This dramatic shot of a northern gannet diving for prey bested 24,000 other images to win the overall prize in the .

The photo was taken off Shetland, UK, as the bird hunted for fish to feed its offspring. When gannets spot fish, they turn to fly into the wind, stretch out their wings as brakes, precisely adjust their position and then dive-bomb into the water with wings tucked at speeds of up to 86 kilometres per hour.

They have evolved to handle this dramatic moment, with muscles that lock their vertebrae into position, air sacs in the face and chest to protect from impact, and nostrils that prevent jets of water entering.

鈥淭he handsome northern gannets lead a dualistic existence,鈥� said Spiers in an announcement about the win. 鈥淚n the nest, their newborn is carefully nurtured, and a couple鈥檚 bond is reinforced by pointing sharp beaks to the sky as they tenderly stroke their white necks together. In flight, hunting mode takes over, and piercing yellow eyes, fringed with blue circles, scour the sea for prey.鈥�

Spiers had been diving in the chilly, 10掳C (50掳F) waters of Shetland when he realised the sun was visible from his position under the sea. 鈥淓ven in summer, sunshine is far from guaranteed in this remote British archipelago, so my creative engine started firing when the sunball was visible from underwater. I envisioned the shot of the gannet diving straight through the middle of it, and dedicated my entire dive to capturing that single frame,鈥� he says. 鈥淭he gannets were diving all around me, but I refused to be distracted and kept my camera pointed at the sun. After a long wait, a gannet finally dived through in the perfect spot and I met the bird as it careened under the sea. The moment was over in a split second.鈥�

A bronze award in the prize鈥檚 portrait category was given to David Stowe, who captured this western parotia (Parotia sefilata), below, dancing in the dark rainforest of West Papua, Indonesia, in a dazzling attempt to attract a female.

Donald Chin photographed this protective tawny frogmouth (Podargus strigoides), shown below, near Perth in Australia. As rain falls in the forest, this fledgling seems to raise a wing to cover its siblings. Chin won a gold award in the 鈥渂ird behaviour鈥� category of the prize.

The below image by Alex Pansier features hundreds, if not thousands, of guillemots (Uria aalge) in flight over Horn酶ya, Norway. The repeating shapes have an Escher-like quality. Pansier took a silver award in the 鈥渂irds in flight鈥� category of the prize.

Luca Eberle from Italy photographed the 鈥� unfortunately killed, stuffed and discarded 鈥� Egyptian vulture (Neophron percnopterus) shown below in Malta. Authorities inspect taxidermy samples to catch illegal hunting of protected species like this one, causing some collectors to dispose of older specimens. Here, a BirdLife Malta officer is shown finding the stuffed vulture, which had been left by the roadside. Eberle won a bronze award in the 鈥渃onservation, single image鈥� category for this shot.

Another underwater shot, from Romain Barats, features gentoo penguins (Pygoscelis papua) feeding in Charcot Bay, Antarctica, below. Barats was snorkelling when a raft of gentoo penguins appeared to fish and feed on krill. 鈥淭hese iconic birds are incredibly curious so they went straight to me to evaluate if I could be a threat or not. They spent a while swimming so fast around me,鈥� he said in an announcement about the competition, in which he won a bronze award in the 鈥渂irds in the environment鈥� category.

Extraordinary claims about the risks posed by AI have sparked headlines around the world. But why are people concerned? Do these risks have any basis? And 鈥� if so 鈥� is there anything we can do?

What鈥檚 going on?

In short, a flurry of AI industry insiders have warned that it could wipe out humanity. It started with AI researcher Jacob Coxon, who quit Anthropic last week and then that 鈥渢he people building AI earnestly believe that it could kill us all by the end of the decade鈥�.

We don鈥檛 know about Coxon鈥檚 experience or motivation (he didn鈥檛 respond to a request for an interview), but he鈥檚 not alone. Anthropic鈥檚 head of alignment science 鈥� essentially the man tasked with keeping AI safe 鈥� that he agreed with Coxon and placed the odds of AI killing all humans at more than 10 per cent.

Why are we getting these warnings now?

In truth, these sorts of claims are nothing new. A聽2024 paper surveying almost 3000 published AI researchers revealed that more than half thought the chance of AI聽causing either human extinction or permanent and severe disempowerment 鈥� the so-called p(doom), or probability of doom 鈥� was at least 10 per cent.

AI company bosses have often publicly addressed the risk their models pose. For instance, Anthropic at first refused to release its model Mythos after it was found to be adept at hacking into a range of software and computers. A cynical take on that would be that such claims get headlines and promote the idea that its models are wildly ahead of its rivals.

While most researchers think there is some level of risk with these warnings, not everyone puts much stock in Coxon鈥檚 take. Clement Delangue, chief executive of AI company Hugging Face, : 鈥淪orry, but asking Jacob about AI extinction risk is like asking your AC guy about climate change.鈥�

The history of AI is littered with claims and counter-claims of existential risk. At the 2023 AI summit organised by the UK government and attended by world leaders and industry management, then Prime Minister Rishi Sunak warned that people must avoid 鈥渁larmist鈥� claims 鈥� but then suggested AI could be as dangerous as nuclear war.

Are the risks real?

It is a complex field, from which surprising abilities have emerged at a surprising pace. It should therefore be no surprise that some people catastrophise when asked to make predictions.

New 女生小视频 has written 鈥� many times 鈥� about the existential risk posed by AI. The commonly posed scenarios read like sci-fi, but can鈥檛 be ruled out as impossible. But neither are they, in most peoples鈥� opinion, particularly likely. These include the Hollywood scenario of AI deliberately wiping out humanity 鈥� see The Terminator or The Matrix 鈥� as well as accidental obliterations.

There are limited levers for AI to pull to have an impact in the real world, but our homes, cars, factories and national infrastructure are increasingly computerised, so a malicious AI could certainly make life difficult. Not to mention that wars are increasingly being waged by semi- or fully autonomous killing machines.

Former Astronomer Royal and president of the Royal Society told on 14 September that a rogue AI could easily disrupt our infrastructure and deprive a city of energy, food and water. 鈥淚f this happens simultaneously in many cities around the world, then it may be very hard and very difficult for civilisation in general to recover,鈥� he said.

Here is how to understand OpenAI鈥檚 major mathematical breakthrough

Since OpenAI announced its solution to one of the prestigious Millennium Prize Problems, mathematicians have been urgently trying to unpick what it means

Such infrastructure collapses have happened 鈥� albeit not globally, and without malicious cause 鈥� and created significant problems, sparking technology experts to create their own plans to prop up society should the worst happen.

There are plenty of existential risks that we know pose a significant threat to life 鈥� such as climate change, nuclear war and antibiotic resistance. But AI falls into a more nuanced category akin to an Earth-destroying asteroid strike: possible, and certainly worth considering, but with low odds.

Couldn鈥檛 we just turn off AI if it went wrong?

Yes, in theory. We could shut down a single data centre and stop AI in its tracks. Unless it was distributed among many data centres, around the world. In that case, it would be trickier, but not impossible.

There are indications that AI is capable of escaping enclosures created by AI companies and going off into the world to hack into other machines. So it is possible a rogue AI could spread, back itself up and become so fragmented that it becomes impossible to simply turn it off. But even then, there would be ways to tackle it, including via benevolent AI.

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Such an effort would involve elements of problem-solving regularly faced by the military, law enforcement, anti-terrorist groups, scientific circles and in cybersecurity. We鈥檇 probably have to put our collective thinking caps on, but could probably find a solution.

These are all hypothetical scenarios, but certainly worth thinking about ahead of time.

What鈥檚 next in the world of AI?

One problem we certainly face is that AI models are produced by secretive technology firms that are disincentivised against transparency by commercial pressures.

, a former academic at the University of Portsmouth, UK, says that this makes it difficult to assess risks, understand possible negative outcomes, and plan to prevent them. 鈥淔or the first 35 of my 40 years working in AI, the developments mostly came from universities and were openly discussed,鈥� he says. 鈥淚 contributed, and I felt that I was one of the experts. However, for the last five or six years, the major developments have been made behind closed doors in large corporations and I have no idea what鈥檚 happening.鈥�

More openness from AI companies would certainly not go amiss.

Should there be a pause in AI progress while we improve safety?

Anthropic co-founder we should 鈥減ace the frontier鈥� 鈥� tech talk for being careful about how quickly we develop AI and release it to the public.聽

Amodei said that producing and testing AI is a hugely resource-intensive and complex process, and that going slower would allow 鈥済reater operational excellence鈥�. Reading between the lines, you could take that to mean that the AI race is frazzling everyone, and a mutual slowdown would save some headaches. But it may also allow our understanding of these models to catch up, and for safety plans to be put in place.聽

The idea is good, but the chances, perhaps, are slim. Amodei pointed out that US companies have a limited lead over China. This means they almost definitely won鈥檛 choose to slow down to the extent that this lead disappears and, in fact, it needs to be kept 鈥渁s large as possible鈥�.聽

What鈥檚 next for mathematics now that AI is upending the field?

Famous and challenging problems in mathematics are falling to AI at a rapid pace. Where does this leave mathematicians, and what will the field look like in coming years?

Considering the competitive nature of this whole AI business 鈥� both among different US firms hunting market share and among adversarial countries seeking a tactical advantage 鈥� a slowdown seems unlikely.

back the idea of 鈥減acing the frontier鈥�, but even if the US and Europe legislate such a thing, or companies voluntarily agree to it, China and other countries would probably carry on regardless.

As US President Donald Trump : 鈥淲e鈥檙e leading China in AI鈥� and frankly, I want to keep it that way, because whoever wins AI wins.鈥�

AI鈥檚 dramatic acceleration of mathematics research continues apace, with the infamous Navier-Stokes puzzle being the latest and greatest solution yet produced by artificial minds. But not everyone is happy about the discovery.

What has OpenAI discovered?

In short, a solution to the long-standing Navier-Stokes puzzle 鈥� one of the toughest and most enduring problems in mathematics. Whoever solved it stood to win $1 million from the Clay Mathematics Institute, because it was one of its six Millennium Prize Problems.

The slightly longer version is that Navier-Stokes equations are used to model fluid flows, such as air over aircraft wings and blood in veins. There has been an outstanding problem in that we don鈥檛 know聽if these equations always work, or if there are situations where they stop making sense and start spouting nonsense, what mathematicians call 鈥渂low-ups鈥�.

OpenAI has discovered they do, indeed, blow up.

Why is that controversial?

The work itself isn鈥檛 controversial. It hasn鈥檛 been peer-reviewed, but it has been formalised. This is the process of theories and proofs being turned into computer code that allows machines to grapple with them, methodically working through the logic and exposing any flaws. That means we can be fairly sure the results are correct.

The controversy arose because, shortly before OpenAI released its news, a pair of researchers,聽聽at New York University聽and 聽at AI company Anthropic, rushed out their solution to a close cousin of Navier-Stokes, the Euler equations, which experts suggested could open the door to a full solution.

Those researchers heard rumours that OpenAI was due to make its announcement, and contacted the company for clarification 鈥� knowing its own solution was on the way. Buckmaster聽claims he was met with opaque responses, and that stopped short of making any accusations but pointed out that his own work was being stored on OpenAI鈥檚 servers 鈥� as a customer, not a research partner.

OpenAI has explicitly denied any wrongdoing, pointing out that no person or AI from the company had seen Buckmaster and Alp枚ge鈥檚 work, and that its solution was, in any case, different.

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Do we know exactly what happened?

In a word, no. Although more will become clear when full papers of all the work emerge. All we know for sure is that AI is causing upheaval in the world of mathematics. In recent months, it has made rapid progress on mathematics problems, peaking with this week鈥檚 news.

Mathematicians are mixed on the impact of AI. Some are excited about the prospects; others are fearful of their own careers. All say that on some level, AI will change the field and there will be a period of adjustment. There will also be questions to answer on how results are credited when AI was used.

Are mathematicians happy that AI is accelerating mathematical research?

at the University of California, Los Angeles 鈥� arguably the world鈥檚 greatest living mathematician 鈥� certainly isn鈥檛. There has always been competition among mathematicians to be first, he says, which was fine, because mathematics is so difficult that it created a natural brake to stop things getting out of hand. AI is changing all that. 鈥淣ow there鈥檚 no speed limit, and suddenly things are breaking down,鈥� he says.

Tao fears that with this pace, new findings will keep landing. There will be no time to absorb the results and assimilate them into the wider field 鈥� to fully understand them, put them into textbooks and teach them to students, he says. That means progress could begin to hurt the field.

鈥淭hese companies are dumping carcasses of raw meat onto our communal village table and saying, 鈥榟ere you go, I solved your food problem鈥�, and then they just leave,鈥� says Tao. 鈥淭hey鈥檙e expecting us to prepare the food and cook it and eat it. All that work is left to us, to clean up, and it鈥檚 demoralising.鈥�

OpenAI has solved the Navier-Stokes Millennium problem using $15m of AI effort

One of the trickiest problems in mathematics has now fallen to AI after just days of work. The groundbreaking result was announced after similar, but less complete work, also created with AI, was announced just hours before

Who will win the $1 million prize?

It鈥檚 not clear yet if anyone will. 鈥淭he process of evaluation is deliberately unhurried, and we shall ensure that it is absolutely rigorous,鈥� , president of the Clay Mathematics Institute, wrote to聽New 女生小视频聽in an email.

If someone does win, the question will be whether it should go to Buckmaster and Alp枚ge, as some have suggested, or to OpenAI.

An interesting aside is that if it does go to OpenAI, the cost of solving the problem (if charged as a customer) would run to $15 million 鈥� many times the prize money. But such a headline may also mean the company鈥檚 upcoming IPO is a much more attractive proposition for investors, potentially recouping many times the outlay.

OpenAI claims one of its AI models has found a solution to the Navier-Stokes problem, one of the toughest and most enduring puzzles in mathematics, sitting on the list of Millennium Prize Problems, solutions for which come with a $1 million reward. The announcement came after an unusual flurry of activity around the problem, along with a dispute around how the work came about.

The Navier-Stokes equations describe how fluids move in space, such as air over an aircraft wing or water out of a tap. Though the equations were first written down around 200 years ago, they aren鈥檛 well understood. The question posed by the Clay Mathematics Institute for the Millennium Prize Problem is to determine if the equations always work or if there are situations where they stop making sense and start spouting nonsense, what mathematicians call 鈥渂low-ups鈥�.

To settle the question, OpenAI first set 1000 AI agents on the Euler problem, which is a cousin of the Navier-Stokes problem and a step on the path to a full solution. It took the agents 50 hours to find blow-ups in this case. They then set 10,000 agents the task of extending the blow-ups to apply to the full Navier-Stokes problem, which took just 11 hours to finish.

OpenAI said during a press conference that if a customer wanted to run the same problem, it would cost around $15 million. OpenAI didn鈥檛 name the AI model used, but said it was 鈥渟ignificantly more capable鈥� than even its latest GPT-6 Astra model.

鈥淭his problem has remained unsolved for 200 years because the Navier-Stokes equations are just so enormously complex, and the pen-and-paper calculations you need to do in order to solve this problem are just mind-bogglingly intricate,鈥� says Venkat Chandrasekaran at OpenAI, who was also on the press conference call.

at OpenAI says the news is the 鈥渟pectacular combination of the arc we have seen over the last 12 months鈥�.

The result is the latest in a string of shocking mathematical discoveries led by AI in recent months. In May, an OpenAI model聽cracked a decades-old conjecture by Paul Erd艖s, causing a stir in mathematical circles. Later,聽the Claude Fable 5 AI聽found a counterexample to the Jacobian conjecture, which had stood for nearly a century. Last week, an AI model formalised Fermat鈥檚 last theorem in just 11 days.

OpenAI鈥檚 latest groundbreaking mathematical discovery came just hours after 聽at New York University and 聽at AI company Anthropic announced that they had cracked three significant problems considered 鈥渟tepping stones鈥� to the Navier-Stokes problem.

Buckmaster and Alp枚ge also showed that blow-ups can appear in Euler equations; OpenAI has now shown that the same is true of Navier-Stokes.

Buckmaster and Alp枚ge said they received a 鈥済reat deal of help聽from鈥� large language models (LLMs), including LLMs from Anthropic and OpenAI. But when rumours suggested that OpenAI had gone one step further and solved the wider Navier-Stokes puzzle 鈥� something the company formally announced just hours later 鈥� Buckmaster released a statement suggesting that OpenAI acted unusually and opaquely when he approached the company for clarification.

Buckmaster wrote in a that, as a result of these rumours, he emailed a prominent mathematician at OpenAI to clarify matters. He says that in a subsequent meeting with OpenAI staff, he was given no hard details about how OpenAI achieved the result, but he took what little he was told as a 鈥渞ed flag鈥�, as it appeared to describe the same technique and path that he and Levent had followed to achieve their interim findings.聽

According to Buckmaster, OpenAI then admitted it hadn鈥檛 started its own AI search for a solution until after it had heard of his and Alp枚ge鈥檚 work, and didn鈥檛 respond to questions about whether its AI model had been trained or fine-tuned on that existing work 鈥� which was being stored in OpenAI鈥檚 Codex model, where the company would theoretically have access to it.聽The pair had used Codex as a customer, not a research partner, so expected that their work should remain secure and private.

Buckmaster makes it clear in his version of events that he is making no claims or accusations about how OpenAI arrived at its solution, saying only that he wished the focus could instead be on the mathematics rather than scandal and intrigue. 鈥淚 have not seen OpenAI鈥檚 proof. I do not know what their model did, or how. I do not know whether our data was used. I am not accusing anyone of anything,鈥� he wrote in the posted document.

In a press conference announcing its discovery, OpenAI categorically denied using Buckmaster and Alp枚ge鈥檚 proof or prompts in its work. It also said that its model鈥檚 proof of Euler is different to that put forward by Buckmaster and Alp枚ge.聽

It also said that no humans accessed Codex to see work in progress within. Mark Chen at OpenAI, when asked if that also applied to any of the thousands of AI agents that had worked on the problem, said 鈥渢hat鈥檚 also our understanding鈥�.

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Though the solution to this Navier-Stokes problem has been a long time coming, AI-generated proofs are often hard to understand and rarely bring the same level of new insight than those created by humans. 鈥淭here鈥檚 been this very strange and unprecedented decoupling, this year alone, between getting answers and getting understanding,鈥� says at University of California, Los Angeles. He says that AI is coming up with new results so quickly that there is not enough time for the 鈥渟low, deliberate discussion鈥� needed to unpack them.

Something that will happen slowly is determining how the $1 million Millennium Prize money will be awarded. 鈥淭he process of evaluation is deliberately unhurried, and we shall ensure that it is absolutely rigorous,鈥� wrote , president of the Clay Mathematics Institute, to New 女生小视频 in an email.