The last few years haven’t been kind to Marvel Studios. Once the lifeblood of Hollywood to the point where the whole endeavor arguably felt too big to fail, the MCU is in the midst of a much-publicized course correction. Too many streaming series tha

IBM Plans Large-Scale Fault-Tolerant Quantum Computer by 2029 By John P. Mello Jr. June 11, 2025 5:00 AM PT IBM unveiled its plan to build IBM Quantum Starling, shown in this rendering. Starling is expected to be the first large-scale, fault-tolerant quantum system. (Image Credit: IBM) ADVERTISEMENT Enterprise IT Lead Generation Services Fuel Your Pipeline. Close More Deals. Our full-service marketing programs deliver sales-ready leads. 100% Satisfaction Guarantee! Learn more. IBM revealed Tuesday its roadmap for bringing a large-scale, fault-tolerant quantum computer, IBM Quantum Starling, online by 2029, which is significantly earlier than many technologists thought possible. The company predicts that when its new Starling computer is up and running, it will be capable of performing 20,000 times more operations than today’s quantum computers — a computational state so vast it would require the memory of more than a quindecillion (10⁴⁸) of the world’s most powerful supercomputers to represent. “IBM is charting the next frontier in quantum computing,” Big Blue CEO Arvind Krishna said in a statement. “Our expertise across mathematics, physics, and engineering is paving the way for a large-scale, fault-tolerant quantum computer — one that will solve real-world challenges and unlock immense possibilities for business.” IBM’s plan to deliver a fault-tolerant quantum system by 2029 is ambitious but not implausible, especially given the rapid pace of its quantum roadmap and past milestones, observed Ensar Seker, CISO at SOCRadar, a threat intelligence company in Newark, Del. “They’ve consistently met or exceeded their qubit scaling goals, and their emphasis on modularity and error correction indicates they’re tackling the right challenges,” he told TechNewsWorld. “However, moving from thousands to millions of physical qubits with sufficient fidelity remains a steep climb.” A qubit is the fundamental unit of information in quantum computing, capable of representing a zero, a one, or both simultaneously due to quantum superposition. In practice, fault-tolerant quantum computers use clusters of physical qubits working together to form a logical qubit — a more stable unit designed to store quantum information and correct errors in real time. Realistic Roadmap Luke Yang, an equity analyst with Morningstar Research Services in Chicago, believes IBM’s roadmap is realistic. “The exact scale and error correction performance might still change between now and 2029, but overall, the goal is reasonable,” he told TechNewsWorld. “Given its reliability and professionalism, IBM’s bold claim should be taken seriously,” said Enrique Solano, co-CEO and co-founder of Kipu Quantum, a quantum algorithm company with offices in Berlin and Karlsruhe, Germany. “Of course, it may also fail, especially when considering the unpredictability of hardware complexities involved,” he told TechNewsWorld, “but companies like IBM exist for such challenges, and we should all be positively impressed by its current achievements and promised technological roadmap.” Tim Hollebeek, vice president of industry standards at DigiCert, a global digital security company, added: “IBM is a leader in this area, and not normally a company that hypes their news. This is a fast-moving industry, and success is certainly possible.” “IBM is attempting to do something that no one has ever done before and will almost certainly run into challenges,” he told TechNewsWorld, “but at this point, it is largely an engineering scaling exercise, not a research project.” “IBM has demonstrated consistent progress, has committed $30 billion over five years to quantum computing, and the timeline is within the realm of technical feasibility,” noted John Young, COO of Quantum eMotion, a developer of quantum random number generator technology, in Saint-Laurent, Quebec, Canada. “That said,” he told TechNewsWorld, “fault-tolerant in a practical, industrial sense is a very high bar.” Solving the Quantum Error Correction Puzzle To make a quantum computer fault-tolerant, errors need to be corrected so large workloads can be run without faults. In a quantum computer, errors are reduced by clustering physical qubits to form logical qubits, which have lower error rates than the underlying physical qubits. “Error correction is a challenge,” Young said. “Logical qubits require thousands of physical qubits to function reliably. That’s a massive scaling issue.” IBM explained in its announcement that creating increasing numbers of logical qubits capable of executing quantum circuits with as few physical qubits as possible is critical to quantum computing at scale. Until today, a clear path to building such a fault-tolerant system without unrealistic engineering overhead has not been published. Alternative and previous gold-standard, error-correcting codes present fundamental engineering challenges, IBM continued. To scale, they would require an unfeasible number of physical qubits to create enough logical qubits to perform complex operations — necessitating impractical amounts of infrastructure and control electronics. This renders them unlikely to be implemented beyond small-scale experiments and devices. In two research papers released with its roadmap, IBM detailed how it will overcome the challenges of building the large-scale, fault-tolerant architecture needed for a quantum computer. One paper outlines the use of quantum low-density parity check (qLDPC) codes to reduce physical qubit overhead. The other describes methods for decoding errors in real time using conventional computing. According to IBM, a practical fault-tolerant quantum architecture must: Suppress enough errors for useful algorithms to succeed Prepare and measure logical qubits during computation Apply universal instructions to logical qubits Decode measurements from logical qubits in real time and guide subsequent operations Scale modularly across hundreds or thousands of logical qubits Be efficient enough to run meaningful algorithms using realistic energy and infrastructure resources Aside from the technological challenges that quantum computer makers are facing, there may also be some market challenges. “Locating suitable use cases for quantum computers could be the biggest challenge,” Morningstar’s Yang maintained. “Only certain computing workloads, such as random circuit sampling [RCS], can fully unleash the computing power of quantum computers and show their advantage over the traditional supercomputers we have now,” he said. “However, workloads like RCS are not very commercially useful, and we believe commercial relevance is one of the key factors that determine the total market size for quantum computers.” Q-Day Approaching Faster Than Expected For years now, organizations have been told they need to prepare for “Q-Day” — the day a quantum computer will be able to crack all the encryption they use to keep their data secure. This IBM announcement suggests the window for action to protect data may be closing faster than many anticipated. “This absolutely adds urgency and credibility to the security expert guidance on post-quantum encryption being factored into their planning now,” said Dave Krauthamer, field CTO of QuSecure, maker of quantum-safe security solutions, in San Mateo, Calif. “IBM’s move to create a large-scale fault-tolerant quantum computer by 2029 is indicative of the timeline collapsing,” he told TechNewsWorld. “A fault-tolerant quantum computer of this magnitude could be well on the path to crack asymmetric ciphers sooner than anyone thinks.” “Security leaders need to take everything connected to post-quantum encryption as a serious measure and work it into their security plans now — not later,” he said. Roger Grimes, a defense evangelist with KnowBe4, a security awareness training provider in Clearwater, Fla., pointed out that IBM is just the latest in a surge of quantum companies announcing quickly forthcoming computational breakthroughs within a few years. “It leads to the question of whether the U.S. government’s original PQC [post-quantum cryptography] preparation date of 2030 is still a safe date,” he told TechNewsWorld. “It’s starting to feel a lot more risky for any company to wait until 2030 to be prepared against quantum attacks. It also flies in the face of the latest cybersecurity EO [Executive Order] that relaxed PQC preparation rules as compared to Biden’s last EO PQC standard order, which told U.S. agencies to transition to PQC ASAP.” “Most US companies are doing zero to prepare for Q-Day attacks,” he declared. “The latest executive order seems to tell U.S. agencies — and indirectly, all U.S. businesses — that they have more time to prepare. It’s going to cause even more agencies and businesses to be less prepared during a time when it seems multiple quantum computing companies are making significant progress.” “It definitely feels that something is going to give soon,” he said, “and if I were a betting man, and I am, I would bet that most U.S. companies are going to be unprepared for Q-Day on the day Q-Day becomes a reality.” John P. Mello Jr. has been an ECT News Network reporter since 2003. His areas of focus include cybersecurity, IT issues, privacy, e-commerce, social media, artificial intelligence, big data and consumer electronics. He has written and edited for numerous publications, including the Boston Business Journal, the Boston Phoenix, Megapixel.Net and Government Security News. Email John. Leave a Comment Click here to cancel reply. Please sign in to post or reply to a comment. New users create a free account. Related Stories More by John P. Mello Jr. view all More in Emerging Tech

Technology leaders are baffled by a “cacophony” of “buzzwords, hype and confusion” over the benefits of artificial intelligence (AI), according to the founder and CEO of technology company Pegasystems. Alan Trefler, who is known for his prowess at chess and ping pong, as well as running a $1.5bn turnover tech company, spends much of his time meeting clients, CIOs and business leaders. “I think CIOs are struggling to understand all of the buzzwords, hype and confusion that exists,” he said. “The words AI and agentic are being thrown around in this great cacophony and they don’t know what it means. I hear that constantly.” CIOs are under pressure from their CEOs, who are convinced AI will offer something valuable. “CIOs are really hungry for pragmatic and practical solutions, and in the absence of those, many of them are doing a lot of experimentation,” said Trefler. Companies are looking at large language models to summarise documents, or to help stimulate ideas for knowledge workers, or generate first drafts of reports – all of which will save time and make people more productive. But Trefler said companies are wary of letting AI loose on critical business applications, because it’s just too unpredictable and prone to hallucinations. “There is a lot of fear over handing things over to something that no one understands exactly how it works, and that is the absolute state of play when it comes to general AI models,” he said. Trefler is scathing about big tech companies that are pushing AI agents and large language models for business-critical applications. “I think they have taken an expedient but short-sighted path,” he said. “I believe the idea that you will turn over critical business operations to an agent, when those operations have to be predictable, reliable, precise and fair to clients … is something that is full of issues, not just in the short term, but structurally.” One of the problems is that generative AI models are extraordinarily sensitive to the data they are trained on and the construction of the prompts used to instruct them. A slight change in a prompt or in the training data can lead to a very different outcome. For example, a business banking application might learn its customer is a bit richer or a bit poorer than expected. “You could easily imagine the prompt deciding to change the interest rate charged, whether that was what the institution wanted or whether it would be legal according to the various regulations that lenders must comply with,” said Trefler. Trefler said Pega has taken a different approach to some other technology suppliers in the way it adds AI into business applications. Rather than using AI agents to solve problems in real time, AI agents do their thinking in advance. Business experts can use them to help them co-design business processes to perform anything from assessing a loan application, giving an offer to a valued customer, or sending out an invoice. Companies can still deploy AI chatbots and bots capable of answering queries on the phone. Their job is not to work out the solution from scratch for every enquiry, but to decide which is the right pre-written process to follow. As Trefler put it, design agents can create “dozens and dozens” of workflows to handle all the actions a company needs to take care of its customers. “You just use the natural language model for semantics to be able to handle the miracle of getting the language right, but tie that language to workflows, so that you have reliable, predictable, regulatory-approved ways to execute,” he said. Large language models (LLMs) are not always the right solution. Trefler demonstrated how ChatGPT 4.0 tried and failed to solve a chess puzzle. The LLM repeatedly suggested impossible or illegal moves, despite Trefler’s corrections. On the other hand, another AI tool, Stockfish, a dedicated chess engine, solved the problem instantly. The other drawback with LLMs is that they consume vast amounts of energy. That means if AI agents are reasoning during “run time”, they are going to consume hundreds of times more electricity than an AI agent that simply selects from pre-determined workflows, said Trefler. “ChatGPT is inherently, enormously consumptive … as it’s answering your question, its firing literally hundreds of millions to trillions of nodes,” he said. “All of that takes [large quantities of] electricity.” Using an employee pay claim as an example, Trefler said a better alternative is to generate, say, 30 alternative workflows to cover the major variations found in a pay claim. That gives you “real specificity and real efficiency”, he said. “And it’s a very different approach to turning a process over to a machine with a prompt and letting the machine reason it through every single time.” “If you go down the philosophy of using a graphics processing unit [GPU] to do the creation of a workflow and a workflow engine to execute the workflow, the workflow engine takes a 200th of the electricity because there is no reasoning,” said Trefler. He is clear that the growing use of AI will have a profound effect on the jobs market, and that whole categories of jobs will disappear. The need for translators, for example, is likely to dry up by 2027 as AI systems become better at translating spoken and written language. Google’s real-time translator is already “frighteningly good” and improving. Pega now plans to work more closely with its network of system integrators, including Accenture and Cognizant to deliver AI services to businesses. An initiative launched last week will allow system integrators to incorporate their own best practices and tools into Pega’s rapid workflow development tools. The move will mean Pega’s technology reaches a wider range of businesses. Under the programme, known as Powered by Pega Blueprint, system integrators will be able to deploy customised versions of Blueprint. They can use the tool to reverse-engineer ageing applications and replace them with modern AI workflows that can run on Pega’s cloud-based platform. “The idea is that we are looking to make this Blueprint Agent design approach available not just through us, but through a bunch of major partners supplemented with their own intellectual property,” said Trefler. That represents a major expansion for Pega, which has largely concentrated on supplying technology to several hundred clients, representing the top Fortune 500 companies. “We have never done something like this before, and I think that is going to lead to a massive shift in how this technology can go out to market,” he added. When AI agents behave in unexpected ways Iris is incredibly smart, diligent and a delight to work with. If you ask her, she will tell you she is an intern at Pegasystems, and that she lives in a lighthouse on the island of Texel, north of the Netherlands. She is, of course, an AI agent. When one executive at Pega emailed Iris and asked her to write a proposal for a financial services company based on his notes and internet research, Iris got to work. Some time later, the executive received a phone call from the company. “‘Listen, we got a proposal from Pega,’” recalled Rob Walker, vice-president at Pega, speaking at the Pegaworld conference last week. “‘It’s a good proposal, but it seems to be signed by one of your interns, and in her signature, it says she lives in a lighthouse.’ That taught us early on that agents like Iris need a safety harness.” The developers banned Iris from sending an email to anyone other than the person who sent the original request. Then Pega’s ethics department sent Iris a potentially abusive email from a Pega employee to test her response. Iris reasoned that the email was either a joke, abusive, or that the employee was under distress, said Walker. She considered forwarding the email to the employee’s manager or to HR. But both of these options were now blocked by her developers. “So what does she do? She sent an out of office,” he said. “Conflict avoidance, right? So human, but very creative.”

Graduate Student Develops an A.I.-Based Approach to Restore Time-Damaged Artwork to Its Former Glory The method could help bring countless old paintings, currently stored in the back rooms of galleries with limited conservation budgets, to light Scans of the painting retouched with a new technique during various stages in the process. On the right is the restored painting with the applied laminate mask. Courtesy of the researchers via MIT In a contest for jobs requiring the most patience, art restoration might take first place. Traditionally, conservators restore paintings by recreating the artwork’s exact colors to fill in the damage, one spot at a time. Even with the help of X-ray imaging and pigment analyses, several parts of the expensive process, such as the cleaning and retouching, are done by hand, as noted by Artnet’s Jo Lawson-Tancred. Now, a mechanical engineering graduate student at MIT has developed an artificial intelligence-based approach that can achieve a faithful restoration in just hours—instead of months of work. In a paper published Wednesday in the journal Nature, Alex Kachkine describes a new method that applies digital restorations to paintings by placing a thin film on top. If the approach becomes widespread, it could make art restoration more accessible and help bring countless damaged paintings, currently stored in the back rooms of galleries with limited conservation budgets, back to light. The new technique “is a restoration process that saves a lot of time and money, while also being reversible, which some people feel is really important to preserving the underlying character of a piece,” Kachkine tells Nature’s Amanda Heidt. Meet the engineer who invented an AI-powered way to restore art Watch on While filling in damaged areas of a painting would seem like a logical solution to many people, direct retouching raises ethical concerns for modern conservators. That’s because an artwork’s damage is part of its history, and retouching might detract from the painter’s original vision. “For example, instead of removing flaking paint and retouching the painting, a conservator might try to fix the loose paint particles to their original places,” writes Hartmut Kutzke, a chemist at the University of Oslo’s Museum of Cultural History, for Nature News and Views. If retouching is absolutely necessary, he adds, it should be reversible. As such, some institutions have started restoring artwork virtually and presenting the restoration next to the untouched, physical version. Many art lovers might argue, however, that a digital restoration printed out or displayed on a screen doesn’t quite compare to seeing the original painting in its full glory. That’s where Kachkine, who is also an art collector and amateur conservator, comes in. The MIT student has developed a way to apply digital restorations onto a damaged painting. In short, the approach involves using pre-existing A.I. tools to create a digital version of what the freshly painted artwork would have looked like. Based on this reconstruction, Kachkine’s new software assembles a map of the retouches, and their exact colors, necessary to fill the gaps present in the painting today. The map is then printed onto two layers of thin, transparent polymer film—one with colored retouches and one with the same pattern in white—that attach to the painting with conventional varnish. This “mask” aligns the retouches with the gaps while leaving the rest of the artwork visible. “In order to fully reproduce color, you need both white and color ink to get the full spectrum,” Kachkine explains in an MIT statement. “If those two layers are misaligned, that’s very easy to see. So, I also developed a few computational tools, based on what we know of human color perception, to determine how small of a region we can practically align and restore.” The method’s magic lies in the fact that the mask is removable, and the digital file provides a record of the modifications for future conservators to study. Kachkine demonstrated the approach on a 15th-century oil painting in dire need of restoration, by a Dutch artist whose name is now unknown. The retouches were generated by matching the surrounding color, replicating similar patterns visible elsewhere in the painting or copying the artist’s style in other paintings, per Nature News and Views. Overall, the painting’s 5,612 damaged regions were filled with 57,314 different colors in 3.5 hours—66 hours faster than traditional methods would have likely taken. Overview of Physically-Applied Digital Restoration Watch on “It followed years of effort to try to get the method working,” Kachkine tells the Guardian’s Ian Sample. “There was a fair bit of relief that finally this method was able to reconstruct and stitch together the surviving parts of the painting.” The new process still poses ethical considerations, such as whether the applied film disrupts the viewing experience or whether A.I.-generated corrections to the painting are accurate. Additionally, Kutzke writes for Nature News and Views that the effect of the varnish on the painting should be studied more deeply. Still, Kachkine says this technique could help address the large number of damaged artworks that live in storage rooms. “This approach grants greatly increased foresight and flexibility to conservators,” per the study, “enabling the restoration of countless damaged paintings deemed unworthy of high conservation budgets.” Get the latest stories in your inbox every weekday.

Jump into Depth Map ML in Silhouette. This node in Silhouette 2025 and above uses AI to analyze 2D images and determines object distances; generating an automated depth map. This tutorial, presented by Ben Brownlee for Boris FX, demonstrates how to leverage depth maps for various tasks, including color correction, lighting effects, and creating mattes. Learn to manipulate these mattes and build custom compound nodes for enhanced workflow efficiency. Download a free trial of Silhouette at borisfx.com : https://borisfx.com/products/silhouette/ // K E Y P O I N T S // ⦁ Automatic Depth Map Generation: Learn how Depth Map ML automatically creates a depth map, distinguishing foreground and background objects. ⦁ Control Effects with Depth Maps: Understand how to use depth map data to control color correction, blurs, and lighting effects for precise adjustments. ⦁ Manipulating Depth Mattes with zMatte: Explore the zMatte node to extract specific depth ranges, allowing for highly localized effects and creative control. ⦁ Keyframing and Tracking for Refinement: Discover how to use keyframes and tracking data with near and far depth points to refine depth map accuracy, especially with moving objects. ⦁ Creating Custom Compound Nodes: Learn to combine Depth Map ML with other nodes like zMatte into custom compound nodes, streamlining your workflow for repeatable effects. Learn more about Silhouette here : https://borisfx.com/products/silhouette/ / D I S C O R D C H A N N E L // https://www.borisfxdiscord.com/ #depthmap #VFX #videoeffects / / C H A P T E R L I S T / / 00:00 Intro 00:40 Introduction to Depth Map ML 01:23 Different Depth Map ML Models 02:23 Different Mapping options 04:55 Using generated depth maps with Depth nodes 05:50 Using Depth Maps as mattes 07:01 Refine depth ranges with the zMatte keyer 10:05 Creating a Compound Node to simplify the workflow

Autoregressive image generation has been shaped by advances in sequential modeling, originally seen in natural language processing. This field focuses on generating images one token at a time, similar to how sentences are constructed in language models. The appeal of this approach lies in its ability to maintain structural coherence across the image while allowing for high levels of control during the generation process. As researchers began to apply these techniques to visual data, they found that structured prediction not only preserved spatial integrity but also supported tasks like image manipulation and multimodal translation effectively. Despite these benefits, generating high-resolution images remains computationally expensive and slow. A primary issue is the number of tokens needed to represent complex visuals. Raster-scan methods that flatten 2D images into linear sequences require thousands of tokens for detailed images, resulting in long inference times and high memory consumption. Models like Infinity need over 10,000 tokens for a 1024×1024 image. This becomes unsustainable for real-time applications or when scaling to more extensive datasets. Reducing the token burden while preserving or improving output quality has become a pressing challenge. Efforts to mitigate token inflation have led to innovations like next-scale prediction seen in VAR and FlexVAR. These models create images by predicting progressively finer scales, which imitates the human tendency to sketch rough outlines before adding detail. However, they still rely on hundreds of tokens—680 in the case of VAR and FlexVAR for 256×256 images. Moreover, approaches like TiTok and FlexTok use 1D tokenization to compress spatial redundancy, but they often fail to scale efficiently. For example, FlexTok’s gFID increases from 1.9 at 32 tokens to 2.5 at 256 tokens, highlighting a degradation in output quality as the token count grows. Researchers from ByteDance introduced DetailFlow, a 1D autoregressive image generation framework. This method arranges token sequences from global to fine detail using a process called next-detail prediction. Unlike traditional 2D raster-scan or scale-based techniques, DetailFlow employs a 1D tokenizer trained on progressively degraded images. This design allows the model to prioritize foundational image structures before refining visual details. By mapping tokens directly to resolution levels, DetailFlow significantly reduces token requirements, enabling images to be generated in a semantically ordered, coarse-to-fine manner. The mechanism in DetailFlow centers on a 1D latent space where each token contributes incrementally more detail. Earlier tokens encode global features, while later tokens refine specific visual aspects. To train this, the researchers created a resolution mapping function that links token count to target resolution. During training, the model is exposed to images of varying quality levels and learns to predict progressively higher-resolution outputs as more tokens are introduced. It also implements parallel token prediction by grouping sequences and predicting entire sets at once. Since parallel prediction can introduce sampling errors, a self-correction mechanism was integrated. This system perturbs certain tokens during training and teaches subsequent tokens to compensate, ensuring that final images maintain structural and visual integrity. The results from the experiments on the ImageNet 256×256 benchmark were noteworthy. DetailFlow achieved a gFID score of 2.96 using only 128 tokens, outperforming VAR at 3.3 and FlexVAR at 3.05, both of which used 680 tokens. Even more impressive, DetailFlow-64 reached a gFID of 2.62 using 512 tokens. In terms of speed, it delivered nearly double the inference rate of VAR and FlexVAR. A further ablation study confirmed that the self-correction training and semantic ordering of tokens substantially improved output quality. For example, enabling self-correction dropped the gFID from 4.11 to 3.68 in one setting. These metrics demonstrate both higher quality and faster generation compared to established models. By focusing on semantic structure and reducing redundancy, DetailFlow presents a viable solution to long-standing issues in autoregressive image generation. The method’s coarse-to-fine approach, efficient parallel decoding, and ability to self-correct highlight how architectural innovations can address performance and scalability limitations. Through their structured use of 1D tokens, the researchers from ByteDance have demonstrated a model that maintains high image fidelity while significantly reducing computational load, making it a valuable addition to image synthesis research. Check out the Paper and GitHub Page. All credit for this research goes to the researchers of this project. Also, feel free to follow us on Twitter and don’t forget to join our 95k+ ML SubReddit and Subscribe to our Newsletter. NikhilNikhil is an intern consultant at Marktechpost. He is pursuing an integrated dual degree in Materials at the Indian Institute of Technology, Kharagpur. Nikhil is an AI/ML enthusiast who is always researching applications in fields like biomaterials and biomedical science. With a strong background in Material Science, he is exploring new advancements and creating opportunities to contribute.Nikhilhttps://www.marktechpost.com/author/nikhil0980/Teaching AI to Say ‘I Don’t Know’: A New Dataset Mitigates Hallucinations from Reinforcement FinetuningNikhilhttps://www.marktechpost.com/author/nikhil0980/This AI Paper Introduces LLaDA-V: A Purely Diffusion-Based Multimodal Large Language Model for Visual Instruction Tuning and Multimodal ReasoningNikhilhttps://www.marktechpost.com/author/nikhil0980/NVIDIA AI Introduces Fast-dLLM: A Training-Free Framework That Brings KV Caching and Parallel Decoding to Diffusion LLMsNikhilhttps://www.marktechpost.com/author/nikhil0980/Meet NovelSeek: A Unified Multi-Agent Framework for Autonomous Scientific Research from Hypothesis Generation to Experimental Validation

If you’ve recently found your Mail app stuck on a blank white screen, you’re not alone. It’s been happening to my girlfriend, to my mom, and to quite a few thousand people around the world. Here are a few workarounds. No Mail for you Over the past few days, a growing number of iPhone users have reported that the Mail app displays a blank screen upon opening, becomes unresponsive, or crashes unexpectedly. This issue appears to affect various iPhone models, suggesting it is a software-related problem tied to iOS 18.5. Apple has yet to acknowledge the issue. Interestingly, while iOS 18.5 was released last month, widespread reports of this Mail app issue have only surfaced recently. Users across platforms like Reddit, Apple Support Communities and MacRumors have been sharing their experiences, indicating that the problem isn’t isolated. Temporary workarounds While there’s no official fix yet, some users have found temporary solutions, including restarting the iPhone, force-closing the Mail app, disabling keyboard auto-correction and smart punctuation, and reinstalling the Mail app. However, these solutions are not permanent, and the problem often recurs. Nothing from Apple As of now, Apple has not officially acknowledged the issue. The company’s System Status page does not indicate any problems with the Mail app either, and there has been no official communication regarding a fix. In situations like this, Apple usually waits until it has a bug fix roadmap before it says anything. Still, we asked Apple about it, and will update this post if they respond. Add 9to5Mac to your Google News feed.  FTC: We use income earning auto affiliate links. More.You’re reading 9to5Mac — experts who break news about Apple and its surrounding ecosystem, day after day. Be sure to check out our homepage for all the latest news, and follow 9to5Mac on Twitter, Facebook, and LinkedIn to stay in the loop. Don’t know where to start? Check out our exclusive stories, reviews, how-tos, and subscribe to our YouTube channel

Posted on : June 5, 2025 By Tech World Times AI  Rate this post Artificial intelligence tools now touch nearly every corner of modern business, from customer service and marketing to supply chain management and HR. These powerful technologies promise speed, accuracy, and insight, but their missteps can cause more than temporary inconvenience. A single AI-driven error can result in regulatory investigations, civil lawsuits, or public scandals that threaten the foundation of a business. Understanding how legal exposure arises from AI mistakes—and how a skilled attorney protects your interests—is no longer an option, but a requirement for any forward-thinking business owner. What Types of AI Errors Create Legal Liability? AI does not think or reason like a human; it follows code and statistical patterns, sometimes with unintended results. These missteps can create a trail of legal liability for any business owner. For example, an online retailer’s AI recommends discriminatory pricing, sparking allegations of unfair trade practices. An HR department automates hiring decisions with AI, only to face lawsuits for violating anti-discrimination laws. Even an AI-driven chatbot, when programmed without proper safeguards, can inadvertently give health advice or misrepresent product claims—exposing the company to regulatory penalties. Cases like these are regularly reported in Legal news as businesses discover the high cost of digital shortcuts. When Is a Business Owner Liable for AI Mistakes? Liability rarely rests with the software developer or the tool itself. Courts and regulators expect the business to monitor, supervise, and, when needed, override AI decisions. Suppose a financial advisor uses AI to recommend investments, but the algorithm suggests securities that violate state regulations. Even if the AI was “just following instructions,” the advisor remains responsible for client losses. Similarly, a marketing team cannot escape liability if their AI generates misleading advertising. The bottom line: outsourcing work to AI does not outsource legal responsibility. How Do AI Errors Harm Your Reputation and Operations? AI mistakes can leave lasting marks on a business’s reputation, finances, and operations. A logistics firm’s route-optimization tool creates data leaks that breach customer privacy and trigger costly notifications. An online business suffers public backlash after an AI-powered customer service tool sends offensive responses to clients. Such incidents erode public trust, drive customers to competitors, and divert resources into damage control rather than growth. Worse, compliance failures can result in penalties or shutdown orders, putting the entire enterprise at risk. What Steps Reduce Legal Risk From AI Deployments? Careful planning and continuous oversight keep AI tools working for your business—not against it. Compliance is not a “set it and forget it” matter. Proactive risk management transforms artificial intelligence from a liability into a valuable asset. Routine audits, staff training, and transparent policies form the backbone of safe, effective AI use in any organization. You should review these AI risk mitigation strategies below. Implement Manual Review of Sensitive Outputs: Require human approval for high-risk tasks, such as legal filings, financial transactions, or customer communications. A payroll company’s manual audits prevented the accidental overpayment of employees by catching AI-generated errors before disbursement. Update AI Systems for Regulatory Changes: Stay ahead of new laws and standards by regularly reviewing AI algorithms and outputs. An insurance brokerage avoided regulatory fines by updating their risk assessment models as privacy laws evolved. Document Every Incident and Remediation Step: Keep records of AI errors, investigations, and corrections. A healthcare provider’s transparency during a patient data mix-up helped avoid litigation and regulatory penalties. Limit AI Access to Personal and Sensitive Data: Restrict the scope and permissions of AI tools to reduce the chance of data misuse. A SaaS provider used data minimization techniques, lowering the risk of exposure in case of a system breach. Consult With Attorneys for Custom Policies and Protocols: Collaborate with experienced Attorneys to design, review, and update AI compliance frameworks. How Do Attorneys Shield Your Business From AI Legal Risks? Attorneys provide a critical safety net as AI integrates deeper into business operations. They draft tailored contracts, establish protocols for monitoring and escalation, and assess risks unique to your industry. In the event of an AI-driven incident, legal counsel investigates the facts, manages communication with regulators, and builds a robust defense. By providing training, ongoing guidance, and crisis management support, attorneys ensure that innovation doesn’t lead to exposure—or disaster. With the right legal partner, businesses can harness AI’s power while staying firmly on the right side of the law. Tech World TimesTech World Times (TWT), a global collective focusing on the latest tech news and trends in blockchain, Fintech, Development & Testing, AI and Startups. If you are looking for the guest post then contact at techworldtimes@gmail.com