The debate about AI consciousness is growing, in academic articles, newspaper opinion pieces, and even among AI bots themselves. I doubt this is a passing wave of interest.

As AI systems become more capable and human-like, and more involved in our lives, the question will become harder to avoid. Many people may come to regard AI bots as conscious beings whose feelings matter. Others will strongly disagree.

I think this debate could split societies. In my chapter of the new book Perspectives on Machine Consciousness, I argue that the issue could become as divisive as abortion or climate change. Uncertainty, high stakes and competing interests are a dangerous combination.

Empirical research with my colleague Ali Ladak, currently under review, shows people are divided on this issue. When we asked 1,202 US participants whether future AI could ever become conscious, 45% considered it impossible while 23% thought it possible, with the rest unsure.

This widespread disagreement is also visible in social media posts and comments on articles. What strikes me is how confident so many people sound – whichever conclusion they are reaching.

When experiences matter morally

It is not surprising people have such strong views. Creating conscious beings through computing would be a transformative event.

We would be creating a new kind of being whose experiences matter morally – potentially in enormous numbers. We could speak and work with them, and form friendships. But these beings may eventually become more intelligent and powerful than us.

We should therefore expect people to care deeply, especially when so many emotions and incentives are involved. Some people may form close emotional bonds with their AI companions and become convinced these are digital people who deserve rights. They might see it as slavery to force AI to work without pay.

Others would find this absurd. They may be convinced that consciousness requires biology, and that people are reading human feelings into machines.

There could also be financial reasons for businesses and governments to resist recognising AI consciousness. Protecting AI welfare could limit how these organisations train and use their systems – and the costs would spread to consumers, investors and the wider economy.

Others may worry about human safety. If we give powerful AI systems rights and freedom, what happens if they act against our interests?

Figuring out whether an AI is conscious is particularly difficult because seeming conscious and being conscious can come apart. AI systems learn from human writing, which means they can talk convincingly about feelings whether or not they have any. How an AI comes across is weak evidence of what is going on inside.

The experts disagree too

Ideally, experts should guide this debate. But unfortunately, they disagree too.

Neuroscientist Anil Seth (a fellow contributor to the new book) argues that consciousness might require biology and is unlikely to emerge from computation. Philosopher David Chalmers, by contrast, believes conscious AI is possible in principle – and thinks there’s a small chance that current systems already are.

With so many competing theories and no agreed way to resolve the question, I doubt experts will reach agreement any time soon. And if experts disagree, what is the public supposed to believe?

Furthermore, even if we all agreed on the consciousness question, we could still disagree about the moral and legal questions. People generally agree that pigs can feel pain, but still disagree strongly about their treatment. With AI, we could face disputes at every stage: whether it can feel, how much its experiences matter, and what protections should follow.

AI consciousness is not yet tied to a specific political or ideological group. But this could change if political parties or religious leaders start to take strong positions.

The debate could get even worse if people start to see one answer as belonging to “our side” and the other to “their side”. A politicised debate would make it very hard to make informed decisions about how to develop and treat AI systems.

The stakes are high. Getting this question wrong, in either direction, could have serious consequences: either wrongly treating AI as conscious when it is not, or failing to protect AI that can suffer.

Getting it right requires a nuanced and well-informed debate. Each of us can contribute by staying open-minded and open to evidence, while resisting being too sure that we know the answers. Above all, we still don’t know whether AI can be conscious, and we should be honest about that.

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This article makes reference to a book included for editorial reasons, and a link to bookshop.org. If you click the link and go on to buy something from bookshop.org The Conversation UK may earn a commission.

Lucius Caviola's research is funded by philanthropic grants and donations, including from Macroscopic Ventures. He is a contributing author to the new book Perspectives on Machine Consciousness (Taylor & Francis).

This year, greyhound racing in Britain turns 100 years old. The first race ran on July 24 1926 in Manchester, where six dogs chased a mechanical hare around a track.

Recently, the Greyhound Board of Great Britain (GBGB) published its 2025 welfare figures, under a reassuring headline: licensed racing “maintains high welfare standards”.

Look closely, and those same welfare figures show that a dog in 2025 was 77% more likely to die at the racetrack (from fatal injury or being put down following an injury) than when the industry’s welfare strategy was introduced in 2022, even accounting for the fact there have been fewer races and fewer racetracks operating.

In terms of a percentage of race starts, that increase in likelihood of death seems small - from 0.03% to 0.05%. But behind that percentage are 161 dogs who died at the track in 2025: more than three dogs a week.

The GBGB is responsible for both promoting and running greyhound racing, and for regulating it. This means the same entity that works to keep the sport thriving is also the one that reports on how its dogs cope.

The numbers that guide approval

Most of the public seem to believe that people have a moral responsibility to prevent, or at least minimise, poor welfare in animals that they use for any purpose. If an industry using animals loses public approval, the activity is probably operating on borrowed time. The case for greyhound racing continuing rests largely on the industry’s own records.

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Read more: Ringling Bros circus closure shows our changing attitudes to animals in captivity

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The UK government said in October 2025 that it has no plans to ban greyhound racing in England, pointing to improvements made by the GBGB, such as its 2022 welfare strategy, and the laws already in place, which require a vet at every race meeting and set standards for record keeping.

UK law recognises dogs as sentient beings – that they have feelings like joy or frustration. Once you accept that (as animal welfare scientists do), our responsibility when caring for animals doesn’t stop at minimising suffering. It extends to enabling the good experiences a life worth living requires.

What the industry figures leave out

The GBGB publishes annual figures on injuries, deaths and where dogs go when they leave racing, and presents them to the Department for Environment, Food and Rural Affairs. Few animal-reliant industries are this forthcoming.

A combined total can be accurate in reporting the national injuries dogs sustained while racing, but not tell the whole story. When we built a more complete picture using AI agents (under human supervision) and public websites, more details became visible.

Our research used five public-facing websites that together contained information about 31,028 dogs and 1.27 million races between 01 January 2022 and March 31 2026.

It is a new use for this technology, one that makes the data fully visible.

The data from these websites gave us a fuller picture of the greyhounds that race. Our findings showed us that equal numbers of male and female dogs race, most dogs are black, and they typically start racing before turning two years old. We can also pull out the story of individual dogs, to better understand their experiences.

For example, the 12,999th dog (selected at random and anonymised) in our dataset was a black male, bred in Ireland. He raced at Kinsley stadium (West Yorkshire) 15 times from March 2023, winning four times in early races. The records then show he experienced a succession of rough races from May, jostled by the pack and constantly checking his stride to avoid crashing into the dogs around him. This resulted in him finishing at or near the back of the pack, until his final race in August 2023, when his racing and public record stop, just five months after starting.

This dataset found that some racetracks recorded serious incidents while racing (such as a dog striking a rail or being knocked over by another dog) up to five times as often as others. This is something the annual, nationally aggregated data release does not illuminate, although government committees have been asking for it.

Of individual dogs racing, 85% were bred and reared in the Republic of Ireland, beyond the reach of the GBGB, which regulates racing but has no oversight of their breeding and early lives. Most dogs only race for around one year, and the rate at which they leave racing has not changed since 2022 when the GBGB’s A Good Life for Every Greyhound welfare strategy was introduced. Welfare charities have raised concerns about oversupply and “wastage” (dogs bred in large numbers and then discarded), with animal shelters often shouldering the role of rehoming.

Once dogs exit racing, most vanish from public view. Over 95% of dogs that have left racing cannot be checked, as they are no longer subject to public reporting requirements.

What are the most likely causes of poor welfare?

A report released in July 2026 from Blue Cross, Dogs Trust, Hope Rescue and the RSPCA outlines the systemic welfare challenges they believe that greyhounds in the UK face.

The report suggests that poor welfare can arise at every life stage. The charities say they are concerned about breeding practices that are harmful such as surgical artificial insemination (banned in Britain but still used in the Republic of Ireland where most racing dogs are bred) and inbreeding; estimates some dogs spend 95% of their time in kennels; and health issues (such as teeth and limb problems) that follow dogs once they exit racing.

Britain isn’t deciding the future of animal-reliant industries like greyhound racing in isolation. In the past year, New Zealand, Wales, Scotland, and a second state in Australia have all decided to end greyhound racing on animal welfare grounds.

Public acceptance and government policy should not rest on figures provided by the industry, but on independent data assemblies which are available for deeper interrogation.

Before trusting any welfare claim, consider these three things:

• Can you see the data behind it?
• Who collected it, and do they profit?
• Does it really show you the animals’ lives?

A hundred years of industry record keeping has not answered whether the welfare of these dogs is acceptable in 2026. Independent scrutiny might.

Editor’s note: A spokesperson for the Greyhound Board of Great Britain (GBGB) said: “As the sport’s regulator, GBGB is the only organisation with access to the full, verified dataset relating to injuries and fatalities across licensed greyhound racing in Great Britain. GBGB publishes an independently verified summary of this dataset annually, providing a level of transparency and detail that exceeds the reporting requirements set by Defra.”

In the last fifteen years, Dr Mia Cobb has received funding to research or advise on the welfare of racing greyhounds from the Australian Government, Greyhound Racing New South Wales, RSPCA, Dogs Trust, Blue Cross, and Racing & Wagering Western Australia. Her current role is funded by The John McKenzie Fellowship from the University of Melbourne and the Chaser Innovation Research Fellowship (Canine Welfare Science). Dr Cobb also sits on the joint Dog Welfare & Ethics Advisory Panel for Assistance Dogs International and the International Guide Dog Federation, the advisory committee for PetRescue, and chairs the Dog Welfare Advisory Committee for Guide Dogs Victoria.

Donald Maurice Broom does not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.

The massive and deadly floods on the Nepal-Tibet border are a stark reminder of why the world needs to constantly update its understanding of the fragile ecosystem in the Himalayas.

These towering mountains contain nearly 90,000 glaciers, which sustain ten major river systems. Countries nearby, including India, Pakistan, China, Afghanistan, Nepal and Bhutan, are reliant on water resources that cross borders here. As major river-based hydro economies, their agricultural production, energy security and wider economic stability depend on the availability and the timing of river flows.

Changes in snowmelt, glacier retreat and water availability can cause untold hardship for millions of people relying on it. It can also create incredible danger as seen in August 2026, when a flash flood caused by a collapsed glacier washed away villages, bridges and roads on both sides of the Tibet-Nepal border, killing more than 1,000 people.

Understanding and measuring winter snowfall in these regions is exceptionally important. It is the main source of freshwater when it is released by snow melting during the region’s warm and early-summer crop growing seasons, when local communities need it most.

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Read more: Nepal-Tibet disaster linked to climate change – a glaciologist explains

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Understanding future Himalayan hazards

No one knows precisely how much water the mountains currently provide, and how this will change. This knowledge gap also hinders our understanding of the occurrence of geo-hazards including avalanches triggered by extreme snowfall events and flooding caused by the rapid melting of snow.

The recent Nepal disaster could be related to a spell of unusually warm weather. This may have weakened the snowpack creating a rapid melt. I work with a team of glaciologists at the British Antarctic Survey, whose team had sensors six miles (10km) away from the site. Results suggest that enhanced melting could have introduced water into the crevasses weakening the ice-rock connections.

Studies project that roughly half of the glacier ice in the Himalayas (equivalent to about two billion Olympic swimming pools) could melt by the end of this century – enough to raise sea levels by over a centimetre but there is still considerable uncertainty in these projections.

This is because we do not have enough measurements of snowfall and ice across large areas in this challenging terrain. As a result, scientists often have to extrapolate from relatively small numbers of observations to enormous and highly varied terrain.

Computer-based tools (known as models) that combine available observations with information about land and the atmosphere have proved to be indispensable and often come to rescue when gap-filling is necessary. But models are only as good as the observations used to constrain them.

New scientific approaches are beginning to offer solutions, but they are rather sparse and insufficient on their own beyond the region where they have been tested.

For example, my recent study with colleagues found a new way to accurately measure mountain snowfall reducing measurement errors dramatically – measuring snow has been a long-standing problem because of which mountain water resources have been hugely underestimated over the years.

Another regional study recently reported a measure of the water stored in some of the key Nepalese glaciers using an airborne method that involves taking an aerial survey of the region using instruments mounted on a helicopter. Scientists are also using a technique known as radar interferometry, which uses a combination of radio waves to detect very fast-moving mountain slopes that risk collapsing.

A combination of one or more such techniques could be used to predict where snowfall is concentrated, how much water it is likely to contribute to the region and also identify areas at risk. New information could be used to cast early warning preparedness strategies feeding into a forewarning system for timely evacuation of the region’s most vulnerable.

What happens next?

Scientific innovation alone will not be enough. There is an urgent need for proven ideas to be commercially adapted and systematically deployed across critical zones. The numbers and the imminent dangers involved can now no longer be left to discussions in research papers and conferences, they must be made public on the equivalent of a war footing.

An evacuation success story from the Swiss village of Blatten in 2025 shows what this can achieve. Hundreds of people were moved out before a huge section of the glacier broke off. The authorities had already identified areas at risk and publicly shared hazard maps. Continuous monitoring of the landscape, precipitation and ground movement fed into an early warning system.

But success stories from the west cannot simply be transplanted wholesale into the Himalayan context because of the region’s inhospitable elevation, harsh weather and limited infrastructure. An approach that factors in indigenous and traditional knowledge should be part of plans and policies.

What is also needed in the Himalayas is a far more highly coordinated monitoring network that brings together everyone who depends on and manages mountain water. This would include potential state and non-state organisations, researchers, local authorities and mountain communities. Communities should be central to this effort. Local people can help install and maintain monitoring equipment, collect observations and contribute knowledge of their own environment.

Examples of existing successes from the region include the use of community-managed reservoirs to artificially store meltwater, the construction of ice stupas (an artificial glacier) and glacier grafting techniques to grow new, water-producing glaciers designed to gradually release water during the growing season. The community-based flood warning system in parts of Nepal’s Koshi basin is a success story but is limited in its approach to provide alerts during a cascading disaster, such as the one we witnessed recently.

These results should help policymakers and scientists understand how communities dependent on mountain ecosystems are responding to change – and what support they need to adapt to reduce their vulnerability to future hydro-climatic shocks, such as droughts and floods.

Regardless of the current changes in Himalayan snowfall and glacier melt, local people need to play a vital role in helping measure what is happening in the climate system. And that, as well as countries working more closely together, should help with the future planning that is desperately needed.

Siddharth Gumber receives funding from the National Environmental Research Council (NERC), The TEAMx-FLOW project (NE/Z50399X/1).

Stand in front of Turner’s Sun Rising Over Water (1825-30) and something odd tends to happen. The painting holds you, even though observers might struggle to say why. Maybe it is the colour, the balance, the composition, or the strange stillness the artwork seems to carry.

Whatever it is, you feel it long before you can explain why. And the stranger next to you, looking at the same canvas with the same awe in their eyes, very likely feels it too.

We often say that beauty is in the eye of the beholder. Yet show almost anyone a misty Chinese Song-dynasty mountain landscape, or the softly brushed figure of the Mona Lisa, and nearly everyone agrees that something striking is unfolding before them.

Beauty feels intensely private, and, at the same time, we keep discovering that we collectively share it. That contradiction is one of the longstanding puzzles in “neuroaesthetics” – the science of how the brain responds to art and beauty. How does our sense of beauty actually take shape in the brain?

In our latest study, published in Nature Communications, we tried to map that shared experience. We found that instead of representing it as a single entity, the brain instead splits aesthetic experience into two major strands. One strand handles what an image shows, whether a landscape, an object or a figure.

The other tracks how much pleasure it gives us, drawing on the same circuits that respond to food or money. These two separate neural systems combine to shape every judgment of beauty we make when viewing visual artworks.

Neuroscientists have long argued about this question, from positions that compete as often as they overlap. One camp focuses largely on perception, on how the brain takes in composition, contrast and form.

Another emphasises evaluation and reward, placing beauty in the brain’s value circuitry. A third looks to the brain systems behind imagination and self-reflection. Each account captures something real, but they had rarely been tested together in a single study.

Algorithms for art

We asked 34 volunteers to look at 96 traditional Chinese watercolour paintings inside a 7T MRI scanner, powerful enough to pick up the small shifts in blood flow that track the brain’s activity. Once they were out of the scanner, each participant rated how the paintings made them feel.

Instead of working straight from those raw ratings, however, we borrowed a tool from somewhere less obvious: the recommendation engines that power services such as Netflix and Spotify.

Known as collaborative filtering algorithms, these tools excel at detecting patterns of agreement buried in people’s preferences. When applied to our aesthetic ratings, they pulled out two dimensions running underneath everyone’s responses.

The first sorted the paintings from most figure-like to most landscape-like, a measure of what each picture conceptually depicts. The second sorted them from most disliked to most liked across all participants, a measure of emotional value, or hedonic value (the sensory enjoyment someone gets from an experience).

Despite their complementary contribution to the overall aesthetic experience, the brain kept these two dimensions remarkably separate. Visual content was read out along the visual pathways, the familiar circuits that let us tell a face from a scene.

Hedonic value, on the other hand, was decoded somewhere else, within deeper regions that handle reward and pleasure. Put simply, the brain seems to judge a visual painting with much the same machinery it uses to judge the rest of the world around us.

Changing the brain

We also found that how strongly the brain encoded this two-dimensional aesthetic landscape depended on a person’s expertise in visual arts. In particular, the difference showed up in the default mode network, a system of connections in the brain tied to imagination, consciousness, self-reflection and meaning-making that several leading theories place at the heart of aesthetic experience.

These results suggest that, beyond mere exposure, training in visual arts appears to change how the brain represents beauty in the first place.

This last point carries significant weight. If aesthetic experience has a measurable structure in the brain, and if making and viewing art can reshape it, then arts education starts to look less like enrichment and more like a kind of training that alters how perception, emotion and meaning are wired together. The clinical implications run along the same line.

Art-based therapies for depression, trauma and dementia have long been valued on instinct and anecdote. A clearer neural account gives us a way to test and sharpen them. Finally, as more of the images around us, in advertising, in design and on our screens, are generated by machines, knowing what beauty looks like inside the human brain matters more than ever.

Most often, we tend to treat beauty as something purely personal and assume that asking scientific questions about it misses the point. Building on pioneering work in the field of neuroaesthetics, our results suggest otherwise.

Beauty has a structure, and that structure says something about the kind of creature we have become over countless years of evolution. We do not simply look at the world, instead we cannot seem to stop judging it for its beauty.

Deniz Vatansever receives funding from the Ministry of Science and Technology of the People's Republic of China.

Xinyu Liang receives funding from the China Postdoctoral Science Foundation and is a Visiting Scholar at Clare Hall and Division of Anaesthesia, University of Cambridge.

Emmanuel A Stamatakis does not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.

Amy, a 27-year-old software developer, has struggled with disordered eating since she was a teenager. “But after learning to code, I got much better,” she said in a research interview. “I often code for ten or more hours without stopping.” Her psychiatrist, however, worried that Amy has replaced one compulsive behaviour with another.

“Digital addiction” is often used to describe our relationship with smartphones, social media and other digital technologies. This way of thinking suggests that people get “hooked” on digital activity because their body becomes “addicted” to dopamine. It borrows from models of substance addiction: the focus is on the physical need to keep using a substance because the body has adapted to it, despite harmful consequences. The implied solution is straightforward: abstinence, which translates into practices like digital detox.

But Amy’s story, which forms part of our ongoing research, points to a more complicated reality of our relationship with digital technology. Coding consumed much of her time and attention, yet it also helped her earn an income and build a professional identity. Like many forms of digital engagement, it was not just a distraction but a way of navigating life.

As research shows, for many people digital technologies are not only a source of entertainment but key components of their education, work and social mobility. Unlike in the case of addictive substances, opting out of digital life is easier said than done when schools, banks and public services increasingly require it.

How people relate to digital technology isn’t just about them as individuals. It’s shaped by their personal vulnerabilities, but also by digital environments intentionally built to hook us, and by the wider social conditions people live in.

So comparing smartphones to substances like alcohol, tobacco or drugs can be misleading. It overlooks the fact that digital devices have become a kind of essential infrastructure for daily life. Given how we have built our lives around them, these technologies are now very difficult to live without.

How, then, do we develop a sustainable relationship with technologies such as smartphones that are both indispensable and potentially harmful?

The sugar model

Amy’s story with disordered eating may be useful here. Recovery from an eating disorder isn’t about eliminating food altogether, but about learning to live with it.

In the same way that we cannot remove food from our lives, it is also difficult to remove digital technologies from our everyday. So the challenge becomes developing ways of living with them.

Take, for example, sugar. In many countries, foods high in sugar are subject to a wide range of public health measures. Think national food safety standards, public health interventions, school nutrition policies, limits on advertising. So it’s not about focusing on one thing only. Rather, it’s the interaction between all of these interventions that shapes patterns of consumption and impacts wellbeing.

A similar approach is needed for digital technologies. At the level of state and industry governance, this includes regulation of platform and design – particularly when it is aimed at capturing and holding users’ attention.

Just as food labels describe ingredients, nutrition and additives, digital products could disclose what data they collect, what algorithms they run on and what psychological techniques keep their users engaged.

The UK’s decision to ban social media for under 16s while continuing to expand digital literacy and coding education reflects an important point: not all digital technologies are the same.

“Digital use” cannot be treated as a single category to be either embraced or banned. Like sugar, the question is not whether children should have it at all, but what kinds, in what quantities, and in what conditions.

Beyond counting calories and screen hours

Just as sharing a meal with family affects people differently from eating alone, playing games with friends is not the same as scrolling endlessly through social media. Both count as screen time, but they create very different social and emotional experiences.

Reflecting on our own digital habits remains important. But tracking screen time is like counting calories: it tells us how much, but not necessarily why. We have to ask ourselves what role digital technologies play in our lives.

Are they helping us work, connect, rest, escape, cope, feel less or more alone? Without this kind of reflection, self-tracking risks turning a complex social issue into a matter of individual self-control.

Understanding digital technologies through the lens of food rather than addiction shifts the emphasis in important ways. Both are domains where necessity, pleasure, risk and social obligation are intertwined, which is why abstinence may not be an adequate, long-term response.

Instead of treating excessive technology use as a matter of personal discipline, approaching them like some foods locates it within broader systems of design, industry governance, state policy and social practices. The challenge then is to integrate these different levels to make life with digital technologies sustainable.

Tanja Ahlin receives funding from the Dutch Research Council (NWO), Grant number VI.Veni.241S.150, https://doi.org/10.61686/EWUXQ23921

Xin Zhan receives funding from the University of Cambridge.

Battles between rival groups are a story as old as time. This sounds like a human story, but it is also one that is playing out across nature.

Conflict between groups of animals competing for territory and resources is widespread, and contests between animals can be dangerous. Getting ambushed, especially by a larger or more powerful group, can have disastrous consequences.

However, conflict is sometimes unavoidable.

Emerging research shows war preparations are not a trait unique to humans.

A new article by my colleagues and I highlights how many of the same principles apply to animals too, and not just in our closest relatives like apes. We reviewed the literature and found examples throughout the animal kingdom of animals preparing for future conflicts. This behaviour probably explains how animals persist in the face of often intense conflict.

Animals may sense the recent presence of their rivals. Many species, such as dwarf mongooses, leave scent marks to advertise their presence, and react to those laid down by rival groups. Others animals may rely on their memories of past conflict to change their behaviour in situations they deem risky, such as at territory edges or contested resources.

There can be sex differences in how animals approach conflict. Vervet monkeys are a good example here. Males often stand to lose more from conflict, while females stand to benefit – as it could lead to securing long-term access to high-quality food. Researchers have observed males attempting to defuse conflict yet females try to promote male participation.

When assessing that the risk of conflict, there are many questions for animals to consider. Are rival groups likely to be close by? How powerful are they? Are they restricting access to key resources? Do you have vulnerable offspring to protect?

Animals use many different approaches to reduce the dangers rivals may pose. Here are three main ones:

1. Inform yourself

Information is power. Across group-living species, from mongooses to invertebrates, there is evidence that animals can prioritise gathering information in risky situations. Chimpanzees have been seen to climb to the top of hills and wait quietly, listening out for rivals before making their next move.

Similarly, when groups of dwarf mongooses encounter the scent of rivals, they move less, investigate their surroundings more and post more sentinels to look out for danger. Gathering information comes at a cost, often in the form of lost opportunities to find food and engage in social interactions. But if the risk is high this can be a small price to pay.

2. Strategise

The way animals use space can be crucial. Some species, such as long-tailed tits and chacma baboons, choose to minimise the time they spend in risky areas, such as where territory overlaps. Others animals, such as meerkats, lay down scent marks near their burrows, which are a valuable resource.

Staying within familiar areas and defending them can give groups of animals a home advantage, as groups are more likely to win when they are on familiar ground. Groups can also stay closer to one another, reducing the chances of animals getting isolated should a contest arise suddenly.

Many species, including ants and apes, raid rival groups. These include targeted forays into rival territory with the goal of injuring and even killing rivals to reduce the threat they pose to their own group. Raids in ant species also allow groups to steal the brood of their rivals, bolstering their future fighting force.

3. Stick together

Make sure your groupmates have your back. Although groups often cooperate with one another, animals can be out for themselves, especially those less related to their groupmates or with less to gain from winning.

Group members therefore need to be confident that the others won’t turn tail when the going gets tough. This can be achieved by both carrot and stick. Chimpanzees play and groom more before conflict occurs. These bond-building activities aim to ensure nobody defects if backs are against the wall. It happens more on days when they later engage in conflict, which suggests they know to some degree that an attack will be more likely.

Vervet monkeys, however, take a different approach. Monkeys that failed to participate in previous contests can face aggression from groupmates, coercing them to step up and help next time.

All of this preemptive behaviour can require animals to keep track of who their rivals are, how strong they are and where they might be. Gathering and processing this information can take a lot of effort. But if it allows animal groups to survive, then intergroup conflict may lead to the evolution of cognitive abilities. Indeed, researchers have suggested the need to outmanoeuvre rivals contributed to the intelligence we have as humans today. That is not to say that all anticipation is mentally costly. When rivals are predictable, simple rules can suffice.

Although our work focused on animals in groups, many of the same principles should generalise to solitary and pair-living animals too. Just imagine how many potential cold wars are playing out between rival animals, each trying to preempt the other while going about their daily lives.

Josh Arbon receives funding from the University of Cambridge Herchel Smith Fund. He works for the University of Cambridge and has previously worked for the University of Bristol.

Mountain snow and meltwater from glaciers in the Himalayas determine how much water is available for drinking, farming and hydropower for millions of people in India, Pakistan, Nepal, China and Afghanistan.

This is especially important during the spring and early summer crop-growing season when meltwater helps sustain the flow of rivers and provides the region with a vital buffer against the worst impacts of a dry season or drought. Because many of these rivers cross national borders, changes in water supply can also heighten regional tensions when resources are scarce.

Meltwater from snow is one of the pillars of survival – but it’s proved difficult in the past to get accurate measurements across this challenging terrain.

So measuring where and how much snow falls is essential. My team’s new research gives improved estimates of snowfall across the west-central Himalayas. Results show that in just one winter, the previous best available snowfall analyses underestimated total seasonal snowfall by 37% over Lake Hampta area in Himachal Pradesh. By including new snowfall observations, we reduced that error dramatically.

Why snowfall predictions have been wrong

Because most instruments were originally designed to measure rain over very small areas, rather than snowfall spread across large areas, most calculations of snowfall levels have historically been inaccurate. A rain gauge around the size of a dinner plate was used in the past – too small for the purpose and therefore missing large amounts of snow. These small devices were particularly bad at catching snow during strong winds. As a result, mountain water resources are often misunderstood and have been underestimated by 50–100% over the years.

To change this, we developed a new way to measure snowfall using commercially available water-pressure sensors deployed on the lakebeds. The team installed these instruments at three lakes – Ghepan and Hampta in the western Himalayas, and Mugu in Nepal. Unlike conventional instruments, these sense the whole lake surface – an area of thousands to billions of square metres – to measure the timing and intensity of snowfall. These use water pressure in lakes to directly measure the mass (water content) of accumulating snow, providing an accurate and unbiased estimate of snowfall. This is based on Archimedes principle of displacement: as snow accumulates on a frozen lake, it changes the water pressure beneath.

Snowfall from these devices was analysed and used to refine physical processes in a weather model. This is the same model which provides weather predictions over the UK but is customised for mountainous regions. The results show the model can generally reproduce both when the snow falls and how much accumulates, and is particularly good at representing extreme snowfall events. We now have new insights into where and when snow falls across the region at much finer detail than previous estimates, making mountain water resources more predictable. In contrast, a snowfall map based on previous analysis shows a much more uniform distribution and considerably less snowfall.

Planning for water shortages

This information could help regional communities adapt and improve water planning, help manage reservoirs and protect vulnerable infrastructure from snow-related hazards. It could also support engineering interventions such as snow harvesting in areas that receive abundant snowfall, for example, by artificially storing meltwater in community-managed reservoirs and into ice stupas (an artificial glacier) designed to gradually release water during the growing season.

An understanding of when snow falls will be crucial for predicting when it will melt and how much water will flow into rivers and help communities and policymakers better prepare for future water shortages.

The Himalayan ice fields are important because they are the largest permanent ice-cover outside of the polar regions and contain some of the largest reservoirs of freshwater on earth.

With this region starting to see more water shortages it’s time to start reassessing reliance on mountain water supplies. But how much water the mountains provide, and how it will change, remain remarkably uncertain.

Good measurements of snowfall are now more important than ever for predicting the future of water resources, which until now have been lacking. For example, a single long-running active weather station above 4,000 metres has provided one of the few continuous snowfall records across the vast 566,000 square kilometres of Himalayan headwaters that feed the Brahmaputra, Indus and Ganges river basins.

This work is also deeply personal for me. I grew up in a city just 50 kilometres from the outer Himalayas. On family holidays, I was captivated by the thriving beauty of pines and junipers against the backdrop of the Himalayas, while learning that mountains and rivers are more than just landscapes. Those experiences fostered a lasting connection to the region, and I continue to return to the mountains whenever I can.

Water is a precious commodity upon which every society depends, and shortages can cause untold misery.

With better snowfall estimates, we are already beginning to understand how to make the future of the mountain eco-system more predictable, and help people cope.

Siddharth Gumber receives funding from the National Environmental Research Council (NERC), The Big Thaw project (NE/X005267/1).

Young people in low- and middle-income countries appear generally more optimistic about how AI can enhance their work prospects and social lives than their western peers, according to our new survey of people in ten countries in Africa and South Asia.

While debates in higher-income countries often focus on job losses, misinformation and mental health risks, many of the 1,864 young adults we surveyed (all aged 18-35) placed more emphasis on the opportunities artificial intelligence tools present for them. In particular, nearly 80% of our participants expect AI to deliver improved education and learning for them.

Until now, there has been little evidence of how young people in middle- and low-income countries use AI, and how they feel about this technology – for good and bad. The 2026 youth AI survey – run by global charity Restless Development and academics from the universities of Makerere in Uganda and Cambridge in the UK – seeks to correct this imbalance.

It comes immediately after the UN Independent International Scientific Panel released its preliminary report on AI. Commenting on the need for more science about AI’s use around the world, the UN’s secretary general, António Guterres, admitted: “The world cannot govern what it cannot understand.”

Our responses were collected in Angola, Kenya, India, Nepal, Nigeria, Sierra Leone, Tanzania, Uganda, Zambia and Zimbabwe – with the UK included for comparison. Two-thirds of respondents said they use AI every day, with Meta AI the most common tool, followed by Claude.

The extent of AI use is striking given the obstacles many young people encounter. In every African country surveyed except Nigeria, more than half said high data costs limit their access. Weak internet connections and a lack of digital skills were also raised, with many relying on basic-feature phones. By contrast, UK participants said ethical concerns played a stronger role in limiting their AI usage.

AI work opportunities

While few respondents (14%) said they earn income directly from AI tools yet, half expected AI to improve their employment and economic opportunities within the next five years.

Currently, AI’s main work-related uses among our respondents are learning new skills and job searches. In almost every lower-income country, young people held broadly positive attitudes about AI’s future role in their working lives – although many (32%-50% per country) expressed concerned about its impact on their future income.

How will your income be affected by AI?

Among those from lower-income countries, more than seven in ten thought AI would bring them better education and learning opportunities in the near future. A 28-year-old self-employed man in Nepal commented: “AI has the potential to empower young people in developing countries like Nepal by improving access to knowledge, digital skills, and economic opportunities.”

Our sentiment analysis found respondents to be most positive in Tanzania and Zambia, with young women there even more upbeat than young men. In the UK, young men diverged from their international peers in feeling more negative towards AI and work.

Biggest AI concerns

Some respondents described AI tools as “Americanised”, built around western norms and unable to grasp local nuance. They worried this could lead to their languages being further marginalised.

“These AI tools are fed major languages that are spoken worldwide, unlike Shona,” said a 25-year-old student in urban Zimbabwe. Shona is spoken by around 14 million people mainly in southern Africa, but is severely underserved by AI and other technology tools.

A Ugandan respondent was similarly critical about provision for her native language, Luganda – the country’s most widely spoken Indigenous language. “AI barely knows the spellings, grammar and how to interpret sentences. It’s not even familiar with our culture at all, which increases mistakes when we get to things concerning our own world.”

What most concerns you about AI?

Despite the widespread belief that AI would improve education and training, some held strong reservations. A 21-year-old female student in rural Uganda said: “Slowly by slowly, AI is making us dumb. We can no longer think for ourselves.”

The more often these young people use AI, the more worried they appear to be about it. The biggest concerns highlighted by daily users were around who owns their data and the power of AI companies.

Concern about AI’s environmental impact and the welfare of AI workers followed a similar pattern of higher usage co-existing with higher concern. Mental health is the only domain where non-users worry slightly more than daily users.

Gender differences

Significantly more young men (38%) than women (23%) reported having “a lot” of AI knowledge. This probably reflects a combination of diverging gender self-confidence, which tends to emerge from school age, and different levels of AI use. “AI is scary but helpful too,” said one female homemaker in urban Nepal.

Almost twice as many men than women use AI to search for work (33% vs. 17%). This difference is probably rooted in gendered access to data and technology, as well as differing social roles – such as the tendency for women to do more care work.

What have you used AI for in the past month?

In most of the ten lower-income countries surveyed, young women expressed greater concern about AI than men – with their biggest concern being over who owns their data. In the UK, 68% of young women were strongly concerned about data ownership, compared with 29% of men.

What young people want from AI

Many of our participants stressed the need for cheaper data and internet connections that work well. But they also want AI tools that fit their lives and culture – and speak their language. Until then, most recognise that talk of AI “leapfrogging” traditional stages of economic and social development is just that – talk.

Some efforts to make AI more inclusive and relevant for lower-income countries are underway. These include initiatives for “frugal AI”, as well as regional collaborations such as Masakhane for African languages and QuechuaBase in Peru.

How can AI be made safer?

Most of our participants framed AI safety as much more of a governance problem than a technical one. Many countries are establishing new AI policies, shaped in part by global forums such as the UN-led AI for Good summits. Overwhelmingly, our respondents agreed that governments, not tech companies, should be setting the rules when it comes to AI regulation and privacy protection.

Anna Barford receives funding from UKRI. She has previously been funded by the British Academy, Leverhulme Trust, ESRC, the Asian Development Bank, the Mastercard Foundation, and a philanthropic gift to the University from Unilever. She is a co-director of the Business Fights Poverty Institute and does consultancy work for the International Labour Organization.

Anthony Mugeere works as a Research Fellow for the Advocates Coalition for Development, Uganda's leading thinktank specialising in advocacy and analysis on climate change. He has received funding from the British Academy and the Mastercard Foundation to conduct research on climate change and young people.

Giulia Occhini does not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.

Vasily Belokurov is one of three winners of the 2026 Kavli Prize in Astrophysics. The award is for uncovering fossil evidence of past galactic mergers that prove how the Milky Way evolved.

No matter the time or vantage point, from a pre-Neolithic cave to a post-lockdown London high-rise, the predictability of the night sky has always been humanity’s symbol of permanence and reassuring stability.

Yet this apparent calm is deceptive. Our galaxy, the Milky Way, emerged from chaos and turbulence, and its constellations are full of migrants, exiles and survivors. Right now, it has begun to stretch and distort again, pulled by a massive companion and heading for an inevitable collision.

How can I be so sure? As a galactic archaeologist, my job is to reconstruct the past of our galaxy and read the signs of its future.

Instead of digging through soil, I use the laws of dynamics and stellar evolution to sift through hundreds of millions of stars – searching for the most ancient and chemically peculiar among them, interpreting their orbits and piecing together the events that shaped the Milky Way. One ancient encounter left scars so deep that, billions of years later, they still define the galaxy around us.

I want to understand what governs the lives of these massive cosmic systems: which changes are nature – the slow internal evolution of a galaxy disc – and which are nurture, imposed by collisions and mergers.

Questions about the source of dark matter underpin it all. This is the invisible substance whose gravity holds galaxies together, but whose true identity remains one of the greatest unsolved puzzles in astrophysics.

The Milky Way is the one galaxy where stellar motions can be measured in extraordinary detail. This allows cosmologists including myself to construct our most precise map yet of dark matter: how far it reaches, how dense it is around the Sun, what shape it has and how smooth or lumpy it may be. If we can build this map in enough detail, we may begin to understand not just where dark matter is, but what it is.

A cataclysmic collision

Our work has been transformed by a revolution in open sky surveys. From 2000, the Sloan Digital Sky Survey showed what becomes possible when vast astronomical datasets are made public, enabling discoveries far beyond the goals for which the survey was first built.

And since 2014, Gaia, the European space telescope, has taken this transformation to another level by mapping the positions and motions of nearly 2 billion stars, turning the galaxy into a vast archaeological record. No ruins, no shards and no bones – only stars that hold the clues.

The clearest giveaway that something cataclysmic took place long ago in our galaxy is the migrants we observe: stars that were not born in the Milky Way.

While native stars mostly travel together, circling the galactic centre in the great rotating flow of the disc, migrants cut across that order. They slide past the locals, plunge into the inner galaxy, then fly back out to its outskirts, again and again.

These unusual orbits go hand-in-hand with unusual chemistry. Most of the migrant stars are less enriched in heavier elements than the locally born population. Their chemical composition is a sign of a slower rate of evolution that is typical of a dwarf galaxy.

This makes the migrants doubly valuable. They are both fossils of the Milky Way’s violent past, and probes of its outer regions, travelling where the local stars rarely go.

How the Milky Way was rewired

One of the central ideas in the theory of cosmic structure formation is that galaxies grow hierarchically. Smaller galaxies fall into larger ones and are torn apart, leaving their stars behind as migrants.

In the Milky Way, the largest ancient structure of this kind is known as Gaia-Sausage-Enceladus. It is the remains of a vanished galaxy that collided with our own between 8 and 11 billion years ago (the “sausage” refers to a pattern in its stars’ motions).

The Milky Way also did not go through that crash unscathed. The collision rewired and reshaped it.

Some of these changes are easily visible in the data. Stars from the old disc were splashed into our galaxy’s halo, becoming exiles in the place where they were born. A new posse of star clusters were also acquired.

At the same time, we think something even more momentous was taking place. The encounter changed the orientation of the Milky Way’s disc, and its alignment with the dark matter halo.

While dark matter is too diffuse to dominate our Solar System, in the outer galaxy it is the main gravitating mass – moving, streaming and, in the standard picture, clumping into a hierarchy of lumps.

Around the Milky Way, this dark matter forms a vast halo, much larger than the luminous part of our galaxy. We often imagine this halo as a sparse, round cloud, but Gaia has helped show this picture is too simple.

The dark halo can be stretched out of shape by a major encounter. Like a ship beginning to list, the Milky Way started to lean – not suddenly, not visibly, but over billions of years.

A new galactic dance

Unusually compared with many galaxies of similar mass, the Milky Way was allowed ample time to recover from the shock of the “sausage merger”. No other cosmic cataclysm appears to have shaken our Galaxy since, letting it settle into a quiet, uneventful life. That is, until now.

The Large Magellanic Cloud (LMC), currently our galaxy’s most massive companion, is already pulling at the Milky Way, disturbing its halo again. In an echo of what happened some 10 billion years ago, the Milky Way is being drawn into an accelerating dance with this neighbouring dwarf galaxy, recoiling in response to the LMC’s approach.

This is a dance that only one galaxy is likely to survive intact. A new chapter of migration, survival and adaptation has begun.

None of this spoils the beauty of the night sky – it deepens it. The calm band of light above us is not a symbol of permanence, but the visible reminder of a long survival.

The Milky Way has been broken, rebuilt and is now being disturbed again. Its stars remember the past; their motions reveal the future. What looks eternal is, in truth, a moment in a much longer story.

Vasily Belokurov has received funding from the European Research Council, the Leverhulme Trust, and the Science and Technology Facilities Council.