China's latest spacecraft aims to bring 'groundbreaking' samples back from asteroid near Mars
China launched a spacecraft that promises to return samples from an asteroid near Mars and yield "groundbreaking discoveries and expand humanity's knowledge of the cosmos," the country's space agency said.
The Tianwen-2 probe launched early on Thursday from southern China aboard the workhorse Long March 3-B rocket.
The probe will collect samples from the asteroid 2016HO3 and explore the main-belt comet 311P, which lies even farther from Earth than Mars, according to the China National Space Administration.
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Shan Zhongde, head of the CNSA, was quoted as saying the Tianwen-2 mission represents a "significant step in China's new journey of interplanetary exploration" and over its decade-long mission will "yield groundbreaking discoveries and expand humanity's knowledge of the cosmos".
Samples from 2016HO3 are due to be returned in about two years. The asteroids, chosen for their relatively stable orbits, hopefully will offer clues into the formation of Earth, such as the origins of water.
China earlier returned rock samples from the moon's far side back to Earth in a historic mission and has welcomed international cooperation.
However, any cooperation with the US hinges on removing an American law banning direct bilateral cooperation with NASA.
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The near side of the Moon is seen from Earth, and the far side faces outer space. The far side is also known to have mountains and impact craters, and is much more difficult to reach.
China also operates the three person-crewed Tiangong - or "Heavenly Palace" - space station, making the country a major player in a new era of space exploration and the use of permanent stations to conduct experiments in space, especially since the station was entirely Chinese-built after the country was excluded from the International Space Station over US national security concerns.
China's space programme is controlled by the People's Liberation Army, the military branch of the ruling Communist Party.
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The country's space programme has grown rapidly in the more than 20 years since it first put a man in space, only the third country to do so under its own speed.
The space agency has landed an unmanned explorer on Mars and a rover on the far side of the Moon. It aims to put a person on the moon before 2030.
A future Tianwen-4 Jupiter mission will explore Jupiter, although details haven't been released.
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CNN
28 minutes ago
- CNN
Small businesses struggle under Trump's tariff whiplash: ‘I'm so angry that my government has done this to me'
For some small businesses, the last week brought even more twists and turns to the past two months of President Donald Trump's chaotic tariffs. The situation was already confusing, with stops and starts of tariffs at different levels. Then on Wednesday, a US court said Trump overstepped his authority in imposing most of those import levies – only for an appeals court on Thursday to pause the previous court's ruling. The confusion has made it challenging for some small companies to plan, business owners told CNN. In certain cases, they have had to consider changing their product strategy, looking into shifting their supply chains, reducing staff hours or delaying products. 'My fear is, if this continues, there's going to be like the mass extinction of small businesses,' Julie Robbins, CEO of Ohio-based guitar pedal maker EarthQuaker Devices, told CNN. Trump announced blanket tariffs across the globe on April 2, and since then, his plans have changed on a regular basis. In early April, he issued a 90-day pause on reciprocal tariffs almost everywhere except China. Then, after ratcheting up total tariffs on Chinese imports to 145%, he declared smartphones and certain other electronics would be exempt from the reciprocal tariffs. The US and China agreed in May to roll back reciprocal tariffs for 90 days. And in late May, he threatened smartphone makers like Apple with 25% tariffs if they don't make their phones in the US. He also agreed to push back levies on imports from the European Union until July 9. Those are only some of his changes, which can come at any time of day via the White House, social media posts or other avenues. The whiplash has been hard for companies to keep up with. Even major brands like apparel giant Gap are feeling the impact of tariffs, but small companies with far fewer resources are in an even tougher spot. The National Federation of Independent Business Small Business Optimism Index fell by 1.6 points in April, dipping below the 51-year average for the second consecutive month. The organization's chief economist, Bill Dunkelberg, cited uncertainty as a 'major impediment' for small business owners in a press release. 'It's the sort of more smaller, kind of more niche… brands that are going to really, really get hit by this,' Jack Leathem, an analyst at market research firm Canalys, told CNN in April. Some small business owners have had to make difficult decisions as they've grappled with the impact of tariffs. EveAnna Manley, whose company Manley Labs makes high-end electronics for recording studios, has had to cut her employees' hours by 25%. The reciprocal tariffs that China imposed on the US have been particularly challenging, she says, since China has become a major market for her business. Manley says it took 'decades' for her to 'get the best Chinese importers.' Overall, Manley Labs' sales are down more than 19% compared to last year, she told CNN, which has frozen the company's product development efforts. 'It's just a freaking mess right now,' she said in late May, before this past week's court rulings on Trump's tariffs. 'And I'm so angry that my own government has done this to me.' The best thing small businesses can do right now is to be flexible and diversify their sourcing and procurement strategies, says Tala Akhavan, chief operating officer of Pietra, a platform that helps brands with sourcing, production and logistics among other services. That's what Intuition Robotics, which makes a home robot designed to be a companion for older adults, is doing, according to chief strategy officer Assaf Gad. The company also makes money off its digital subscription accounts, according to Gad, giving it the flexibility to look into a 'plan B' outside of China for producing the company's hardware. Sudden changes in tariff policies haven't really impacted the company's decisions because it's planning for the next nine to 12 months rather than the short term, he said in mid-May. Trump's tariffs have encouraged Gad to think about expanding Intuition Robotics into international markets. 'Maybe this is also a good time to say, 'Let's not put all the eggs in one basket,'' he said, 'and, you know, start looking on other kind of territories that will reduce the risk for us going forward.' But for some companies, finding a plan B isn't so easy. That's the case for Sarah O'Leary, CEO of Willow, which makes wearable breast pumps and accessories. As a medical device company, Willow can't simply just move its manufacturing, O'Leary told CNN. The company had to pause exporting one product it produces in China for postpartum recovery at one point because it became too expensive. The ruling on Wednesday aiming to block many tariffs brought some relief, O'Leary said in an emailed statement on Thursday evening. But she acknowledged that there's still 'so much uncertainty,' adding that 'the chaos will persist.' Any tariffs, even low ones, would be difficult for a small company like hers to absorb, she said in mid-May. 'We don't build our products with that much margin,' she said. 'And so, unfortunately, we are in a position where we have to evaluate what we can do to survive in those contexts.'
WIRED
28 minutes ago
- WIRED
How to Make AI Faster and Smarter—With a Little Help from Physics
Jun 1, 2025 7:00 AM Rose Yu has drawn on the principles of fluid dynamics to improve deep learning systems that predict traffic, model the climate, and stabilize drones during flight. Photograph: Peggy Peattie for Quanta Magazine The original version of this story appeared in Quanta Magazine. When she was 10 years old, Rose Yu got a birthday present that would change her life—and, potentially, the way we study physics. Her uncle got her a computer. That was a rare commodity in China 25 years ago, and the gift did not go unused. At first, Yu mainly played computer games, but in middle school she won an award for web design. It was the first of many computer-related honors. Yu went on to major in computer science at Zhejiang University, where she won a prize for innovative research. For her graduate studies, she chose the University of Southern California (USC), partly because the same uncle—who was the only person she knew in the United States—was then working at the Jet Propulsion Laboratory in nearby Pasadena. Yu earned her doctorate in 2017 with an award for best dissertation. Her most recent honor came in January, when President Joe Biden, in his last week in office, gave her a Presidential Early Career Award. Yu, now an associate professor at the University of California, San Diego (UCSD), is a leader in a field known as 'physics-guided deep learning,' having spent years incorporating our knowledge of physics into artificial neural networks. The work has not only introduced novel techniques for building and training these systems, but it's also allowed her to make progress on several real-world applications. She has drawn on principles of fluid dynamics to improve traffic predictions, sped up simulations of turbulence to enhance our understanding of hurricanes, and devised tools that helped predict the spread of Covid-19. This work has brought Yu closer to her grand dream—deploying a suite of digital lab assistants that she calls AI Scientist. She now envisions what she calls a 'partnership' between human researchers and AI tools, fully based on the tenets of physics and thus capable of yielding new scientific insights. Combining inputs from a team of such assistants, in her opinion, may be the best way to boost the discovery process. Quanta spoke with Yu about turbulence in its many guises, how to get more out of AI, and how it might get us out of urban gridlock. The interview has been condensed and edited for clarity. Yu on the UCSD campus, where she is an associate professor. Photograph: Peggy Peattie for Quanta Magazine When did you first try to combine physics with deep learning? Rose Yu: It started with traffic. I was a grad student at USC, and the campus is right near the intersection of I-10 and I-110. To get anywhere, you have to go through a lot of traffic, which can be very annoying. In 2016, I began to wonder whether I could do anything about this. Deep learning—which uses multilayered neural networks to elicit patterns from data—was getting really hot back then. There was already a lot of excitement about applications in image classification, but images are just static things. I wondered whether deep learning could help with problems where things are constantly changing. I wasn't the first person to consider this, but my colleagues and I did find a novel way of framing the problem. What was your new approach? First, we thought of traffic in terms of the physical process of diffusion. In our model, the flow of traffic over a network of roads is analogous to the flow of fluids over a surface—motions that are governed by the laws of fluid dynamics. But our main innovation was to think of traffic as a graph, from the mathematical field of graph theory. Sensors, which monitor traffic on highways and other roads, serve as the nodes of this graph. And the edges of the graph represent the roads (and distances) between those sensors. Yu's interest in computers began with a gift for her 10th birthday. Photograph: Peggy Peattie for Quanta Magazine A graph provides a snapshot of the entire road network at a given time, telling you the average velocity of cars at every point on the graph. When you put together a series of these snapshots, spaced every five minutes apart, you get a good picture of how traffic is evolving. From there, you can try to predict what will happen in the future. The big challenge in deep learning is that you need a lot of data to train the neural network. Fortunately, one of my advisers, Cyrus Shahabi, had worked for many years on the problem of traffic forecasting, and he'd accumulated a vast amount of LA traffic data that I had access to. So how good were your predictions? Prior to our work, people could only make traffic forecasts that were reliable for about 15 minutes. Our forecasts were valid for one hour—a big improvement. Our code was deployed by Google Maps in 2018. A bit later, Google invited me to become a visiting researcher. That's about when you began working on climate modeling, right? Yes, that started in 2018, when I gave a talk at the Lawrence Berkeley National Laboratory. Afterward, I spoke with scientists there, and we looked for a problem that would be a good testbed for physics-guided deep learning. We settled on predicting the evolution of turbulent flow, which is a key factor in climate models, as well as an area of major uncertainty. Familiar examples of turbulence are the swirling patterns you see after pouring milk into a cup of coffee and giving it a stir. In the oceans, swirls like this can span thousands of miles. Predictions of turbulent behavior that are based on solving the Navier-Stokes equation, which describes the flow of fluids, are considered the gold standard in this field. But the required calculations are very slow, which is why we don't have good models for predicting hurricanes and tropical cyclones. The heavy congestion of Los Angeles first inspired Yu to model highway traffic as the flow of fluids. Photograph: Peggy Peattie for Quanta Magazine And deep learning can help? The basic idea is that deep neural networks that are trained on our best numerical simulations can learn to imitate—or as we say, 'emulate'—those simulations. They do that by recognizing properties and patterns buried within the data. They don't have to go through time-consuming, brute-force calculations to find approximate solutions. Our models sped up predictions by a factor of 20 in two-dimensional settings and by a factor of 1,000 in three-dimensional settings. Something like our turbulence prediction module might someday be inserted into bigger climate models that can do better at predicting things like hurricanes. Where else does turbulence show up? It's pretty much everywhere. Turbulence in blood flow, for instance, can lead to strokes or heart attacks. And when I was a postdoc at Caltech, I coauthored a paper that looked into stabilizing drones. Propellor-generated airflows interact with the ground to create turbulence. That, in turn, can cause the drone to wobble. We used a neural network to model the turbulence, and that led to better control of drones during takeoffs and landings. I'm currently working with scientists at UCSD and General Atomics on fusion power. One of the keys to success is learning how to control the plasma, which is a hot, ionized phase of matter. At temperatures of about 100 million degrees, different kinds of turbulence arise within the plasma, and physics-based numerical models that characterize that behavior are very slow. We're developing a deep learning model that should be able to predict the plasma's behavior in a split second, but this is still a work in progress. Yu and doctoral student Jianke Yang in her office at UCSD. Photograph: Peggy Peattie for Quanta Magazine Where did your AI Scientist idea come from? In the past couple of years, my group has developed AI algorithms that can automatically discover symmetry principles from data. For example, our algorithm identified the Lorentz symmetry, which has to do with the constancy of the speed of light. Our algorithm also identified rotational symmetry—the fact, for example, that a sphere doesn't look any different regardless of how you rotate it—which is something it was not specifically trained to know about. While these are well-known properties, our tools also have the capability to discover new symmetries presently unknown to physics, which would constitute a huge breakthrough. It then occurred to me that if our tools can discover symmetries from raw data, why don't we try to generalize this? These tools could also generate research ideas or new hypotheses in science. That was the genesis of AI Scientist. What exactly is AI Scientist—just a fancy kind of neural net? It's not a single neural network, but rather an ensemble of computer programs that can help scientists make new discoveries. My group has already developed algorithms that can help with individual tasks, such as weather forecasting, identifying the drivers of global temperature rise, or trying to discover causal relationships like the effects of vaccination policies on disease transmission. We're now building a broader 'foundation' model that's versatile enough to handle multiple tasks. Scientists gather data from all types of instruments, and we want our model to include a variety of data types—numbers, text, images, and videos. We have an early prototype, but we want to make our model more comprehensive, more intelligent and better trained before we release it. That could happen within a couple of years. What do you imagine it could do? AI can assist in practically every step of the scientific discovery process. When I say 'AI Scientist,' I really mean an AI scientific assistant. The literature survey stage in an experiment, for example, typically requires a massive data-gathering and organization effort. But now, a large language model can read and summarize thousands of books during a single lunch break. What AI is not good at is judging scientific validity. In this case, it can't compete with an experienced researcher. While AI could help with hypothesis generation, the design of experiments and data analysis, it still cannot carry out sophisticated experiments. How far would you like to see the concept go? As I picture it, an AI Scientist could relieve researchers of some of the drudgery while letting people handle the creative aspects of science. That's something we're particularly good at. Rest assured, the goal is not to replace human scientists. I don't envision—nor would I ever want to see—a machine substituting for, or interfering with, human creativity. Original story reprinted with permission from Quanta Magazine, an editorially independent publication of the Simons Foundation whose mission is to enhance public understanding of science by covering research developments and trends in mathematics and the physical and life sciences.
Yahoo
33 minutes ago
- Yahoo
The USWNT basks in the return of Naomi Girma – their ‘security blanket'
Compared to the past few windows, Saturday's 3-0 win was a game where the U.S. women's national team looked in complete control. The attack kept the pressure on China, continuing to threaten their defense as it frequently adjusted the height of its line of confrontation. Catarina Macario provided a goal and an assist; Lindsey Heaps and Sam Coffey scored from their midfield roles. Advertisement However, head coach Emma Hayes' post-match press conference started with a question about the long-awaited return of Naomi Girma and how the team benefited from her 90-minute shift. 'We've missed her, we really have,' Hayes said. 'Just in terms of the way we control the game; her, in a deeper space, just making decisions when to play forward, when not to.' As the first million-dollar transfer in women's soccer history, this year has only intensified the scrutiny that comes with being one of the world's best players in her position. Her time at Chelsea was not as smooth as the club and player had hoped. She exited her debut in early March with a calf injury, feeling the strain having gone nearly four months without playing a club match. Her first minutes back with Chelsea came in mid-April and this international window marked her first with the U.S. in 2025. 'I gave her a hug after the game,' said midfielder Coffey, who scored her second U.S. goal against China. 'Having her on the field is like having a security blanket, and just like being wrapped in it.' Advertisement After some tense and at times disjointed performances against Japan in the SheBelieves Cup and Brazil in a pair of friendlies, the USWNT dominated the entire match on Saturday. The defense played its part, with Coffey shuttling around to shield the back-line and the partnership between Girma and Emily Sonnett giving goalkeeper Phallon Tullis-Joyce ample coverage whenever China reached the final third. Those threats were few and far between. The USWNT dominated the chance-creation game, generating 3.01 expected goals (xG) while holding China to 0.18 xG. 'It does feel natural now,' Girma said of returning to the national team. 'I mean, I was able to watch what we did before, and I think a lot of what Emma wants to do is layer on what we had done in the past year. I think the changes are good and easy for me to kind of adapt to, with that base knowledge of how we want to play. 'It was just nice to be back on the field.' Advertisement As was often the case during the triumphant run to Olympic gold last summer, Girma was at the heart of the team's build-up. She logged a staggering 138 touches, per TruMedia, 41 more than the team's second-most involved player (Avery Patterson, with the right-back notching 97 touches). Girma completed 95.3 percent of her 129 pass attempts, helping determine how the USWNT worked to break through China's defensive structure. She also put in a defensive shift that embodied working smarter, not harder. She was not throwing herself into many challenges, though much of that work was done well before the ball even reached the U.S. defense. Still, she was quick on mop up duty, leading the USWNT with seven ball clearances (nobody else had more than three) while winning all three ground duels and her only aerial duel. Having her in the back-line only helped the midfield feel more confident as they engaged defensively, with peace of mind that she was in position if they failed to win the ball. Advertisement 'I can't put into words what she means to this team,' Coffey said. 'I think everybody sees it on the field, but off the field as well. She's just a joy in this environment and such a light for us. We have missed her so much. I thought she was exceptional today, as she always is.' In a year characterized by frequent rotation across Hayes' squads and lineups, Girma's return represents a different type of variable for the team. Throughout 2025, Hayes has called on a number of center-backs, each auditioning to be Girma's primary partner. Sonnett represents a vital holdover from the team's last World Cup win in 2019, having established herself as a hard-nosed veteran along the back-line. Emily Sams came off the bench against China, while Tara McKeown has earned five caps this year. The latter two in particular are emblematic of Hayes' examination of her broader player pool, with both stepping into more important roles given positional absences. Not only has Girma been missing, but so has her partner last summer, Tierna Davidson, who tore her ACL in April. While Sams, McKeown, Sonnett and others have stepped into their roles, none can quite match the same comfortable benchmark established by Girma. Advertisement 'I mean, she's a world-class player,' Hayes said. 'I thought she brought something to our performance that we're looking for, so I'm delighted to have her back.' Once she returned to playing regular minutes for Chelsea in mid-April, she was eased back into the fold. While Chelsea kept clean sheets in each of her final four performances of the WSL season, only two of those matches saw Girma play all 90 minutes. 'It was a lot of transition for me,' Girma said on Friday regarding her first months with Chelsea. 'I think it was a huge learning experience for me. You always have those moments in your career where you're up and down, up and down, up and down, so it was definitely like that. 'But I think it was a good five months of getting settled, getting to know my team-mates, getting used to playing there, playing with a new team, and living in a new country. So it's been really positive so far, and I've really enjoyed it.' Advertisement Girma logged her 46th cap, an impressive total for a 24-year-old defender who seems destined to be the bedrock of this team for years to come. With its world class center-back in the lineup, the United States put together its most composed performance of the year. Then again, that revelation hardly comes as a surprise given Girma's floor-raising performances since her debut in 2022. This article originally appeared in The Athletic. US Women's national team, Soccer, NWSL, UK Women's Football 2025 The Athletic Media Company
