Google DeepMind has once again used large language models to discover new solutions to long-standing problems in math and computer science. This time the firm has shown that its approach can not only tackle unsolved theoretical puzzles, but improve a range of important real-world processes as well. Google DeepMind's new tool, called AlphaEvolve, uses the Gemini 2.0 family of large language modelsto produce code for a wide range of different tasks. LLMs are known to be hit and miss at coding. The twist here is that AlphaEvolve scores each of Gemini’s suggestions, throwing out the bad and tweaking the good, in an iterative process, until it has produced the best algorithm it can. In many cases, the results are more efficient or more accurate than the best existingsolutions. “You can see it as a sort of super coding agent,” says Pushmeet Kohli, a vice president at Google DeepMind who leads its AI for Science teams. “It doesn’t just propose a piece of code or an edit, it actually produces a result that maybe nobody was aware of.” In particular, AlphaEvolve came up with a way to improve the software Google uses to allocate jobs to its many millions of servers around the world. Google DeepMind claims the company has been using this new software across all of its data centers for more than a year, freeing up 0.7% of Google's total computing resources. That might not sound like much, but at Google’s scale it’s huge. Jakob Moosbauer, a mathematician at the University of Warwick in the UK, is impressed. He says the way AlphaEvolve searches for algorithms that produce specific solutions—rather than searching for the solutions themselves—makes it especially powerful. “It makes the approach applicable to such a wide range of problems,” he says. “AI is becoming a tool that will be essential in mathematics and computer science.” AlphaEvolve continues a line of work that Google DeepMind has been pursuing for years. Its vision is that AI can help to advance human knowledge across math and science. In 2022, it developed AlphaTensor, a model that found a faster way to solve matrix multiplications—a fundamental problem in computer science—beating a record that had stood for more than 50 years. In 2023, it revealed AlphaDev, which discovered faster ways to perform a number of basic calculations performed by computers trillions of times a day. AlphaTensor and AlphaDev both turn math problems into a kind of game, then search for a winning series of moves. FunSearch, which arrived in late 2023, swapped out game-playing AI and replaced it with LLMs that can generate code. Because LLMs can carry out a range of tasks, FunSearch can take on a wider variety of problems than its predecessors, which were trained to play just one type of game. The tool was used to crack a famous unsolved problem in pure mathematics. AlphaEvolve is the next generation of FunSearch. Instead of coming up with short snippets of code to solve a specific problem, as FunSearch did, it can produce programs that are hundreds of lines long. This makes it applicable to a much wider variety of problems.     In theory, AlphaEvolve could be applied to any problem that can be described in code and that has solutions that can be evaluated by a computer. “Algorithms run the world around us, so the impact of that is huge,” says Matej Balog, a researcher at Google DeepMind who leads the algorithm discovery team. Survival of the fittest Here’s how it works: AlphaEvolve can be prompted like any LLM. Give it a description of the problem and any extra hints you want, such as previous solutions, and AlphaEvolve will get Gemini 2.0 Flashto generate multiple blocks of code to solve the problem. It then takes these candidate solutions, runs them to see how accurate or efficient they are, and scores them according to a range of relevant metrics. Does this code produce the correct result? Does it run faster than previous solutions? And so on. AlphaEvolve then takes the best of the current batch of solutions and asks Gemini to improve them. Sometimes AlphaEvolve will throw a previous solution back into the mix to prevent Gemini from hitting a dead end. When it gets stuck, AlphaEvolve can also call on Gemini 2.0 Pro, the most powerful of Google DeepMind’s LLMs. The idea is to generate many solutions with the faster Flash but add solutions from the slower Pro when needed. These rounds of generation, scoring, and regeneration continue until Gemini fails to come up with anything better than what it already has. Number games The team tested AlphaEvolve on a range of different problems. For example, they looked at matrix multiplication again to see how a general-purpose tool like AlphaEvolve compared to the specialized AlphaTensor. Matrices are grids of numbers. Matrix multiplication is a basic computation that underpins many applications, from AI to computer graphics, yet nobody knows the fastest way to do it. “It’s kind of unbelievable that it’s still an open question,” says Balog. The team gave AlphaEvolve a description of the problem and an example of a standard algorithm for solving it. The tool not only produced new algorithms that could calculate 14 different sizes of matrix faster than any existing approach, it also improved on AlphaTensor’s record-beating result for multipying two four-by-four matrices. AlphaEvolve scored 16,000 candidates suggested by Gemini to find the winning solution, but that’s still more efficient than AlphaTensor, says Balog. AlphaTensor’s solution also only worked when a matrix was filled with 0s and 1s. AlphaEvolve solves the problem with other numbers too. “The result on matrix multiplication is very impressive,” says Moosbauer. “This new algorithm has the potential to speed up computations in practice.” Manuel Kauers, a mathematician at Johannes Kepler University in Linz, Austria, agrees: “The improvement for matrices is likely to have practical relevance.” By coincidence, Kauers and a colleague have just used a different computational technique to find some of the speedups AlphaEvolve came up with. The pair posted a paper online reporting their results last week. “It is great to see that we are moving forward with the understanding of matrix multiplication,” says Kauers. “Every technique that helps is a welcome contribution to this effort.” Real-world problems Matrix multiplication was just one breakthrough. In total, Google DeepMind tested AlphaEvolve on more than 50 different types of well-known math puzzles, including problems in Fourier analysis, the minimum overlap problem, and kissing numbers. AlphaEvolve matched the best existing solutions in 75% of cases and found better solutions in 20% of cases.   Google DeepMind then applied AlphaEvolve to a handful of real-world problems. As well as coming up with a more efficient algorithm for managing computational resources across data centers, the tool found a way to reduce the power consumption of Google’s specialized tensor processing unit chips. AlphaEvolve even found a way to speed up the training of Gemini itself, by producing a more efficient algorithm for managing a certain type of computation used in the training process. Google DeepMind plans to continue exploring potential applications of its tool. One limitation is that AlphaEvolve can’t be used for problems with solutions that need to be scored by a person, such as lab experiments that are subject to interpretation.    Moosbauer also points out that while AlphaEvolve may produce impressive new results across a wide range of problems, it gives little theoretical insight into how it arrived at those solutions. That’s a drawback when it comes to advancing human understanding.   Even so, tools like AlphaEvolve are set to change the way researchers work. “I don't think we are finished,” says Kohli. “There is much further that we can go in terms of how powerful this type of approach is.” #google #deepminds #new #agent #uses

Source: Wright & Wright ArchitectsProposed scheme on Oxford High Street Wright & Wright Architects have secured planning permission for a major retrofit scheme for All Souls College, Oxford. The project is intended to create new academic and public-facing spaces within a group of conjoined historic buildings on Oxford’s High Street, expanding the college’s presence beyond its historic quadrangles. The scheme is intended to restore original architectural features across a series of existing buildings and shoprfronts, while introducing new facilities across multiple floors. The upper levels will include three seminar rooms, a lecture theatre with capacity for 100 people, fellows’ studies, staff offices and a rooftop garden. Historic elements such as 17th-century stone walls, ancient roof timbers and 18th-century ceilings are to be retained and revealed as part of the works. Clare Wright, founding partner at Wright & Wright, said: “It has been a privilege to help realise the College’s ambition to remodel an extraordinary set of historic buildings on an important site on Oxford’s High Street. In an era when city centres are languishing, this pioneering project can help preserve the soul of Oxford.” David Renton, estates bursar at All Souls College, said: “Our ambition is to create a place of learning that connects directly with the life of the city, while honouring the historic complexity of the site. We are excited to work with Wright & Wright to bring this vision to life.” Roof gardenSource: Wright & Wright Architects Front elevationSource: Wright & Wright Architects Proposed sectionSource: Wright & Wright Architects Front elevationSource: Wright & Wright Architects Study roomSource: Wright & Wright Architects 1/5 show caption The ground floor proposals include replacing existing shopfronts with bespoke joinery designed to reflect the proportions of earlier facades. The scheme also seeks to enhance the covered passageway linking the High Street to the Covered Market with improved signage, lighting and finishes. The project, comprising three conjoined structures, is described by the architects as a low-energy, deep retrofit. The design aims to reduce carbon emissions while safeguarding the building’s historic fabric. Later modern interventions are to be stripped out in order to improve environmental performance and expose earlier features. Founded in 1438, All Souls is one of the University of Oxford’s oldest and most academically distinctive colleges, known for its postgraduate-only focus on research and absence of undergraduates. Wright & Wright’s wider body of work includes projects for the British Museum, Lambeth Palace Library, the British Academy and several other Oxford colleges. A construction timeline has not yet been confirmed. Project team Architect: Wright & Wright Architects Structural & Civil Engineer: engineersHRW M&E, Sustainability Engineer: Max Fordham Acoustic Engineer: Max Fordham Heritage Consultant: Donald Insall Associates Project Manager: Ridge and Partners Cost Consultant: Gardiner & Theobald Property Management: Cluttons Planning Consultant: Carter Jonas Fire Consultant: OFR Consultants Landscape Consultant: Harris Bugg AV Consultant: Hewshott Retail Consultant: KLM Real Estate Accessibility Consultant: Mima Group Ltd Archaeological Consultant: Oxford Archaeology Building Regulations PD: Ridge and Partners Principal Designer Advisor: Stroma Visual Impact Assessment: Neaves Urbanism #wright #ampamp #secure #planning #oxford

Google DeepMind has once again used large language models to discover new solutions to long-standing problems in math and computer science. This time the firm has shown that its approach can not only tackle unsolved theoretical puzzles, but improve a range of important real-world processes as well. Google DeepMind's new tool, called AlphaEvolve, uses the Gemini 2.0 family of large language modelsto produce code for a wide range of different tasks. LLMs are known to be hit and miss at coding. The twist here is that AlphaEvolve scores each of Gemini’s suggestions, throwing out the bad and tweaking the good, in an iterative process, until it has produced the best algorithm it can. In many cases, the results are more efficient or more accurate than the best existingsolutions. “You can see it as a sort of super coding agent,” says Pushmeet Kohli, a vice president at Google DeepMind who leads its AI for Science teams. “It doesn’t just propose a piece of code or an edit, it actually produces a result that maybe nobody was aware of.” In particular, AlphaEvolve came up with a way to improve the software Google uses to allocate jobs to its many millions of servers around the world. Google DeepMind claims the company has been using this new software across all of its data centers for more than a year, freeing up 0.7% of Google's total computing resources. That might not sound like much, but at Google’s scale it’s huge. Jakob Moosbauer, a mathematician at the University of Warwick in the UK, is impressed. He says the way AlphaEvolve searches for algorithms that produce specific solutions—rather than searching for the solutions themselves—makes it especially powerful. “It makes the approach applicable to such a wide range of problems,” he says. “AI is becoming a tool that will be essential in mathematics and computer science.” AlphaEvolve continues a line of work that Google DeepMind has been pursuing for years. Its vision is that AI can help to advance human knowledge across math and science. In 2022, it developed AlphaTensor, a model that found a faster way to solve matrix multiplications—a fundamental problem in computer science—beating a record that had stood for more than 50 years. In 2023, it revealed AlphaDev, which discovered faster ways to perform a number of basic calculations performed by computers trillions of times a day. AlphaTensor and AlphaDev both turn math problems into a kind of game, then search for a winning series of moves. FunSearch, which arrived in late 2023, swapped out game-playing AI and replaced it with LLMs that can generate code. Because LLMs can carry out a range of tasks, FunSearch can take on a wider variety of problems than its predecessors, which were trained to play just one type of game. The tool was used to crack a famous unsolved problem in pure mathematics. AlphaEvolve is the next generation of FunSearch. Instead of coming up with short snippets of code to solve a specific problem, as FunSearch did, it can produce programs that are hundreds of lines long. This makes it applicable to a much wider variety of problems.     In theory, AlphaEvolve could be applied to any problem that can be described in code and that has solutions that can be evaluated by a computer. “Algorithms run the world around us, so the impact of that is huge,” says Matej Balog, a researcher at Google DeepMind who leads the algorithm discovery team. Survival of the fittest Here’s how it works: AlphaEvolve can be prompted like any LLM. Give it a description of the problem and any extra hints you want, such as previous solutions, and AlphaEvolve will get Gemini 2.0 Flashto generate multiple blocks of code to solve the problem. It then takes these candidate solutions, runs them to see how accurate or efficient they are, and scores them according to a range of relevant metrics. Does this code produce the correct result? Does it run faster than previous solutions? And so on. AlphaEvolve then takes the best of the current batch of solutions and asks Gemini to improve them. Sometimes AlphaEvolve will throw a previous solution back into the mix to prevent Gemini from hitting a dead end. When it gets stuck, AlphaEvolve can also call on Gemini 2.0 Pro, the most powerful of Google DeepMind’s LLMs. The idea is to generate many solutions with the faster Flash but add solutions from the slower Pro when needed. These rounds of generation, scoring, and regeneration continue until Gemini fails to come up with anything better than what it already has. Number games The team tested AlphaEvolve on a range of different problems. For example, they looked at matrix multiplication again to see how a general-purpose tool like AlphaEvolve compared to the specialized AlphaTensor. Matrices are grids of numbers. Matrix multiplication is a basic computation that underpins many applications, from AI to computer graphics, yet nobody knows the fastest way to do it. “It’s kind of unbelievable that it’s still an open question,” says Balog. The team gave AlphaEvolve a description of the problem and an example of a standard algorithm for solving it. The tool not only produced new algorithms that could calculate 14 different sizes of matrix faster than any existing approach, it also improved on AlphaTensor’s record-beating result for multipying two four-by-four matrices. AlphaEvolve scored 16,000 candidates suggested by Gemini to find the winning solution, but that’s still more efficient than AlphaTensor, says Balog. AlphaTensor’s solution also only worked when a matrix was filled with 0s and 1s. AlphaEvolve solves the problem with other numbers too. “The result on matrix multiplication is very impressive,” says Moosbauer. “This new algorithm has the potential to speed up computations in practice.” Manuel Kauers, a mathematician at Johannes Kepler University in Linz, Austria, agrees: “The improvement for matrices is likely to have practical relevance.” By coincidence, Kauers and a colleague have just used a different computational technique to find some of the speedups AlphaEvolve came up with. The pair posted a paper online reporting their results last week. “It is great to see that we are moving forward with the understanding of matrix multiplication,” says Kauers. “Every technique that helps is a welcome contribution to this effort.” Real-world problems Matrix multiplication was just one breakthrough. In total, Google DeepMind tested AlphaEvolve on more than 50 different types of well-known math puzzles, including problems in Fourier analysis, the minimum overlap problem, and kissing numbers. AlphaEvolve matched the best existing solutions in 75% of cases and found better solutions in 20% of cases.   Google DeepMind then applied AlphaEvolve to a handful of real-world problems. As well as coming up with a more efficient algorithm for managing computational resources across data centers, the tool found a way to reduce the power consumption of Google’s specialized tensor processing unit chips. AlphaEvolve even found a way to speed up the training of Gemini itself, by producing a more efficient algorithm for managing a certain type of computation used in the training process. Google DeepMind plans to continue exploring potential applications of its tool. One limitation is that AlphaEvolve can’t be used for problems with solutions that need to be scored by a person, such as lab experiments that are subject to interpretation.    Moosbauer also points out that while AlphaEvolve may produce impressive new results across a wide range of problems, it gives little theoretical insight into how it arrived at those solutions. That’s a drawback when it comes to advancing human understanding.   Even so, tools like AlphaEvolve are set to change the way researchers work. “I don't think we are finished,” says Kohli. “There is much further that we can go in terms of how powerful this type of approach is.” #google #deepminds #new #uses #large

Google DeepMind has once again used large language models to discover new solutions to long-standing problems in math and computer science. This time the firm has shown that its approach can not only tackle unsolved theoretical puzzles, but improve a range of important real-world processes as well. Google DeepMind’s new tool, called AlphaEvolve, uses the Gemini 2.0 family of large language modelsto produce code for a wide range of different tasks. LLMs are known to be hit and miss at coding. The twist here is that AlphaEvolve scores each of Gemini’s suggestions, throwing out the bad and tweaking the good, in an iterative process, until it has produced the best algorithm it can. In many cases, the results are more efficient or more accurate than the best existingsolutions. “You can see it as a sort of super coding agent,” says Pushmeet Kohli, a vice president at Google DeepMind who leads its AI for Science teams. “It doesn’t just propose a piece of code or an edit, it actually produces a result that maybe nobody was aware of.” In particular, AlphaEvolve came up with a way to improve the software Google uses to allocate jobs to its many millions of servers around the world. Google DeepMind claims the company has been using this new software across all of its data centers for more than a year, freeing up 0.7% of Google’s total computing resources. That might not sound like much, but at Google’s scale it’s huge. Jakob Moosbauer, a mathematician at the University of Warwick in the UK, is impressed. He says the way AlphaEvolve searches for algorithms that produce specific solutions—rather than searching for the solutions themselves—makes it especially powerful. “It makes the approach applicable to such a wide range of problems,” he says. “AI is becoming a tool that will be essential in mathematics and computer science.” AlphaEvolve continues a line of work that Google DeepMind has been pursuing for years. Its vision is that AI can help to advance human knowledge across math and science. In 2022, it developed AlphaTensor, a model that found a faster way to solve matrix multiplications—a fundamental problem in computer science—beating a record that had stood for more than 50 years. In 2023, it revealed AlphaDev, which discovered faster ways to perform a number of basic calculations performed by computers trillions of times a day. AlphaTensor and AlphaDev both turn math problems into a kind of game, then search for a winning series of moves. FunSearch, which arrived in late 2023, swapped out game-playing AI and replaced it with LLMs that can generate code. Because LLMs can carry out a range of tasks, FunSearch can take on a wider variety of problems than its predecessors, which were trained to play just one type of game. The tool was used to crack a famous unsolved problem in pure mathematics. AlphaEvolve is the next generation of FunSearch. Instead of coming up with short snippets of code to solve a specific problem, as FunSearch did, it can produce programs that are hundreds of lines long. This makes it applicable to a much wider variety of problems.     In theory, AlphaEvolve could be applied to any problem that can be described in code and that has solutions that can be evaluated by a computer. “Algorithms run the world around us, so the impact of that is huge,” says Matej Balog, a researcher at Google DeepMind who leads the algorithm discovery team. Survival of the fittest Here’s how it works: AlphaEvolve can be prompted like any LLM. Give it a description of the problem and any extra hints you want, such as previous solutions, and AlphaEvolve will get Gemini 2.0 Flashto generate multiple blocks of code to solve the problem. It then takes these candidate solutions, runs them to see how accurate or efficient they are, and scores them according to a range of relevant metrics. Does this code produce the correct result? Does it run faster than previous solutions? And so on. AlphaEvolve then takes the best of the current batch of solutions and asks Gemini to improve them. Sometimes AlphaEvolve will throw a previous solution back into the mix to prevent Gemini from hitting a dead end. When it gets stuck, AlphaEvolve can also call on Gemini 2.0 Pro, the most powerful of Google DeepMind’s LLMs. The idea is to generate many solutions with the faster Flash but add solutions from the slower Pro when needed. These rounds of generation, scoring, and regeneration continue until Gemini fails to come up with anything better than what it already has. Number games The team tested AlphaEvolve on a range of different problems. For example, they looked at matrix multiplication again to see how a general-purpose tool like AlphaEvolve compared to the specialized AlphaTensor. Matrices are grids of numbers. Matrix multiplication is a basic computation that underpins many applications, from AI to computer graphics, yet nobody knows the fastest way to do it. “It’s kind of unbelievable that it’s still an open question,” says Balog. The team gave AlphaEvolve a description of the problem and an example of a standard algorithm for solving it. The tool not only produced new algorithms that could calculate 14 different sizes of matrix faster than any existing approach, it also improved on AlphaTensor’s record-beating result for multipying two four-by-four matrices. AlphaEvolve scored 16,000 candidates suggested by Gemini to find the winning solution, but that’s still more efficient than AlphaTensor, says Balog. AlphaTensor’s solution also only worked when a matrix was filled with 0s and 1s. AlphaEvolve solves the problem with other numbers too. “The result on matrix multiplication is very impressive,” says Moosbauer. “This new algorithm has the potential to speed up computations in practice.” Manuel Kauers, a mathematician at Johannes Kepler University in Linz, Austria, agrees: “The improvement for matrices is likely to have practical relevance.” By coincidence, Kauers and a colleague have just used a different computational technique to find some of the speedups AlphaEvolve came up with. The pair posted a paper online reporting their results last week. “It is great to see that we are moving forward with the understanding of matrix multiplication,” says Kauers. “Every technique that helps is a welcome contribution to this effort.” Real-world problems Matrix multiplication was just one breakthrough. In total, Google DeepMind tested AlphaEvolve on more than 50 different types of well-known math puzzles, including problems in Fourier analysis, the minimum overlap problem, and kissing numbers. AlphaEvolve matched the best existing solutions in 75% of cases and found better solutions in 20% of cases.   Google DeepMind then applied AlphaEvolve to a handful of real-world problems. As well as coming up with a more efficient algorithm for managing computational resources across data centers, the tool found a way to reduce the power consumption of Google’s specialized tensor processing unit chips. AlphaEvolve even found a way to speed up the training of Gemini itself, by producing a more efficient algorithm for managing a certain type of computation used in the training process. Google DeepMind plans to continue exploring potential applications of its tool. One limitation is that AlphaEvolve can’t be used for problems with solutions that need to be scored by a person, such as lab experiments that are subject to interpretation.    Moosbauer also points out that while AlphaEvolve may produce impressive new results across a wide range of problems, it gives little theoretical insight into how it arrived at those solutions. That’s a drawback when it comes to advancing human understanding.   Even so, tools like AlphaEvolve are set to change the way researchers work. “I don’t think we are finished,” says Kohli. “There is much further that we can go in terms of how powerful this type of approach is.” #google #deepminds #new #agent #uses

We may earn a commission from links on this page. Deal pricing and availability subject to change after time of publication.If you’re getting excited for spring and summer home improvement projects, or if you plan to expand your yard care equipment collection, the expense of new tools can be an important consideration. Getting deals on new cordless tools is a game-changer for DIYers everywhere, and the following deals can get you up to 67% off right now. Ryobi cordless tools are a go-to for many DIY enthusiasts because of the variety of tools they make that fit their batteries, their affordability, and their quality, so saving some money on this brand will allow you to add to your quality cordless tool kit as you go.DriversPower drivers have come a long way from back when you only had a drill with one or two speeds and a few attachments. 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Here are some deals of cutting tools that will get you through almost any DIY job.The Ryobi 18-volt jig saw is on sale for 60% off its usual price. This saw comes with one 2-amp-hour battery and one 4-amp-hour battery and a charger. A jig saw is good for making cuts that aren’t in a straight line and can be handy for trimming pieces that need custom shapes, like flooring cut to fit around a doorway.The Ryobi 18-volt, 18-inch hedge trimmer is on sale for 65% off its usual price. This trimmer comes with one 2-amp-hour battery and one 4-amp-hour battery.The Ryobi 18-volt oscillating tool is on sale for 65% off its typical price. This tool comes with one 2-amp-hour battery and one 4-amp-hour battery and a charger. 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Here are some deals on sanding and finishing cordless tools.The 18-volt Ryobi hand planer is on sale for 69% off its regular price. This kit comes with one 2-amp-hour battery and one 4-amp-hour battery, and a charger. A hand planer can be used to smooth and shape rough boardsThe 18-volt Ryobi orbital sander is on sale for 62% off its usual price. This kit comes with one 2-amp-hour battery and one 4-amp-hour battery, and a charger. A random orbital sander can help smooth rough surfaces and prep for paint. 18-volt Ryobi hand planer at Home Depot at Home Depot Batteries, chargers, and accessoriesWhile it might be an afterthought when investing in cordless tools, batteries and accessories can really make a difference in the amount of time and effort it takes to complete a project. Having extra fresh batteries means you can use multiple tools at a time without stopping to swap batteries, allowing you to work faster. The Ryobi 18-volt battery starter kit is on sale for 67% off its regular price. It comes with one 2-amp-hour battery and two 4-amp-hour batteries. This is a good kit if you're starting out or if you’re expanding your kit to allow you to use multiple tools simultaneously.The Ryobi gutter cleaner attachment for your Expand-It yard maintenance tool allows you to use the base edger/string trimmer handle to clear leaves and debris from your gutter. It’s on sale for 39% off its usual price.The Ryobi 18-volt 7 and ½ inch fan is on sale for 62% off its typical price. The fan comes with one 2-amp-hour battery and one 4-amp-hour battery, and a charger. Having a fan can do more than just cool you down while you’re working. You can use it to help dry paint faster or blow dust away from where you’re working to keep your paint dust-free. #these #ryobi #cordless #tools #are

Remedy hopes FBC: Firebreak can defeat the "predatory" live-service trap - with help from small budgets, Game Pass, and PS Plus "It's very much about spreading the risk." Image credit: Remedy News by Chris Tapsell Deputy Editor Published on May 14, 2025 I am quietly optimistic about FBC: Firebreak, Remedy's upcoming co-op, PvE Control spin-off. Having played a couple of hours of it, particularly towards the end of my time, as the systemic complexity began to compound, its coming together at the last minute almost feels like a strength, as I wrote in our big FBC: Firebreak preview. But naturally at a time when live-service, multiplayer-only games - particularly ones from studios known for the purely single-player output - are hardly the most popular pitches in the world, there is plenty of scepticism out there. Speaking to game director Mike Kayatta, lead designer Anssi Hyytiäinen, and PR director Thomas Puha, however, the sense was - maybe predictably - that the studio remained bullish. They gave a few reasons. "For me, at least one element there in that conversation is always the predatory practices in player retention," Hyytiäinen explains. Instead of battle passes, daily objectives and the like, for instance, Firebreak has a somewhat old-school approach of a set of upgrades to your weapons and kits, and a set of unlockable perks, both earned through the main loop of playing rounds of the game, collecting resources and spending them on said unlocks. FBC: Firebreak gameplay trailer.Watch on YouTube As Hyytiäinen puts it, Remedy has intentionally aimed to make it something you can dip in and out of, "the session lengths are short - we're almost thinking: we don't have time to play games ourselves, what kind of game would we like to play that doesn't need to be sucking us in? You know when you lock in, you have 12 phases of stuff that has happened, that just reading through that is already your time gone for the night?" Kayatta makes similar noises, describing the studio's inspiration in Left 4 Dead, as a case of looking back and saying, "Wow, wasn't that game great - but also from the perspective of nostalgia, like: wasn't it great when you could kind of buy a game, and it might be supported over time, but ultimately, I can pick it up and put it down? And I can enjoy that game on my own terms, and not on the game's terms. Because it feels like so many things that tried to come for Left 4 Dead went in the opposite direction, where they're like, "Oh here's the fun gameplay - oh yeah but: battle passes and seasons!"" "I'm not saying all those things are always evil..." "I'm not saying all those things are always evil," he adds, "it's not true. But ultimately, it is something that starts to try to tell you how to engage, and not just like, when you're in the game, but actually when you're out of the game, when do you come back to the game? How often do you need to play the game? Check in, do this, do that." The inspiration instead, he says, "is more of a meta inspiration to say: what if we came back and we said, let's just focus on making a game that people like, and if they only play it for an hour a week and then they need to step away for six months, what if that was okay? What if it's okay that they go play other stuff? What if we built a game and a mandate - and a product, even, on the business level - around this idea that that's actually okay for a multiplayer game, and just get it in people's hands?" The natural follow-on there is Remedy's decision to put Firebreak immediately onto Xbox Game Pass and PlayStation Plus. I asked Kayatta whether that helped take the pressure off a little, amongst the wider, tougher context of similar multiplayer games - most notably the high-profile failure and ultimately pulling from sale of Sony's Concord - having some difficult launches. "From a wider industry perspective, obviously, there's always the question of: is it cannibalising sales? But of course, it's not like people put things on Game Pass for free either," Kayatta says. "I wouldn't call it a safety net for us," he adds, in that "games can't just survive exclusively by trying to be available in one service or another service." Still, he notes that Game Pass and PS Plus were both "a very welcome partnership". "We never used 'games-as-a-service', 'live-service' with this game, very consciously," adds PR director Thomas Puha, during a separate conversation. "The connotation of those games is, you know, it's gonna be 'battle pass' this, Call of Duty, Destiny, all these things that were so far from that." "a very contested market - but then everything is contested..." Puha adds, too, that the studio is aware it's "a very contested market - but then everything is contested. There's tons of games. It's not like there isn't a contest with single-player games, I mean we're up against, you know, Sony heavy-hitters... there's no point in trying to build something where you've got to be playing every day, because then the machinery we've got to build here is not something we could even afford - so what's the point?" According to Puha, the studio's other backstop here, meanwhile, is its shift in approach towards making multiple games at once, which Remedy's been doing since 2016. "It's very much about spreading the risk," he says, "you can see it's near impossible to survive with making a single game and then hoping that it, against all odds, does phenomenally well, and you get some money out of it. Part of our business model is that we work on multiple things, and we've said we try to ship something every year." The last point the team is keen to make is that FBC: Firebreak has been made with a significantly tighter budget than what you might consider some of its multiplayer competitors. "Of course, not just taking crazy risks by doing massive budgets and so on" is also a factor, as Puha puts it. "Of course we need this game to do well, but then we have realistic expectations." The team is still only around 50 developers strong, for instance - Control's team, by comparison, was in the low hundreds. "It's not like a 200-person, five-year dev cycle." To Kayatta, FBC: Firebreak's director, it also just comes back to the reasons why it exists in the first place. "I think there's a lot of cynicism around multiplayer games," he says. "And I think there's double cynicism about multiplayer games that come from, traditionally, single-player studios. And obviously I feel that - I mean I'm also a gamer. "But at the end of the day, one thing that is quite nice about working on this project here is that I know - because I live it every day - that this did not come from any amount of cynicism. It has just come from passion and creative people have been given a license to say, you know, what is fun? What do we just want to do? What's interesting?" This article is based on a press event at Remedy's headquarters, for which it assisted with travel and accommodation. #remedy #hopes #fbc #firebreak #can

St. Andrew’s Playground Park. Photo by Scott Norsworthy Doors Open Toronto, an annual event organized by the City of Toronto in partnership with the wider community, will be returning to the city later this month. The event provides rare public access for one weekend to architecturally and culturally significant buildings across the city. The theme for 2025 is “The City is Your Playground; Get Ready to Play,” which aims to explore Toronto’s playful development through the city’s most architecturally, historically, culturally and socially significant buildings and sites. The free weekend-long celebration of architecture and public space is taking place on Saturday, May 24 and Sunday, May 25, and will feature more than 150 sites for exploration, walking tours, and more. As part of this year’s festival, the TSA has organized three special programs reflecting on this year’s theme including a public panel discussion exploring how play can be built into our cities and three one-hour playground tours led by their designers: Sara Jackman Playground, St. Andrew’s Playground Park, and St. James Park Playground. This year will also see the return of the TSA’s Paper City, hosted for the first time at Toronto City Hall, which invites kids and their families to be the architects, city planners and builders of a paper metropolis. Additionally, Doors Open Toronto 2025 will see the return of Open Studio, one of the TSA’s most popular initiatives providing behind-the-scenes access to architecture studios across the city. This year’s edition will include the participation of 12 studios, as well as the Ontario Association of Architectsand the schools of architecture at the University of Toronto and Toronto Metropolitan University. For more information and to view the full schedule, click here. The post Doors Open Toronto 2025 returning to city this month appeared first on Canadian Architect. #doors #open #toronto #returning #city

News Mario Kart World, Donkey Kong Bananza “Could Not Be Realised” on Original Switch – President Nintendo president Shuntaro Furukawa spoke about how the older hardware of the Switch couldn't support new gameplay experiences. Posted By Joelle Daniels | On 14th, May. 2025 While Nintendo has already revealed its forecasts for the sales of the Switch 2 for the next fiscal year, president Shuntaro Furukawa also spoke about the original Switch during an investor Q&A. As caught by GamesRadar, Furukawa mentioned that one of the main reasons for Nintendo to create the Switch 2 was so that developers could work with upgraded hardware. Answering a question about whether the Switch 2 would just be a stopgap console release sandwiched between the original Switch and a more radical new offering down the line, Furukawa spoke about why Nintendo made the choices it did with the upcoming console. In his answer, he said that offering new gameplay experiences was one of the key reasons for the new hardware. “Over the course of these eight years and counting, it has become necessary to increase hardware processing performance in order for software developers to realize one of Nintendo’s most valued tenets: offering new gameplay experiences.” Furukawa said. “Thus, this time we decided to design the hardware with enhanced processing capability. We believe that Mario Kart World and Donkey Kong Bananza offer new gameplay experiences that could not be realized on Nintendo Switch.” Furukawa also spoke about the company’s plans for games going forward, and how it will ultimately make the upcoming Switch 2 the “new standard” for the kind of hardware that developers and customers can expect from the company. “Going forward, we will develop a variety of new titles centered on Nintendo Switch 2 and offer new gameplay experiences,” he said. “We aim to make Nintendo Switch 2 the new standard for Nintendo Switch, as hardware that continues to provide fresh surprises to our consumers.” During the same Q&A, Furukawa had also spoken about the company’s target of selling 15 million units of the Switch 2. He also noted that the conservative forecast for the new console largely came about because of its higher cost, which in turn is expected to add more hurdles when it comes to wider adoption by the player base. “We’ve set projected sales for the Nintendo Switch 2 in the ongoing fiscal year at 15 million units,” said Furukawa. “This figure is based on a target comparable to sales of the original Nintendo Switch during the 10-month period from its launch in March 2017 to the end of December the same year.” “Since the Switch 2 is priced higher than the original Switch, we recognize that there will be considerable hurdles in achieving the console’s widespread adoption in the early stages,” he continued. While Nintendo is gearing up for the June 5 launch of the console, in its recent earnings report, the company also noted that it will continue to support the original Switch with more game releases down the line. Some of the games noted to be coming out for the older console include Pokemon Legends: Z-A, Metroid Prime 4: Beyond, Rhythm Heaven Groove, Tomodachi Life: Living the Dream, and Pokemon Champions. The company has also noted that the $449.99 price tag for the Switch 2 might get affected down the line because of the affects of the US’s tariffs on imported goods. Tagged With: Elden Ring: Nightreign Publisher:Bandai Namco Developer:FromSoftware Platforms:PS5, Xbox Series X, PS4, Xbox One, PCView More DOOM: The Dark Ages Publisher:Bethesda Softworks Developer:id Software Platforms:Xbox Series X, PS5, PCView More Death Stranding 2: On the Beach Publisher:Sony Developer:Kojima Productions Platforms:PS5View More Amazing Articles You Might Want To Check Out! Mario Kart World, Donkey Kong Bananza “Could Not Be Realised” on Original Switch – President Nintendo president Shuntaro Furukawa spoke about how the older hardware of the Switch couldn't support new gam... 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View More Source: https://gamingbolt.com/mario-kart-world-donkey-kong-bananza-could-not-be-realised-on-original-switch-president #mario #kart #world #donkey #kong #bananza #could #not #realised #original #switch #president

A fresh coat of paint Google announces Material 3 Expressive, a colorful evolution of Android design Google's take on Android is getting brighter and bolder later this year. Ryan Whitwam – May 13, 2025 2:57 pm | 32 Credit: Google Credit: Google Story text Size Small Standard Large Width * Standard Wide Links Standard Orange * Subscribers only   Learn more Google accidentally showed off its big Android design refresh last week, but now Material 3 Expressive is official. Google says the new interface will begin with Android OS, but it will eventually expand across the full Google app ecosystem, bringing a more lively vibe to Gmail, Google Photos, and more. Material 3 Expressive won't be entirely unfamiliar—it shares some basic design elements with the Material You system Google launched four years ago. Material 3 Expressive is a bolder take on the same aesthetic, featuring "springy" animations, brighter colors, and new shapes. Material 3 Expressive As we learned from Google's slipup, Material 3 Expressive is the result of numerous user studies, which included more than 18,000 participants. Google explored how people parse information on their phone, finding design themes that supposedly support quicker and easier interactions. Often, that seems to result in making certain UI elements larger and easier to spot. Google claims people can find these important buttons four times faster than they can with older Material You interfaces. What you can expect from Material 3 Expressive Animations will abound in future versions of Android, and Google emphasizes the springy aspect. The style is supposed to feel natural and fun, with more UI elements connected to dynamic haptics. Colors are also getting an overhaul, but this isn't a complete rethink of Material You. The theming engine will pick bolder colors to improve the visual separation of UI elements, and you will see those colors in more places. Google says it's also updating Android's typography, featuring more contrast between headers and body text. This will change across Google's apps to help users parse information faster. Certain buttons will also come in a wider variety of shapes, and the labels will have varying text weights. Likewise, the style and shape of status bar icons will change to be more readable. The notification shade and quick settings may be one of the more dramatic shifts for Material 3 Expressive. Google will finally let you pin more controls to the compact view, and the quick settings tiles will be resizable. The background of the notification shade will use a blur effect on your background image as well. While Android 16 is just around the corner, and elements of Material 3 Expressive are hiding in the latest betas, this is not the default UI for Google's new version. Android 16 will launch in June with the Material You interface you know and (maybe) love. Later this year, Material 3 Expressive will come to Pixel phones and the Pixel Watch via an update. And what of non-Google devices? Like Material You, Material 3 Expressive is an open design framework. Any of Google's partners can implement it in their Android skin, and developers will be able to take advantage of design templates and new animation APIs to make their apps blend in with Material 3 Expressive. But will they? History suggests that Google's design preferences are rarely reflected in software from companies like Samsung and OnePlus. Motorola has traditionally hewed closer to Google's design language, but even Moto didn't fully embrace Material You. If you love the vibes of Material 3 Expressive, you'll probably need a Pixel to get the full effect. Ryan Whitwam Senior Technology Reporter Ryan Whitwam Senior Technology Reporter Ryan Whitwam is a senior technology reporter at Ars Technica, covering the ways Google, AI, and mobile technology continue to change the world. Over his 20-year career, he's written for Android Police, ExtremeTech, Wirecutter, NY Times, and more. He has reviewed more phones than most people will ever own. You can follow him on Bluesky, where you will see photos of his dozens of mechanical keyboards. 32 Comments Source: https://arstechnica.com/gadgets/2025/05/google-reveals-vibrant-material-3-expressive-coming-soon-to-a-pixel-near-you/ #google #announces #material #expressive #colorful #evolution #android #design

On a crisp morning in early April, Dan McEvoy and Bjoern Bingham cut clean lines down a wide run at the Heavenly Ski Resort in South Lake Tahoe, then ducked under a rope line cordoning off a patch of untouched snow.  They side-stepped up a small incline, poled past a row of Jeffrey pines, then dropped their packs.  The pair of climate researchers from the Desert Research Institute (DRI) in Reno, Nevada, skied down to this research plot in the middle of the resort to test out a new way to take the temperature of the Sierra Nevada snowpack. They were equipped with an experimental infrared device that can take readings as it’s lowered down a hole in the snow to the ground. The Sierra’s frozen reservoir provides about a third of California’s water and most of what comes out of the faucets, shower heads, and sprinklers in the towns and cities of northwestern Nevada. As it melts through the spring and summer, dam operators, water agencies, and communities have to manage the flow of billions of gallons of runoff, storing up enough to get through the inevitable dry summer months without allowing reservoirs and canals to flood. The need for better snowpack temperature data has become increasingly critical for predicting when the water will flow down the mountains, as climate change fuels hotter weather, melts snow faster, and drives rapid swings between very wet and very dry periods.  In the past, it has been arduous work to gather such snowpack observations. Now, a new generation of tools, techniques, and models promises to ease that process, improve water forecasts, and help California and other states safely manage one of their largest sources of water in the face of increasingly severe droughts and flooding. Observers, however, fear that any such advances could be undercut by the Trump administration’s cutbacks across federal agencies, including the one that oversees federal snowpack monitoring and survey work. That could jeopardize ongoing efforts to produce the water data and forecasts on which Western communities rely. “If we don’t have those measurements, it’s like driving your car around without a fuel gauge,” says Larry O’Neill, Oregon’s state climatologist. “We won’t know how much water is up in the mountains, and whether there’s enough to last through the summer.” The birth of snow surveys The snow survey program in the US was born near Lake Tahoe, the largest alpine lake in North America, around the turn of the 20th century.  Without any reliable way of knowing how much water would flow down the mountain each spring, lakefront home and business owners, fearing floods, implored dam operators to release water early in the spring. Downstream communities and farmers pushed back, however, demanding that the dam was used to hold onto as much water as possible to avoid shortages later in the year.  In 1908, James Church, a classics professor at the University of Nevada, Reno, whose passion for hiking around the mountains sparked an interest in the science of snow, invented a device that helped resolve the so-called Lake Tahoe Water Wars: the Mt. Rose snow sampler, named after the peak of a Sierra spur that juts into Nevada. James Church, a professor of classics at the University of Nevada, Reno, became a pioneer in the field of snow surveys.COURTESY OF UNIVERSITY OF NEVADA, RENO It’s a simple enough device, with sections of tube that screw together, a sharpened end, and measurement ticks along the side. Snow surveyors measure the depth of the snow by plunging the sampler down to the ground. They then weigh the filled tube on a specialized scale to calculate the water content of the snow.  Church used the device to take measurements at various points across the range, and calibrated his water forecasts by comparing his readings against the rising and falling levels of Lake Tahoe.  It worked so well that the US began a federal snow survey program in the mid-1930s, which evolved into the one carried on today by the Department of Agriculture’s Natural Resources Conservation Service (NRCS). Throughout the winter, hundreds of snow surveyors across the American West head up to established locations on snowshoes, backcountry skis, or snowmobiles to deploy their Mt. Rose samplers, which have barely changed over more than a century.  In the 1960s, the US government also began setting up a network of permanent monitoring sites across the mountains, now known as the SNOTEL network. There are more than 900 stations continuously transmitting readings from across Western states and Alaska. They’re equipped with sensors that measure air temperature, snow depth, and soil moisture, and include pressure-sensitive “snow pillows” that weigh the snow to determine the water content.  The data from the snow surveys and SNOTEL sites all flows into snow depth and snow water content reports that the NRCS publishes, along with forecasts of the amount of water that will fill the streams and reservoirs through the spring and summer. Taking the temperature None of these survey and monitoring programs, however, provide the temperature throughout the snowpack.  The Sierra Nevada snowpack can reach more than 6 meters (20 feet), and the temperature within it may vary widely, especially toward the top. Readings taken at increments throughout can determine what’s known as the cold content, or the amount of energy required to shift the snowpack to a uniform temperature of 32˚F.  Knowing the cold content of the snowpack helps researchers understand the conditions under which it will begin to rapidly melt, particularly as it warms up in the spring or after rain falls on top of the snow. If the temperature of the snow, for example, is close to 32˚F even at several feet deep, a few warm days could easily set it melting. If, on the other hand, the temperature measurements show a colder profile throughout the middle, the snowpack is more stable and will hold up longer as the weather warms. Bjoern Bingham, a research scientist at the Desert Research Institute, digs at snowpit at a research plot within the Heavenly Ski Resort, near South Lake Tahoe, California. JAMES TEMPLE The problem is that taking the temperature of the entire snowpack has been, until now, tough and time-consuming work. When researchers do it at all, they mainly do so by digging snow pits down to the ground and then taking readings with probe thermometers along an inside wall. There have been a variety of efforts to take continuous remote readings from sensors attached to fences, wires, or towers, which the snowpack eventually buries. But the movement and weight of the dense shifting snow tends to break the devices or snap the structures they’re assembled upon.“They rarely last a season,” McAvoy says. Anne Heggli, a professor of mountain hydrometeorology at DRI, happened upon the idea of using an infrared device to solve this problem during a tour of the institute’s campus in 2019, when she learned that researchers there were using an infrared meat thermometer to take contactless readings of the snow surface. In 2021, Heggli began collaborating with RPM Systems, a gadget manufacturing company, to design an infrared device optimized for snowpack field conditions. The resulting snow temperature profiler is skinny enough to fit down a hole dug by snow surveyors and dangles on a cord marked off at 10-centimeter (4-inch) increments. Bingham and Daniel McEvoy, an associate research professor at the Desert Research Institute, work together to take temperature readings from inside the snowpit as well as from within the hole left behind by a snow sampler.JAMES TEMPLE At Heavenly on that April morning, Bingham, a staff scientist at DRI, slowly fed the device down a snow sampler hole, calling out temperature readings at each marking. McEvoy scribbled them down on a worksheet fastened to his clipboard as he used a probe thermometer to take readings of his own from within a snow pit the pair had dug down to the ground. They were comparing the measurements to assess the reliability of the infrared device in the field, but the eventual aim is to eliminate the need to dig snow pits. The hope is that state and federal surveyors could simply carry along a snow temperature profiler and drop it into the snowpack survey holes they’re creating anyway, to gather regular snowpack temperature readings from across the mountains. In 2023, the US Bureau of Reclamation, the federal agency that operates many of the nation’s dams, funded a three-year research project to explore the use of the infrared gadgets in determining snowpack temperatures. Through it, the DRI research team has now handed devices out to 20 snow survey teams across California, Colorado, Idaho, Montana, Nevada, and Utah to test their use in the field and supplement the snowpack data they’re collecting. The Snow Lab The DRI research project is one piece of a wider effort to obtain snowpack temperature data across the mountains of the West. By early May, the snow depth had dropped from an April peak of 114 inches to 24 inches (2.9 meters to 0.6 meters) at the UC Berkeley Central Sierra Snow Lab, an aging wooden structure perched in the high mountains northwest of Lake Tahoe. Megan Mason, a research scientist at the lab, used a backcountry ski shovel to dig out a trio of instruments from what was left of the pitted snowpack behind the building. Each one featured different types of temperature sensors, arrayed along a strong polymer beam meant to hold up under the weight and movement of the Sierra snowpack.  She was pulling up the devices after running the last set of observations for the season, as part of an effort to develop a resilient system that can survive the winter and transmit hourly temperature readings. The lab is working on the project, dubbed the California Cold Content Initiative, in collaboration with the state’s Department of Water Resources. California is the only western state that opted to maintain its own snow survey program and run its own permanent monitoring stations, all of which are managed by the water department.  The plan is to determine which instruments held up and functioned best this winter. Then, they can begin testing the most promising approaches at several additional sites next season. Eventually, the goal is to attach the devices at more than 100 of California’s snow monitoring stations, says Andrew Schwartz, the director of the lab. The NRCS is conducting a similar research effort at select SNOTEL sites equipped with a beaded temperature cable. One such cable is visible at the Heavenly SNOTEL station, next to where McEvoy and Bingham dug their snow pit, strung vertically between an arm extended from the main tower and the snow-covered ground.  DRI's Bjoern Bingham feeds the snow temperature profiler, an infrared device, down a hole in the Sierra snowpack.JAMES TEMPLE Schwartz said that the different research groups are communicating and collaborating openly on the projects, all of which promise to provide complementary information, expanding the database of snowpack temperature readings across the West. For decades, agencies and researchers generally produced water forecasts using relatively simple regression models that translated the amount of water in the snowpack into the amount of water that will flow down the mountain, based largely on the historic relationships between those variables.  But these models are becoming less reliable as climate change alters temperatures, snow levels, melt rates, and evaporation, and otherwise drives alpine weather patterns outside of historic patterns. “As we have years that scatter further and more frequently from the norm, our models aren’t prepared,” Heggli says. Plugging direct temperature observations into more sophisticated models that have emerged in recent years, Schwartz says, promises to significantly improve the accuracy of water forecasts. That, in turn, should help communities manage through droughts and prevent dams from overtopping even as climate change fuels alternately wetter, drier, warmer, and weirder weather. About a quarter of the world’s population relies on water stored in mountain snow and glaciers, and climate change is disrupting the hydrological cycles that sustain these natural frozen reservoirs in many parts of the world. So any advances in observations and modeling could deliver broader global benefits. Ominous weather There’s an obvious threat to this progress, though. Even if these projects work as well as hoped, it’s not clear how widely these tools and techniques will be deployed at a time when the White House is gutting staff across federal agencies, terminating thousands of scientific grants, and striving to eliminate tens of billions of dollars in funding at research departments.  The Trump administration has fired or put on administrative leave nearly 6,000 employees across the USDA, or 6% of the department’s workforce. Those cutbacks have reached regional NRCS offices, according to reporting by local and trade outlets. That includes more than half of the roles at the Portland office, according to O’Neill, the state climatologist. Those reductions prompted a bipartisan group of legislators to call on the Secretary of Agriculture to restore the positions, warning the losses could impair water data and analyses that are crucial for the state’s “agriculture, wildland fire, hydropower, timber, and tourism sectors,” as the Statesman Journal reported. There are more than 80 active SNOTEL stations in Oregon. The fear is there won’t be enough people left to reach all the sites this summer to replace batteries, solar panels, and drifting or broken sensors, which could quickly undermine the reliability of the data or cut off the flow of information.  “Staff and budget reductions at NRCS will make it impossible to maintain SNOTEL instruments and conduct routine manual observations, leading to inoperability of the network within a year,” the lawmakers warned. The USDA and NRCS didn’t respond to inquiries from MIT Technology Review.  DRI’s Daniel McEvoy scribbles down temperature readings at the Heavenly site.JAMES TEMPLE If the federal cutbacks deplete the data coming back from SNOTEL stations or federal snow survey work, the DRI infrared method could at least “still offer a simplistic way of measuring the snowpack temperatures” in places where state and regional agencies continue to carry out surveys, McAvoy says. But most researchers stress the field needs more surveys, stations, sensors, and readings to understand how the climate and water cycles are changing from month to month and season to season. Heggli stresses that there should be broad bipartisan support for programs that collect snowpack data and provide the water forecasts that farmers and communities rely on.  “This is how we account for one of, if not the, most valuable resource we have,” she says. “In the West, we go into a seasonal drought every summer; our snowpack is what trickles down and gets us through that drought. We need to know how much we have.” Source: https://www.technologyreview.com/2025/05/14/1116395/why-climate-researchers-are-taking-the-temperature-of-mountain-snow/ #why #climate #researchers #are #taking #the #temperature #mountain #snow