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These experimental ship designs rely on smaller nd potentially safer molten salt nuclear reactors. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. A graphic rendering of a containership that is powered by small modular nuclear reactors. Breakthroughs, discoveries, and DIY tips sent six days a week. By signing up, you confirm you are 16+, will receive newsletters and promotional content and agree to our Terms of Use and acknowledge the data practices in our Privacy Policy .

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Masayoshi Son speaks at an annual SoftBank World event in Tokyo on Tuesday. SoftBank Group founder Masayoshi Son said that in the not-so-distant future, nuclear fusion technology will offer the most realistic solution for powering AI data centers' ballooning needs. Natural gas will provide the bulk of data centers' power needs for the time being, Son said Tuesday. But he predicted that nuclear fusion -- the process by which the sun and other stars generate energy -- has a role to play, predicting the world will need 3 terawatts of data center capacity in 2040. Many technological and financial challenges remain for fusion technology to become a viable energy source, however, according to BloombergNEF. "Fusion will become the main source of a new kind of cheaper, clean and safe energy here on Earth," said Son, 68, at an annual SoftBank World event in Tokyo.

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Plus: NATO is building a network to stop Russian attackers in their tracks. I was really looking forward to July 4, and not just because I love a poolside barbecue. This year the American holiday also marked a big symbolic deadline for US nuclear power. Last year the Trump administration set a goal to see three new microreactors achieve criticality, a technical milestone establishing that a reactor can sustain a chain reaction, by the nation's 250th birthday. And just in time, not just three, but four reactors did so. But achieving criticality doesn't mean a reactor is ready to provide electricity for the grid (or at all, for that matter).

From AI with Hannah Fry to David Attenborough's early days, these are the five must-watch science documentaries of the year to date, says Bethan Ackerley In 2015, an amateur trophy hunter from the US shot and killed the largest lion in Africa. The vitriol unleashed after Cecil's death isn't surprising (or entirely unwarranted), but what is remarkable is how this delicately-crafted film uses the case as a locus for all sorts of arguments about conservation. A symbol in life and in death, Cecil and other large, charismatic animals exist in a complex balance with humans who, one way or another, invariably stake a claim on them. Almost everyone in the world now needs to have some knowledge of how AI technologies work, from all the chatbots they encounter to driverless cars and more. Mathematician Hannah Fry is an excellent person to impart such knowledge: across three episodes, she guides us through recent cases where AI has become entangled with very human problems.

Safety verification ensures that a system avoids undesired behaviour. Liveness complements safety, ensuring that the system also achieves its desired objectives. A complete specification of functional correctness must combine both safety and liveness. Proving with mathematical certainty that a system satisfies a safety property demands presenting an appropriate inductive invariant of the system, whereas proving liveness requires showing a measure of progress witnessed by a ranking function. Neural model checking has recently introduced a data-driven approach to the formal verification of reactive systems, albeit focusing on ranking functions and thus addressing liveness properties only.

Stellarators are magnetic confinement devices under active development to deliver steady-state carbon-free fusion energy. Their design involves a high-dimensional, constrained optimization problem that requires expensive physics simulations and significant domain expertise. Recent advances in plasma physics and open-source tools have made stellarator optimization more accessible. However, broader community progress is currently bottlenecked by the lack of standardized optimization problems with strong baselines and datasets that enable data-driven approaches, particularly for quasi-isodynamic (QI) stellarator configurations, considered as a promising path to commercial fusion due to their inherent resilience to currentdriven disruptions. Here, we release an open dataset of diverse QI-like stellarator plasma boundary shapes, paired with their ideal magnetohydrodynamic (MHD) equilibria and performance metrics. We generated this dataset by sampling a variety of QI fields and optimizing corresponding stellarator plasma boundaries. We introduce three optimization benchmarks of increasing complexity: (1) a singleobjective geometric optimization problem, (2) a "simple-to-build" QI stellarator, and (3) a multi-objective ideal-MHD stable QI stellarator that investigates trade-offs between compactness and coil simplicity. For every benchmark, we provide reference code, evaluation scripts, and strong baselines based on classical optimization techniques. Finally, we show how learned models trained on our dataset can efficiently generate novel, feasible configurations without querying expensive physics oracles.

Plus: Autonomous drones may have killed soldiers for the first time. Imagine tuning in to the opening kickoff of a World Cup match and seeing a player intentionally kick the ball out of bounds. You may question the logic of surrendering possession seconds into a game. If you were Jesse Davis, though, you'd know that this play could be a prime setup to score. Davis is a professor of computer science at KU Leuven in Belgium and head of its Sports Analytics Lab, which has been at the vanguard of a data awakening in soccer. Using AI and data analytics, his team has uncovered hidden tactical patterns and challenged long-held assumptions about how the game should be played.