The State Department wants US diplomats to fight data localization around the world. The policy position is correct. It’s just that the messenger has spent the last few months systematically destroying every reason anyone might listen.
In the State Department cable, dated February 18 and signed by U.S. Secretary of State Marco Rubio, the agency said such laws would “disrupt global data flows, increase costs and cybersecurity risks, limit Artificial Intelligence (AI) and cloud services, and expand government control in ways that can undermine civil liberties and enable censorship.”
The cable said the Trump administration was pushing for “a more assertive international data policy” and that diplomats should “counter unnecessarily burdensome regulations, such as data localization mandates.”
Now, if you’ve been reading Techdirt for any length of time, you know we’ve long been critical of data localization mandates. They really are bad for the internet. They fracture the global internet into national fiefdoms. They raise costs. They can actually weaken cybersecurity by forcing data onto local infrastructure that may be less secure. And in authoritarian or semi-authoritarian countries, data localization is often a thinly veiled mechanism for government surveillance and control of information. Requiring that data stay within a country’s borders makes it a whole lot easier for that country’s government to demand access to it.
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So on the merits, the policy position described in the cable is basically correct. Data sovereignty mandates do tend to hurt the open internet, and the US pushing back on them has, historically, been a genuinely good thing for global internet freedom. Indeed, the US State Department has a long history of pushing back on such efforts.
But the US already blew its credibility on this issue before this administration even took office. Remember the TikTok ban? That was a bipartisan effort—both Trump and Biden supported it—to do the exact same “data sovereignty” nonsense we’re now telling other countries not to do.
While the justification kept changing depending on the day and who you talked to, many of its supporters (including those in the Supreme Court who blessed that travesty) insisted that it was perfectly legitimate to force a “data localization” plan on TikTok because “ooh, scary foreigners shouldn’t have American data.” Literally this was the Supreme Court’s conclusion:
But Congress has determined that divestiture is necessary to address its well-supported national security concerns regarding TikTok’s data collection practices and relationship with a foreign adversary.
So both parties, both of the last two presidents, and the entirety of the Supreme Court announced to the world “it’s totally fine to force a foreign company to not just be required to hold data locally, but even to be forced to sell off local operations to a favored oligarch.”
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That alone would make this diplomatic push awkward. But let’s talk about why it lands as completely absurd right now.
The reason data sovereignty initiatives have been “gathering pace,” as Reuters puts it, is in no small part because of the behavior of this very administration. Countries—and especially our allies in Europe—are rushing to build digital walls because the US government has spent the last few months torching every alliance, cozying up to dictators, kicking off arbitrary trade wars, and generally making it abundantly clear that it has zero respect for the norms, rules, or institutions that underpin international cooperation.
You cannot spend your days insulting and threatening your closest allies, engaging in wildly protectionist trade policies, and signaling to the world that no agreement or partnership is safe from your whims, and then turn around and demand that those same allies keep the data pipeline wide open for American tech companies.
This would be like setting your neighbor’s house on fire and then asking to borrow their garden hose. And everyone sees exactly what’s happening:
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Bert Hubert, a Dutch cloud computing expert and former member of the board that regulates the Dutch intelligence services, said Europe’s increasing wariness of America’s tech companies may be spurring Washington to take a more aggressive tack.
“Where the previous administration attempted to woo European customers, the current one is demanding that Europeans disregard their own data privacy regulations that could hinder American business,” he said.
And then there’s what the cable actually reveals about its real motivations. The cable reportedly frames data sovereignty as a threat to “Artificial Intelligence (AI) and cloud services,” which is a pretty revealing tell. It strips away any pretense that this is about internet freedom or civil liberties. What it actually says is: “American AI companies need access to your citizens’ data to train their models, and we’d appreciate it if you’d stop putting up barriers to that.”
This is the diplomatic equivalent of saying the quiet part loud. The US isn’t making a principled argument about the open internet here. It’s making a commercial demand dressed up in freedom rhetoric. And that’s not exactly a compelling pitch to countries that are already worried about the dominance of US tech firms and the lack of meaningful privacy protections in the US.
The cable also takes a swipe at the GDPR specifically, calling it an example of “unnecessarily burdensome data processing restrictions.” Look, the GDPR has plenty of problems and we’ve written about many of them. But when the US government is publicly calling Europe’s flagship privacy law a burden it wants to fight, while simultaneously offering no credible privacy framework of its own, it’s hard to see how that’s going to win hearts and minds.
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Meanwhile, Rubio has also been ordering diplomats to fight against the EU’s Digital Services Act, and the US reportedly wants to set up a portal to help Europeans “bypass” content moderation rules around hate speech and terrorist content.
So the diplomatic message from the US to Europe is currently: ignore your privacy laws, ignore your content moderation laws, give our companies access to your data for AI training, and also we might slap tariffs on you tomorrow. Good luck getting anyone to take the “open internet” pitch seriously after that.
The deeply frustrating thing about all of this is that there really is a strong case to be made against data localization. The open, global internet has been one of the most powerful engines of innovation, communication, and human rights in history, and fragmenting it into national data silos is genuinely dangerous. But making that case requires credibility. It requires being the kind of partner that other countries can trust with their citizens’ data. It requires demonstrating, through your own behavior, that you believe in the rule of law, in stable institutions, and in respecting the sovereignty of your allies even while you advocate for open data flows.
Henry Farrell and Martha Finnemore’s 2013 Foreign Affairs piece on “The End of Hypocrisy” keeps proving prescient. A huge part of America’s moral power around the world resulted from the clear hypocrisy between America’s stated values and the ones we repeatedly failed to uphold. But it was a convenient myth that we could pretend to hold the moral high ground, and use that as a form of soft power to demand better of others. That falls apart entirely with administrations like Trump’s, where the idea of soft power, or even the moral high ground, is seen as woke nonsense. The Trump administration refuses to understand the power of that myth.
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But now it’s gone. And that has a real cost: the policy position in Rubio’s cable is exactly right. The US should be pushing back on problematic data localization and “data sovereignty” laws. They’re bad for the open internet and good for local surveillance. This is an argument worth making—and we’ve surrendered the ability to make it credibly at precisely the moment it most needs to be made.
Foreign diplomats aren’t stupid. They can see that we demanded TikTok localize or divest while telling them localization is bad. They can see that we’re attacking their privacy laws while offering nothing in return. They can see that we’re framing this as “freedom” while the cable itself reveals it’s about feeding data to American AI companies. The policy is correct. The hypocrisy is total. And the result is that we’ve handed every country in the world a perfectly reasonable justification to ignore us.
South Korean authorities made a serious blunder as they sought to showcase their crackdown on online fraud and cybercrime. According to local reports, Seoul’s National Tax Service (NTS) released a press statement detailing an on-site investigation targeting 124 high-profile tax fraud suspects. In the process, it also published a photo… Read Entire Article Source link
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Today’s Connections: Sports Edition is a tough one unless you’re really familiar with a certain sports romance show and book series. If you are, you should have no problems with the blue category. If you’re struggling with today’s puzzle but still want to solve it, read on for hints and the answers.
Connections: Sports Edition is published by The Athletic, the subscription-based sports journalism site owned by The Times. It doesn’t appear in the NYT Games app, but it does in The Athletic’s own app. Or you can play it for free online.
Hints for today’s Connections: Sports Edition groups
Here are four hints for the groupings in today’s Connections: Sports Edition puzzle, ranked from the easiest yellow group to the tough (and sometimes bizarre) purple group.
Yellow group hint: Lone Star State.
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Green group hint: Support the team.
Blue group hint: Hockey love story.
Purple group hint: Not short.
Answers for today’s Connections: Sports Edition groups
Sometimes a visually compelling metaphor is all you need to get an otherwise complicated idea across. In the summer of 2001, a Tulane physics professor named John P. Perdew came up with a banger. He wanted to convey the hierarchy of computational complexity inherent in the behavior of electrons in materials. He called it “Jacob’s Ladder.” He was appropriating an idea from the Book of Genesis, in which Jacob dreamed of a ladder “set up on the earth, and the top of it reached to heaven. And behold the angels of God ascending and descending on it.”
Jacob’s Ladder represented a gradient and so too did Perdew’s ladder, not of spirit but of computation. At the lowest rung, the math was the simplest and least computationally draining, with materials represented as a smoothed-over, cartoon version of the atomic realm. As you climbed the ladder, using increasingly more intensive mathematics and compute power, descriptions of atomic reality became more precise. And at the very top, nature was perfectly described via impossibly intensive computation—something like what God might see.
With this metaphor in mind, we propose to extend Jacob’s Ladder beyond Perdew’s version, to encompass all computational approaches to simulating the behavior of electrons. And instead of climbing rung by rung toward an unreachable summit, we have an idea to bend the ladder so that even the very top lies within our grasp. Specifically, we at Microsoft envision a hybrid approach. It starts with using quantum computers to generate exquisitely accurate data about the behavior of electrons—data that would be prohibitively expensive to compute classically. This quantum-generated data will then train AI models running on classical machines, which can predict the properties of materials with remarkable speed. By combining quantum accuracy with AI-driven speed, we can ascend Jacob’s Ladder faster, designing new materials with novel properties and at a fraction of the cost.
At the base of Jacob’s Ladder are classical models that treat atoms as simple balls connected by springs—fast enough to handle millions of atoms over long times but with the lowest precision. Moving up along the black line, semiempirical methods add some quantum mechanical calculations. Next are approximations based on Hartree-Fock (HF) and density functional theory (DFT), which include full quantum behavior of individual electrons but model their interactions in an averaged way. The greater accuracy requires significant computing power, which limits them to simulating molecules with no more than a few hundred atoms. At the top are coupled-cluster and full configuration interaction (FCI) methods—exquisitely accurate but, at the moment, restricted to tiny molecules or subsets of electrons due to the large computational costs involved. Quantum computing can bend the accuracy-versus-cost curve at the top of Jacob’s Ladder [orange line], making highly accurate calculations feasible for large systems. AI, trained on this quantum-accurate data, can flatten this curve [purple line], enabling rapid predictions for similar systems at a fraction of the cost of classical computing.Source: Microsoft Quantum
In our approach, the base of Jacob’s Ladder still starts with classical models that treat atoms as simple balls connected by springs—models that are fast enough to handle millions of atoms over long times, but with the lowest precision. As we ascend the ladder, some quantum mechanical calculations are added to semiempirical methods. Eventually, we’ll get to the full quantum behavior of individual electrons but with their interactions modeled in an averaged way; this greater accuracy requires significant compute power, which means you can only simulate molecules of no more than a few hundred atoms. At the top will be the most computationally intensive methods—prohibitively expensive on classical computers but tractable on quantum computers.
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In the coming years, quantum computing and AI will become critical tools in the pursuit of new materials science and chemistry. When combined, their forces will multiply. We believe that by using quantum computers to train AI on quantum data, the result will be hyperaccurate AI models that can reach ever higher rungs of computational complexity without the prohibitive computational costs.
This powerful combination of quantum computing and AI could unlock unprecedented advances in chemical discovery, materials design, and our understanding of complex reaction mechanisms. Chemical and materials innovations already play a vital—if often invisible—role in our daily lives. These discoveries shape the modern world: new drugs to help treat disease more effectively, improving health and extending life expectancy; everyday products like toothpaste, sunscreen, and cleaning supplies that are safe and effective; cleaner fuels and longer-lasting batteries; improved fertilizers and pesticides to boost global food production; and biodegradable plastics and recyclable materials to shrink our environmental footprint. In short, chemical discovery is a behind-the-scenes force that greatly enhances our everyday lives.
The potential is vast. Anywhere AI is already in use, this new quantum-enhanced AI could drastically improve results. These models could, for instance, scan for previously unknown catalysts that could fix atmospheric carbon and so mitigate climate change. They could discover novel chemical reactions to turn waste plastics into useful raw materials and remove toxic “forever chemicals” from the environment. They could uncover new battery chemistries for safer, more compact energy storage. They could supercharge drug discovery for personalized medicine.
And that would just be the beginning. We believe quantum-enhanced AI will open up new frontiers in materials science and reshape our ability to understand and manipulate matter at its most fundamental level. Here’s how.
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How Quantum Computing Will Revolutionize Chemistry
To understand how quantum computing and AI could help bend Jacob’s Ladder, it’s useful to look at the classical approximation techniques that are currently used in chemistry. In atoms and molecules, electrons interact with one another in complex ways called electron correlations. These correlations are crucial for accurately describing chemical systems. Many computational methods, such as density functional theory (DFT) or the Hartree-Fock method, simplify these interactions by replacing the intricate correlations with averaged ones, assuming that each electron moves within an average field created by all other electrons. Such approximations work in many cases, but they can’t provide a full description of the system.
A joint project between Microsoft and Pacific Northwest National Laboratory used AI and high-performance computing to identify potential materials for battery electrolytes. The most promising were synthesized [top and middle] and tested [bottom] at PNNL. Dan DeLong/Microsoft
Electron correlation is particularly important in systems where the electrons are strongly interacting—as in materials with unusual electronic properties, like high-temperature superconductors—or when there are many possible arrangements of electrons with similar energies—such as compounds containing certain metal atoms that are crucial for catalytic processes.
In these cases, the simplified approach of DFT or Hartree-Fock breaks down, and more sophisticated methods are needed. As the number of possible electron configurations increases, we quickly reach an “exponential wall” in computational complexity, beyond which classical methods become infeasible.
Enter the quantum computer. Unlike classical bits, which are either on or off, qubits can exist in superpositions—effectively coexisting in multiple states simultaneously. This should allow them to represent many electron configurations at once, mirroring the complex quantum behavior of correlated electrons. Because quantum computers operate on the same principles as the electron systems they will simulate, they will be able to accurately simulate even strongly correlated systems—where electrons are so interdependent that their behavior must be calculated collectively.
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AI’s Role in Advancing Computational Chemistry
At present, even the computationally cheap methods at the bottom of Jacob’s Ladder are slow, and the ones higher up the ladder are slower still. AI models have emerged as powerful accelerators to such calculations because they can serve as emulators that predict simulation outcomes without running the full calculations. The models can speed up the time it takes to solve problems up and down the ladder by orders of magnitude.
This acceleration opens up entirely new scales of scientific exploration. In 2023 and 2024, we collaborated with researchers at Pacific Northwest National Laboratory (PNNL) on using advanced AI models to evaluate over 32 million potential battery materials, looking for safer, cheaper, and more environmentally friendly options. This enormous pool of candidates would have taken about 20 years to explore using traditional methods. And yet, within less than a week, that list was narrowed to 500,000 stable materials and then to 800 highly promising candidates. Throughout the evaluation, the AI models replaced expensive and time-consuming quantum chemistry calculations, in some cases delivering insights half a million times as fast as would otherwise have been the case.
We then used high-performance computing (HPC) to validate the most promising materials with DFT and AI-accelerated molecular dynamics simulations. The PNNL team then spent about nine months synthesizing and testing one of the candidates—a solid-state electrolyte that uses sodium, which is cheap and abundant, and some other materials, with 70 percent less lithium than conventional lithium-ion designs. The team then built a prototype solid-state battery that they tested over a range of temperatures.
This potential battery breakthrough isn’t unique. AI models have also dramatically accelerated research in climate science, fluid dynamics, astrophysics, protein design, and chemical and biological discovery. By replacing traditional simulations that can take days or weeks to run, AI is reshaping the pace and scope of scientific research across disciplines.
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However, these AI models are only as good as the quality and diversity of their training data. Whether sourced from high-fidelity simulations or carefully curated experimental results, these data must accurately represent the underlying physical phenomena to ensure reliable predictions. Poor or biased data can lead to misleading outcomes. By contrast, high-quality, diverse datasets—such as those full-accuracy quantum simulations—enable models to generalize across systems and uncover new scientific insights. This is the promise of using quantum computing for training AI models.
How to Accelerate Chemical Discovery
The real breakthrough will come from strategically combining quantum computing’s and AI’s unique strengths. AI already excels at learning patterns and making rapid predictions. Quantum computers, which are still being scaled up to be practically useful, will excel at capturing electron correlations that classical computers can only approximate. So if you train classical models on quantum-generated data, you’ll get the best of both worlds: the accuracy of quantum delivered at the speed of AI.
As we learned from the Microsoft-PNNL collaboration on electrolytes, AI models alone can greatly speed up chemical discovery. In the future, quantum-accurate AI models will tackle even bigger challenges. Consider the basic discovery process, which we can think of as a funnel. Scientists begin with a vast pool of candidate molecules or materials at the wide-mouthed top, narrowing them down using filters based on desired properties—such as boiling point, conductivity, viscosity, or reactivity. Crucially, the effectiveness of this screening process depends heavily on the accuracy of the models used to predict these properties. Inaccurate predictions can create a “leaky” funnel, where promising candidates are mistakenly discarded or poor ones are mistakenly advanced.
Quantum-accurate AI models will dramatically improve the precision of chemical-property predictions. They’ll be able to help identify “first-time right” candidates, sending only the most promising molecules to the lab for synthesis and testing—which will save both time and cost.
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Another key aspect of the discovery process is understanding the chemical reactions that govern how new substances are formed and behave. Think of these reactions as a network of roads winding through a mountainous landscape, where each road represents a possible reaction step, from starting materials to final products. The outcome of a reaction depends on how quickly it travels down each path, which in turn is determined by the energy barriers along the way—like mountain passes that must be crossed. To find the most efficient route, we need accurate calculations of these barrier heights, so that we can identify the lowest passes and chart the fastest path through the reaction landscape.
Even small errors in estimating these barriers can lead to incorrect predictions about which products will form. Case in point: A slight miscalculation in the energy barrier of an environmental reaction could mean the difference between labeling a compound a “forever chemical” or one that safely degrades over time.
Accurate modeling of reaction rates is also essential for designing catalysts—substances that speed up and steer reactions in desired directions. Catalysts are crucial in industrial chemical production, carbon capture, and biological processes, among many other things. Here, too, quantum-accurate AI models can play a transformative role by providing the high-fidelity data needed to predict reaction outcomes and design better catalysts.
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Once trained, these AI models, powered by quantum-accurate data, will revolutionize computational chemistry by delivering quantum-level precision. And once the AI models, which run on classical computers, are trained with quantum computing data, researchers will be able to run high-accuracy simulations on laptops or desktop computers, rather than relying on massive supercomputers or future quantum hardware. By making advanced chemical modeling more accessible, these tools will democratize discovery and empower a broader community of scientists to tackle some of the most pressing challenges in health, energy, and sustainability.
Remaining Challenges for AI and Quantum Computing
By now, you’re probably wondering: When will this transformative future arrive? It’s true thatquantum computers still struggle with error rates and limited lifetimes of usable qubits. And they still need to scale to the size required for meaningful chemistry simulations. Meaningful chemistry simulations beyond the reach of classical computation will require hundreds to thousands of high-quality qubits with error rates of around 10-15, or one error in a quadrillion operations. Achieving this level of reliability will require fault tolerance through redundant encoding of quantum information in logical qubits, each consisting of hundreds of physical qubits, thus requiring a total of about a million physical qubits. Current AI models for chemical-property predictions may not have to be fully redesigned. We expect that it will be sufficient to start with models pretrained on classical data and then fine-tune them with a few results from quantum computers.
Despite some open questions, the potential rewards in terms of scientific understanding and technological breakthroughs make our proposal a compelling direction for the field. The quantum computing industry has begun to move beyond the early noisy prototypes, and high-fidelity quantum computers with low error rates could be possible within a decade.
Realizing the full potential of quantum-enhanced AI for chemical discovery will require focused collaboration between chemists and materials scientists who understand the target problems, experts in quantum computing who are building the hardware, and AI researchers who are developing the algorithms. Done right, quantum-enhanced AI could start to tackle the world’s toughest challenges—from climate change to disease—years ahead of anyone’s expectations.
The Trump administration is purposefully cruel. That much cannot be argued, not when it has deliberately sent deportees to foreign torture prisons, dumped them in war-torn countries with histories of human rights abuses, and stranded people its has been ordered to release far from home without their IDs, phones, or money.
This regime loves to inflict pain. Its desire to erase as many minorities from this country as possible has led it to do things no competent government would ever do, especially not one that serves a nation long known as a land of hope and opportunity. The people who first landed here were escaping religious persecution. (They then went on to eradicate the people who actually lived here, but stick with me for a moment.) People seeking the same refuge from persecution are now being ejected from this country as quickly as possible.
The good news is that a federal court has at least pumped the brakes on one such DHS effort. In Minnesota — where Trump has used benefits fraud allegations as justification for a “surge” that has resulted in two murders committed by federal officers (so far!) — a federal judge has just told the administration it can’t just suddenly declare an end to refugee status.
The longtime government policy has been that refugees — vetted and legally admitted individuals — who are yet to adjust to lawful permanent resident status cannot be detained on that basis alone.
With Operation PARRIS (Post-Admission Refugee Reverification and Integrity Strengthening), the Trump administration wants to change that.
In a pair of memos issued in December 2025 and February 2026 — which Law Dork has covered extensively — the Department of Homeland Security has purported to change that policy by rescinding and re-rescinding the 2010 U.S. Immigration and Customs Enforcement policy that most recently enunciated that policy for applying the relevant provision — 8 U.S.C. 1159 — of the Refugee Act of 1980.
What used to be a normal part of the “give me your tired, huddled masses” ideal that once represented this Land of Opportunity is no longer. The Trump administration is now claiming it can simply pretend existing law no longer matters. And while it is true that Congress could decide to rewrite or overturn the 1980 law, it cannot simply be ignored just because the DHS sent out a couple of memos telling federal officers they’re free to ignore existing law.
Fortunately, this Minnesota court isn’t going to sit by while the administration pretends the only interpretation of the law is the one it recently wrote for itself. From the opinion [PDF]:
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When the clock strikes 12:00 a.m. on the 366th day after a refugee was lawfully admitted to the United States, according to the Government, 8 U.S.C. § 1159(a) gives Department of Homeland Security officials the power to arrest and detain that refugee with no limits on the length of detention. Because § 1159(a) provides no such power, the Court will issue a preliminary injunction enjoining Defendants from arresting or detaining refugees in Minnesota on the basis that have not yet been adjusted to lawful permanent resident status—which, by law, cannot occur until one year has passed. The Court will not allow federal authorities to use a new and erroneous statutory interpretation to terrorize refugees who immigrated to this country under the promise that they would be welcomed and allowed to live in peace, far from the persecution they fled.
You see the obvious evil here, right? A refugee — at earliest — cannot secure lawful permanent status until after one year has passed. Trump’s DHS says refugees applying for permanent residence can be arrested and detained indefinitely 24 hours after they’ve been here for a year. The court is right: this not only flips the law on its head, it completely destroys an American ideal that made this nation of a beacon of hope for oppressed people around the world.
Decades ago, as a nation, we made a solemn promise to refugees fleeing persecution: that after rigorous vetting, they would be welcomed to the United States and given the opportunity to rebuild their lives. We assured them that they could care for their families, earn a living, contribute to their communities, and live in peace here in the United States. We promised them the hope that one day they could achieve the American Dream.
The Government’s new policy breaks that promise—without congressional authorization—and raises serious constitutional concerns. The new policy turns the refugees’ American Dream into a dystopian nightmare.
A government that retains any notion of serving the public good would never have attempted to enact this policy. Only a government filled with unjustified hatred of “others” would dare to destroy the American Dream. And only a regime so laden with craven bigots would dare to drape themselves in the flag while shitting on what actually makes this country great.
And, it must be noted, this is only a temporary block. The court is going to allow the government to defend its actions. I don’t think the government will win, but it will certainly kick this up the ladder to the appellate level. That’s fine, so long as the restraining order stays in place while the government cooks up a defense for its blatant racism. With any luck, this will stick all the way to the Supreme Court… and then hopefully after that review as well. No one who truly loves America would back this effort. And no one who only claims to love America while strip-mining it of its greatness should be allowed to turn this great nation into a “dystopian nightmare.”
Entering SKALA codes during RBMK operation. (Credit: Pripyat-Film studio)
Running a nuclear power plant isn’t an easy task, even with the level of automation available to a 1980s Soviet RBMK reactor. In their continuing efforts to build a full-sized, functional replica of an RBMK control room as at the Chornobyl Nuclear Power Plant – retired in the early 2000s – the [Chornobyl Family] channel has now moved on to the SKALA system.
Previously we saw how they replicated the visually very striking control panel for the reactor core, with its many buttons and status lights. SKALA is essentially the industrial control system, with multiple V-3M processor racks (‘frames’), each with 20k 24-bit words of RAM. Although less powerful than a PDP-11, its task was to gather all the sensor information and process them in real-time, which was done in dedicated racks.
Output from SKALA’s DREG program were also the last messages from the doomed #4 reactor. Unfortunately an industrial control system can only do so much if its operators have opted to disable every single safety feature. By the time the accident unfolded, the hardware was unable to even keep up with the rapid changes, and not all sensor information could even be recorded on the high-speed drum printer or RTA-80 teletypes, leaving gaps in our knowledge of the accident.
(Credit: Chornobyl Family, YouTube)
Setting up a genuine RTA-80 teletype is still one of the goals, but these old systems are not easy to use. Same with the original software that ran on these V-3M computer frames, which was loaded from paper tape (the ‘library’), including the aforementioned DREG program. This process creates executable code that is put on magnetic tapes, with magnetic tape also used for storage.
(Credit: Chornobyl Family, YouTube)
The workings of the SKALA system and its individual programs including KRV, DREG and PRIZMA are explained in the video, each having its own focus on a part of the RBMK reactor’s status and overall health. Interacting with SKALA occurs via a special keyboard, on which the operator enters command codes to change e.g. set points, with parameters encoded in this code.
Using this method, RBMK operators can set and request values, with parameters and any error codes displayed on a dedicated display. There is also the Mnemonic Display for the SKALA system which provides feedback to the operator on the status of the SKALA system, including any faults.
Although to many people the control system of a power plant is just the control room, with its many confusing buttons, switches, lights and displays, there is actually a lot more to it, with systems SKALA and its associated hardware an often overlooked aspect. It’s great to see this kind of knowledge being preserved, and even poured into a physical model that simulates the experience of using the system.
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The long-lived nature of nuclear power reactors means that even today 1960s and 1970s-era industrial automation system are still in active use, but once the final reactor goes offline – or is modernized during refurbishing – a lot of the institutional knowledge of these systems tends to vanish and with it a big part of history.
The ballot also includes nominees for delegate-elect/director-elect offices submitted by division and region nominating committees, as well as IEEE Technical Activities vice president-elect; IEEE-USA president-elect; and IEEE Standards Association board of governors members-at-large.
Those elected take office on 1 January 2027.
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IEEE members who want to run for an office, except for IEEE president-elect, who have not been nominated, must submit their petition intention to the IEEE Board of Directors by 1 April. Petitions should be sent to the IEEE Corporate Governance staff at elections@ieee.org. The petition intention deadline for IEEE president-elect was 31 December.
Election Updates
Regional elections will also take place. Eligible voting members in IEEE Region 1 (Northeastern U.S.) and Region 2 (Eastern U.S.) will elect the future IEEE Region 2 delegate-elect/director-elect (Eastern and Northeastern U.S.) for the 2027—2028 term. Members in the future IEEE Region 10 (North Asia) will elect the IEEE Region 10 delegate-elect/director-elect for the same term. These changes reflect IEEE’s upcoming region realignment, as outlined in The Institute’s September 2024 article, “How Region Realignment Will Impact IEEE Elections.”
Beginning this year, only professional members will be eligible to vote in IEEE’s annual election or sign related petitions. Ballots will be created for eligible voting members on record as of 31 March. To ensure voting eligibility, all members should review and update their contact information and communication preferences by that date.
To support sustainability initiatives, the “Candidate Biographies and Statements” booklet will no longer be available in print. Members can access the candidate biographies and statements within their electronic ballot, view them on the annual election website, or download the digital booklet. Members are also encouraged to vote electronically.
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For more information about the offices up for election, the process for getting on the annual ballot, and deadlines, visit the website or email elections@ieee.org.
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Security researchers say a highly sophisticated iPhone exploitation toolkit dubbed “Coruna,” which possibly originated from a U.S. government contractor, has spread from suspected Russian espionage operations to crypto-stealing criminal campaigns. Apple has patched the exploited vulnerabilities in newer iOS versions, but tens of thousands of devices may have already been compromised. An anonymous reader quotes an excerpt from Wired’s report: Security researchers at Google on Tuesday released a report describing what they’re calling “Coruna,” a highly sophisticated iPhone hacking toolkit that includes five complete hacking techniques capable of bypassing all the defenses of an iPhone to silently install malware on a device when it visits a website containing the exploitation code. In total, Coruna takes advantage of 23 distinct vulnerabilities in iOS, a rare collection of hacking components that suggests it was created by a well-resourced, likely state-sponsored group of hackers.
In fact, Google traces components of Coruna to hacking techniques it spotted in use in February of last year and attributed to what it describes only as a “customer of a surveillance company.” Then, five months later, Google says a more complete version of Coruna reappeared in what appears to have been an espionage campaign carried out by a suspected Russian spy group, which hid the hacking code in a common visitor-counting component of Ukrainian websites. Finally, Google spotted Coruna in use yet again in what seems to have been a purely profit-focused hacking campaign, infecting Chinese-language crypto and gambling sites to deliver malware that steals victims cryptocurrency.
Conspicuously absent from Google’s report is any mention of who the original surveillance company “customer” that deployed Coruna may have been. But the mobile security company iVerify, which also analyzed a version of Coruna it obtained from one of the infected Chinese sites, suggests the code may well have started life as a hacking kit built for or purchased by the US government. Google and iVerify both note that Coruna contains multiple components previously used in a hacking operation known as “Triangulation” that was discovered targeting Russian cybersecurity firm Kaspersky in 2023, which the Russian government claimed was the work of the NSA. (The US government didn’t respond to Russia’s claim.)
Coruna’s code also appears to have been originally written by English-speaking coders, notes iVerify’s cofounder Rocky Cole. “It’s highly sophisticated, took millions of dollars to develop, and it bears the hallmarks of other modules that have been publicly attributed to the US government,” Cole tells WIRED. “This is the first example we’ve seen of very likely US government tools — based on what the code is telling us — spinning out of control and being used by both our adversaries and cybercriminal groups.” Regardless of Coruna’s origin, Google warns that a highly valuable and rare hacking toolkit appears to have traveled through a series of unlikely hands, and now exists in the wild where it could still be adopted — or adapted — by any hacker group seeking to target iPhone users. “How this proliferation occurred is unclear, but suggests an active market for ‘second hand’ zero-day exploits,” Google’s report reads. “Beyond these identified exploits, multiple threat actors have now acquired advanced exploitation techniques that can be re-used and modified with newly identified vulnerabilities.”
Apple CarPlay is quite useful for techie drivers who want to use their iPhones for navigation, entertainment, and other tasks while driving. But even though this car interface is designed to reduce distractions and let you focus more on your driving, the fact that you have to take one hand off the steering wheel and your eyes off the road to manipulate it can be dangerous at times.
That’s why you should turn on Apple CarPlay voice commands. To do so, you need to go to CarPlay’s settings (when you’re parked), choose Accessibility, scroll down to Physical and Motor, and then turn on Voice Control. You’ll also find the ability to change CarPlay’s text size in this menu, which is one of the useful CarPlay features most users miss out on. After you’ve turned on Voice Control, you should see its icon appear under the signal bar on the left side of your display.
There are quite a few commands available, but we’re listing the most useful ones we found and now use daily. So, if you want additional convenience without compromising on safety, here are some of the Apple CarPlay voice commands you need to start using.
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Stop or start listening
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While voice control is convenient if you’re driving alone, it might become an issue if you have a passenger and want to enjoy a conversation. So, if you want Apple CarPlay to stop listening to you and mistake your words for commands, you can simply make it stop by saying, “Stop Listening.” When it registers this command, the Voice Control icon will gray out and will have a slash running through it.
Once you’ve dropped off your passenger or if you want to make some changes on your car’s infotainment screen, you can just say, “Start Listening.” This will cause the Voice Control icon to revert back to its original color, and CarPlay will be ready to receive your commands again.
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Note that these commands only work on the Apple CarPlay system, so it does not automatically mute the microphone in your car. So, telling Apple CarPlay to “Stop Listening” will not stop the other party from hearing you if you’re on a call. Aside from that, CarPlay will still respond to voice commands, even if you mute the speakers.
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Open app
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This is one of the most useful voice commands on Apple CarPlay, as it lets you quickly open the app that you want without having to swipe through the home screen. This is especially great if you’ve installed some of the best CarPlay apps but don’t want to take your eyes off the road just to look for them on your car’s infotainment display.
This command is pretty simple to use — all you need to do is say Open “name of app.” For example, if you’re tired of listening to Apple Podcasts and want to play your favorite Spotify playlist instead, you can just say “Open Spotify,” and it will then pull up your playlists. You can also say “Open Calendar” while you’re driving home to ensure that you’re not forgetting anything on your schedule. Personally, I use Voice Control to launch my smart home app from my car by saying “Open SmartLife” as I approach my neighborhood. That way, I can just tap on the scenes I’ve set up to turn on my air conditioning units and arrive to a cool home.
The only downside to this is that it sometimes has trouble recognizing complicated app names. For example, it readily understands when I say “Open Maps” or “Open Brave.” But when I asked it to “Open SpotHero” or “Open OnTheWay,” it refused to respond.
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Pan directions
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You still need to use Siri if you want to set a destination on your preferred navigation app and avoid traffic jams and accidents. But once you’re on your way and want to see what the traffic is like several miles ahead without dragging your finger along the map, you can instead say “Pan Left,” “Pan Right,” “Pan Down,” or “Pan Up.” This is particularly useful if you find yourself in a traffic jam and you’re deciding between taking that longer alternative route that your navigation app has ignored so far or sticking with its recommended path instead.
Apple has also introduced other commands for using your navigation app with your voice, like “Zoom In,” “Zoom Out,” or “Route Overview.” Unfortunately, I tried all these other commands on Apple Maps, Google Maps, and Waze, and none of them worked — they only recognized the “Pan” voice commands that I issued. This is a bummer, especially as all these other commands are quite useful, but I guess we’ll have to wait for further updates to iOS before they work properly (or maybe I need a new iPhone).
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Siri
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Many modern vehicles have a button on the steering wheel that will activate your phone’s voice assistant, whether you’re using Android or iOS. However, if you retrofitted your older vehicle with an Apple CarPlay or Android Auto screen, which is one of the cool car gadgets you can get from Amazon, you will not have this option — you’ll have to extend your hand to press and hold the home or apps icon on the touchscreen, making it a bit more inconvenient to call up Siri.
But with Voice Control turned on, you can just say “Siri” and the trusty Apple voice assistant will instantly pop up on your display. You can then use it to ask for directions, play a track that just popped into your memory, call saved contacts, or even open your garage door if you’ve set up home automation. This makes it one of the CarPlay features you should definitely check out after updating to iOS 26.
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Swipe left or right
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No, the “swipe left” and “swipe right” voice commands do not let you pick or reject potential dates on Tinder. Instead, they allow you to move between screens when you’re on the CarPlay home screen. This command is particularly useful if you use CarPlay widgets, which Apple introduced in iOS 26.
There are some CarPlay widgets that are surprisingly useful, like Dynamic Lyrics for Carpool Karaoke. But if you find yourself lost and need to check directions, you can just say “Swipe Left” to go back to the main view and see your map, destination, and now playing song, instead of swiping your finger across the display. And when you want to return to the widgets view, you can just say “Swipe Right,” and CarPlay will take you back to the song lyrics (or whatever widget you were last viewing).
It is often much easier to just swipe your finger across the screen, but this command is still a convenient backup for those who don’t want to take their hands off the steering wheel. More importantly, some car brands, like Mazda, deactivate the touchscreen function while you’re driving, meaning this will be the only option you have if you want to change screens without touching the command control dial.
Need to fill up the tank but don’t want to drain the wallet in the process? You might want to think about hitting the gas station on Sundays, at least in 2026. That’s according to a new nationwide analysis from GasBuddy, which found that Sunday is the cheapest day of the week to buy gas in most of America. Remember: That’s most, not all. Alaska, Delaware, Indiana, Kansas, Montana, Ohio, Pennsylvania, South Dakota, and Wyoming are all exceptions to the rule.
Another trend that remains true for much of the country: From coast to coast, prices typically climb higher during the middle of the week. And even though there are some exceptions to this data, as well, there’s a single fact that applies to every single one of these 50 states: Per their findings, Sunday is no state’s worst day to fill up. Saturday’s the best in Kansas, Pennsylvania, South Dakota, and Wyoming, while Tuesday’s the best in Montana alone.
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How GasBuddy was able to narrow down the data
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The discount fill-up app’s report looked at daily statewide average gas prices over the past year and pinpointed the most consistent weekday pricing patterns. Across much of America, it looks like prices start going up on Monday, peak around midweek, and start going back down again heading into the weekend. That’s a steady weekly rhythm most of us can depend on. In most states, the difference between the lowest-priced day and the most expensive one can be anywhere from 4 to 9 cents per gallon. For motorists filling a standard 12- to 16-gallon tank, that difference is going to add up over time.
Most of us know to expect some modest fluctuations in gas prices from week to week. However, according to GasBuddy, some states actually experience much more dramatic price swings than others. It’s due to a pattern known as price cycling, and it’s most prevalent in states like Michigan, Indiana, Ohio, Florida, Texas, and some West Coast areas. There, prices often spike much more sharply on a given day before gradually declining over the next several days. In these places, the difference between the highest and lowest points in a weekly cycle can be way more dramatic: as much as 15 to 45 cents per gallon. Don’t forget that gas prices always add an extra 9/10 of a cent to add insult to injury.
At the base of Jacob’s Ladder are classical models that treat atoms as simple balls connected by springs—fast enough to handle millions of atoms over long times but with the lowest precision. Moving up along the black line, semiempirical methods add some quantum mechanical calculations. Next are approximations based on Hartree-Fock (HF) and density functional theory (DFT), which include full quantum behavior of individual electrons but model their interactions in an averaged way. The greater accuracy requires significant computing power, which limits them to simulating molecules with no more than a few hundred atoms. At the top are coupled-cluster and full configuration interaction (FCI) methods—exquisitely accurate but, at the moment, restricted to tiny molecules or subsets of electrons due to the large computational costs involved. Quantum computing can bend the accuracy-versus-cost curve at the top of Jacob’s Ladder [orange line], making highly accurate calculations feasible for large systems. AI, trained on this quantum-accurate data, can flatten this curve [purple line], enabling rapid predictions for similar systems at a fraction of the cost of classical computing.Source: Microsoft Quantum
A joint project between Microsoft and Pacific Northwest National Laboratory used AI and high-performance computing to identify potential materials for battery electrolytes. The most promising were synthesized [top and middle] and tested [bottom] at PNNL. Dan DeLong/Microsoft
