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Science Simplified: What Is Artificial Intelligence?

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Saturday, March 23, 2024

Artificial intelligence, particularly through machine learning, is revolutionizing how we solve complex problems in various fields including science, medicine, and technology. Facilities like Argonne National Laboratory are leading these advancements, using AI to predict complex system behaviors, improve material selection, and assist in global challenges like disease and climate change.What Is Artificial Intelligence?Artificial intelligence (AI) is the collective term for computer technologies and techniques that help solve complex problems by imitating the brain’s ability to learn.AI helps computers recognize patterns hidden within a lot of information, solve problems, and adjust to changes in processes as they happen, much faster than humans can.VIDEOIn this Science 101: What is Artificial Intelligence video, Argonne National Laboratory scientists Taylor Childers and Bethany Lusch discuss AI — the computer technologies and techniques that help solve complex problems by imitating the brain’s ability to learn. Researchers use AI to be better and faster at tackling the most difficult problems in science, medicine, and technology, and help drive discovery in those areas. This could range from helping us understand how COVID-19 attacks the human body to finding ways to manage traffic jams. Researchers use AI to be better and faster at tackling the most difficult problems in science, medicine, and technology, and help drive discovery in those areas. This could range from helping us understand how COVID-19 attacks the human body to finding ways to manage traffic jams.Many Department of Energy (DOE) facilities, like Argonne National Laboratory, assist in developing some the most advanced AI technologies available. Today, they are used in areas of study ranging from chemistry to environmental and manufacturing sciences to medicine and the universe.AI is used to help make models of complex systems, like engines or weather, and predict what might happen if certain parts of those systems changed — for example, if a different fuel was used or temperatures increased steadily.But there are many more uses for AI.A key tool in Argonne’s AI toolbox is a type of technique called machine learning that gets smarter or more accurate as it gets more data to learn from. Machine learning is really helpful in identifying specific objects hidden within a bigger, more crowded picture.In a popular example, a machine learning model was trained to recognize the main features of cats and dogs by showing it many images. Later, the model was able to identify cats and dogs from pictures of mixed animals.Similar machine learning models can help scientists identify, for example, one type of galaxy from another when they receive object-packed images from space telescopes.Machine learning is just one of many AI techniques that help us learn more quickly and accurately. They can help choose the right molecule or chemical for a new material and may one day guide new experiments on their own.Argonne has worked with many organizations around the world to become a leader in artificial intelligence use and development, this includes applying AI to:Improve battery life for cars and energy.Build better climate models that can predict wildfires, hurricanes, and other disasters, and help our communities and power companies protect against them.Find those parts of viruses that attack our cells and develop drugs to fight them.Credit: Argonne National LaboratoryWhat is Artificial Intelligence?Analyzing large complex data to perform human tasks at computer speeds.Artificial intelligence (AI) is now a part of our daily lives, helping to simplify basic tasks, such as voice recognition, content recommendations or photo searches based on people or objects they contain. Scientists are using AI in similar ways to advance our understanding of the world around us. It can help them analyze mountains of data faster, and has provided better solutions. Different AI techniques are used in many research areas, from materials science and medicine to climate change and the cosmos.For example, we can train AI to recognize complex patterns by viewing many different examples. Researchers can use this capability to find new and improved materials for things like solar cells or medicine by training AI on all the known materials for that application. Then AI can help researchers zero in on other promising materials that can be fabricated and tested in a laboratory.

What Is Artificial Intelligence? Artificial intelligence (AI) is the collective term for computer technologies and techniques that help solve complex problems by imitating the brain’s...

AI Artificial Intelligence General Concept

Artificial intelligence, particularly through machine learning, is revolutionizing how we solve complex problems in various fields including science, medicine, and technology. Facilities like Argonne National Laboratory are leading these advancements, using AI to predict complex system behaviors, improve material selection, and assist in global challenges like disease and climate change.

What Is Artificial Intelligence?

Artificial intelligence (AI) is the collective term for computer technologies and techniques that help solve complex problems by imitating the brain’s ability to learn.

AI helps computers recognize patterns hidden within a lot of information, solve problems, and adjust to changes in processes as they happen, much faster than humans can.


In this Science 101: What is Artificial Intelligence video, Argonne National Laboratory scientists Taylor Childers and Bethany Lusch discuss AI — the computer technologies and techniques that help solve complex problems by imitating the brain’s ability to learn. Researchers use AI to be better and faster at tackling the most difficult problems in science, medicine, and technology, and help drive discovery in those areas. This could range from helping us understand how COVID-19 attacks the human body to finding ways to manage traffic jams.

Researchers use AI to be better and faster at tackling the most difficult problems in science, medicine, and technology, and help drive discovery in those areas. This could range from helping us understand how COVID-19 attacks the human body to finding ways to manage traffic jams.

Many Department of Energy (DOE) facilities, like Argonne National Laboratory, assist in developing some the most advanced AI technologies available. Today, they are used in areas of study ranging from chemistry to environmental and manufacturing sciences to medicine and the universe.

AI is used to help make models of complex systems, like engines or weather, and predict what might happen if certain parts of those systems changed — for example, if a different fuel was used or temperatures increased steadily.

But there are many more uses for AI.

A key tool in Argonne’s AI toolbox is a type of technique called machine learning that gets smarter or more accurate as it gets more data to learn from. Machine learning is really helpful in identifying specific objects hidden within a bigger, more crowded picture.

In a popular example, a machine learning model was trained to recognize the main features of cats and dogs by showing it many images. Later, the model was able to identify cats and dogs from pictures of mixed animals.

Similar machine learning models can help scientists identify, for example, one type of galaxy from another when they receive object-packed images from space telescopes.

Machine learning is just one of many AI techniques that help us learn more quickly and accurately. They can help choose the right molecule or chemical for a new material and may one day guide new experiments on their own.

Argonne has worked with many organizations around the world to become a leader in artificial intelligence use and development, this includes applying AI to:

  • Improve battery life for cars and energy.
  • Build better climate models that can predict wildfires, hurricanes, and other disasters, and help our communities and power companies protect against them.
  • Find those parts of viruses that attack our cells and develop drugs to fight them.
What Is Artificial Intelligence Infographic

Credit: Argonne National Laboratory

What is Artificial Intelligence?

Analyzing large complex data to perform human tasks at computer speeds.

Artificial intelligence (AI) is now a part of our daily lives, helping to simplify basic tasks, such as voice recognition, content recommendations or photo searches based on people or objects they contain. Scientists are using AI in similar ways to advance our understanding of the world around us. It can help them analyze mountains of data faster, and has provided better solutions. Different AI techniques are used in many research areas, from materials science and medicine to climate change and the cosmos.

For example, we can train AI to recognize complex patterns by viewing many different examples. Researchers can use this capability to find new and improved materials for things like solar cells or medicine by training AI on all the known materials for that application. Then AI can help researchers zero in on other promising materials that can be fabricated and tested in a laboratory.

Read the full story here.
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Nobel Prize in Economics Awarded for Research on Science, Technology and Growth

Joel Mokyr, Philippe Aghion and Peter Howitt share the Nobel economics prize for work that underlines the importance of investing in research and development

October 14, 20254 min readEconomics Nobel Honors Work Linking Scientific Research to ProsperityJoel Mokyr, Philippe Aghion and Peter Howitt share the Nobel economics prize for work that underlines the importance of investing in research and developmentBy Philip Ball & Nature magazine Joel Mokyr, Philippe Aghion and Peter Howitt, winners of the 2025 Economics Nobel prize. Northwestern University, Patrick Imbert/Collège de France, Ashley McCabe/Brown UniversityThe 2025 Sveriges Riksbank Prize for Economic Sciences in Memory of Alfred Nobel has been awarded to three researchers who have shown how technological and scientific innovation, coupled to market competition, drive economic growth.One half of the prize goes to economic-historian Joel Mokyr of Northwestern University in Evanston, Illinois, and the other half is split between the economic theorists Philippe Aghion of the Collège de France and the London School of Economics and Peter Howitt of Brown University in Providence, Rhode Island.“I can’t find the words to express what I feel,” Aghion said. He says he will use the money for research in his laboratory at the Collège de France.On supporting science journalismIf you're enjoying this article, consider supporting our award-winning journalism by subscribing. By purchasing a subscription you are helping to ensure the future of impactful stories about the discoveries and ideas shaping our world today.The award “underlies the importance in investing in science for innovation and long-term economic growth”, says economist Diane Coyle of the University of Cambridge. “It's great to see the Nobel prize recognize the importance of this topic,” adds innovation policy researcher Richard Jones of the University of Manchester, UK. “It's important that economists understand the conditions that lead to technological progress,” he adds. The winners, says Coyle, “have long been on people’s list of potential candidates”.Old isn’t goldEconomic growth at a rate of about 1-2 per cent annually is the norm for industrialized nations today. But such growth rates did not happen in earlier times, despite technological innovations, such as the windmill and the printing press.Mokyr showed that the key difference between now and then was what he calls “useful knowledge”, or innovations based on scientific understanding. One example is the advances made during the Industrial Revolution, beginning in the eighteenth century, when improvements in steam engines could be made systematic rather than by trial and error.Aghion and Howitt, for their part, clarified the market mechanisms behind sustained growth in recent times. In 1992 they presented a model showing how competition between companies selling new products allows innovations to enter the marketplace and displaces older products: a process they called creative destruction.Underlying growth, in other words, is a steady churn of businesses and products. The researchers showed how companies invest in research and development (R&D) to improve their chances of finding a new product, and predicted the optimal level of such investment.Entrepreneurial stateAccording to economist Ufuk Akcigit of the University of Chicago, Aghion and Howitt highlight an important aspect of economic growth, which is that spending on R&D does not by itself guarantee higher rates of growth: “Unless we replace inefficient firms from the economy, we cannot make space for newcomers with new ideas and better technologies.”“When a new entrepreneur emerges, they have every incentive to come up with a radical new technology,” Akcigit says. “As soon as they become an incumbent, their incentive vanishes” and they no longer invest in R&D to drive innovation.Thus, because companies cannot expect to remain at the forefront of innovation indefinitely, the incentive for investing in R&D coming from market forces alone declines as a company’s market share grows. To guarantee the societal benefits of constant innovation, the model suggests that it is in society’s interests for the state to subsidize R&D, so long as the return is not merely incremental improvements.The work of all three laureates also acknowledges the complex social consequences of growth. In the early days of the Industrial Revolution there were concerns about how mechanisation would cause unemployment of manual workers – a worry echoed today with the increasing use of AI in place of human labour. But Mokyr showed that in fact early mechanization led to the creation of new jobs.Creative destruction, meanwhile, leads to companies failing and jobs being lost. Aghion and Howitt emphasized that society needs safety nets and constructive negotiation of conflicts to navigate such problems.Their model “recognizes the messiness and complexity of how innovation happens in real economies”, says Coyle. “The idea that a country’s productivity level increases by companies going bust and new ones coming in is a difficult sell, but the evidence that that’s part of the mechanism is pretty strong.”Timely messageThis year’s award comes at a time when funding for scientific research is under threat in the United States and around the world. “It’s a very timely message when we’re seeing the United States undermining so much of its science base,” says Coyle. Aghion said, “I don’t welcome the protectionist wave in the US” and added that “openness is a driver of growth. I see dark clouds accumulating”. to translate high-tech innovations into market value.Economic historian Kerstin Enflo, a member of the Nobel prize awarding committee, denied that the award was intended as a comment on the direction of US policies. “It is only about celebrating the work [the laureates] have done”, she said at the press conference.Green growthMore recently, researchers are questioning the ‘growth-at-all-costs’ narrative not least because of the ways to pursue growth has led to environmental degradation, including global warming.“How can we make sure we innovate greener?” Aghion asked. “Firms don’t spontaneously do this. So how can we redirect growth towards green?” Mokyr’s work showed that growth can sometimes be self-correcting in the sense of producing innovations needed to solve such problems. But that is not a given and requires well-crafted policies to nurture innovation without promoting inequality and unsustainability. “We need to harness the productivity potential and minimize the negative effects”, said Aghion.This article is reproduced with permission and was first published on October 13, 2025.

Science
Technology
Nature

What humans might learn from nature’s real-life zombies

Zombies, it turns out, are real — and science journalist Mindy Weisberger can give you plenty of examples of them. She’s read up on the fungi that take over flies’ bodies, partially digesting them from the inside out before forcing them to climb up blades of grass, so that fungal spores can explode out from […]

Cicadas can be infected by a fungal parasite that turns them into zombies. | Chip Somodevilla/Getty Images Zombies, it turns out, are real — and science journalist Mindy Weisberger can give you plenty of examples of them. She’s read up on the fungi that take over flies’ bodies, partially digesting them from the inside out before forcing them to climb up blades of grass, so that fungal spores can explode out from their swollen corpses and claim new victims.  She’s considered the hairworms that grow inside of crickets before inducing their hosts to toss themselves into a nearby body of water, where the worms emerge from the crickets’ exoskeleton in a miniature but all-too-real imitation of the alien in Alien.  She’s even researched the snails that fall victim to certain flatworms. The flatworms’ larvae need to be eaten by birds to reach the next stage of their lifecycle, so broodsacs full of larvae take up residence in the snails’ eyestalks and turn them into pulsing, colorful, caterpillar-like bird-lures. The parasite also manipulates the snails into wandering into the open in order to increase the odds that a bird will spot the snails and devour both their eyestalks and the larvae within them.  Weisberger dug into these specific nightmare-inducing examples of parasitic mind-control — and many others — as part of her effort to understand real-life “zombification” in her book, Rise of the Zombie Bugs. What she found was that these natural zombie stories are not only sources of inspiration for horrifying fiction — they could also inspire researchers who are trying to better understand everything from immune responses to pest control.  So we spoke to Weisberger about research on real-life zombies for Unexplainable, Vox’s science podcast. What follows is a version of our conversation, edited for clarity and length. There’s much more in the full podcast, so listen to Unexplainable wherever you get podcasts, including Apple Podcasts and Spotify. Let’s start by just defining some terms. What do we mean when we say “zombifier,” or “zombie?” Sure. A zombifier is an organism that manipulates the behavior of its host, and a zombie is an organism that is being manipulated to behave in a way that it normally would not, and which only benefits the parasite that’s manipulating it.  Let’s say you catch a cold — you’re gonna change your behavior because you’re feeling sick. You feel like you need to rest more, you need to drink more water. These are all things that help you recover, that help you fight off the infection. So in a certain sense, that’s the cold virus generating a change in behavior, but it’s a behavioral change that actually benefits you.  For a zombie, the changes to its behavior are not something that benefit the host. They only benefit the parasite. That’s what makes it a zombie. So it’d be like if I got sick and instead of going into my room and trying to sleep it off, I went and I licked everybody that I could lick in order to spread it.  Yeah, exactly. There are zombifying viruses; there are zombifying fungi; there are insects that are able to zombify their hosts. There are worms that can zombify their hosts. Most of the organisms that they infect are arthropods — bugs. (I do have to apologize to entomologists, because as far as entomologists are concerned, bugs are only insects with sucking mouth parts. However, as we all know, colloquially, “bugs” covers a much broader range.) What are some of the biggest categories of mysteries about how [zombifiers do what they do]? Some of the biggest mysteries start with the moment that the host is infected, because obviously a body’s first response to any kind of infection is going to be an immune response. The first thing that a zombifier needs to do is to somehow get past that. That’s a big question for zombifiers, from viruses to wasps to fungi to worms: When they get inside an organism where they’re not supposed to be, how exactly are they telling their host immune system, “No, there’s nothing to see here! Just go about your business! You don’t need to worry about me!”  Another one is, once it gets to the point of manipulation, what are the cues? How does it decide “OK, now’s the right time to get this host moving to a place where I need to be”?  The third big question is obviously the nuts and bolts of: How is it manipulating behavior? The thing about this field is that there is still so much that scientists are piecing together about the precise mechanisms of how this works. Behavior is something that is just super complicated, even in insects.   So, when we look at, for example, the wasp that parasitizes orb-weaving spiders, scientists have found that in the spiders that are zombified, what the wasp does — it lays an egg on the spider. The egg hatches, and the wasp larva essentially piggybacks on the spider and drinks from it like it’s a living juice box.   And the spider just goes about its business until the larva is ready to reproduce. And then somehow the wasp larvae is manipulating the spider to think that it’s time to molt, so that the spider makes a different type of web than it normally does, something called a resting web. It’s reinforced, and it’s meant to support the spider and protect the spider while it’s molting.  And then once that web is done, the wasp larvae drains the spider dry, the spider’s empty husk of a corpse drops to the ground, and the wasp larva builds its cocoon and sets itself up in the spider’s final web to hang out until it becomes an adult wasp. What scientists found is that when spiders start making that final web, their little spider brains are being flooded with ecdysteroids, which is the hormone that the spider naturally releases when it’s ready to build a molting web. And scientists aren’t sure yet: Is the larvae actually producing the ecdysteroids? Is it somehow triggering its production in the spider through another compound? That’s something that they’re still figuring out. Why is it important to understand how this behavior manipulation works? In a lot of ways, this is looking at sort of really big questions about how behavior works, which is something that scientists are still piecing together, on so many levels for all different types of organisms, because there are so many factors that shape behavior. Some of them are genetics; some of them are biochemical; some of them have to do with environments; some of them have to do with social relationships. So, this is one way of trying to understand behavior writ large.  You mentioned that these insects suppress the immune systems of their hosts. Is there stuff that we could learn from that about how immune systems work in general? Oh yeah. Looking at the immunosuppressive aspect of zombifiers is definitely something that is a huge area of interest, because that could inform the development of immunosuppressive drugs, which is something that is just something that would be hugely beneficial to people.  Not that this should be all about what’s in it for me, but that is usually a consideration for scientific research: Could there potentially be applications for this that have medical applications? And so, there is not yet a direct line between any research into how zombifiers evade their host’s immune system and the development of some kind of pharmaceutical immunosuppressive. But that’s definitely something that is part of the mix when scientists are following that line of investigation. I think about all the insects that invade homes, some of which are beneficial, some of which are less so. Could we potentially borrow from this to fight off pests? Pest control is definitely one avenue that scientists have explored. Is there some way that we can take what we’re seeing these zombifiers do to insects and apply it to insects that we don’t like?  So baculoviruses — which are these viruses that infect caterpillars and make them climb and then dissolve their bodies into goo — this is something that has been deployed as a strategy for pest control in China and in Europe, in the US, in Brazil.  These types of viruses are an interesting alternative to traditional insecticides because they are very targeted. They’re less toxic to the environment. They’re not harmful to insects that are not their host species and they’re not toxic to people. But they’re also not as quick as I think the insecticides that people have gotten used to. And people like things to be quick and they like them to be absolute.  So what seems like the best way is perhaps to incorporate this alongside insecticides, and use this along with other approaches, because there are a lot of benefits to just going full-on zombie warfare to get rid of our agricultural pests. Could humans be zombified this way? Like, are we also susceptible to this? Well, there are some types of pathogens that are known to manipulate behavior in mammals and indeed in humans too. So rabies, of course. There have been medical cases of rabies-infected humans that are thousands of years old with documentation of heightened aggression. So there is already a virus among us that can manipulate human behavior.  And recently, there have been studies into Toxoplasma gondii, which is the pathogen that causes toxoplasmosis. Its definitive host is cats. It’s very entrenched amongst human populations. And in fact, many, many people, millions of people, carry Toxoplasma gondii, but it doesn’t cause any symptoms. It tends to be dangerous in people that are pregnant or in immunocompromised people. Most of the people who are carrying Toxoplasma gondii have no symptoms.  However, there have been studies recently in the last 10, 15 years or so, that have looked at people who are carrying the parasite and have found that there does seem to be evidence of certain types of behavior: of being more risk-taking, of being bolder. And what’s interesting about it is that Toxoplasma gondii is known for manipulating behavior in rodents. And what it does is it makes them bolder and less afraid of cats.  What? Because Toxoplasma gondii needs to reproduce inside cats. So it infects rodents, and then to get back into a cat, it makes the rodent less afraid of and attracted to the smell of cat pee. And that brings the rodent closer to a cat than it would normally go. And then once it’s eaten, then the parasite is back inside the cat.  And scientists have found that this is true for other animals too. So hyena cubs that are infected with Toxoplasma gondii are bolder around lions and are more likely to be eaten by lions. Chimpanzees that are infected with Toxoplasma gondii lose their fear of jaguars. And some studies found that people who are infected with Toxoplasma gondii are more likely to make risky business decisions or be bolder in traffic. There’s still a lot of work to be done because obviously human behavior is its own form of complicated. But there is some evidence that seems to suggest that Toxoplasma gondii can shape human behavior, too. What?  Did I just blow your mind? So there could literally at this moment be zombifiers within us shaping us in some way? It’s entirely possible. There are so many things that make us who we are that shape how we behave. There are environmental factors; there are social factors. But, you know, there might also be zombifiers.

Science
Animals
Chemicals

Laurent Demanet appointed co-director of MIT Center for Computational Science and Engineering

Applied mathematics professor will join fellow co-director Nicolas Hadjiconstantinou in leading the cross-cutting center.

Laurent Demanet, MIT professor of applied mathematics, has been appointed co-director of the MIT Center for Computational Science and Engineering (CCSE), effective Sept. 1.Demanet, who holds a joint appointment in the departments of Mathematics and Earth, Atmospheric and Planetary Sciences — where he previously served as director of the Earth Resources Laboratory — succeeds Youssef Marzouk, who is now serving as the associate dean of the MIT Schwarzman College of Computing.Joining co-director Nicolas Hadjiconstantinou, the Quentin Berg (1937) Professor of Mechanical Engineering, Demanet will help lead CCSE, supporting students, faculty, and researchers while fostering a vibrant community of innovation and discovery in computational science and engineering (CSE).“Laurent’s ability to translate concepts of computational science and engineering into understandable, real-world applications is an invaluable asset to CCSE. His interdisciplinary experience is a benefit to the visibility and impact of CSE research and education. I look forward to working with him,” says Dan Huttenlocher, dean of the MIT Schwarzman College of Computing and the Henry Ellis Warren Professor of Electrical Engineering and Computer Science.“I’m pleased to welcome Laurent into his new role as co-director of CCSE. His work greatly supports the cross-cutting methodology at the heart of the computational science and engineering community. I’m excited for CCSE to have a co-director from the School of Science, and eager to see the center continue to broaden its connections across MIT,” says Asu Ozdaglar, deputy dean of the MIT Schwarzman College of Computing, department head of Electrical Engineering and Computer Science, and MathWorks Professor.Established in 2008, CCSE was incorporated into the MIT Schwarzman College of Computing as one of its core academic units in January 2020. An interdisciplinary research and education center dedicated to pioneering applications of computation, CCSE houses faculty, researchers, and students from a range of MIT schools, such as the schools of Engineering, Science, Architecture and Planning, and the MIT Sloan School of Management, as well as other units of the college.“I look forward to working with Nicolas and the college leadership on raising the profile of CCSE on campus and globally. We will be pursuing a set of initiatives that span from enhancing the visibility of our research and strengthening our CSE PhD program, to expanding professional education offerings and deepening engagement with our alumni and with industry,” says Demanet.Demanet’s research lies at the intersection of applied mathematics and scientific computing to visualize the structures beneath Earth’s surface. He also has a strong interest in scientific computing, machine learning, inverse problems, and wave propagation. Through his position as principal investigator of the Imaging and Computing Group, Demanet and his students aim to answer fundamental questions in computational seismic imaging to increase the quality and accuracy of mapping and the projection of changes in Earth’s geological structures. The implications of his work are rooted in environmental monitoring, water resources and geothermal energy, and the understanding of seismic hazards, among others.He joined the MIT faculty in 2009. He received an Alfred P. Sloan Research Fellowship and the U.S. Air Force Young Investigator Award in 2011, and a CAREER award from the National Science Foundation in 2012. He also held the Class of 1954 Career Development Professorship from 2013 to 2016. Prior to coming to MIT, Demanet held the Szegö Assistant Professorship at Stanford University. He completed his undergraduate studies in mathematical engineering and theoretical physics at Universite de Louvain in Belgium, and earned a PhD in applied and computational mathematics at Caltech, where he was awarded the William P. Carey Prize for best dissertation in the mathematical sciences.

Science
Education
Water

Scientists Reveal That the Red Sea Completely Vanished 6.2 Million Years Ago

KAUST researchers discovered that the Red Sea experienced a massive disruption 6.2 million years ago, completely transforming its marine life. Researchers at King Abdullah University of Science and Technology (KAUST) have confirmed that the Red Sea once completely dried up around 6.2 million years ago, only to be suddenly refilled by a catastrophic influx of [...]

New research shows the Red Sea dried out 6.2 million years ago before being suddenly flooded by the Indian Ocean. (Artist’s concept). Credit: SciTechDaily.comKAUST researchers discovered that the Red Sea experienced a massive disruption 6.2 million years ago, completely transforming its marine life. Researchers at King Abdullah University of Science and Technology (KAUST) have confirmed that the Red Sea once completely dried up around 6.2 million years ago, only to be suddenly refilled by a catastrophic influx of water from the Indian Ocean. Their work places a precise timeline on a remarkable event that reshaped the basin’s history. By combining seismic imaging, microfossil analysis, and geochemical dating, the team discovered that this transformation occurred within just 100,000 years, an exceptionally short span in geological terms. During this period, the Red Sea shifted from being linked to the Mediterranean to becoming a desolate salt basin. The dry phase ended when a powerful flood cut through volcanic ridges, opening the Bab el-Mandab strait and restoring the Red Sea’s connection to the global oceans. “Our findings show that the Red Sea basin records one of the most extreme environmental events on Earth, when it dried out completely and was then suddenly reflooded about 6.2 million years ago,” said lead author Dr. Tihana Pensa of KAUST. “The flood transformed the basin, restored marine conditions, and established the Red Sea’s lasting connection to the Indian Ocean.” How the Indian Ocean Flooded the Red Sea The Red Sea was initially connected from the north to the Mediterranean through a shallow sill. This connection was severed, drying the Red Sea into a barren salt desert. In the south of the Red Sea, near the Hanish Islands, a volcanic ridge separates the sea from the Indian Ocean. But around 6.2 million years ago, seawater from the Indian Ocean surged across this barrier in a catastrophic flood. The torrent carved a 320-kilometer-long submarine canyon that is still visible today on the seafloor. The flood rapidly refilled the basin, drowning the salt flats and restoring normal marine conditions in less than 100,000 years. This event happened nearly a million years before the Mediterranean was refilled by the famous Zanclean flood, giving the Red Sea a unique story of rebirth. Why the Red Sea Matters Geologically The Red Sea formed by the separation of the Arabian Plate from the African Plate beginning 30 million years ago. Initially, the sea was a narrow rift valley filled with lakes, then became a wider gulf when it was flooded from the Mediterranean 23 million years ago. Marine life thrived initially, as seen by the fossil reefs along the northern coast near Duba and Umlujj. However, evaporation and poor seawater circulation increased salinity, causing the extinction of marine life between 15 and 6 million years ago. Additionally, the basin was filled with layers of salt and gypsum. This culminated in the complete desiccation of the Red Sea. The catastrophic flood from the Indian Ocean restored marine life in the Red, which persists in the coral reefs to the present. All in all, the Red Sea is a natural laboratory for understanding how oceans are born, how salt giants accumulate, and how climate and tectonics interact over millions of years. The discovery highlights how closely the Red Sea’s history is linked with global ocean change. It also shows that the region has experienced environmental extremes before, only to return as a thriving marine ecosystem. “This paper adds to our knowledge about the processes that form and expand oceans on Earth. It also maintains KAUST’s leading position in Red Sea research,” said co-author KAUST Professor Abdulkader Al Afifi. Reference: “Desiccation of the Red Sea basin at the start of the Messinian salinity crisis was followed by major erosion and reflooding from the Indian Ocean” by Tihana Pensa, Antonio Delgado Huertas and Abdulkader M. Afifi, 9 August 2025, Communications Earth & Environment.DOI: 10.1038/s43247-025-02642-1 Never miss a breakthrough: Join the SciTechDaily newsletter.Follow us on Google, Discover, and News.

Science
Technology
Climate

The Sun’s Poles Hold the Key to Its Three Greatest Mysteries

The Sun’s poles may hold answers to long-standing mysteries about magnetic cycles, solar wind, and space weather. The polar regions of the Sun remain one of the least explored areas in solar science. Although satellites and ground-based observatories have captured remarkable details of the Sun’s surface, atmosphere, and magnetic field, nearly all of these views [...]

The Sun’s polar regions, long hidden from our Earth-bound perspective, are a critical frontier in solar physics, holding the secrets to the solar magnetic cycle and the origin of the fast solar wind. An upcoming mission is designed to achieve an unprecedented polar orbit, promising to finally reveal these uncharted territories and transform our ability to predict space weather. Credit: Image courtesy of Zhenyong Hou and Jiasheng Wang at Peking University. Beijing Zhongke Journal Publising Co. Ltd.The Sun’s poles may hold answers to long-standing mysteries about magnetic cycles, solar wind, and space weather. The polar regions of the Sun remain one of the least explored areas in solar science. Although satellites and ground-based observatories have captured remarkable details of the Sun’s surface, atmosphere, and magnetic field, nearly all of these views come from the ecliptic plane, the narrow orbital path followed by Earth and most other planets. This restricted perspective means scientists have only limited knowledge of what occurs near the solar poles. Yet these regions are critical. Their magnetic fields and dynamic activity are central to the solar magnetic cycle and provide both mass and energy to the fast solar wind. These processes ultimately shape solar behavior and influence space weather that can reach Earth. Why the Poles Matter On the surface, the poles may seem calm compared to the Sun’s more active mid-latitudes (around ±35°), where sunspots, solar flares, and coronal mass ejections (CMEs) are common. However, research shows that polar magnetic fields contribute directly to the global solar dynamo and may act as the foundation for the next solar cycle by helping establish the Sun’s dipole magnetic field. Observations from the Ulysses mission further revealed that the fast solar wind originates mainly from vast coronal holes in the polar regions. For this reason, gaining a clearer view of the Sun’s poles is essential to addressing three of the most fundamental questions in solar physics: 1) How does the solar dynamo work and drive the solar magnetic cycle? The solar magnetic cycle refers to the periodic variation in sunspot number on the solar surface, typically on a time scale of approximately 11 years. During each cycle, the Sun’s magnetic poles undergo a reversal, with the magnetic polarities of the north and south poles switching. The Sun’s global magnetic fields are generated through a dynamo process. Key to this process are the differential rotation of the Sun that generates the active regions, and the meridional circulation that transport magnetic flux toward the poles. Yet, decades of helioseismic investigations have revealed conflicting results about the flow patterns deep within the convection zone. Some studies even suggest poleward flows at the base of the convection zone, challenging the classical dynamo models. High-latitude observations of the magnetic fields and plasma motions could provide the missing evidence to refine or rethink these models. 2) What drives the fast solar wind? The fast solar wind – a supersonic stream of charged particles – originates primarily from the polar coronal holes, and permeates the majority of the heliospheric volume, dominating the physical environment of interplanetary space. However, critical details regarding the origin of this wind remain unresolved. Does the wind originate from dense plumes within coronal holes or from the less dense regions between them? Are wave-driven processes, magnetic reconnection, or some combination of both responsible for accelerating the plasma in the wind? Direct polar imaging and in-situ measurements are required to settle the debate. 3) How do space weather events propagate through the solar system? Heliospheric space weather refers to the disturbances in the heliospheric environment caused by the solar wind and solar eruptive activities. Extreme space weather events, such as large solar flares and CMEs, can significantly trigger space environmental disturbances such as severe geomagnetic and ionospheric storms, as well as spectacular aurora phenomena, posing a serious threat to the safety of high-tech activities of human beings. To accurately predict these events, scientists must track how magnetic structures and plasma flows evolve globally, not just from the limited ecliptic view. Observations from a vantage point out of the ecliptic would provide an overlook of the CME propagation in the ecliptic plane. Past Efforts Scientists have long recognized the importance of solar polar observations. The Ulysses mission, launched in 1990, was the first spacecraft to leave the ecliptic plane and sample the solar wind over the poles. Its in-situ instruments confirmed key properties of the fast solar wind but lacked imaging capability. More recently, the European Space Agency’s Solar Orbiter has been gradually moving out of the ecliptic plane and is expected to reach latitudes of around 34° in a few years. While this represents a remarkable progress, it still falls far short of the vantage needed for a true polar view. A number of ambitious mission concepts have been proposed over the past decades, including the Solar Polar Imager (SPI), the POLAR Investigation of the Sun (POLARIS), the Solar Polar ORbit Telescope (SPORT), the Solaris mission, and the High Inclination Solar Mission (HISM). Some envisioned using advanced propulsion, such as solar sails, to reach high inclinations. Others relied on gravity assists to incrementally tilt their orbits. Each of these missions would carry both remote-sensing and in-situ instruments to image the Sun’s poles and measure key physical parameters above the poles. The SPO Mission The Solar Polar-orbit Observatory (SPO) is designed specifically to overcome the limitations of past and current missions. Scheduled for launch in January 2029, SPO will use a Jupiter gravity assist (JGA) to bend its trajectory out of the ecliptic plane. After several Earth flybys and a carefully planned encounter with Jupiter, the spacecraft will settle into a 1.5-year orbit with a perihelion of about 1 AU and an inclination of up to 75°. In its extended mission, SPO could climb to 80°, offering the most direct view of the poles ever achieved. The 15-year lifetime of the mission (including an 8-year extended mission period) will allow it to cover both solar minimum and maximum, including the crucial period around 2035 when the next solar maximum and expected polar magnetic field reversal will occur. During the whole lifetime, SPO will repeatedly pass over both poles, with extended high-latitude observation windows lasting more than 1000 days. The SPO mission aims at breakthroughs on the three scientific questions mentioned above. To meet its ambitious objectives, SPO will carry a suite of several remote-sensing and in-situ instruments. Together, they will provide a comprehensive view of the Sun’s poles. The remote-sensing instruments include the Magnetic and Helioseismic Imager (MHI) to measure magnetic fields and plasma flows at the surface, the Extreme Ultraviolet Telescope (EUT) and the X-ray Imaging Telescope (XIT) to capture dynamic events in the solar upper atmosphere, the VISible-light CORonagraph (VISCOR) and the Very Large Angle CORonagraph (VLACOR) to track the solar corona and solar wind streams out to 45 solar radii (at 1 AU). The in-situ package includes a magnetometer and particle detectors to sample the solar wind and interplanetary magnetic field directly. By combining these observations, SPO will not only capture images of the poles for the first time but also connect them to the flows of plasma and magnetic energy that shape the heliosphere. SPO will not operate in isolation. It is expected to work in concert with a growing fleet of solar missions. These include the STEREO Mission, the Hinode satellite, the Solar Dynamics Observatory (SDO), the Interface Region Imaging Spectrograph (IRIS), the Advanced Space-based Solar Observatory (ASO-S), the Solar Orbiter, the Aditya-L1 mission, the PUNCH mission, as well as the upcoming L5 missions (e.g., ESA’s Vigil mission and China’s LAVSO mission). Together, these assets will form an unprecedented observational network. SPO’s polar vantage will provide the missing piece, enabling nearly global 4π coverage of the Sun for the first time in human history. Looking Ahead The Sun remains our closest star, yet in many ways it is still a mystery. With SPO, scientists are poised to unlock some of its deepest secrets. The solar polar regions, once hidden from view, will finally come into focus, reshaping our understanding of the star that sustains life on Earth. The implications of SPO extend far beyond academic curiosity. A deeper understanding of the solar dynamo could improve predictions of the solar cycle, which in turn affects space weather forecasts. Insights into the fast solar wind will enhance our ability to model the heliospheric environment, critical for spacecraft design and astronaut safety. Most importantly, better monitoring of space weather events could help protect modern technological infrastructure — from navigation and communications satellites to aviation and terrestrial power systems. Reference: “Probing Solar Polar Regions” by Yuanyong Deng, Hui Tian, Jie Jiang, Shuhong Yang, Hao Li, Robert Cameron, Laurent Gizon, Louise Harra, Robert F. Wimmer-Schweingruber, Frédéric Auchère, Xianyong Bai, Luis Rubio Bellot, Linjie Chen, Pengfei Chen, Lakshmi Pradeep Chitta, Jackie Davies, Fabio Favata, Li Feng, Xueshang Feng, Weiqun Gan, Don Hassler, Jiansen He, Junfeng Hou, Zhenyong Hou, Chunlan Jin, Wenya Li, Jiaben Lin, Dibyendu Nandy, Vaibhav Pant, Marco Romoli, Taro Sakao, Sayamanthula Krishna Prasad, Fang Shen, Yang Su, Shin Toriumi, Durgesh Tripathi, Linghua Wang, Jingjing Wang, Lidong Xia, Ming Xiong, Yihua Yan, Liping Yang, Shangbin Yang, Mei Zhang, Guiping Zhou, Xiaoshuai Zhu, Jingxiu Wang and Chi Wang, 29 August 2025, Chinese Journal of Space Science.DOI: 10.11728/cjss2025.04.2025-0054 Never miss a breakthrough: Join the SciTechDaily newsletter.Follow us on Google and Google News.

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