The New York Times alleges Microsoft actively encouraged OpenAI to steal its copyrighted work, reports Ars Technica, citing a new (and heavily redacted) court filing Thursday: NYT's motion comes after the [U.S.] Supreme Court sided with Cox Communications in a case where Sony tried and failed to claim that Cox was contributing to music piracy as an Internet service provider, which set a new standard for contributory infringement. Moving forward, plaintiffs will have to prove that parties intentionally acted to induce illegal conduct. Recognizing that the legal precedent has changed, the NYT now wants to amend its complaint to align its contributory infringement claim against Microsoft with that new standard... A Microsoft spokesperson told Ars that the company views the amended complaint as "a last-ditch effort by the plaintiff to save its claim from unfavorable precedent set in other recent rulings..."

The updated complaint seeks to specify that [Microsoft's] supercomputer was tailor-made to help OpenAI infringe and allege that it was built for the explicit purpose of training AI on copyrighted works without permission. And as the NYT alleged, its articles were more heavily weighted by this system, as both firms hoped to train models on the highest-quality journalism possible, so that level of writing could be confidently mimicked in outputs. By building this "unusually complex" machine, Microsoft not only helped select the works that were infringed but also provided a means to seize copyrighted works without permission, the NYT alleged. "Microsoft specifically designed it for the purpose of using essentially the whole Internet — curated to disproportionately feature Times Works — to train the most capable LLM in history," the NYT alleged... Similarly as problematic for the NYT are hallucinations where Microsoft and OpenAI models falsely cite the NYT for content that they never published... "Users who ask a search engine what The Times has written on a subject should be provided with neither an unauthorized copy nor an inaccurate forgery of a Times article, but a link to the article itself," the NYT alleged...

In a statement provided to Ars, OpenAI spokesperson Drew Pusateri reiterated the AI firm's often-repeated claims that AI training on copyrighted works is indisputably fair use... OpenAI has argued that "ChatGPT is not a substitute for a Times subscription," the NYT reported, partly because "they transformed the material for a different use."
An OpenAI spokesperson told Ars Technica that OpenAI's models "empower innovation," while a New York Times spokesperson insisted that Microsoft "actively encouraged OpenAI to steal our copyrighted works... [O]ur core claims remain the same from the day we filed this lawsuit — that Microsoft and OpenAI stole millions of The Times's copyrighted works to compete with our products and illegally enrich themselves."

The article speculates that the case's most extreme outcome "could require OpenAI and Microsoft to wipe models and start over. The NYT has also asked for permanent injunctive relief to prevent future infringement, as well as extensive damages..."

A peer-reviewed Nature critique argues that Microsoft's 2025 Majorana quantum-computing breakthrough -- and its claim that it could enable "a truly meaningful quantum computer not in decades, as some have predicted, but in years" -- is fundamentally flawed. According to Dr Henry Legg, a lecturer at the University of St Andrews, the claims were undermined by omitted data, selective plotting, and basic Python errors that concealed alternative results. Microsoft, for its part, says the bugs were minor and stands by its findings and roadmap. The Register reports: "Last year they claimed to be years, not decades from a 'topological quantum supercomputer,'" Legg told The Register in an email. "My feeling is that they are centuries, not decades away. If it works at all -- and, based on what I have seen, the most likely scenario is that it doesn't work." Based on his analysis of the research Microsoft published in 2025, Legg argues that the company's claims about finding and being able to control the elusive Majorana particle to build a topological superconductor do not withstand scrutiny.

"I demonstrate that Microsoft's tune-up software is flawed and that coding errors resulted in incorrect statements to peer reviewers," said Legg. "Raw data, which was omitted from the original paper, also appears to indicate Microsoft's devices contain considerable disorder and are not compatible with the existence of a topological gap. In other words, the prerequisites for Microsoft's claims do not appear to be met, but this was obscured because this data did not appear in the original publication."

Essentially, Microsoft has proposed a Topological Gap Protocol (TGP) that can be used to detect the phase transition deemed to be a prerequisite for conducting quantum calculations using Majorana particles. Legg argues that based on his analysis of underlying transport data (measurements of particle change) -- omitted from the original publication -- Microsoft chose to focus on results that supported its thesis and ignored data that could be interpreted as a negative result. As he notes in his critique: "The TGP plotting code was set to highlight only the largest purportedly topological region."

"The primary consequence was the omission of other regions that passed their tune-up protocol (the TGP)," said Legg. "When peer reviewers asked if other regions existed, Microsoft inaccurately stated that they had investigated the only region passing the protocol within the explored range. This was not correct." Legg also argues that Microsoft mishandled its code. "The code antisymmetrized bias voltage based on array index rather than physical value," his analysis says.

In other words, Microsoft's researchers made a basic programming mistake by evaluating the array index -- the number identifying a value's position in an array -- instead of the value to which the index refers. "There were two pretty basic Python programming errors that hid these alternative regions," Legg explained. "Their plotting software was hardcoded with a filter (zbp_cluster_numbers=[1]) that forced it to display only the single largest region, concealing other successful results from their phase maps. Changing this to zbp_cluster_numbers=[1,2] shows already a second region." Legg added: "The TGP software transformed the data by simply reversing a Python array (x[::-1]) based on its index position, ignoring the actual physical bias voltages."

Longtime Slashdot reader hackingbear shares a report from TOP500: The 67th edition of the TOP500 list of the world's most powerful supercomputers was announced today at the ISC 2026 conference in Hamburg, Germany. LineShine, a previously unlisted system installed in China, debuts at No. 1, displacing El Capitan as the world's most powerful supercomputer as measured by the High Performance Linpack (HPL) benchmark. LineShine achieved 2.198 Exaflop/s on HPL -- about 80 percent of its 2.736 Exaflop/s theoretical peak -- making it the first system on the TOP500 to exceed two exaflops of sustained double-precision performance using CPUs only.

Installed at the National Supercomputing Centre in Shenzhen (NSCS) and built by the Shenzhen Cloud Computing Center, the system is based on a custom Chinese processor and the "LingKun" platform: 13.79 million cores across 304-core LX2 processors running at 1.55 GHz, linked by the proprietary LingQi interconnect and running Kylin OS. LineShine draws approximately 42.2 megawatts of power, for an efficiency of 52.07 Gigaflops/Watt. Its debut marks the first time since 2017 that a Chinese system has led the TOP500, and it also takes over the No. 1 position on the HPCG ranking with 22.00 HPCG-Petaflop/s. On the HPL-MxP mixed-precision benchmark, LineShine reached 7.92 Exaflop/s for fourth place, a comparatively modest 3.6x speedup over its HPL score that points to a CPU-only design without dedicated low-precision accelerators. While impressive, "the results may say more about Beijing's desire to show self-sufficiency in computing systems than its standing in the global AI race," reports Reuters.

Reuters interviewed tech and policy experts who said that the results "do not mean that China has the world's fastest computer for AI work because of changes in the computing industry in recent years and the methods used to compile the list." The reports notes that LineShine "ranked fourth on a benchmark test designed to simulate computing work that is more similar to AI."

Jimmy Goodrich, a senior fellow at the University of California's Institute for Global Conflict and Cooperation, said: "If the hyperscalers submitted their systems, this 'world's fastest' would not crack the top five." Addison Snell, CEO of Intersect360 Research, a firm that focuses on supercomputers, added: "I'm not surprised it's the number one system. What I'm surprised by is that they submitted it and want recognition for it."

An anonymous reader quotes a report from Ars Technica: At its Code with Claude developer conference on Wednesday, Anthropic announced a deal with SpaceX to utilize the entire compute capacity of the latter's data center in Memphis, Tennessee. On stage at the conference, CEO Dario Amodei said the deal was intended to increase usage limits for Anthropic's Pro and Max plan subscribers. The announcement was accompanied by an increase in those usage limits; Anthropic doubled Claude Code's five-hour window limits for Pro and Max subscribers, removed the peak-hours limit reduction on Claude Code for those same accounts, and raised API limits for its Opus model. The table [here] outlining the Opus changes was shared in the company's blog post on the topic.

Anthropic claims the deal gives the company access to more than 300 megawatts of new compute capacity. For its part, SpaceX focused its announcement on the capability of the Colossus 1 supercomputer that's at the center of the deal. "Colossus 1 features over 220,000 NVIDIA GPUs, including dense deployments of H100, H200, and next-generation GB200 accelerators," SpaceX wrote. Additionally, Anthropic "expressed interest" in working with SpaceX to build up "multiple gigawatts" of orbital compute capacity, tying into a recent (but unproven) focus on exploring orbital data centers as an answer to the problem that "compute required to train and operate the next generation of these systems is outpacing what terrestrial power, land, and cooling can deliver on the timelines that matter." "I spent a lot of time last week with senior members of the Anthropic team to understand what they do to ensure Claude is good for humanity and was impressed," Elon Musk said on Wednesday. "No one set off my evil detector."

An anonymous reader quotes a report from CNN: A hacker has allegedly stolen a massive trove of sensitive data -- including highly classified defense documents and missile schematics -- from a state-run Chinese supercomputer in what could potentially constitute the largest known heist of data from China. The dataset, which allegedly contains more than 10 petabytes of sensitive information, is believed by experts to have been obtained from the National Supercomputing Center (NSCC) in Tianjin -- a centralized hub that provides infrastructure services for more than 6,000 clients across China, including advanced science and defense agencies.

Cyber experts who have spoken to the alleged hacker and reviewed samples of the stolen data they posted online say they appeared to gain entry to the massive computer with comparative ease and were able to siphon out huge amounts of data over the course of multiple months without being detected. An account calling itself FlamingChina posted a sample of the alleged dataset on an anonymous Telegram channel on February 6, claiming it contained "research across various fields including aerospace engineering, military research, bioinformatics, fusion simulation and more." The group alleges the information is linked to "top organizations" including the Aviation Industry Corporation of China, the Commercial Aircraft Corporation of China, and the National University of Defense Technology.

Cyber security experts who have reviewed the data say the group is offering a limited preview of the alleged dataset, for thousands of dollars, with full access priced at hundreds of thousands of dollars. Payment was requested in cryptocurrency. CNN cannot verify the origins of the alleged dataset and the claims made by FlamingChina, but spoke with multiple experts whose initial assessment of the leak indicated it was genuine. The alleged sample data appeared to include documents marked "secret" in Chinese, along with technical files, animated simulations and renderings of defense equipment including bombs and missiles.

The UK's science minister is announcing details of a five-year, £2.5 billion investment in nuclear fusion, reports the Times of London, "including building one of the world's first prototype fusion power plants in Nottinghamshire and developing a UK sector projected to employ 10,000 people by 2030." Despite the potentially transformative impact of fusion, which in theory could provide limitless clean energy and create a £12 trillion global market, no country has managed to use this fledgling technology to generate useable electricity... [T]he UK is backing a spherical tokamak design... investing an initial £1.3 billion into a prototype fusion power plant called Step (Spherical Tokamak for Energy Production) on the site of a decommissioned coal-fired power station at West Burton in Nottinghamshire. Paul Methven, chief executive of the government-owned UK Industrial Fusion Solutions, which is delivering the Step project, said the aim is to get the reactor operating early in the 2040s. "It's quite an aggressive programme," he said. "We need to show that we can achieve genuine 'wall socket' energy — which has not been done before."

On Monday, [science minister] Vallance will also announce £180 million for a facility in Culham, Oxfordshire, to manufacture tritium fuel and £50 million for training 2,000 scientists and engineers in fusion-related disciplines. The government is also buying a £45 million fusion-dedicated AI supercomputer called Sunrise to model plasma physics. Scientists at the UK Atomic Energy Authority last year developed an AI model that can rapidly simulate how the ultra-hot fuel in a fusion power plant will behave, cutting calculations that previously took days down to seconds...

Vallance will also announce new support and collaboration for the many fusion, robotics, engineering and AI start-ups working in Britain, to develop a strong supply chain for a new fusion sector. One of those companies, Tokamak Energy, which spun out from the UK Atomic Energy Authority in 2009, has already built a smaller reactor that has informed the Step design. In March 2022, it became the first private organisation in the world to surpass 100 million degrees Celsius in its reactor.

Longtime Slashdot reader schwit1 shares a report from CBS News: A former Google engineer has been found guilty on multiple federal charges for stealing the tech giant's trade secrets on artificial intelligence to benefit Chinese companies he secretly worked for, federal prosecutors said. According to the U.S. Attorney's Office for the Northern District of California, a jury on Thursday convicted Linwei Ding on seven counts of economic espionage and seven counts of theft of trade secrets, following an 11-day trial. The 38-year-old, also known as Leon Ding, was hired by Google in 2019 and was a resident of Newark.

According to evidence presented at trial, Ding stole more than 2,000 pages of confidential information containing Google AI trade secrets between May 2022 and April 2023. He uploaded the information to his personal Google Cloud account. Around the same time, Ding secretly affiliated himself with two Chinese-based technology companies. Around June 2022, prosecutors said Ding was in discussions to be the chief technology officer for an early-stage tech company. Several months later, he was in the process of founding his own AI and machine learning company in China, acting as the company's CEO. Prosecutors said Ding told investors that he could build an AI supercomputer by copying and modifying Google's technology.

In late 2023, prosecutors said Ding downloaded the trade secrets to his own personal computer before resigning from Google. According to the superseding indictment, Google uncovered the uploads after finding out that Ding presented himself as CEO of one of the companies during an Beijing investor conference. Around the same time, Ding told his manager he was leaving the company and booked a one-way flight to Beijing. "Silicon Valley is at the forefront of artificial intelligence innovation, pioneering transformative work that drives economic growth and strengthens our national security. The jury delivered a clear message today that the theft of this valuable technology will not go unpunished," U.S. Attorney Craig Missakian said in a statement.

An R&D lab under America's Energy Department annnounced this week that "Neuromorphic computers, inspired by the architecture of the human brain, are proving surprisingly adept at solving complex mathematical problems that underpin scientific and engineering challenges."

Phys.org publishes the announcement from Sandia National Lab: In a paper published in Nature Machine Intelligence, Sandia National Laboratories computational neuroscientists Brad Theilman and Brad Aimone describe a novel algorithm that enables neuromorphic hardware to tackle partial differential equations, or PDEs — the mathematical foundation for modeling phenomena such as fluid dynamics, electromagnetic fields and structural mechanics. The findings show that neuromorphic computing can not only handle these equations, but do so with remarkable efficiency. The work could pave the way for the world's first neuromorphic supercomputer, potentially revolutionizing energy-efficient computing for national security applications and beyond...

"We're just starting to have computational systems that can exhibit intelligent-like behavior. But they look nothing like the brain, and the amount of resources that they require is ridiculous, frankly," Theilman said.For decades, experts have believed that neuromorphic computers were best suited for tasks like recognizing patterns or accelerating artificial neural networks. These systems weren't expected to excel at solving rigorous mathematical problems like PDEs, which are typically tackled by traditional supercomputers. But for Aimone and Theilman, the results weren't surprising. The researchers believe the brain itself performs complex computations constantly, even if we don't consciously realize it. "Pick any sort of motor control task — like hitting a tennis ball or swinging a bat at a baseball," Aimone said. "These are very sophisticated computations. They are exascale-level problems that our brains are capable of doing very cheaply..."

Their research also raises intriguing questions about the nature of intelligence and computation. The algorithm developed by Theilman and Aimone retains strong similarities to the structure and dynamics of cortical networks in the brain. "We based our circuit on a relatively well-known model in the computational neuroscience world," Theilman said. "We've shown the model has a natural but non-obvious link to PDEs, and that link hasn't been made until now — 12 years after the model was introduced." The researchers believe that neuromorphic computing could help bridge the gap between neuroscience and applied mathematics, offering new insights into how the brain processes information. "Diseases of the brain could be diseases of computation," Aimone said. "But we don't have a solid grasp on how the brain performs computations yet." If their hunch is correct, neuromorphic computing could offer clues to better understand and treat neurological conditions like Alzheimer's and Parkinson's.

An anonymous reader quotes a report from the Associated Press: Mexico unveiled plans Wednesday to build what it claims will be Latin America's most powerful supercomputer -- a project the government says will help the country capitalize on the rapidly evolving uses of artificial intelligence and exponentially expand the country's computing capacity. Dubbed "Coatlicue" for the Mexica goddess considered the earth mother, the supercomputer would be seven times more powerful than the region's current leader in Brazil, Jose Merino, head of the Telecommunications and Digital Transformation Agency.

President Claudia Sheinbaum said during her morning news briefing that the location for the project had not been decided yet, but construction will begin next year. "We're very excited," said Sheinbaum, an academic and climate scientist. "It is going to allow Mexico to fully get in on the use of artificial intelligence and the processing of data that today we don't have the capacity to do." Merino said that Mexico's most powerful supercomputer operates at 2.3 petaflops -- a unit to measure computing speed, meaning it can perform one quadrillion operations per second. Coatlicue would have a capacity of 314 petaflops.

The U.S. has formed a $1 billion partnership with AMD to construct two supercomputers that will tackle large scientific problems ranging from nuclear power to cancer treatments to national security, said Energy Secretary Chris Wright and AMD CEO Lisa Su. From a report: The U.S. is building the two machines to ensure the country has enough supercomputers to run increasingly complex experiments that require harnessing enormous amounts of data-crunching capability. The machines can accelerate the process of making scientific discoveries in areas the U.S. is focused on.

Energy Secretary Wright said the systems would "supercharge" advances in nuclear power and fusion energy, technologies for defense and national security, and the development of drugs. Scientists and companies are trying to replicate fusion, the reaction that fuels the sun, by jamming light atoms in a plasma gas under intense heat and pressure to release massive amounts of energy. "We've made great progress, but plasmas are unstable, and we need to recreate the center of the sun on Earth," Wright told Reuters.

Google announced Wednesday that its quantum computer achieved the first verifiable quantum advantage, running a new algorithm 13,000 times faster than a top supercomputer. The algorithm, called Quantum Echoes, was published in the journal Nature. The results can be replicated on another quantum computer of similar quality, something Google had not demonstrated before. The quantum computer uses a chip called Willow, which was announced in December 2024. Hartmut Neven, head of Google's Quantum AI research lab, called the work a demonstration of the first algorithm with verifiable quantum advantage and a milestone on the software track.

Michel H. Devoret, who won this year's Nobel Prize in Physics and joined Google in 2023, said future quantum computers will run calculations impossible with classical algorithms. Google stopped short of claiming the work would have practical uses on its own. Instead, the company said Quantum Echoes demonstrated a technique that could be applied to other algorithms in drug discovery and materials science.

A second paper published Wednesday on arXiv showed how the method could be applied to nuclear magnetic resonance. The experiment involved a relatively small quantum system that fell short of full practical quantum advantage because it was not able to work faster than a traditional computer. Google exhaustively red-teamed the research, putting some researchers to work trying to disprove its own results.

Prineha Narang, a professor at UCLA, called the advance meaningful. The quantum computer tested two molecules, one with 15 atoms and another with 28 atoms. Results on the quantum computer matched traditional NMR and revealed information not usually available from NMR. Google's research competes against Microsoft, IBM, universities and efforts in China. The Chinese government has committed more than $15.2 billion to quantum research. Previous claims of quantum advantage have been met with skepticism.

BrianFagioli writes: The United States Mint is honoring Steve Jobs and Apple with a new coin for 2026. Part of the American Innovation $1 Coin Program, California's entry depicts a young Jobs seated before rolling northern California hills, accompanied by the words "Make Something Wonderful." The reflective design, created by Elana Hagler and sculpted by Phebe Hemphill, captures how Jobs's surroundings and vision shaped Apple's mission to make technology feel intuitive and human.

The 2026 series also celebrates Dr. Norman Borlaug for Iowa, the Cray-1 supercomputer for Wisconsin, and mobile refrigeration for Minnesota. The obverse of all coins features the Statue of Liberty and a special Liberty Bell mark commemorating the nation's Semiquincentennial. The Steve Jobs coin stands out as one of the few times the U.S. Mint has recognized a modern tech innovator, and some collectors are already calling it one of the most exciting releases in years.

The New York Times: John Clarke, Michel H. Devoret and John M. Martinis were awarded the Nobel Prize in Physics on Tuesday in Sweden for showing that two properties of quantum mechanics, the physical laws that rule the subatomic realm, could be observed on a system large enough to see with the naked eye. They will share a prize of 11 million Swedish kroner, or around $1.17 million.

"There is no advanced technology today that does not rely on quantum mechanics," Olle Eriksson, chairman of the Nobel Committee for Physics, said during the announcement of the award. The laureates' discoveries, he added, paved the way for technologies like the cellphone, cameras and fiber optic cables.

It also helped lay the groundwork for current attempts to build a quantum computer, a device that could compute and process information at speeds that would not be possible with classical computer. Martinis worked at Google from 2014 to 2020 to build a quantum computer and led the quantum supremacy experiment in 2019. Devoret is cited in Google's recent breakthrough where its Willow quantum chip solved a problem in five minutes that the world's most advanced supercomputer could never solve.

The three laureates conducted experiments with electrical circuits that demonstrated quantum mechanical tunneling and quantized energy levels in systems large enough to hold in the hand. Clarke is a professor at the University of California, Berkeley. Devoret joined his research group in the 1980s and is now at Yale University and UC Santa Barbara. Martinis also joined the group in the 1980s and is currently at UC Santa Barbara and co-founded Qolab, a startup developing utility-scale superconducting quantum computers.

Microsoft will invest $30 billion in the U.K. through 2028 to expand AI infrastructure and operations, including building the country's largest supercomputer with 23,000 GPUs in partnership with Nscale. CNBC reports: On a call with reporters on Tuesday, Microsoft President Brad Smith said his stance on the U.K. has warmed over the years. He previously criticized the country over its attempt in 2023 to block the tech giant's $69 billion acquisition of video game developer Activision-Blizzard. The deal was cleared by the U.K.s competition regulator later that year.

"I haven't always been optimistic every single day about the business climate in the U.K.," Smith said. However, he added, "I am very encouraged by the steps that the government has taken over the last few years." "Just a few years ago, this kind of investment would have been inconceivable because of the regulatory climate then and because there just wasn't the need or demand for this kind of large AI investment," Smith said. Microsoft's announcement comes as President Donald Trump embarks on a state visit to Britain where he's expected to sign a new deal with U.K. Prime Minister Keir Starmer "to unlock investment and collaboration in AI, Quantum, and Nuclear technologies," the government said in a statement late Tuesday.

Europe on Friday inaugurated Jupiter, its first exascale supercomputer and the most powerful AI machine on the continent. Built in Germany with 24,000 Nvidia chips, the 500-million-euro system aims to close the AI gap with the US and China while also advancing climate modeling, neuroscience, and renewable energy research. France 24 reports: Based at Juelich Supercomputing Centre in western Germany, it is Europe's first "exascale" supercomputer -- meaning it will be able to perform at least one quintillion (or one billion billion) calculations per second. The United States already has three such computers, all operated by the Department of Energy. Jupiter is housed in a centre covering some 3,600 meters (38,000 square feet) -- about half the size of a football pitch -- containing racks of processors, and packed with about 24,000 Nvidia chips, which are favored by the AI industry.

Half the 500 million euros ($580 million) to develop and run the system over the next few years comes from the European Union and the rest from Germany. Its vast computing power can be accessed by researchers across numerous fields as well as companies for purposes such as training AI models. "Jupiter is a leap forward in the performance of computing in Europe," Thomas Lippert, head of the Juelich centre, told AFP, adding that it was 20 times more powerful than any other computer in Germany. [...]

Yes, Jupiter will require on average around 11 megawatts of power, according to estimates -- equivalent to the energy used to power thousands of homes or a small industrial plant. But its operators insist that Jupiter is the most energy-efficient among the fastest computer systems in the world. It uses the latest, most energy-efficient hardware, has water-cooling systems and the waste heat that it generates will be used to heat nearby buildings, according to the Juelich centre.

Igor Babuschkin, co-founder of xAI, has left the company to start Babuschkin Ventures, a VC firm focused on AI safety and humanity-advancing startups. TechCrunch reports: Babuschkin led engineering teams at xAI and helped build the startup into one of Silicon Valley's leading AI model developers just a few years after it was founded. "Today was my last day at xAI, the company that I helped start with Elon Musk in 2023," Babuschkin wrote in the post. "I still remember the day I first met Elon, we talked for hours about AI and what the future might hold. We both felt that a new AI company with a different kind of mission was needed."

Babuschkin is leaving xAI to launch his own venture capital firm, Babuschkin Ventures, which he says will support AI safety research and back startups that "advance humanity and unlock the mysteries of our universe." The xAI co-founder says he was inspired to start the firm after a dinner with Max Tegmark, the founder of the Future of Life Institute, in which they discussed how AI systems could be built safely to encourage the flourishing of future generations. In his post, Babuschkin says his parents immigrated to the U.S. from Russia in pursuit of a better life for their children.

Prior to co-founding xAI, Babuschkin was part of a research team at Google DeepMind that pioneered AlphaStar in 2019, a breakthrough AI system that could defeat top-ranked players at the video game StarCraft. Babuschkin also worked as a researcher at OpenAI in the years before it released ChatGPT. In his post, Babuschkin details some of the challenges he and Musk faced in building up xAI. He notes that industry veterans called xAI's goal of building its Memphis, Tennessee supercomputer in just three months "impossible." [...] Nevertheless, Babuschkin says he's already looking back fondly on his time at xAI, and "feels like a proud parent, driving away after sending their kid away to college." "I learned 2 priceless lessons from Elon: #1 be fearless in rolling up your sleeves to personally dig into technical problems, #2 have a maniacal sense of urgency," said Babuschkin.

The U.S. Department of Energy has greenlit four federal sites for private sector AI datacenters and nuclear-powered energy projects, aligning with Trump's directive to fast-track AI infrastructure using government land. "The four that have been finalized are the Idaho National Laboratory, Oak Ridge Reservation, Paducah Gaseous Diffusion Plant, and Savannah River Site," reports The Register. "These will now move forward to invite companies in the private sector to build AI datacenter projects plus any necessary energy sources to power them, including nuclear generation." The Register reports: "By leveraging DoE land assets for the deployment of AI and energy infrastructure, we are taking a bold step to accelerate the next Manhattan Project -- ensuring US AI and energy leadership," Energy Secretary Chris Wright said in a statement. Ironically -- or perhaps not -- Oak Ridge Reservation was established in the early 1940s as part of the original Manhattan Project to develop the first atomic bomb, and is home to the Oak Ridge National Laboratory (ORNL) that operates the Frontier exascale supercomputer, and the Y-12 National Security Complex which supports US nuclear weapons programs.

The other sites are also involved with either nuclear research or atomic weapons in one way or another, which may hint at the administration's intentions for how the datacenters should be powered. All four locations are positioned to host new bit barns as well as power generation to bolster grid reliability, strengthen national security, and reduce energy costs, Wright claimed. [...] In light of this tight time frame, the DoE says that partners may be selected by the end of the year. Details regarding project scope, eligibility requirements, and submission guidelines for each site are expected to be released in the coming months.

An anonymous reader quotes a report from Live Science: In a world first, scientists have demonstrated an enigmatic phenomenon in quantum computing that could pave the way for fault-tolerant machines that are far more powerful than any supercomputer. The process, called "magic state distillation," was first proposed 20 years ago, but its use in logical qubits has eluded scientists ever since. It has long been considered crucial for producing the high-quality resources, known as "magic states," needed to fulfill the full potential of quantum computers. [...] Now, however, scientists with QuEra say they have demonstrated magic state distillation in practice for the first time on logical qubits. They outlined their findings in a new study published July 14 in the journal Nature.

In the study, using the Gemini neutral-atom quantum computer, the scientists distilled five imperfect magic states into a single, cleaner magic state. They performed this separately on a Distance-3 and a Distance-5 logical qubit, demonstrating that it scales with the quality of the logical qubit. "A greater distance means better logical qubits. A Distance-2, for instance, means that you can detect an error but not correct it. Distance-3 means that you can detect and correct a single error. Distance-5 would mean that you can detect and correct up to two errors, and so on, and so on," [explained Yuval Boger, chief commercial officer at QuEra who was not personally involved in the research]. "So the greater the distance, the higher fidelity of the qubit is -- and we liken it to distilling crude oil into a jet fuel."

As a result of the distillation process, the fidelity of the final magic state exceeded that of any input. This proved that fault-tolerant magic state distillation worked in practice, the scientists said. This means that a quantum computer that uses both logical qubits and high-quality magic states to run non-Clifford gates is now possible. "We're seeing sort of a shift from a few years ago," Boger said. "The challenge was: can quantum computers be built at all? Then it was: can errors be detected and corrected? Us and Google and others have shown that, yes, that can be done. Now it's about: can we make these computers truly useful? And to make one computer truly useful, other than making them larger, you want them to be able to run programs that cannot be simulated on classical computers."

An anonymous reader quotes a report from TechCrunch: One of Microsoft's latest AI models can accurately predict air quality, hurricanes, typhoons, and other weather-related phenomena, the company claims. In a paper published in the journal Nature and an accompanying blog post this week, Microsoft detailed Aurora, which the tech giant says can forecast atmospheric events with greater precision and speed than traditional meteorological approaches. Aurora, which has been trained on more than a million hours of data from satellites, radar and weather stations, simulations, and forecasts, can be fine-tuned with additional data to make predictions for particular weather events.

AI weather models are nothing new. Google DeepMind has released a handful over the past several years, including WeatherNext, which the lab claims beats some of the world's best forecasting systems. Microsoft is positioning Aurora as one of the field's top performers -- and a potential boon for labs studying weather science. In experiments, Aurora predicted Typhoon Doksuri's landfall in the Philippines four days in advance of the actual event, beating some expert predictions, Microsoft says. The model also bested the National Hurricane Center in forecasting five-day tropical cyclone tracks for the 2022-2023 season, and successfully predicted the 2022 Iraq sandstorm.

While Aurora required substantial computing infrastructure to train, Microsoft says the model is highly efficient to run. It generates forecasts in seconds compared to the hours traditional systems take using supercomputer hardware. Microsoft, which has made the source code and model weights publicly available, says that it's incorporating Aurora's AI modeling into its MSN Weather app via a specialized version of the model that produces hourly forecasts, including for clouds.

Amazon's decade-old acquisition of Annapurna Labs has emerged as a pivotal element in its AI strategy, with the once-secretive Israeli chip design startup now powering AWS infrastructure. The $350 million deal, struck in 2015 after initial talks between Annapurna co-founder Nafea Bshara and Amazon executive James Hamilton, has equipped the tech giant with custom silicon capabilities critical to its cloud computing dominance.

Annapurna's chips, particularly the Trainium processor for AI model training and Graviton for general-purpose computing, now form the foundation of Amazon's AI infrastructure. The company is deploying hundreds of thousands of Trainium chips in its Project Rainier supercomputer being delivered to AI startup Anthropic this year. Amazon CEO Andy Jassy, who led AWS when the acquisition occurred, described it as "one of the most important moments" in AWS history.