Those questions remain open. But to us, the most important lesson may be that work is not one thing. A monthly cash transfer can reduce one kind of work while increasing another. It can lower earnings from gigs people take mainly to pay the bills, while freeing up time to spend on work that is meaningful, socially valuable or personally sustaining. For artists in this program, $1,000 a month did not buy a vacation or a chance to slack off. It bought time for work they valued more. That distinction matters, particularly as debates over the use of basic income policies grow alongside advances in AI and automation. The question is not only whether people work when they receive cash with no strings attached. It is what kind of work becomes possible when financial pressures ease. Joanna was previously funded by Creatives Rebuild New York to conduct an independent evaluation of their Guaranteed Income for Artists program. Doug Noonan receives funding from the National Endowment for the Arts. He previously was funded by Creatives Rebuild New York to conduct an independent evaluation of their Guaranteed Income for Artists program.

AI algorithms such as facial recognition systems produce probabilities, not facts. Matthew Horwood/Getty Images In Baltimore County, Maryland on Oct. 20, 2025, a 17-year-old student named Taki Allen was sitting outside his high school after football practice when an artificial intelligence-enhanced surveillance camera falsely identified the Doritos bag in his pocket as a gun. Within moments police cars arrived, officers drew their weapons and Allen was forced to his knees and handcuffed while they searched him. All they found was a crumpled bag of chips. The AI’s misidentification and the human decisions that followed turned a normal evening into a traumatic confrontation. On Dec. 24, 2025, Angela Lipps, a Tennessee grandmother, was released after spending five months in jail because facial recognition software had incorrectly connected her to fraud crimes in North Dakota, a state she had never visited. Police had arrested her at gunpoint while she was babysitting her four grandchildren. These are unfortunate examples of how AI can lead to mistreatment of people because of technical flaws as well as misplaced human faith in the technology’s supposed objectivity. These cases involve different tools, but the underlying issue is the same. AI systems produce probabilities, and people treat them as certainties. We are researchers who study the intersection of technology, law and public administration. In researching how police departments use AI and how digital technologies operate in a democratic society, we have seen how quickly the shift from probabilistic prediction to operational certainty happens in practice. AI policing tools are used in dozens of U.S. cities, although no public registry tracks the full footprint. The tools ingest historical crime data and score neighborhoods on predicted risk so officers can be routed toward the resulting hot spots. The mechanism is straightforward, but its consequence is not. Once a system signals a possible threat, the question is no longer how certain the prediction is but what to do about it. A statistical output turns into a deployment decision, and the uncertainty that produced it gets lost on the way. A matter of probabilities When generative AI models such as ChatGPT or Claude respond to human requests, they are not searching a database and pulling out facts. They are predicting the most likely answer based on patterns in data they have been trained on. When asked, “Who invented the light bulb?” the models do not go to a source or fact-check a finding. They generate a statistically probable answer which is “Thomas Edison.” The reply might be right, but it might not capture the full story – such as Joseph Swan’s parallel invention at the same time as Edison’s. The danger arises when people believe that the model is retrieving truth rather than generating likelihoods. This distinction matters. The most probable response is not the same as a factually verified answer, complete with context. Police handcuffed teenager Taki Allen at gunpoint after an AI camera system incorrectly indicated he had a gun. This reality can be highly problematic for policing and law. For example, when law enforcement agencies use AI systems trained on geographical data to estimate where criminal activity is likely to occur, the algorithms analyze historical crime data and geographic patterns. These systems generate statistical risk scores or heat maps for locations based on prior incidents. But such predictions may have little bearing on who was involved in a new crime in the area, even if an algorithm generates information that sounds authoritative. Some researchers have argued that predictive policing systems do not increase the likelihood that racial minorities will be arrested more often relative to traditional policing practices. The broader concern, however, is not limited to measurable disparities in arrest outcomes alone. It is about how probabilistic predictions can become standardized operational decisions absent further verification. Artificial intelligence researchers caution against using these models in isolation for crime and legal proceedings or decision-making. Research at the University of Virginia’s Digital Technology for Democracy Lab with police chiefs shows that some law enforcement groups follow strict policies that dictate when technology is used in tandem with, or in place of, human discretion, while others have no such policy. What most users do not realize is that AI systems rarely produce binary answers: yes or no, a positive identification or a negative one. They generate probabilities. Some systems assign scores that assess the system’s confidence in a prediction. In those cases, engineers set a confidence threshold, a level of certainty that determines when the system should trigger an alert about a possible threat. You can think of this threshold as settings on a control knob. A 95% confidence level, for example, indicates that the model considers its interpretation to be highly likely. A low threshold catches more potential threats but increases false alarms. A high threshold reduces mistakes but risks missing real dangers. Either way, these algorithmic thresholds are often invisible to the public and are set quietly by vendors or agencies, even though they shape when police action begins. Angela Lipps was unjustly jailed for more than five months based on a mistake by a facial recognition system. Where to draw the line In medicine, these kinds of trade-offs are explicit. Diagnostic tools are calibrated on the relative harm of different errors. In infectious disease settings, for instance, systems that detect infections are often designed to accept more false positives to avoid missing contagious individuals. Then medical professionals look into the human cases. And the algorithm-based decisions are subject to professional standards, ethics reviews and regulatory oversight. In policing, an AI system must balance false positives, where the system flags a threat that does not exist, and false negatives, where it fails to detect a real danger. The trade-off carries significant consequences. A lower threshold may generate more alerts and allow officers to intervene earlier, but it also increases the risk of mistaken identifications, which happened to Angela Lipps, or escalated encounters like the one Taki Allen experienced. A higher threshold may reduce wrongful interventions but could allow legitimate threats to go undetected. Some law enforcement agencies argue that acting on imperfect signals is preferable to missing serious risks. But lowering the bar for algorithmic alerts based on probabilistic estimates effectively expands the number of people subjected to police attention. It is important to realize that these thresholds are not neutral features of the technology; they are choices embedded by the creators in the model’s code. Decisions about where to draw the line determine when an algorithmic suspicion becomes a real-world police action, even though the public rarely sees or debates how those thresholds are set. Limits of optimization Developers often use several methods to determine where to set a confidence threshold. Techniques such as “receiver operating characteristic curve analysis” examine how changing the threshold for an alert alters the balance between correctly identifying real events and mistakenly flagging harmless ones. Precision–recall analysis examines a similar trade-off, asking how accurate the system’s alerts are relative to the number of incidents it successfully detects. These approaches could help calibrate systems more responsibly by testing how often an algorithm wrongly flags people or locations. Fine-tuning can improve system performance. But the techniques cannot resolve the underlying question of how much algorithmic uncertainty society is willing to tolerate. In law, legal standards of proof determine how convincing evidence must be before a judge or jury can rule in favor of a plaintiff or defendant. Courts use formal standards of proof depending on the stakes, such as probable cause, preponderance of the evidence and beyond a reasonable doubt. These standards reflect a societal judgment about how much uncertainty is acceptable before exercising legal authority. A court does not accept a guess or a prediction; it follows a process to weigh evidence. Unlike humans, an AI model does not usually say, “I’m not sure.” A model typically has confidence in its reply, even when the answer is incorrect. Stakes are rising as AI enters the courtroom, law enforcement, the classroom, the doctor’s office and the public sector. It is important for people to understand that AI does not know things the way many assume it does. It does not distinguish between “maybe” and “definitely.” That is up to us. We believe that technologists should design systems that admit uncertainty and need to educate users about how to interpret AI outputs responsibly. This article has been updated to correct the location of an AI system’s false detection of a gun. It was in Baltimore County, Maryland. Maria Lungu is affiliated with the Digital Technology for Democracy Lab at the University of Virginia, Kennesaw State University, and the Center for DI and Digital Policy (CAIDP). Steven L. Johnson is affiliated with the Digital Technology for Democracy Lab at the University of Virginia.

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. Here’s what you need to know about the cruise ship hantavirus outbreak Last week, eight passengers aboard a Dutch-flagged cruise ship contracted a type of hantavirus transmitted by rats. Three have…

The Information: Sources: Kuaishou plans to spin off its Kling AI video unit for an IPO in 2027 and is seeking a $20B valuation in pre-IPO funding talks with potential investors  —  Chinese social media giant Kuaishou Technology is planning to spin off its Kling AI video business ahead of an initial public offering next year …

The partnership is among a startling wave of multi-billion-dollar AI compute arrangements between tech giants and neocloud vendors.

The timing is notable. PayPal, which owns Venmo, is restructuring to spin Venmo off as a standalone business unit — a move widely seen as laying the groundwork for a potential sale. Stripe has reportedly expressed interest in buying PayPal outright.

A step-by-step guide to understanding distributed data, lazy logic, and your first DataFrame. The post PySpark for Beginners: Mastering the Basics appeared first on Towards Data Science.

The campaign quietly compromises aerospace and drone operators to exfiltrate GIS files, terrain models, and GPS data and gain a clear picture of adversaries' world view.

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Inti Landauro / Reuters: The European Commission says it is in ongoing discussions with OpenAI and Anthropic to access their latest AI models; OpenAI is “proactively offering” access  —  The European Commission is in ongoing discussions with U.S. artificial intelligence giants OpenAI and Anthropic, a spokesperson said on Monday.

Cable firm Cox's Supreme Court win may help all tech providers, not just ISPs.