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Monday, October 5, 2026

6 Guidelines for Governing AI

<img src="https://spectrum.ieee.org/media-library/illustration-of-floating-hands-rearranging-various-ai-chat-boxes-and-tasks.jpg?id=68052366&width=1200&height=400&coordinates=0%2C1042%2C0%2C1042"/> For the first 10 years of my career, I worked in product management and data analytics by myself. I wrote database queries that pulled numbers out of corporate systems, built statistical models to predict what customers would buy, and shipped data pipelines that moved information between business systems.I built and scaled analytics teams at <a href="https://corporate.bestbuy.com/" rel="noopener noreferrer" target="_blank">Best Buy</a> and <a href="https://corporate.target.com/about" rel="noopener noreferrer" target="_blank">Target</a>, studying how customers shop and what stores should stock. Today I lead enterprise AI transformation at <a href="https://corporate.lowes.com/who-we-are" rel="noopener noreferrer" target="_blank">Lowe’s</a>, the Fortune 100 home improvement retailer.The goal is not to sell <a href="https://spectrum.ieee.org/70-years-of-artificial-intelligence" target="_self">artificial intelligence</a>; it is to use it to deliver useful expertise at the moment a customer needs it. In retail and other customer-facing industries, virtual assistants can help people address everyday questions—such as how to repair a leaky faucet—while guiding them toward relevant products, services, or next steps. As these capabilities become more common, technology roles are changing. The work is no longer <a href="https://spectrum.ieee.org/ieee-ai-3119-standards" target="_self">limited to building AI systems</a>; it also includes defining how they operate: which decisions they can make autonomously, when they must escalate to a person, and which actions must remain off-limits.That shift—from <a href="https://spectrum.ieee.org/two-new-ai-ethics-certifications" target="_self">building AI systems to governing them</a>—is coming for anyone who is accountable for what such systems produce. Not the casual user typing into a chatbot but the engineers, product managers, analysts, and business operators who sign off on work a machine drafted.It is the subject of the book I recently coauthored, <a href="https://a.co/d/06iT1WDW" rel="noopener noreferrer" target="_blank">The Enterprise Brain</a>. I call the change the “governor shift,” from executing tasks yourself to setting the intent, principles, and boundaries within systems that execute them for you.Business operators might not write code; they will decide which pricing exceptions an agent may approve and which it must escalate.That is governing.A 2025 report from <a href="https://www.media.mit.edu/groups/nanda/overview/" rel="noopener noreferrer" target="_blank">MIT Media Lab’s Project NANDA</a> found that, despite an estimated US $30 billion to $40 billion in enterprise generative-AI investment, the vast majority of organizations in its dataset had not yet demonstrated measurable profit-and-loss impact. The report estimated that only about 5 percent of integrated pilots were generating substantial value, underscoring how difficult it remains to move from experimentation to scaled business outcomes.Researchers named the pattern the <a href="https://mlq.ai/media/quarterly_decks/v0.1_State_of_AI_in_Business_2025_Report.pdf" rel="noopener noreferrer" target="_blank">GenAI Divide</a>, the term I adopted for the book.The companies rarely lack technology; they use the same models as the 5 percent that are winners. But they lack people who can direct the systems and stand behind the results. <h2>Guidelines to follow</h2>Here are six guidelines.<ul><li>Recognize when you have become “human middleware.” In software, “middleware” is the code that sits between two systems and passes information back and forth. Many of us have become its human version. Take an honest look at your week. How much time is spent pulling data out of one tool, reformatting it, and routing it to another team? I call this the “administrator trap,” which is set by the architecture, not by the people caught in it. Relaying is what AI agents now do well. But they cannot judge which numbers deserve attention, which risks are real, or which compromises are worth making.</li></ul><ul><li>Trade rules for principles. For many years, workers used rules to manage their work. Refunds for a product over a certain amount needed a signature from upper management, for example. Writing code needed two reviewers. Rules work at human speed. But rules break when a system makes thousands of decisions per hour and meets situations no rulebook anticipated, such as a complaint covered by three different policies. A rule says to do exactly this specific thing; a principle says to achieve the outcome without crossing certain lines. Governing AI means writing those principles in priority order so the system settles its own conflicts the way a well-led team does when the manager is not available. Never harm the customer. Tell the truth even if the company loses a sale. Protect the economics, and then move quickly. Underneath sits a question of decision rights: the formal authority over who or what may make a given call. Writing down the answers in what I call a “library of principles” is now core leadership work, whether you’re a technologist or a business owner.</li></ul><ul><li>Write your culture into your code. Many companies have turned their values into posters that hang on office walls. But an AI agent cannot read the posters. Instead, write your governance as code. Include your values and policies as machine-readable instructions that the AI agent will follow automatically. Do so in three layers. The top is the constitution, which states the rules an agent may never break, and never state a fact it cannot support. The second layer is the doctrine: how the business competes and the acceptable trade-offs to get there, such as protecting a long-term relationship over a short-term sale. At the bottom sits the playbook, which has the tactics used for one task.</li></ul><ul><li>Install a trust thermostat, not a trust switch. The question that stalls nearly every company’s AI deployment is some version of: “What if it tells our biggest customer something wrong, or quotes a price we will not honor?” It might. Treating trust as a switch leaves two bad options: an unsupervised system or a human reviewing every transaction—which would cost more than the automation would save. The alternative is a thermostat. Every decision an agent makes carries a confidence score measured against the principles set. Above an agreed threshold, it proceeds alone; below it, a human decides. That person’s answer is fed back into the learning loop so the next similar case clears the threshold on its own. Every decision stays transparent, auditable, and explainable—which is what I call a glass box.</li></ul><ul><li>Fix context before you govern. You cannot govern a system that cannot see the whole picture. Ask your best employee about a project, and they will pull together the budget, the contract clause, and the customer’s last complaint because they know it all by heart. Most enterprise AI fails that test, because the information sits scattered across applications that store it in incompatible formats. I describe the full loop as Connections, Context, Reasoning, Actions, and Governance (CCRAG). Connections feed in raw information such as transactions and service records. Context weaves it into a context graph, which is a single connected picture of the business that gives agents something close to memory. Reasoning makes the decisions. Actions carry them back into the business systems. Governance keeps things aligned with the company’s intent. Most organizations obsess over the reasoning in the middle and underinvest in context and governance, which is exactly where humans play a role. Context compounds: Every interaction makes the graph richer and harder to reproduce.</li></ul><ul><li>Learn to lead by exception. The most important change in habit comes last. Most of us have been trained to check every report and every number because we never knew where an error could surface. In a governed system, the machine tells you which cases it could not resolve confidently. Routine workflows go untouched, and your attention goes to the small portion that is ambiguous, unfamiliar, or high stakes. At first, that might feel like losing control, but it is the opposite. It is what makes a self-scaling enterprise possible, an organization whose output grows without its head count growing in proportion. People were not removed from the loop; they were raised above it.</li></ul><h2>The identity question</h2>When I talk with people about the shift, their resistance is rarely about technical issues. More often it is about identity: If the AI does the doing, what do I do?I have watched capable people freeze on that question. I’ve also asked myself the question.Doing was never really the job, though. Judgment was. Doing was just how we expressed it.AI has not made judgment less valuable. It has made it the scarcest resource in the organization because, for the first time, one person’s judgment, written down well, can guide thousands of decisions each day.Judgment has a twin we talk less about: taste. Judgment tells you whether an answer is sound. Taste tells you whether the question was worth asking and which of a hundred defensible options to offer the customer. A machine will happily generate all 100 options, but it cannot tell you which is best.The AI transition rewards instincts many IEEE members already have: systems thinking, precision about requirements, and honesty about failure modes. The tools have changed, but the discipline has not.Governing is where taste and judgment stop being soft words and become the work itself.The people who treat it that way, rather than as a step away from engineering, will define the profession in the age of AI. Reference: https://ift.tt/QtDCviU

Smart Car Researcher Wants to Eliminate Stoplights

<img src="https://spectrum.ieee.org/media-library/a-middle-aged-greek-man-with-facial-hair-smiling-against-an-illustrated-street-map-background.jpg?id=68036422&width=1245&height=700&coordinates=0%2C187%2C0%2C188"/> <a href="https://christosgcassandras.org/" rel="noopener noreferrer" target="_blank">Christos Cassandras</a> has spent more than 40 years studying how computers and machines make decisions. Lately, his work has focused on a problem nearly every driver knows well: sitting at a red light with no cross traffic in sight, wondering why it takes so long for the light to turn green.Cassandras, an IEEE Life Fellow, is a professor at <a href="https://www.bu.edu/" rel="noopener noreferrer" target="_blank">Boston University</a>, where he headed its <a href="https://www.bu.edu/eng/academics/departments-and-divisions/systems-engineering/" rel="noopener noreferrer" target="_blank">systems engineering division</a>, a graduate program he helped create.<h3>Christos Cassandras</h3> Employer Boston UniversityTitle ProfessorMember grade Life FellowAlma maters Yale, Stanford, and HarvardHe is the recipient of this year’s <a href="https://ieee-itss.org/" rel="noopener noreferrer" target="_blank">IEEE Intelligent Transportation Systems Society</a><a href="https://ieee-itss.org/awards/outstanding-research/" rel="noopener noreferrer" target="_blank"> Outstanding Research Award</a> for his work on <a href="https://spectrum.ieee.org/tag/autonomous-vehicles" target="_self">autonomous vehicles</a> and the systems that help them drive—work that could one day get rid of traffic lights altogether.That idea—proving with mathematical models and intelligent algorithms using cars’ relative speeds, mass, and distance as inputs that self-driving cars will behave safely before they are ever let loose on the streets—has become the foundation of Cassandras’s career and the reason his name keeps coming up in conversations about transportation.In the 1990s he theorized that a machine such as a car could be understood as two things at once: an object obeying the laws of physics and a computer processing information and making decisions. This led to what we now call cyber physical systems. That framework is still the standard way engineers describe a car’s mechanical behavior.“Ultimately, the key word is safety,” Cassandras says. “You would not buy a self-driving car unless the manufacturer could guarantee that it’s safe for you.”<h2> Growing up in Greece during a dictatorship</h2>Born in Athens, Cassandras was a teenager during<a href="https://www.britannica.com/topic/the-Colonels" rel="noopener noreferrer" target="_blank"> Greece’s military dictatorship</a>, a period he says scarred him and many others but also taught him some hard lessons. His father was a partner in an insulation manufacturing company, and his mother was a homemaker.His friends pulled Cassandras toward engineering. As a teenager, he ran with a group of classmates who read about philosophy, history, and science. They were especially struck by the <a href="https://airandspace.si.edu/explore/stories/apollo-11-moon-landing" rel="noopener noreferrer" target="_blank">1969 Apollo 11 moon landing</a> and the questions it raised about the technology that made it possible.That same year also marked the <a href="https://www.icann.org/en/blogs/details/the-first-message-transmission-29-10-2019-en" rel="noopener noreferrer" target="_blank">first time a piece of data was sent from one computer to another</a>—an early step toward the creation of the Internet. Several inspiring teachers pushed his curiosity about technology even further.He was awarded a scholarship to study in the United States, and he enrolled at<a href="https://www.yale.edu/" rel="noopener noreferrer" target="_blank"> Yale</a>, where he first studied physics and philosophy before switching to engineering. He earned his bachelor’s degree in engineering and applied science in 1977. At the time, Yale didn’t yet offer separate degrees in fields such as electrical or mechanical engineering.From there, Cassandras went to <a href="https://www.stanford.edu/" rel="noopener noreferrer" target="_blank">Stanford</a>, earning a master’s degree in electrical engineering in 1978. There, he became interested in <a href="https://en.wikipedia.org/wiki/Game_theory" rel="noopener noreferrer" target="_blank">game theory</a>, the mathematical study of decision-making and strategy.A mentor pointed him to <a href="https://www.harvard.edu/" rel="noopener noreferrer" target="_blank">Harvard</a>, home to a leading game theory researcher at the time. His new mentor, Professor <a href="https://seas.harvard.edu/person/yu-chi-ho" rel="noopener noreferrer" target="_blank">Yu-Chi “Larry” Ho</a>, now an IEEE Life Fellow, advised him to abandon game theory, which Ho considered a scientific dead end at the time. Ho suggested a newer, less-explored area of research, that of emerging dynamic systems in modern technology whose behavior could be understood and managed through occurrences of discrete events.Cassandras took the advice. He earned a second master’s degree from Harvard the following year and completed his Ph.D. in applied mathematics there in 1982. He and Ho remain friends and colleagues.<h2> Fiat assembly line births of a new kind of engineering</h2>Ask people in <a href="https://ieee-itss.org/" rel="noopener noreferrer" target="_blank">intelligent transportation systems</a> circles why Cassandras’s name carries so much weight, and the answer traces back to a theoretical shift he helped pioneer starting in the 1980s. Cassandras recognized that human-made systems—computers, factory machines and, eventually, vehicles—operate on a different kind of logic than the one that governs the natural world.The physical world runs on time-driven physics, the same math <a href="https://www.britannica.com/biography/Isaac-Newton" rel="noopener noreferrer" target="_blank">Isaac Newton</a> used centuries ago. But machines built by people, he realized, are better understood as event-driven: They mostly sit still until something happens, like a button click or a part arriving on a conveyor belt.That insight didn’t come from a textbook. It took shape during Cassandras’s early graduate work, when he was handed a real-world problem. He was assigned to figure out how to manage the buffers—the temporary holding areas for car parts—on an assembly line for <a href="https://www.fiat.com/" rel="noopener noreferrer" target="_blank">Fiat</a>, the Italian carmaker.Solving that concrete, practical puzzle helped him see how event-driven thinking could describe an entire class of systems that classical physics-based math couldn’t handle well. This came to be known as <a href="https://link.springer.com/book/10.1007/978-3-030-72274-6" rel="noopener noreferrer" target="_blank">discrete event systems</a>.By the early 1990s, that insight had matured into a formal framework: hybrid systems theory, now known as <a href="https://en.wikipedia.org/wiki/Cyber-physical_system" rel="noopener noreferrer" target="_blank">cyber physical systems</a> theory. The idea is that a machine can be understood as an object obeying the laws of physics and as a computer obeying event-driven dynamics.<h2>Building “event-driven” systems</h2>After completing his doctorate, Cassandras wanted to test himself outside of academia. He spent about a year and a half as a systems engineer at Information and Technology for Production (ITP), a small manufacturing-automation startup in the Boston area. The experience convinced him that the “real world” wasn’t so different from academic life, and he returned to research while continuing to consult for the company for roughly a decade. He has kept up the habit of staying connected to industry throughout his career.In 1984 he joined the <a href="https://www.umass.edu/admissions/first-year-students?utm_campaign=bvk-7134_umass_search_fy27admissions_brand_newyork&utm_medium=paidsearch&utm_source=google&utm_term=university%20of%20massachusetts%20amherst&utm_content=bvk-706915381353&gad_source=1&gad_campaignid=21490020363&gbraid=0AAAAAo2qFQxIzla1V5lpfXZHpK4qipiY3&gclid=CjwKCAjwzNTUBhAjEiwA7zcvWu8D6YmKbwxYUZDSKywzAJpLhc_eVOsRla2-nnwEb05i-qaZFsnipRoC9c0QAvD_BwE" rel="noopener noreferrer" target="_blank">University of Massachusetts Amherst</a> as a faculty member in the <a href="https://www.umass.edu/engineering/electrical-and-computer-engineering" rel="noopener noreferrer" target="_blank">electrical and computer engineering department</a>. Building on the ideas born out of the Fiat project, he helped pioneer research into event-driven systems.That was a shift away from the traditional math used to describe the physical world toward systems built around discrete events. The transition, driven by the rise of computers, became the backbone of Cassandras’s research for the next three decades.In 1997, after years of commuting weekly between Amherst and Boston for his consulting work, he joined Boston University as a tenured full professor in what was then its manufacturing engineering department. As the field evolved in the early 2000s with the rise of the Internet and sensor technology, Cassandras helped establish the university’s systems engineering division. In 2008 he became the research program’s first director, a role he held until stepping down two months ago.<h3> Using math to show that self-driving cars are safe</h3>Cassandras’s recent research has focused on mathematically proving vehicle safety before a car is road-tested. He has developed models built around measurable quantities: a vehicle’s position, speed, and distance to nearby vehicles. That lets connected and automated vehicles cooperate during tricky maneuvers such as changing lanes on a highway in traffic.He has extended the approach to a bigger idea: eliminating traffic lights. One of his more striking projects involves a chronically congested intersection near his office in Boston, where Commonwealth Avenue meets the <a href="https://secretboston.co/bu-bridge-boston/" rel="noopener noreferrer" target="_blank">Boston University Bridge</a>. Using<a href="https://en.wikipedia.org/wiki/Traffic_simulation" rel="noopener noreferrer" target="_blank"> computer simulations</a>, he and his team modeled what would happen if they removed all the traffic lights, and cars simply coordinated with each other instead. The results, he says, showed improvements not only in how smoothly traffic would flow but also in safety and energy efficiency.Cassandras acknowledges that widespread adoption is still far off. For now, only a small percentage of vehicles on the road are capable of that kind of communication. But he’s testing similar coordination systems using small robots in his BU lab, working to bring the idea closer to reality and to exploit the intelligence of cooperating autonomous vehicles even when they are only a small fraction of the actual cars on the road.He says his perspective has evolved in recent years from viewing humans as obstacles to smooth automation toward designing technology that actually works for people.Through projects with Boston on <a href="https://www.bu.edu/articles/2013/if-boston-were-smart-2/" rel="noopener noreferrer" target="_blank">smart city</a> initiatives, he learned that not everyone can afford or access new technology—a lesson that reshaped how he approaches his research.“It’s not just about technology,” he says. “It’s really about technology and people.”<h2>A legacy passed on through his students</h2>His influence in the field has been amplified by some of his students. Several of his Ph.D. students have developed safety algorithms at companies including <a href="https://www.aptiv.com/" rel="noopener noreferrer" target="_blank">Aptiv</a> and <a href="https://zoox.com/" rel="noopener noreferrer" target="_blank">Zoox</a>, both of which build technology for autonomous and connected vehicles. He wrote a book, titled <a href="https://link.springer.com/book/10.1007/978-3-031-27576-0" rel="noopener noreferrer" target="_blank">Safe Autonomy with Control Barrier Functions: Theory and Applications</a>, with one of his former students. It lays out the mathematical foundations of autonomous-vehicle safety, with specific applications to intelligent transportation.<h2>A long relationship with IEEE</h2>Cassandras joined IEEE as a student member while he was a graduate student. He was drawn in by the discounted student conference registration fees, he says.That early, practical decision grew into something bigger: a professional community with which he has stayed connected for more than 40 years.He has served as president of the <a href="https://ieeecss.org/" rel="noopener noreferrer" target="_blank">IEEE Control Systems Society</a>, his primary professional home within IEEE. He has served on the <a href="https://www.ieeecss.org/publications" rel="noopener noreferrer" target="_blank">conference publications committee</a> and is currently involved with <a href="https://spectrum.ieee.org/ieee-publishing-ethics-research-integrity" target="_self">IEEE Publishing Ethics.</a>IEEE’s greatest value to his career, he says, has come through the relationships he has built at its conferences and serving on committees. Connections with colleagues and IEEE leadership have shaped his thinking and opened doors throughout his career, he says.Looking back on more than four decades of work, Cassandras says the goal has never really been about the technology for its own sake.“After all,” he says, “we’re doing all this to make society better and facilitate the comfortable lives of all of humanity.” Reference: https://ift.tt/LRZkznD

Friday, October 2, 2026

Apple changes full-disk access permissions to curb abuse from AI agents

Apple says it is changing its macOS privacy settings to stop third-party app developers from misusing them to access message histories. Friday's <a href="https://developer.apple.com/news/?id=p6zjojqw">announcement</a> comes two weeks after tech columnist Jason Aten <a href="https://www.inc.com/jason-aten/metas-new-muse-ai-agent-read-my-private-messages-i-never-asked-it-to/91408202">said</a> that Meta’s new general-purpose AI agent Muse sent him an unsolicited notification referencing a thread between him and a co-worker over Apple Messages. Aten said he never granted Muse permissions to read his messages and had assumed they were off-limits. Social media last week blew up with masses of people who agreed and said the incident showed that AI assistants given access to calendars, emails, messages, shopping accounts, and other resources are akin to a skill saw or other power tool. While potentially useful, they can do real damage if not used carefully. <h2>He said/she said</h2> Meta CTO David Singleton <a href="https://www.threads.com/@davidsingleton/post/DddI7WtG8ul">joined the fray</a> with a rebuttal that appeared solid. For Muse to access Apple Messages, a user must manually give it two privileges. One is full-disk access, a macOS system-level permission. The other is to enable a Messages connector setting in Muse.<a href="https://arstechnica.com/security/2026/10/apple-changes-full-disk-access-permissions-to-curb-abuse-from-ai-agents/">Read full article</a> <a href="https://arstechnica.com/security/2026/10/apple-changes-full-disk-access-permissions-to-curb-abuse-from-ai-agents/#comments">Comments</a> Reference : https://ift.tt/hfwrFTQ

Engineering Multipole Resonances in Dielectric Metasurfaces for Transmission, Reflection, and Absorption Control

<img src="https://spectrum.ieee.org/media-library/gray-comsol-logo-with-stylized-text-and-rounded-rectangular-emblem-on-left.png?id=68001893&width=980"/> Dielectric metasurfaces have moved to the forefront of nanophotonics, offering flat, low-loss alternatives to conventional bulk optical elements for controlling the amplitude, phase, and polarization of light. These structures are of growing interest to researchers and engineers working on sensing, energy harvesting, and flat optics, as their performance hinges on precisely engineered optical resonances. Full-wave finite element simulation, combined with semianalytical multipole decomposition in the COMSOL Multiphysics® software, gives us a way to not only predict these resonances but also uncover their underlying physical origins.In this webinar, Dr. Pavel Terekhov, postdoctoral researcher at National Institute of Standards and Technology, will trace how a single quadrumer meta-atom, originally studied for its magnetic octupole response, evolves into two distinct light manipulation regimes. He will first revisit the foundational single-particle results that motivated this work, then show how arranging quadrumers into a periodic crystalline silicon metasurface produces anomalous absorption enhancement, governed by two independent multipole mechanisms coexisting in the same structure. Building on this, he will then introduce ongoing work on a gallium nitride metasurface, where the complex interplay of four different multipoles is used to sculpt reflection and transmission spectra including the quasi-bound-states-in-the-continuum (q-BIC) manipulation.Attendees will see how COMSOL Multiphysics® and multipole decomposition connect full-wave simulation and analytical insight, turning abstract resonance behavior into physically interpretable design rules. The broader takeaway is that multipole-based simulation is not just a diagnostic tool but a design strategy: It enables the on-demand tailoring of absorption, reflection, and transmission in dielectric metasurfaces, with direct relevance to sensing, energy harvesting, and future optical device applications.Key Takeaways:<ul><li>Learn about modeling multipole resonances in dielectric metasurfaces using full-wave finite element simulation and semianalytical multipole decomposition in COMSOL Multiphysics®.</li><li>See how multipole-based simulation can be used to understand and control absorption, reflection, and transmission in silicon and gallium nitride metasurfaces.</li><li>Explore how different multipole mechanisms, including quasi-bound states in the continuum (q-BICs), can be engineered to tailor optical responses for sensing, energy harvesting, and flat optics.</li><li>Gain insights into how simulation and multipole decomposition can help researchers and engineers turn complex resonance behavior into practical design strategies for future optical devices.</li></ul><div><a href="https://event.on24.com/wcc/r/5510255/64A1C3695631E727E756BFCA0490437A?utm_source=IEEE" target="_blank">Register now for this free webinar!</a></div> Reference: https://ift.tt/oTfNmAW

Thursday, October 1, 2026

Hacks of 2 federal agencies in a month have spilled a bonanza of sensitive data

The Pentagon is informing more than 2 million current and former military members that their personnel records storing sensitive personal information were stolen over a monthslong compromise of one of its networks. The breach is the second one in recent months to expose sensitive government information. The records, according to one <a href="https://www.reddit.com/r/AirForce/comments/1wmotdr/question_about_data_breach_letter/">notification letter</a> posted to Reddit, included Social Security numbers, names, addresses, sex, race, and occupational specialty. This last category could be particularly valuable to foreign adversaries because it could help their intelligence agencies in identifying high-value military personnel. Starting last October, hackers gained access to a system operated by the <a href="https://dwp.dmdc.osd.mil/dwp/app/about/overview">Defense Manpower Data Center</a>, which collates Department of Defense personnel records. The Pentagon says that the breach compromised the records of 2.8 million living individuals. <h2>A potential boon</h2> The incident is the second time a major network breach in recent months has exposed sensitive US government personnel records that criminal groups or foreign adversaries could use. Last month, the ransomware group ShinyHunters claimed it hacked into FBI systems and stole records of thousands of the agency’s current or former employees. Reuters <a href="https://www.reuters.com/world/hacked-fbi-data-has-sensitive-information-about-employees-intelligence-roles-2026-09-23/?ref=404media.co">reported</a> the job titles in the records included ones related to investigating China or Russia.<a href="https://arstechnica.com/security/2026/10/hacks-of-2-federal-agencies-in-a-month-have-spilled-a-bonanza-of-sensitive-data/">Read full article</a> <a href="https://arstechnica.com/security/2026/10/hacks-of-2-federal-agencies-in-a-month-have-spilled-a-bonanza-of-sensitive-data/#comments">Comments</a> Reference : https://ift.tt/fImL4qU

Memory executives expect RAM shortage to continue through 2028

Micron and Samsung executives this week said the memory shortage will continue for at least the next couple of years. Micron CEO Sanjay Mehrotra expects demand for the firm’s memory to exceed its available supply over that period, he told investors last night. <a href="https://arstechnica.com/gadgets/2025/12/after-nearly-30-years-crucial-will-stop-selling-ram-to-consumers/">Micron no longer sells consumer RAM,</a> and Mehrotra’s statements refer to Micron’s business-to-business sales of high-bandwidth memory (HBM) for AI and DRAM for servers. However, his comments also have implications for consumer devices. Manufacturing capacity is prioritizing memory for AI and servers, limiting the supply of memory manufactured for consumer devices.<a href="https://arstechnica.com/information-technology/2026/10/memory-supplies-are-only-getting-tighter-micron-ceo-says/">Read full article</a> <a href="https://arstechnica.com/information-technology/2026/10/memory-supplies-are-only-getting-tighter-micron-ceo-says/#comments">Comments</a> Reference : https://ift.tt/oPN6KZH

Electricity Theft Is Rampant, but Delhi Found a Fix

<img src="https://spectrum.ieee.org/media-library/three-lightbulbs-glowing-with-the-middle-one-appearing-to-wear-a-black-mask-with-a-menacing-face.jpg?id=67857147&width=1245&height=700&coordinates=0%2C208%2C0%2C208"/> It’s disheartening how much power gets generated and then promptly lost as it travels through grid networks. This leaking of electricity happens when it vanishes as heat as well as when it is pilfered by thieves and nonpaying customers. More than half of the countries that track these metrics <a href="https://data.worldbank.org/indicator/EG.ELC.LOSS.ZS?end=2024&start=1960&view=map&year=20230" target="_blank">lost at least 10 percent of their electricity</a> in 2023, according to the World Bank. Losses topped 20 percent for 24 of those nations. Two countries lost more than half of what they generated. With numbers this high, cutting down on losses makes sense. Electricity demand is rising beyond what many grid operators can supply; reducing waste would help meet some of that demand without having to build new power plants. Plus, when the power comes from fossil fuels, any loss means emitting even more greenhouse gases into the atmosphere. And electric losses hit the bottom lines of power providers, which ultimately pass those costs on to everyone else. The trouble is, reducing electricity losses is a hard and expensive process that takes a long time. Typically, the less maintained the grid infrastructure, the more electricity that’s lost. And the more fragile the region’s law enforcement and government, the more prevalent the power theft. Natural disasters and war make things worse. <h2>Delhi’s Power Grid Comeback</h2>Fixing a power grid requires a systemic approach across many sectors. There’s no one technology that will solve the problem. At the outset, the obstacles to success may feel insurmountable. Equipment across entire grid networks must be updated. Multiple arms of government must agree to reforms and coordinate to ensure power providers are set up to succeed. Regulations must be written or revised, investments made, cultures changed.The city of Delhi did all those things. Over the past 25 years, it cut its electricity losses from about 50 to 5 percent. How the city pulled off that impressive feat is the focus of “<a href="https://spectrum.ieee.org/delhi-electricity-loss" target="_blank">The Epic Comeback of Delhi’s Power Grid</a>” by <a href="https://www.linkedin.com/in/minishajithomas/" target="_blank">Mini Shaji Thomas</a>, an electrical engineer at the university Jamia Millia Islamia who has lived in Delhi since the 1990s. She gives us a view from the inside—as a resident and a <a href="https://spectrum.ieee.org/empowering-women-power-industry" target="_self">power systems expert</a>. Delhi is a shining example, but some other regions have significantly lowered electricity losses over the last quarter century too. The country of <a href="https://data.worldbank.org/indicator/EG.ELC.LOSS.ZS?end=2023&locations=GE&start=1990&view=chart&year=20230" target="_blank">Georgia</a> went from losses of over 16 percent in 2002 to about 8 percent in 2023. In <a href="https://data.worldbank.org/indicator/EG.ELC.LOSS.ZS?end=2023&locations=SG&start=1990&view=chart&year=20230" target="_blank">Singapore</a>, losses dropped from 6.6 percent to a nearly nonexistent 0.2 percent over the same time period. <h2>Global Electricity Theft Crisis</h2>But there are many parts of the world where electricity losses remain a problem or have gotten worse. In <a href="https://data.worldbank.org/indicator/EG.ELC.LOSS.ZS?end=2023&locations=JM&start=1990&view=chart&year=20230" target="_blank">Jamaica</a>, where power theft is rampant, losses have hovered between 21 and 28 percent for years. <a href="https://data.worldbank.org/indicator/EG.ELC.LOSS.ZS?end=2023&locations=AR&start=1990&view=chart&year=20230" target="_blank">Argentina’s losses</a> nearly doubled between 2015 and 2023, going from an all-time low of about 12 percent to an all-time high of nearly 24 percent. The main problem: Transmission and distribution companies lacked the capital to maintain and upgrade their networks, which left equipment operating under stress. A delay in the installation of smart meters has allowed thieves to more easily siphon power and tamper with meters. Thomas says she hopes her account of Delhi’s grid comeback will serve as a blueprint for others. It’s possible to replicate the sweeping changes Delhi made, she says. But it “requires a concerted effort from all stakeholders, customers, the utility, the government, and their employees.” Reference: https://ift.tt/SrBfP5q