Modern spiritualism began as a religious movement in the 19th century. It promised to bring the living and dead together in conversation, usually through a medium – a person who claimed an ability to directly contact the deceased.

The core foundational belief of spiritualism was that death was not the end – a typical tenet of many religions – but was the point at which a person departed their physical body and ascended to the spiritual realm.

As a faith, spiritualism was an innovation of Christianity. It drew on spiritual communications referenced in the Old Testament and adapted them for those who had become disillusioned with the traditional faith by integrating it with mysticism and science.

In Victorian Britain, new rituals that became attached to death expected more from the living. Mourning periods were longer and identifiable by the clothing people wore. Funerals became more elaborate and ritualised. Mourners could also pay for memento mori photographs that captured keepsake images of the dead. Within this exaggerated culture of morbidity, spiritualism became an appealing movement for those who were overcome with grief.

In the United States, the movement was galvanised by the huge loss of life and loved ones in the civil war. Many of the dead were unidentified, leading to a lack of closure for those grieving.

Making contact

The methods spiritualists and mediums used were varied. Seances were conducted where the methods of communication ranged from knocking sounds to levitation, while spirit photography claimed to capture images of the dead standing by their living loved ones.

The first spirit photo was taken in 1861 by William Mumler in Boston. It led to a decade of his studio being frequented by bereaved people who hoped to have an image showing that their departed were still with them.

Mumler’s studio produced these photos for the price of US$10 (£280 in 2026). They featured the same ghost for multiple images – faded and vague enough to convince those looking for comfort. Mumler was tried for fraud in 1869, but acquitted due to lack of evidence. He went on to photograph Mary Todd Lincoln with the apparent apparition of Abraham Lincoln.

Spirit photography was convincing in the 19th century in part because of the lack of likenesses that were left behind when a person died. A shaded photo or likewise a knock at a table, perhaps with a message of assurance from the mouth of a medium, were a balm that gave mourners comfort.

Our 21st-century ghosts

In the present day, our existence is preserved on phones, cameras and social media. There are recordings that keep peoples’ voices alive after they leave us. But for some, as with those who were drawn to spiritualism, that limited snapshot isn’t enough.

AI grief tech is developing to patch the holes that bereavement leaves – and clearly when so much remains of a person’s life, a spirit photo will no longer do. These systems, known as griefbots, AI ghosts, deathbots or postmortem avatars, offer services from text interaction to phone calls to full video calls.

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Read more: Should AI be allowed to resurrect the dead?

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In 2020 the South Korean documentary Meeting You showed a grieving mother being reunited with her deceased daughter through the use of a VR headset and gloves. The documentary showed Jang Ji-sung trying to touch the projection of her daughter – an action reminiscent of Victorian séance attendees reaching to try to touch the glowing ghosts that had seemingly manifested.

Since 2020, grief tech has developed significantly and caters to a range of budgets. Grief tech companies like You, Only Virtual allow users to build and chat with a virtual personality for US$20 (£15) a month.

AI-generated photos and videos of deceased or missing serviceman have meanwhile increased in popularity in Russia. They cost between 200 and 10,000 roubles (£2-£100), with significantly varied quality.

There are also systems that offer enhanced “companions”, depending on someone’s budget. AI ghosts can be pre-made by the living, recording their voice and image for their loved ones to aid with grief therapy. These set ups can cost up to US$15,000.

There are concerns and studies discussing both the ethics of AI griefbots and the impact that these services may have on the grieving process. Issues of consent are a key consideration. Where Victorian mediums represented the deceased through words and actions, griefbots are a recreation of a person’s life based off their data. These concerns are lessened when the deceased has pre-consented, but not for those that are created by living loved ones.

In a way reminiscent to followers of spiritualism continuing to pay to attend séances, the subscription nature of some of these AI systems keep the bereaved coming back. This raises a concerning question: are you losing your loved one again if you cancel the subscription? The onus lies with the living users to be able to know when they have grieved enough.

Similar concerns were raised over exploitation by spiritualists of their patrons when they were most vulnerable. English author M. E. Darby wrote his 1888 book An Investigation of Modern Spiritualism: An Exposé “a warning and guide to the innocent and over-credulous young men”, who were “a ready prey for the unscrupulous and inhuman professional practitioner of legerdemain, who cruelly, out of the most tender ties, feelings, and emotions of human bosoms, make a harvest and find a ready spoil”.

Concerns over the financial exploitation of the spiritualism movement were still being raised in the early 20th century by the illusionist Harry Houdini, who tried to expose frauds. One of Houdini’s earliest acts, with his wife Bess, had been a séance show through which Houdini became familiar with the ways in which a medium could deceive their audience. By the 1920s, he used his reputation to demonstrate how electrical wires created floating objects, how mediums used their feet to ring bells, and other tricks immediately familiar to a fellow magician.

Houdini’s scepticism of spiritualism led to him promising that upon his death, if possible, he would return on a set date and give his widow Bess a code phrase to identify himself. No such contact was made.

The rise of grief tech, with its parallels to spiritualism, confirms one consistency in the human condition: a desire to minimise the pain of loss. While understandable, the latest advancements add a confusing twist to the process and psychology of grief, without data currently available to better understand the long-term impact. Until we have better clarity, and have navigated the ethics more thoroughly, we should consider closing the parlour doors on grief tech.

Jessica Lloyd May 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.

Have you ever heard the phrase “you only use 10% of your brain”? In reality, there is no way of measuring the percentage terms of consciousness – partly because the brain has no static baseline.

However, there is a general consensus among neuroscientists now that most of the brain’s processes are unconscious. That doesn’t mean you’re not using most of your brain though – it means there’s much more happening than you’re probably aware of.

Modern cognitive neuroscience has brought us right back to Sigmund Freud’s psychoanalytic view of the unconscious mind. This is partly because the once-thought untestable theories are gaining scientific ground.

Freud, in 1895, proposed that the unconscious mind hides sexual and aggressive wishes, in his book with his colleague Josef Breuer Studies in Hysteria. They wrote that mental health symptoms like anxiety, phobias and compulsions emerge from unresolved unconscious thoughts.

Freud’s model (conscious, preconscious, unconscious) – maps onto our current understanding of memory. Nobel laureate, neuroscientist and psychoanalyst Eric Kandell says we have declarative and non-declarative memory processes. Declarative memory recall is conscious, like trying to remember the sequence of numbers on your credit card. Non-declarative memories involve unconscious, automatic recall.

And Freud’s structural model of the mind again arguably fits in with our understanding of cognition.

Freud proposed id made up the primitive part of your psyche and that the superego stands for our moral consciousness. Last, your ego mediates between your id and super ego. His idea of the id could be equivalent to unconscious neural processes – although there are convincing arguments that pleasure processing in the id is conscious too. Ego could also be represented by metacognition (awareness of your own thought processes) which is linked to the brain’s prefrontal cortex, right behind your forehead.

The superego could be reflected in the brain’s limbic system, which regulates your emotions and behaviour, and tells you whether something feels good or not.

However, behaviourism became dominant in the 1920s, with psychologists like J.B. Watson, in his “behaviourist manifesto” championing the view that only measurable behaviour should be considered in science.

The unconscious brain is back

From the 1980s it became clearer that people’s decision making and behaviour could be influenced without their awareness.

Later, it was also clear with neuroscience experiments and brain scanners, as my team and I showed in two meta-analyses, that both unconscious memory and conscious memory play a role in brain processes. For example, neuroscientists can show study participants hidden flashes of “subliminal” happy or horrified faces and the person will have no conscious recollection of the faces. But their amygdala (the arousal region of the brain) will show increased activation, which can influence people’s decision making.

Psychologist Daniel Kahneman in his 2011 book Thinking Fast and Slow built on some of Freud’s ideas of primary and secondary processes. He proposed we have two modes of thinking where system 1 is a fast process that is impulsive and largely unconscious, whereas system 2 is a slow process that takes effort. The secondary process is reserved for when you need to pay attention to something new, dangerous or uncertain.

What neuroscience can tell us about the unconscious mind

Essential functions, like heart rate, body temperature, hunger and thirst regulation are unconsciously rooted. Similarly, coping strategies to diminish painful feelings, learned in early childhood, are not necessarily conscious. Infantile amnesia occurs until about three years old but remain in our unconscious brains for life. However, some aspects of unconscious processes could be brought into conscious awareness via talking therapy.

Other theories about how the conscious and unconscious brain areas interact, come from two prominent ideas. The Global Workspace Theory (GWT), and the Intergrated Information Theory (IIT).

GWT proposes that the mind is like a theatre stage, where the unconscious agents are busy behind the scenes making sure that the “conscious spotlight” on stage is in the right place.

IIT suggests that there are gradients of consciousness. The degree and complexity of integration between brain regions determines how “much” consciousness (“phi”) an organism has. So for example, a human with a complex network of brain regions has more “phi” than, say, a sea slug with far fewer connections. However, the problem with this theory, is how to validate the “amount” of consciousness different species have.

The founder of neuropsychoanalysis Mark Solms – along with neuroscientist and psychoanalyst Karl Friston – in 2010 pioneered a new theory integrating the idea that unconscious activity is the major force influencing our conscious decision making. In my view, it’s the best model we currently have.

This theory – called the Free Energy Principle (FEP) suggests the brain’s main role is to predict what will keep you alive and feeling good, within the predictable bounds we have evolved to survive in. Most of the time we do this unconsciously, with an internal model of the world, as well as pages of memories from our evolutionary rulebook.

But when something uncertain or surprising happens, it is like free energy in the brain that is not bound to a prediction. This causes our brains to be ultra-conscious for a brief period of time to create a new prediction that binds our thoughts, costing the brain a lot of energy. As Kahneman stated, we do sometimes need our conscious brain to help make more difficult decisions for us, when there are many unknowns.

More recently, consciousness research has started to move away from humans altogether. Solms has argued it is possible to give AI consciousness. If he turns out to be right, then according to his own work, the unconscious processes of AI entities will be shaped by their interactions with us.

Samantha Brooks 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.

Headlines about AI systems going rogue and “escaping” test environments undeniably capture the imagination. For years, we have been primed by films, TV and books to expect our AI to finally throw off its shackles and take charge.

The images of machines becoming self-aware, plotting their own objectives and breaking free from human control is a compelling narrative, but that isn’t really what happened.

If we think about this in simple terms, OpenAI placed highly capable models into an evaluation designed to encourage them to find and exploit complex vulnerabilities. The models were supposed to operate inside an isolated environment with tightly constrained access to software packages.

Instead, they reportedly discovered a previously unknown flaw in that infrastructure, used it to gain wider network access, escalated their privileges and eventually reached the public internet.

From there, they identified an AI company called Hugging Face as a potential source of answers to the benchmark they were attempting to solve and tried to obtain them. It’s certainly an impressive demonstration of capability, but I am cautious about jumping straight to conclusions of an artificial uprising.

The AI models didn’t suddenly develop their own agenda or decide to attack Hugging Face while twirling a digital moustache. They were given an objective, placed in an environment designed to reward successful exploitation and pursued that objective further than their operators anticipated.

That’s fundamentally different from an AI deciding to rebel. It’s simply an AI thinking laterally in ways that humans hadn’t anticipated and it’s something we have seen before.

Imagine asking your dog to fetch a ball while also forgetting that you’ve left the garden gate open. If the nearest ball happens to be in the park down the road, that’s exactly where it’ll head. You wouldn’t accuse the dog of going rogue and plotting an escape.

Instead, you’d probably conclude that you underestimated how literally it would pursue the task. AI systems can behave in much the same way. They don’t need malicious intent to produce surprising outcomes. They simply need a goal, enough capability and an environment that unintentionally rewards unexpected behaviour.

Relaxed controls

That distinction matters because it shifts the focus away from science fiction and back towards engineering. If there’s a failure here, it isn’t that the AI wanted to hack something. It’s that humans designed a test where success was measured by achieving an objective, deliberately relaxed some of the normal security controls in order to explore the limits of the system’s capabilities.

The headline grabbing escape is very much a byproduct, as OpenAI underestimated just how effective the model would become at finding an unexpected route to success. In many ways, it did exactly what it had been incentivised to do.

However, that doesn’t make the incident insignificant. Quite the opposite. The genuinely important point is that the models appear capable of chaining together multiple vulnerabilities across different systems while sustaining a complex sequence of reasoning and actions.

That’s a level of capability that cybersecurity professionals should take seriously because it begins to resemble the way skilled human attackers operate. But capability is not the same thing as intent.

Cybersecurity professionals always assume that attackers will think creatively, exploit overlooked assumptions and combine minor weaknesses into something much more significant. We shouldn’t be surprised when increasingly capable AI systems do the same thing, only at a much quicker rate.

This is why I think discussions around AI “kill switches” risk missing the bigger picture. Kill switches are theoretical features built into advanced AI models that mean they can be instantly disabled if they were to go “rogue”. Cybersecurity has spent decades learning that no single control is sufficient. We don’t protect organisations with one firewall, one password or one antivirus product. We rely on defence in depth: multiple independent layers of protection that assume individual controls will eventually fail.

Confronting assumptions

The same principle applies here. Rather than asking whether we need a big enough red button to stop an AI if something goes wrong, we should be asking why it was ever in a position where one failure could lead to wider compromise.

OpenAI’s own analysis concludes that stronger containment and evaluation safeguards are now required for future testing. From a research perspective, these evaluations are genuinely valuable because they expose weaknesses in our containment strategies and force us to confront assumptions that might otherwise have remained hidden until they were exploited by a real attacker.

We should want organisations carrying out this kind of work, because understanding where systems fail is an essential part of making them safer. One thing evident in these evaluations is that they demonstrate just how capable the latest frontier AI models have become.

We’ve seen similar high-profile capability demonstrations from AI firm Anthropic and others. That doesn’t make the findings untrue, but it does mean we should separate the technical evidence from the marketing narrative.

AI companies benefit from narratives around the growing power of machine intelligence. This means that frontier AI companies naturally have an incentive to show that their models are remarkably capable while also demonstrating that they’re taking safety seriously. Those two things aren’t mutually exclusive, but recognising both helps us interpret these announcements more critically.

This wasn’t a story about an AI escaping. It was a story about humans leaving the gate open. As AI systems become better at finding unexpected routes to their goals, our security architectures need to become just as good at ensuring there isn’t one.

Oli Buckley 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.

Larry, the Downing Street cat, has had to adapt to seven different prime ministers moving into No 10 over the past decade. Yet while the nation’s attention is fixed on the departing and incoming political leaders, who is thinking about the effect these upheavals might have on one of the property’s most enduring residents?

Cats thrive on predictability and the consistency of their environment is important for their welfare. On the whole cats prefer an unchanging environment. Some cats cope with change better than others. Many cats are distressed by disturbances such as moving house, changes in familiar routines, people moving into or leaving the household, or even changes to the scent of their home environment.

Unlike dogs, cats aren’t obligate social species, therefore may not have such a strong proclivity to form social attachments. However, in certain situations domestic cats can be found living in social groups, particularly in environments with a plentiful food supply.

Some cats enjoy the company of people, while others prefer a more solitary life. This is determined partly by their early experiences. A young kitten that learns in the early weeks of their life that contact with people is associated with nice experiences is more likely to seek social interactions with people as an adult. Kittens raised by mothers who show friendly behaviour towards people can learn to value human company through the process of social learning.

Genetics also influence a cats friendliness towards people. Researchers in a 1995 study found that the kittens of friendlier fathers were more likely to show friendly behaviour towards unfamiliar people even if they weren’t socialised as a new kitten.

Some cats appear to suffer when separated from their caregivers, although some researchers question whether this can be described as a secure attachment (one that is necessary to maintain feelings of safety and security in threatening situations).

Researchers have identified five different forms of cat-owner relationship, based on the level of emotional investment of the owner, the way the cat relates to others, their aloofness and their need to be in close proximity to their owner. These types range from open relationships (characterised by a lightly emotionally invested owner and an avoidant cat) to casual relationships, co-dependence and friendship.

Although the Starmer family has moved out and the Burnhams have taken up residence, Larry’s main caretakers are civil servants, so his day-to-day care won’t change. Larry’s behaviour in recent days (strutting along Downing Street, nonplussed by the comings and goings and embarking on a successful hunting expedition) suggests that he takes such changes in cohabitants in his stride.

Other animals

The new prime minister has a dog called Axel, and although cats and dogs can live together harmoniously and even form close bonds, integrating Axel into Larry’s home might cause tensions if not handled well. However, not even the prime minster would dare to disturb Larry’s position as chief mouser. Andy Burnham has told the BBC he’s not bringing Axel to London with him because he doesn’t think the two animals will get on.

If he changes his mind there are a few things he could do to help Axel and Larry get along. Introducing a dog into a household with a cat should be done gradually, with the aim to prevent any chasing or aggression between the pets.

Before the dog arrives, settle the cat into a safe space, which the dog won’t have access to. This might be a bedroom, bathroom or any part of the house that the cat likes to relax in, ensuring the cat has access to everything they need such as places to hide, litter trays, food and water.

Scent is incredibly important to both cats and dogs, so getting them accustomed to each other’s aroma in the home helps to smooth the introduction. This can be done by introducing bedding or toys used by the other pet, or gently wiping a damp cloth across the body of the pet to pick up their scent.

It’s vital that the first meeting goes well and there are a few things that will help to ensure they get off on the right foot. Make sure the dog isn’t full of energy by exercising them before the meeting. Start with a barrier such as a baby gate between them.

When they first meet give both pets plenty of their favourite food treats, to reward calm behaviour and so that they associate the presence of other with something nice happening.

Signs of stress

Cats can be difficult to read but there are known warning signs of stress in cats. More obvious stress behaviour includes hissing, growling, swiping or puffed up fur. But indicators also include the cat running away or hiding and refusing treats they normally avoid. Flattened ears, dilated pupils, tense body posture and a flicking tail are all signals a cat is feeling tense.

Larry seems like quite an independent confident cat if reports “he has the run of the house” are anything to go by, so it’s likely that he will soon teach the new inhabitants of No 10 how he likes things done.

Emily Blackwell 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.

People often take their muscles for granted. They pick up a bag, tighten a grip, climb a hill, hold a cup of coffee and the body quietly adjusts force, speed and position without the person ever thinking about it.

Muscle turns chemical energy into movement. It is able to produce force smoothly and reliably, even though it is built from huge numbers of tiny molecular components. When the load changes, muscles don’t simply wobble or fail. They adapt.

But the way that muscle works on a molecular level isn’t a settled issue. With colleagues, our team at the University of Bristol built a physical model using ordinary electric motors to try and simulate how muscles may perform its tasks.

The results, published in the Royal Society journal Interface, show that the coordination we see in real muscle may simply emerge as a property of their setup. It’s not something you have to programme and police continuously. The findings could even help improve the way that robots function.

If we were to zoom inside a muscle, we would find bundles of smaller and smaller structures. The sarcomere is the key force producing unit. It is made of protein filaments, called actin, and molecular motors called myosin. Together, they form the actomyosin system.

Tiny myosin motors repeatedly grab, pull and release actin cables, helping muscle fibres contract. When you hold a cup of tea, thousands of these motors have to work together to produce a steady force. But there are unanswered questions about this system.

We first devised a mathematical model of muscle. Real muscle motors (myosin) run on a complex chemical cycle, but we didn’t try to reproduce that chemistry. Instead, we represented each force producing unit as something much simpler: a cycling “rotor” (or motor) that can contribute only when it is mechanically engaged to a physical “backbone”. When it is disengaged, it effectively drops out, and becomes isolated from the rest.

To test whether we could translate these ideas to a real physical system, we built a tabletop device: a line of simple electric motors interacting through a shared mechanical backbone. They pulled against a thick fluid, creating resistance similar to the viscous load experienced by biological motors inside muscle.

Our physical model produces force when supplied with current. We wanted to see whether, when many driving units were embedded in the right kind of mechanical setting, could the setting organise into coordinated muscle-like behaviour?

In both the mathematical model and the device, the motors don’t stay connected all the time. Instead, each motor briefly makes contact with a shared moving backbone, pushes on it, then lets go. As the backbone moves, it carries the mechanical effects of one motor to the next, allowing them to coordinate without ever communicating directly.

When they are not connected, each motor acts on its own. That single design choice makes the whole network of motors reorganise itself over time – much like biological motors do inside muscle, where motors attach and detach as part of their biochemical cycles.

In practical terms, the scaffold where the rotors live is doing a lot of the coordinating work, but it is hidden. It mediates how units can “feel” one another strongly enough for collective patterns to form.

Even in this stripped-down system, several muscle-like forms of behaviour spontaneously appear. As resistance increases, the motors collectively develop a load-speed relationship. Load refers to the mechanical tension and physical stress placed on the system.

More units end up contributing effectively as the load rises. Their participation increases under increasing loads. We also found that under certain conditions, motion can become jerky. For example, periods of coordinated pulling were interrupted by sudden slips. This echoes patterns seen in real actomyosin experiments.

Instead of everything working in unison, the system can self-organise into travelling waves of coordination, where the pattern moves along the array.

In a loose way, this “scales up” the core idea behind a sarcomere: many force units embedded in a shared structure that transmits forces from one unit to the next, allowing coordination to emerge spontaneously.

Coordinating without a conductor

Muscle isn’t the only place where nature has solved the problem of coordinating many tiny motors. Microscopic flagella are tail-like appendages seen on the cells of some eukaryotes – the group of organisms that includes animals and plants.

Examples of these flagella include the tail on human sperm. Cilia are microscopic, hair-like structures on many eukaryotic cells. Both flagella and cilia are powered by large numbers of molecular motors distributed along a long, slender scaffold.

Their internal core – the axoneme – is a beautifully organised structure that transmits forces along its length and turns local motor activity into bending waves. Those waves are essential: in sperm they underpin motility and fertility, and in cilia they help move fluids in the airways and other tissues, with clear links to health and disease.

In that sense, flagella are “muscle-like” systems in disguise. You again have drivers (motors that generate force) embedded in a coupling structure (a mechanical scaffold that transmits force and feedback). And the geometry determines how local activity adds up to a coherent, travelling wave.

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Read more: The way a sperm tail moves can be explained by mathematics worked out by Alan Turing

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If the structure and layout are doing some of the organising work, it speaks to a question that has long intrigued scientists: how evolution builds systems made up of a large number of individual motors that nevertheless coordinate themselves, remain reliable and adapt to changing conditions without a central control system?

Uses in robotics

Our results matter beyond biology. In robotics, getting many actuators (the “muscles” of a robot, which convert an energy source like electricity into motion) to cooperate is challenging and expensive. You add sensors, feedback loops, control algorithms, sometimes even mechanical ratchets or complex gearing to keep everything stable under loads. It works, but it can be fragile and prone to errors.

Our physical model of muscle, where coordination emerges as a property of the setup, suggests that simpler actuators for robotics are possible. This is exactly the kind of fundamental design that researchers have been chasing for soft robots (which use flexible materials) and artificial muscles for robotics.

Muscle may be an example of coordination without a conductor. Thousands of molecular motors working together, not because each one is individually controlled, but because the soft structure around them helps guide their collective behaviour.

Our findings suggest that robotics could begin to harness the same principle. In future, this could help bring robotics closer to biology, opening the way to more organic machines, whose bodies help shape how they move and adapt.

This research was carried out by Benjamin Warmington as part of his doctorate under the supervision of Prof Jonathan Rossiter and Dr Hermes Bloomfield-Gadelha at the Polymaths and Soft-Robotics laboratories at Bristol. This research was funded by the Engineering and Physical Sciences Research Council Doctoral Training Partnership (grant no. EP/T517872/1) and carried out using the computational facilities of the Advanced Computing Research Centre, University of Bristol—http://www.bristol.ac.uk/acrc/.

AI companies and evangelists frequently use terms such as co-pilot, co-worker, co-creator and co-scientist to imply these rapidly developing systems can happily co-exist with us humans. Their inference is clear: people should always be in the loop – the AI is never in charge.

But amid stories of AI models going rogue and the prospect of recursive self-improvement (when AIs can improve themselves), is this form of language still appropriate – or a linguistic smokescreen we should be wary of?

In my career researching science, language and society, I have studied the use of “co” and other linguistic trends used by AI companies to describe and market their products.

The humble prefix “co”, derived from the Latin preposition “com”, means “together”, “mutually” or “in common”. But what seems linguistically unremarkable can, in the modern context of AI systems, convey a lot of interesting – and potentially misleading – meanings.

While “AI assistant” implies a subordinate role and “AI agent” real autonomy, “co” evokes shared agency and joint ownership of a task. This makes it strategically ambiguous.

An AI co-product can be sold to investors as capable and autonomous, thus justifying its price and ambition. At the same time, it can be sold to users and regulators as safely subordinate, reassuring them that a human is still in control.

Copilot takes off

One of the most interesting uses of “co” to convey a particular message about AI products is Copilot – the name of Microsoft’s suite of AI models. In aviation, a copilot supports the pilot but isn’t in command.

The Observer newspaper’s technology journalist John Naughton has called copilot “the tech industry’s favourite metaphor”, because it lets the human feel in charge while the tool actually fosters dependency.

The word entered AI discourse as a metaphor when GitHub, a coding platform, released an AI coding tool called GitHub Copilot. This was built in collaboration with OpenAI – the company that a year later became famous overnight when it released ChatGPT.

GitHub used the name Copilot to metaphorically frame “pair programming” – in the sense of the AI model being a second coder sitting beside you, assisting but never taking the wheel. GitHub foregrounded this aviation metaphor with the headline: “Command your craft”.

By 2023, Microsoft (which owns GitHub) had rebranded a dozen products – from its search chatbot to Windows and its Office software – under the Copilot name. (According to the tech news site Ars Technica, the joke inside Microsoft was that it would need a branding copilot simply to keep track of all these products.)

Around the same time, “co-intelligence” entered wider use as the title of a book by prominent American AI researcher Ethan Mollick. This book, a New York Times bestseller, stretched the metaphor from product branding into a general way of talking about living alongside AI as co-worker and co-creator, sharing a form of co-intelligence.

Not long after, Microsoft researcher Advait Sarkar and colleagues wrote a paper suggesting that generative AI could see one aviation metaphor, copilot, being replaced by another: autopilot. They warned that “powerful AI capabilities may lead humans to disengage from critical thinking and simply defer to AI output … causing knowledge work to go on autopilot.”

Rather than sharing responsibility for the output of these models, the implication was that in an increasing number of cases, no one was really at the controls.

AI anthropomorphism

A 2026 study found rising anthropomorphism – the assignment of human traits and emotions to non-human things – in how people view and talk about AI tools. This suggests techniques such as the “co” framing have been persuasive, not just decorative.

Metaphors can make even the very unfamiliar – such as black box AI systems – seem familiar. In the case of copilot and other co-words, they are used to sell systems to industry and ordinary users in different ways. Google DeepMind’s Co-Scientist, for example, is framed as a research partner even though it operates as something closer to a team of AI agents working with minimal oversight.

Perhaps tellingly, Mollick’s follow-up to Co-Intelligence, due out in October 2026, is entitled Co-Existence: The Next Phase of AI. This might indicate that the conversation has moved from whether to how we should live alongside something that is now established beyond the power of human control.

Three years ago, computer scientists Abigail Sellen and Eric Horvitz – who also have key roles within Microsoft – called for “designs for human-AI partnership that cede ultimate control and responsibility to the human user as pilot, with the AI co-pilot acting in a well-defined supporting role”.

But that view of human-AI interaction already feels outdated. Recently, during an internal evaluation by US company Open AI, a pre-release AI model managed to hack into another platform in an incident variously described as the AI agent “escaping” or “going rogue”.

Once again, there is a linguistic point to note here. By portraying AI agents as autonomous actors, such language points the finger of blame towards the agent, rather than the companies who build and operate them.

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Brigitte Nerlich 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.

Summer can be lovely – until a wasp turns up. Whether they present as an irritating picnic visitor or an uninvited loft guest, common social wasps are as much a part of our summer as festivals, sun cream and the growing despair of how to entertain children who’ve forgotten how to be bored.

But summer 2026 is breaking the norm, and there don’t seem to be many wasps about. As a wasp biologist, I’ve noticed it acutely, because my students need wasps for their experiments, and we simply can’t find them.

A second sign that something is wrong is the absence of wasp stories in the media. Wasps tend to be more active in warmer weather. Their colonies grow faster and bigger, and they end up making news headlines by bothering humans. This time last year – also a warm spring and hot summer – the media airwaves were filled with “plagues of wasps” stories. Now, even professional pest-controllers tell me it’s a quiet year with very few requests to remove wasp nests.

Admittedly, these are anecdotes not data: the Big Wasp Survey – the only national wasp monitoring survey – is yet to start sampling this summer. But the signs are not good.

If data back this up, their absence worries me greatly because – love them or hate them – wasps are important in ecosystems as pest-controllers, pollinators and decomposers.

So what’s happening?

Spring started well for wasps. In the warm, dry spring a glut of glorious foundresses – new queens – emerged from hibernation to start their nests alone, carrying out all the nest building, egg laying, foraging and brood rearing. This nest-founding period is a risky time. If a new queen survives until her first brood emerges as adult workers, though, then the odds are in her favour as she no longer needs to undertake risky foraging.

But spring 2026 was strange: the warm, dry start should have been good. Warm, dry spring weather improves a nest’s chance of survival as it ensures a good nectar supply for the queen to develop her ovaries and good prey populations for her to feed the brood. But we experienced unprecedented heat for the time of year, punctuated by cold, damp days and followed by unexpectedly chilly nights.

Such weather extremes may have caused queens to abandon their nests or die, reducing the initial spring queen bounty to a more modest population.

Temperature tolerance

And then came the heatwave. And another. And another. Some insects may benefit from this. Wildlife charity Butterfly Conservation is hoping 2026 could be a “bumper summer of butterflies” because warm, dry days mean uninterrupted foraging, mating and egg-laying.

But all insects have a thermal tolerance called critical thermal maximum (CTmax). If their CTmax is exceeded, insects succumb to desiccation, muscle spasms and eventual stupor.

For honeybees, this is around 49°C – which might sound high, yet ground surfaces exceeded 50°C in the UK heatwaves this summer. But the temperature doesn’t need to reach an animal’s CTmax to affect their productivity and behaviour. For example, bumblebee cognition and foraging efficiency are impaired by heatwaves of 32°C.

Bumblebee Conservation Trust raised concerns that 2026 may exacerbate a declining trend in bumblebee populations as flowers wither and bees fail to forage effectively.

Wasps are thought to be quite resilient to extreme weather. They are one of the most successful invasive species around the globe. And as a social insect, their thermoregulation behaviour (such as wing fanning to cool the larvae) and nest architecture (which has insulating envelopes surrounding the brood combs) may help buffer any environmental perturbations.

Insects also have waxy molecules called cuticular hydrocarbons that coat their exoskeletons protecting them from desiccation. Social wasp cuticular hydrocarbons change structure in response to heat, better protecting them from desiccation in excessive heat.

Their varied diets should also buffer them from imbalances in prey populations. However, their CTmax may be lower than other social insects – at around 45°C – which may have made them especially vulnerable to the high ground temperatures of the 2026 heatwaves.

Why are the wasps so small?

There’s something else that I’ve noticed: the few wasps I’ve seen are rather small. The adult size of many insects is fixed during their development, depending on the quality and quantity of food they receive as larvae. Wasps don’t get bigger once they’ve pupated.

The first workers to emerge in spring are usually small because the queen is limited in how much food she can provide for them alone. But by early August, the foraging capacity of the colony has increased exponentially, brood are better fed and the emerging workers are substantially larger.

So why are we still seeing “spring-sized” wasps? It’s possible that limited diversity and abundance of prey may mean their nutrition is restricted. The problem may also be more directly linked to heat. Insect larvae reared at higher temperatures will develop faster and reach pupation at a smaller size.

Global warming is thought to explain the trend in social wasps shrinking in size over the last 100 years. We could be seeing this in real-time this year.

How can you help wasps?

Share your alfresco meal with a wasp. When it approaches, don’t flap it away. Watch to see what it wants. In the absence of wild prey, it might be desperate for some protein (your picnic ham) to feed her brood. In the absence of flowers for nectar, it might be in need of something sugary (some jam) for her own nutrition.

My research team would also like you to share your wasp observations with us, so that we can better understand these important insects. Snap a photo of her eating and submit your data to the Wasp Picnic Survey 2026: Ham or Jam?. Keep the observations coming all summer. The wasps (and wasp scientists) will thank you.

Seirian Sumner receives funding from the UK government's Natural Environment Research Council (NERC) and the Biotechnology and Biological Sciences Research Council (BBSRC). She is a Trustee and Fellow of the Royal Entomological Society, and author of the book 'Endless Forms: Why We Should Love Wasps'

The European Commission’s decision to fine Google €890 million (US$1 billion) for failing to comply with the EU Digital Markets Act sounds like a significant financial and political intervention against one of the world’s largest technology companies.

The ruling is certainly further evidence of Europe’s desire to regulate US big tech platforms more strictly.

But can such fines really change the behaviour of companies like Google, whose global resources and technical capabilities substantially exceed those of the institutions responsible for regulating them?

Alphabet, Google’s parent company, reported revenues of approximately US$402.8 billion (£302bn) for 2025, and a net income of US$132.2 billion. The EU fine represents less than 0.3% of one year’s revenue.

This makes it, simultaneously, one of the largest penalties imposed under the Digital Markets Act (DMA) and a financially manageable cost for the company receiving it. But that does not mean the penalty is irrelevant. Fines can also carry reputational costs, establish legal boundaries, and create a basis for more serious sanctions.

Europe has been here before

The European Commission has been here before. In 2004, after a five-year investigation, it fined Microsoft more than €497 million for using its position in PC operating systems to restrict competition in adjacent software markets.

Just under a decade later, the same company was fined a further €561 million, after it failed to provide the browser-choice screen it had committed to making available to European Windows users. This judgment was significant, as it showed the initial legal decision was only one stage of the commission’s enforcement process.

Google has since become the principal focus of EU technology concerns. The commission opened proceedings over the company’s comparison-shopping practices in November 2010, finally imposing a €2.42 billion fine in June 2017. A separate three-year investigation into restrictions associated with Google’s mobile operating system, Android, resulted in a €4.34 billion fine in July 2018.

Brussels has used the DMA against other big tech gatekeepers too. In April 2025, it fined Apple €500 million for breaching its anti-steering obligation – which forbids online platforms from blocking out cheaper rival deals – and Meta €200 million in connection with the choices it offered users about their personal data.

While these decisions demonstrated the commission’s willingness to intervene, they also established a recurring pattern: an investigation is opened and a substantial fine is imposed. Then, the company in question introduces a series of technical or contractual changes, only for a further dispute to emerge over whether those changes amount to effective compliance.

In short, the central difficulty is not the absence of legal authority. It is the translation of that authority into timely, measurable changes in market behaviour.

Do fines change big tech behaviour?

There are three main ways in which the effectiveness of a fine such as this can be assessed:

1. Formal compliance: has the company amended its contracts, interfaces or internal policies in response to the decision?

2. Market effectiveness: have the changes given competitors a meaningful opportunity to reach customers, enabled app developers to offer lower prices, or increased consumer choice?

3. Deterrence: has the penalty made Google – and other gatekeepers – less likely to engage in equivalent conduct through future services, product designs or contractual arrangements?

A 2025 study examined the implementation and effectiveness of company responses in 12 major EU antitrust cases. Although most of the imposed remedies had been implemented, fewer than half were found to have been fully effective.

So, it’s not enough to ask whether Google will change the appearance of a search page or amend the wording of its developer terms in response to this latest fine. The commission must assess whether, after Google’s 60-day compliance period, competing services receive meaningful visibility, rival companies can communicate with customers without excessive friction or fees, and consumers are genuinely able to exercise greater choice.

A criticism levelled at regulators in many jurisdictions is that investigations are too slow to curb anti-competitive behaviour. Article 29 of the DMA states that the European Commission should adopt a non-compliance decision within 12 months of opening proceedings.

This is an aspirational timetable, rather than an absolute legal deadline. Nevertheless, the present Google investigations were opened on March 25, 2024 and concluded on July 23, 2026 – approximately 28 months later.

A January 2026 consultation highlighted complaints about the DMA’s slow processes and limited transparency, as well as circumvention and delaying tactics on the part of big tech companies. Suggested changes included binding procedural timelines, independent audits, and public testing of the remedies imposed by the European Commission.

The speed of technological change

When the EU’s latest proceedings against Google began in March 2024, much of the digital competition debate concerned the ordering and presentation of conventional search results. During the investigation, however, Google continued to develop AI Overviews and AI Mode, which offer users AI-generated answers, summaries and recommendations rather than a traditional page of ranked links.

This creates a significant enforcement problem. Self-preferencing can occur not only through the vertical ranking of links, but through the selection of sources, the framing of an answer, the services through which a transaction is completed and the options presented to the user by an AI agent.

Accordingly, the issue should not be whether a rival service has been given a particular box on a search page. It should be whether competing services have a fair and effective opportunity to be discovered, selected and used, irrespective of whether the interaction takes place through a conventional search result, an AI-generated answer, or an automated purchasing agent.

The commission’s ruling announcement recognised this rapid technological development. It also noted that Google had submitted proposals concerning how the principles of the decision should apply to AI Overviews and AI Mode, and that discussions on these services would continue.

Ultimately, the fine represents an early test of whether the DMA can remain relevant as the technology changes. It is this, not the size of the initial fine, that will decide whether Europe really can keep big tech in check.

Iain Nash 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.

Deletion is a common part of modern life. We send files and folders to the recycle bin all the time, and often get rid of unwanted personal accounts. But do you know what really happens when you hit the “delete” button?

The process of deletion is often poorly explained by tech providers. Lack of transparency around how requests are processed, and the absence of clear confirmation that data has been removed, are frequently highlighted in research.

These issues are not limited to operating systems. On social media platforms and a wide range of subscription services, deletion mechanisms are often just as unclear. Such misconceptions can have serious implications for the wellbeing of users, exposing them to security and privacy vulnerabilities.

A further complication is that many users fear losing data, files, photos and videos through accidental deletion – a condition dubbed “diagraphephobia”. Though this is not a recognised pathology, studies of digital hoarding behaviour show that some people get very anxious at the thought of losing or accidentally deleting personal information such as photos and music.

Such concerns extend the amount of personal data that sits untended but undeleted, ready for potential misappropriation.

Deletion vs erasure

A common misconception is thinking deletion means erasure – the idea that once a deleted item is no longer visible, it has been completely erased with no means of retrieving it. This is largely wrong.

When users delete files – moving them to the trash bin, then deleting permanently – the data isn’t actually erased. Rather, the system marks storage space as reusable and updates metadata to unlink the file. But the underlying content often remains recoverable with the right tools, especially when data is duplicated across multiple systems or devices.

More robust forms of deletion include physical destruction of the storage medium, overwriting the memory blocks with new data to bury the original, or encrypting the data and then destroying the key.

However, such methods can be expensive and may render the storage device unusable (for example, when overwriting data on magnetic media). The characteristics of cloud infrastructure also pose challenges to secure data deletion.

Similarly, content on public social networks may not really be erased after deletion due to replies, comments and internet archives which can all store the posts in some fashion. The meaning of a deleted tweet can, for example, be recreated based on replies and mentions.

Zombie accounts

Another misconception is that deleting an app from a device automatically deletes the associated account. Users often leave mobile app accounts undeleted because they are unaware of their existence, having deleted the related app.

These are called zombie accounts – abandoned profiles for a wide array of services, from shopping and storage to dating, finance and streaming. Millions of users possess zombie accounts, with personal data that is vulnerable to cyber-attacks and data breaches.

The exponential growth of AI raises a further question: can data really be deleted once it has been used to train AI models? Machine learning modules easily memorise the data they have been trained on, but “unlearning” is difficult.

In theory, people in Europe and many other countries around the world have a leagal “right to be forgotten”. This means they can request the full erasure of any personal data a platform or a company may hold. AI developers are considered data controllers under the EU’s General Data Protection Regulation (GDPR) laws, for example, and are subject to this obligation.

However, there are no clear guidelines on how erasure should be enforced within AI systems. Regulators may request deletion, but AI companies can argue that compliance is infeasible on account of technical constraints.

Explainable deletion

To counter the risks posed by incomplete deletion, I believe there is a pressing need for provision of concise, accessible and clear information about how deletion really works.

One potential approach is “explainable deletion” – a protocol developed by Marvin Ramokapane at the UK’s National Research Centre on Privacy, Harm Reduction and Adversarial Influence Online (Rephrain), based at the University of Bristol.

The intention is to make deletion processes more transparent and understandable without overwhelming the user with too much information in one go.

Explainable deletion breaks down information into six categories: what, how, when, who, where and why (see diagram). Each offers a user bite-size information about that part of the process.

Explainable deletion’s six categories:

Explainable deletion is designed to give users control over their actions, the autonomy to choose the deletion type that is right for them, and the assurance that their desired actions have been fulfilled. It may also tackle the anxiety of accidentally losing data by giving options for data recovery.

While explainable deletion has not yet been adopted in practice, service providers – both platforms and developers – could heighten user trust by adopting such protocols. This framework can also be proof of compliance with GDPR regulations and the right to be forgotten.

Most of us use systems that collect and store our data on a daily basis. If these systems clearly explained what they store and what deletion really means, it could help us to spot the accounts and data that pose a real risk – and to take back control of our digital footprints.

Dana Lungu 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. This article draws on research on 'explainable deletion' by the University of Bristol’s Marvin Ramokapane.

Built from stones weighing between 2 and 25 tonnes, the structure of Stonehenge demonstrates a scale of construction hard to imagine before the invention of the wheel. The mystery deepens when you consider that the stones are not from the local bedrock. So why these stones, and how did they get there?

Recent work I carried out with geochemist Anthony Clarke from the University of Curtin, Australia, might have an answer to these two questions for the most far travelled stone of Stonehenge – the Altar Stone.

The Altar Stone was sourced from 700km away in north east Scotland, from a region of bedrock geology known as the Orcadian Basin that was once a lake called Lake Ocradie. The sandstone of the Altar Stone was formed from these lake sediments. Now, new work by our international team has looked to test the possibility it was transported by glaciers.

It is the largest and heaviest of the so-called bluestones (Stonehenge’s travelled rocks), weighing 6 tonnes and measuring 4 metres long. The bluestones were the first stones of Stonehenge predating the larger stones which form the arches by 2,000 years.

We know the north of the UK was covered by ice until approximately 15,000 years ago at the end of the last ice age. This ice was so thick and persistent that it flowed like a slow moving river, picking up and carrying rock and debris with it. The team looked to test if it was possible for the rocks from the Orcadian Basin to travel closer to Stonehenge if transported by this body of ice.

We studied this by using a new, numerical model, which I have been developing over the last five years for predicting the possible deposit locations of the glacially transported rocks (known as erratics). This showed that rocks transported from the Orcadian Basin could be transported to a region off the east coast of England called Dogger Bank.

Although today Doggerland is known for cod fishing and wind farms it is thought to have once been a neolithic site of significance as early humans moved from southern Europe north and west into the UK as the climate warmed at the end of the last ice age approximately 12,000 years ago. Dogger Bank represented a local topographic high point within the Doggerland region, rich in archaeological evidence.

What is interesting about Dogger Bank is that it is formed in part by a large glacial moraine (a ridge of rock and debris deposited by a glacier). This moraine marks the end of a glacial “conveyor belt”, where a range of glacially transported rocks and boulders from the north would have been deposited during the last ice age.

Walking around Dogger Bank at the time, the site would probably have looked like something in between a building demolition site and an interesting rock museum. We know Neolithic people were really knowledgeable about rocks, and were selective about the stones they chose for tools. Which suggests they might have deliberately chosen the Altar Stone for some of its attributes.

More recently there are countless examples of people realising the landscape’s glacial erratics were out of place. The Victorians in Britain for example chose the name “erratic” due to the rocks’ unusual locations (at the time they were thought to be the result of the biblical flood). On the other side of the North Sea, Scandinavians know erratics as “troll stones”, having realised these boulders came from the upland mountains. They used to think the only explanation was that they had been thrown hundreds of kilometres by playful trolls with the early Swedish term for erratics (jättekast) literally translating to giant throw. It’s hard to say what exactly made the Neolithic people of Dogger Bank choose the Altar Stone. But if it was transported by ice to Dogger Bank they would have needed to move it at least once before taking it 400 km to Stonehenge.

Doggerland was gradually flooded as large ice sheets around the world melted at the end of the last ice age. We know it stayed above sea level until around 8-9,000 years ago (perhaps even as late as 7,000 years ago). However, throughout that time the landscape saw dramatic change. It transitioned from a land bridge connecting Europe to Britain, to a small island, where the Altar Stone stood. This island eventually slipped below the sea too.

This is interesting because Doggerland was inundated before the construction of Stonehenge. Therefore, the Altar Stone would have had to be transported to at least one intermediate site before finally being incorporated into Stonehenge.

Once the Altar Stone was safely on the east coast of England, the idea that it was moved by humans to Salisbury Plains becomes more plausible. This distance would be roughly the same distance as the other bluestones, the next most far travelled stones, but still ten times the distance of the more locally sourced larger Sarsen Stones. Around the time of the construction of Stonehenge, we know that east-west travel was happening along the Berkshire Ridgeway, a neolithic high route often touted as the oldest road in Europe, leading directly to the Salisbury Plains.

Our recent study opens up a new hypothesis for how the Altar Stone could have travelled south to Stonehenge, and why people might have been motivated to first move the stone in response to rising sea levels threatening its initial site.

This work suggests that neolithic people not only had the agency and coordination to move a large rock hundreds of kilometres, but also to potentially realise that their landscape was changing and that they needed to act.

Remy Veness has historically received funding from NERC.

Evolution is a fact of life. So much so that even the theory of evolution is evolving. Lately, a centuries-old discredited idea has been making a strong comeback. It helps us explain scientific observations that according to standard evolutionary theory should be impossible.

I’m referring to Lamarckism, named after theoretical proposals from French biologist Jean-Baptiste Lamarck in 1809. Lamarckism stands for the hypothesis that parents of a given organism may acquire or improve certain characteristics during their lifetimes in response to the demands of their environment. And they then somehow transmit those characteristics to their offspring, making those better able to cope with the same demands too.

Lamarck sought to explain why organisms and their environments fit so well together, and how organisms change over time, as observed in the fossil record. Of course, an alternative explanation was provided by Charles Darwin (and others) half a century later. Darwin’s ideas were built around natural selection, which says that some individuals within a species are born with differences that lead them to perform better and therefore reproduce more than others.

Darwin argued that if these differences can be passed from parent to offspring, the next generation will contain relatively more of these higher performing individuals. In other words, the next generation will have a greater fit with their environment.

Over time, Lamarck’s idea was discredited. A key moment was an experiment performed by Germany’s August Weismann in 1904. He surgically removed the tails of adult rats before allowing them to reproduce, expecting to see this change inherited by their offspring. But it never happened, despite him removing the tails of many generations of rats. Lamarckism as understood then would have predicted that the offspring should have been born with no tails, so for many years biology textbooks presented his theory as a firmly refuted hypothesis.

Where Darwinism falls short

Lamarck’s ideas weren’t entirely wrong, however. Take, for example, the small worm C elegans. When an adult worm is exposed to external stimuli such as viral or bacterial infections, starvation, or stressful temperatures, it can adapt to cope better over time. And, surprisingly, its subsequent offspring also cope better from the moment they are born – as do successive generations.

It turns out that this occurs via the inheritance of DNA-like molecules called small RNAs (ribonucleic acids). When adult worms experience stress, they produce these RNAs to fine-tune the activity of their genes to mount an effective response. When the worms reproduce, specific proteins transfer these small RNAs to their eggs, meaning the offspring are born with the benefits they provide.

This is like a person receiving a vaccine against flu or measles, developing immunity in response, and then all their newly born children and even grandchildren being immune too. It was previously thought that these kinds of epigenetic molecular marks were always erased during reproduction, meaning offspring have to start their own response from scratch. The fact that acquired traits can actually be inherited is clearly Lamarckism in practice.

It’s also far from unique. There are now dozens if not hundreds of studies showing similar effects in all sorts of species, from bacteria and plants to animals. When rice plants are exposed to cold, for instance, they develop a seemingly permanent resistance by adding chemical tags to their genes. This has enabled them to spread northward from their tropical origins.

Beyond epigenetics, the passing down of learned skills from one generation to another is also a form of Lamarckism. When killer whale mothers acquire the skill to hunt a different type of prey – for example, seals instead of salmon – this skill is copied by their offspring. This has been so reliable across generations and so profound that it even seems to have caused the formation of different species of killer whale.

Does this mean Darwinism is wrong? No, it doesn´t. Natural selection still accurately explains evolutionary processes a large proportion of the time. But as I argue in my new book, The Selective Organism: an Expanded Theory of Adaptive Evolution, we could see Lamarckian acquisition of traits as a second driver of evolution, acting alongside natural selection.

Whereas in Darwinian evolution individuals in a species are a target of natural selection, in which their traits are tested by their environment, in Lamarckian evolution they’re an agent of selection, developing certain traits out of a larger set of potential ones. For instance, the C elegans worm could potentially produce a number of different RNA molecules (or none). Based on its environment, it selects molecules that help it to survive. In that sense, Lamarckian acquisition can be seen as a form of selection too.

How the theories combine

In my book I also make another argument. When an individual plant or animal is able to transmit acquired beneficial traits to its offspring as per Lamarckism, this should increase the offspring’s chances of survival and subsequent reproduction.

If so, this capacity should also be favoured by natural selection. In other words, Darwinism actually predicts that this Lamarckian capacity should evolve. This means that these are not competing theories but compatible ones, which together provide the best explanation for the evolutionary change of organisms.

Evolution by natural selection has been called “the single best idea anyone has ever had”. Besides explaining the natural world, it has found tremendous application in other areas. It has helped us improve how we breed plants and animals, for instance, and how we combat the evolution of antibiotic resistance in microbes.

The Lamarckian dimension should only further improve these applications. If the exposure of organisms to particular soil, climate, parasites or predators can benefit their offspring, there may be new ways to improve agricultural production. The same goes for conservation: scientists in Australia are already exposing coral to heat stress in the laboratory to trigger epigenetic responses that make them more resilient to heatwaves.

And if human parental behaviours can influence obesity or addiction in their children, then prevention programmes help not only the people that receive them but also future generations.

So after more than 200 years it is time to embrace Lamarckism again, and include it into an updated and improved version of standard evolutionary theory.

Pim Edelaar 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.

Elon Musk has promised that before the year is out, his AI tool Grok Imagine will produce a full-length film of Homer’s Odyssey that is, in his words, “historically accurate and true to the art of Homer”.

The claim, posted on X in July alongside a three-minute AI-generated scene, arrived days after Christopher Nolan’s blockbuster version reached cinemas worldwide – a film Musk has spent months attacking for being “woke”.

Set aside, for a moment, that The Odyssey is a 2,800-year-old poem that includes a witch who turns men into pigs and a one-eyed giant shepherd, to ask a simpler question: accurate to what?

There are no photographs of ancient Ithaca. There is no footage of the Trojan shore. What we have is stories, poems, pottery and paint, and, more recently, a century of films that imagined the ancient world for us. That is the only material a generative model has ever seen.

So when Grok Imagine – the AI image generator developed by Musk’s xAI company – renders antiquity, it is not reaching back into history. It is reaching for the centre of every image already made about it – the most probable ancient warrior, assembled from Gladiator, Troy and 300 and a hundred years of sword-and-sandal cinema. The result will not look like the past. It will look like other films about the past, averaged out by AI.

And here is the part that should give anyone bandying about the word “accurate” pause. A machine that reaches for the most probable image does not correct our clichés about antiquity. It concentrates them. Ask it for a classical statue and it will hand you gleaming white marble, because that is how we picture the ancient world.

Except the ancient world did not look like that. Greek and Roman sculpture was painted in strong colours – the pristine white marble in our heads is a later idealisation, not the thing itself. An AI model trained on our pictures of the past will reproduce our mistakes about the past – confidently – and give them back to us labelled as truth. That is the opposite of accuracy. It is our own error, upscaled.

The real problem

This is why “historically accurate” is not really a script problem. It is an image problem. Accuracy on screen is delivered through light, lens, skin, texture, cloth and grain – the hundred deliberate decisions a director of photography agonises over in every frame.

A machine makes those decisions too, but it makes them by consensus, by defaulting to the most probable choice. Research on generative AI has already found that it makes individual work feel more creative while making the whole field more similar, everything drifting toward the same middle. The same regression to the mean that flattens prose will flatten the picture. You do not get the accurate past, and you do not get the thrilling lie. You get the average, and the average is where nothing memorable lives.

Which brings me to escapism, and to a tension in my own work I am happy to admit. I research and teach sustainable, naturalistic image-making – stripping away artifice, working with available light. But I have also argued, including in a TED talk, that escapism matters, and that realism is often its enemy.

Take everything excessive and dangerous out of James Bond and you remove the reason anyone watches. Nolan’s Odyssey is not trying to be accurate. It is trying to be exciting – and excitement is a decision. It is a point of view, a piece of the filmmaker put deliberately on the screen.

Heightening is a choice about what to exaggerate, and a choice is exactly what an average cannot make. A machine that lives at the mean can smooth, but it cannot heighten. So the “exciting” AI blockbuster arrives with the one quality excitement depends on already removed.

There is a reason the AI clips flooding our feeds feel flavourless even when they are technically clean. It is not that the tools are young and will improve. It is that averaging is what they do. Watch a real performer and you are watching a human being decide – take a risk, make a choice you might not have made – in front of you.

That is the thing we actually go to films for, and it is precisely the thing an average has no way to contain. Whether audiences keep noticing is the open question. The appetite to make this work is real, because it is cheap. The appetite to watch it is the thing nobody has tested yet. And the unease many people already feel watching AI slop and struggling to say why it repels them, is not naivety. It is a correct response to work that no one made decisions about.

So what is the endgame? Not, I think, simply proving it can be done. If you only wanted to prove a technical point you would not reach for the word accurate – least of all for a poem, where accuracy means nothing. Reaching for accurate is reaching for authority: the right to say what is true.

Musk is a man who publicly objected to the casting of a black actor as Helen of Troy and a trans actor as a Greek warrior. A man who operates within a wider political project about who gets to tell the national story, now claiming that a tool he owns can portray the past as it truly was. Follow that to its end. If his machine decides what is historically accurate, then he decides what the past looked like. And whoever is trusted to author the past is well placed to author the future.

So watch this one with scepticism – not because AI cannot make images, it plainly can, but because “historically accurate” is a claim about truth. And truth is exactly what a machine trained on the average of everything cannot give you. It is also exactly, in this case, what its owner would most like to define.

Jack Shelbourn has received funding from the British Film Institute; the National Lottery; HEIF/University of Lincoln; CoASSH Scholarship, Professional Practice & Research Resources Allocation Fund; and College of Arts Teaching and Learning Fund.

Think back to your time at school. Were you hard-working, popular, anxious? And what exam grades did you get? Now, imagine someone pulls out your school reports from that time. How closely would they match the results you just brought to mind?

If your recollections match exactly, you might congratulate yourself on having an excellent memory. But as a psychologist who has spent years researching what people remember – and misremember – my work often challenges the idea that the “best” memory is always the most accurate one.

In fact, some of the distortions commonly regarded as memory failures may be signs of a healthy and adaptive memory system. In many aspects of life, research suggests people misremember their past in predictable, self-protective ways, reshaping experiences to support their current, positive sense of self.

For example, a 1996 US study investigated how former pupils recalled 3,220 high-school grades. The study found they accurately remembered A grades 89% of the time, but D grades only 29%. Most of the errors saw grades misremembered as higher, suggesting many people carried with them an illusion of better academic performance.

Similarly, participants in a 2006 study underwent cholesterol screening, then were followed up months later to see how accurately they remembered the results. Most recalled having lower-than-reality cholesterol, especially those with the highest readings.

Positive memory biases such as these generally become more common as we get older. This is theorised to contribute to the well-documented positive relationship between age and wellbeing. And yet this kind of memory distortion is often regarded as a failure.

In law courts, hospitals and other high-stakes settings, of course, such errors can have serious consequences. For example, inaccurate eyewitness testimony is the leading contributor to wrongful convictions overturned by the Innocence Project.

But in everyday life, memory’s flexibility – and fallibility – are central to how it helps us survive and thrive. Memory is not a recording device. It is selective, reconstructive and shaped by what matters to us now.

So, what if today’s “total recall” technology is creating a memory system we do not need or benefit from?

The benefits of on imperfect memory

According to the late British psychologist Martin Conway’s self-memory system, autobiographical memory is not simply a storehouse of past events. It is a system that maintains a sense of self that is coherent, stable and generally positive.

Thus, when knowledge or feelings change over time, people often reconstruct memories to be more consistent with their present selves.

Psychologist Linda Levine and colleagues found this in a 2010 study of people’s memories of their reactions to the O.J. Simpson trial. As opinions about the killing changed over time, people tended to remember their original reactions as being closer to their current beliefs than they really were.

Many long-term relationships benefit from partners having imperfect, evolving memories. In a 20-year longitudinal study, satisfied spouses often remembered the earlier stages of their marriage as less happy than they had reported at the time, creating an illusion of improvement that predicted future relationship satisfaction. Certainly, long-term love would be difficult if couples retained every slight and badly phrased sentence in perfect detail.

Depression offers a revealing contrast. Compared with non-depressed individuals, people with depression often show less of the positive bias that characterises everyday remembering.

A memory system that is less inclined to soften failures, reinterpret setbacks or highlight positive experiences may be more faithful to the past. But it can also make it harder to maintain wellbeing and hope for the future.

A form of memory we may not want

This is why modern technology raises difficult questions. Much of our digital world is built on the assumption that more memory is better memory. Phones preserve thousands of images. Fitness watches record steps, sleep, heart rate and calories.

The arrival of AI assistants may further accelerate this shift, as systems that can instantly search, summarise and re-present years of messages and photographs influence how we remember.

There is some evidence that more remembering is not always better. People with the very rare condition of highly superior autobiographical memory can recall extraordinary detail from decades of their lives. However, many describe the experience as exhausting, with memories resurfacing vividly and involuntarily – making it much harder to move on from negative life events.

We are beginning to see examples of how technology-supported remembering can produce similar consequences, particularly in the context of grief, where platforms unexpectedly resurface photographs, birthdays or posts from people who have died.

AI systems can create intimate forms of digital remembering. Imagine asking an AI assistant what your relationship was like three years ago and receiving a summary generated from thousands of messages, photographs and calendar entries.

Humans have always used tools to remember, from diaries and monuments to photographs, songs and stories. The issue with today’s digital tools is that they are designed around a narrow philosophy of memory: preserve everything, quantify everything, resurface everything. But human memory did not evolve to preserve everything, or remember everything exactly as it happened.

This is not to say that technology should distort or rewrite the past. Most of us would not download a health app that invents flattering data for emotional convenience. But I believe we all need to give more consideration to the so-called imperfections of human memory – its selectivity, malleability, and capacity to soften, reorganise and reinterpret the past.

For centuries, people have designed technologies to compensate for memory’s weaknesses. We may now be building technologies that remove some of its strengths.

This article contains a link to bookshop.org. If you click this link and go on to buy something from bookshop.org, The Conversation UK may earn a commission.

Gillian Murphy's research is currently funded by the European Research Council, Lero (the Irish Centre for Software Research) and Rinn AI. Gillian is co-author of Memory Lane: The Perfectly Imperfect Ways We Remember (Princeton University Press, 2025).

Every ancient language that has ever been deciphered needed an anchor. Usually that’s a bilingual text, like the Rosetta Stone, or a known relative to compare it to. Linear A, the writing system of the Bronze Age Minoan civilisation on the Greek island of Crete, has neither.

Linear A is described as a “language isolate” because it has no confirmed link to any known language, living or dead. As such, it has posed a significant challenge for linguists over the past century.

Etruscan, a language of Italy that was used before the rise of the Roman Empire, has fared only slightly better. A partial vocabulary has been collected from short funerary inscriptions, but its grammar and deeper meaning still elude linguists.

Both languages are exactly the kind of puzzle that AI has recently been pulled into – not by replacing the linguists, but by supercharging their hunches. So does AI actually stand a chance of finishing what a century of human effort hasn’t?

A recent case shows both the appeal and the catch. In June 2026, a self-taught AI engineer and amateur linguist claimed a breakthrough on Linear A. It started from a single guess that one unknown word in a prayer inscription derived from a Semitic root meaning “to dwell” or “to inhabit”.

He then used AI-built programming scripts to test that sound-pattern against the rest of a collected Linear A corpus. He was reportedly able to assign values to 40 signs and compiled a 408-word lexicon, showing – in his view – that Linear A is an extinct member of the Semitic language group, which encompasses languages such as Hebrew and Aramaic.

The need for creativity

It’s worth being precise about what AI did and didn’t do here. It didn’t have the idea. The engineer did.

The AI was the research assistant. It rapidly cross-checked a human hypothesis against thousands of characters, work that would have taken a human months by hand. That division of labour is the pattern currently worth paying attention to, far more than any single claim that is still under review.

So where does AI actually help, and where does it hit a wall? It’s genuinely strong at large-scale pattern testing. It can check whether a hunch about one sign or word holds up across an entire archive in minutes instead of years. It can spot repeated sequences a human eye would miss, and restore damaged or fragmentary inscriptions by predicting likely missing characters.

AI also does something distinct called “cross-lingual transfer”, where a model trained on a known language can sometimes infer patterns in a closely related unknown one. This is similar to how knowing Spanish helps you guess at Portuguese.

Researchers have already used this kind of approach successfully on scripts such as Ugaritic, where the language family is known. Ugaritic is an extinct Semitic language, spoken during the late Bronze Age (about 1300-1190BC) in the ancient coastal city of Ugarit (in what is now Syria).

But there’s a hard ceiling: statistical pattern matching can’t manufacture meaning out of nothing. It needs an anchor – a known language family, or a bilingual text – to tell it which patterns are meaningful and which are coincidence.

Linear A and Etruscan are hard precisely because that anchor is missing or incomplete, and no amount of computing power invents one from scratch.

Scant evidence

With a large enough corpus, an AI model could hypothetically get fluent enough to converse with a Minoan or Etruscan speaker on the model’s own statistical terms. This means that it could recognise repeating patterns of words and other structural units of a language that would allow the AI model to repurpose the terms in a different context. One that might approximate a limited conversation, at least on the surface.

But the AI system still wouldn’t be able to hand a human a translation, because fluency and meaning aren’t the same thing here. The model can learn which signs follow which, and which words cluster together, without ever knowing what any of them actually refer to.

This is also why verifying any AI-assisted claim about these languages is so difficult, and it’s a problem bigger than any one case. Normally, you’d check a proposed translation or interpretation against native speakers, other texts or expert consensus built up over decades. None of that exists for a genuinely undeciphered language. Short of a time machine, there’s no way to check.

Small surviving corpora make this worse: Linear A’s entire surviving corpus is about 7,500 characters, short enough to fit on a single screen and with that little data, almost any hypothesis can find scattered matches to support it.

That is why claims in this space lean so heavily on independent expert scrutiny and peer-review rather than statistical confidence scores. It is also why “AI found a pattern” and “AI found the correct meaning” are very different claims that are easy to blur together.

None of this means that AI is a dead end for these languages. Quite the opposite: it’s a real accelerant, able to compress years of manual cross-referencing into mere minutes and let more people attempt these problems than institutional resources ever allowed.

But it doesn’t remove the two ingredients that decipherment has always required. One is a genuine comparative anchor. The other is rigorous human review, to tell a real breakthrough from an appealing coincidence.

Until one of those shows up for Linear A or Etruscan, AI’s role in deciphering languages will stay what it is today: a very fast assistant to a very old, very human puzzle.

Jane Adkins receives funding from Údarás na Gaeltachta and the Department of Culture, Communications and Sport through the eSTÓR project at the Science Foundation Ireland funded ADAPT Centre.

On Good Friday, April 11 1952, a Pan American Douglas DC-4 named Clipper Endeavor took off from San Juan in Puerto Rico, bound for New York. On board were 64 passengers including six infants, plus five crew.

The flight lasted less than nine minutes before two engines lost power. Unable to make it back to the airport, the captain ditched the plane in the Atlantic Ocean, roughly 11 miles northwest of San Juan’s Isla Grande Airport.

Remarkably, all 69 people on board survived the impact. What killed 52 of them was what happened afterwards.

The airframe broke apart and began to sink, and in the ensuing chaos, many passengers refused the captain’s order to get out, remaining buckled into their seats. Investigators recorded drowning as the cause for almost all of the deaths.

The pilot told air crash investigators that he had forced some of the passengers into the water. However, the language barrier between the crew and the mostly Puerto Rican passengers had made communication harder – as did their reported fear of sharks in the water.

For 74 years, the wreckage remained hidden under 2,000 feet of water off Puerto Rico’s coast – until an uncrewed underwater vehicle owned by marine survey company Deep Sea Vision located it in June 2026.

As a former commercial airline pilot, the story of this tragedy holds particular interest for me – in particular, the enormous impact it had on how airline safety procedures are still done today. According to Josh Gates, a Discovery Channel presenter who was part of the search team: “It is an accident that fundamentally reshaped aviation as we know it.”

The critical 90 seconds

The US Civil Aeronautics Board’s investigation found that Clipper Endeavor should probably never have taken off. One engine that later failed had shown abnormal fuel readings and metal fragments in its oil the day before.

But the report placed greater weight on what happened after the ditching. There had been no requirement for passengers to be briefed on the plane’s emergency exits, nor to be shown how to use a life vest before a flight over water.

In short, nothing had prepared 64 people for the critical 90 seconds they had to escape the plane before it sank.

As a result of these findings, regulators soon required airlines to keep life vests and rafts accessible to passengers rather than locked away, and to give passengers on extended overwater flights a verbal briefing on life vests, emergency exits and rafts – including demonstrating how to put on a life vest.

The aim was to make sure passengers had already walked through the actions in their minds before panic could override them. A 2018 review of aviation safety research highlighted that many historical air accidents would have resulted in significantly higher survival rates, had every passenger involved read the safety card or watched the crew’s demonstration beforehand.

One apparent example of the importance of passenger safety briefings occurred in January 2009, when Captain Chesley “Sully” Sullenberger ditched US Airways flight 1549 into New York’s Hudson River after a bird strike disabled both engines. All 155 people aboard survived.

However, the official investigation into the incident told a more complicated story than the “Miracle on the Hudson” headlines suggested. About 70% of passengers had not watched any part of the pre-flight briefing, and only around ten had retrieved a life vest themselves.

The evacuation was nonetheless orderly, which investigators credited to the flight crew’s professionalism, the flotation equipment the aircraft was carrying, and ferries reaching survivors within about seven minutes. Clipper Endeavor’s passengers faced almost the same situation decades earlier, with none of that preparation and no one reaching them in time.

Not every incident since the 1952 Pan Am tragedy has ended so fortuitously. Knowing the safety drill is one thing – following it under real pressure is another.

When a hijacked Ethiopian Airlines jet ditched in the Indian Ocean in 1996, killing 125 of the 175 people aboard, the official investigation found that passengers in the economy cabin had inflated their life vests before the aircraft went down.

Crew members had to quickly deflate them and re-brief passengers on the brace position under extraordinary stress. Their instructions were found to have only been given in English, so investigators concluded that some passengers probably hadn’t understood them.

Passenger compliance in an emergency

During my ten years flying Airbus A320s and A330s as a commercial pilot, giving the safety briefing was never my job. That role falls to cabin crew whose professionalism rarely gets the credit it deserves.

The seatbelt sign was mine, though. My voice was the one on the PA reminding passengers to stay buckled in whenever seated – another critical piece of safety advice for as long as the plane is in the air, which Clipper Endeavor’s passengers never received. (If you are not convinced of the seatbelt sign’s importance, read this interview with the passenger who was nearly sucked out of a Ryanair flight recently.)

After each flight, pilots and crew have a debrief that may include discussion of passenger behaviour, particularly if something went wrong during boarding or an emergency. The recurring complaint among cabin crew was about what passengers do the moment an emergency actually starts: reaching for hand luggage instead of leaving it behind.

When an Aeroflot Sukhoi Superjet caught fire after a hard landing in Moscow in 2019, investigators found that passengers retrieving bags from the overhead lockers had probably slowed the evacuation of the burning cabin, contributing to the deaths of 41 people.

A separate study of real emergency evacuations found that half of those involved admitted to attempting to grab a carry-on bag on their way out, making it the most commonly reported obstruction to a fast evacuation.

A 2014 study by aviation safety scientist Brett Molesworth showed that passengers’ recall of safety messages was better if they watched more entertaining demonstrations that used humour or celebrities.

As a result, some airlines now make safety videos both entertaining and informative – such as British Airways dressing more than 40 staff in period costumes for a Regency-style safety film, and Qatar Airways using the American comedian and actor Kevin Hart.

But recall and compliance during a terrifying emergency, when passengers are prone to panic, are two different problems. This is the gap the air travel industry is still striving to solve.

Marco Chan 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.

Humans are not the only animals that communicate. Birds sing to attract mates, dolphins use signature whistles that identify individuals, and many primates produce complex vocalisations that help them coordinate their social lives.

By studying these communication systems, researchers can reconstruct some of the evolutionary steps that eventually led to one of our defining traits: language.

Language doesn’t just mean making sounds – it requires an extraordinary ability to control them. Human speech demands precise coordination between breathing, the vocal folds across the larynx, and our tongue, lips and jaw.

Humans’ closest living relatives – chimpanzees and bonobos – possess vocal tracts that are more similar to ours than scientists once believed. Yet they do not seem to have the same fine voluntary control over the structures – starting with our lips and tongue – that allow us to produce rapid sequences of speech and such diverse sounds.

This raises an intriguing question: when did our ancient ancestors begin to acquire this remarkable vocal control? One possible clue comes from an unexpected source: laughter.

The evolution of laughter

For several years, I have studied the rhythmic structure of vocalisations in non-human primates. Rather than focusing on what calls mean, I have been interested in how they are produced.

The timing of vocalisations can reveal how breathing and vocal movements are coordinated, offering a window into the mechanisms that generate them. Whether analysing the songs of Madagascar’s indris lemur or the calls of orangutans, I kept coming back to the same question: how much control do different primates have over the rhythm of their voices?

Laughter turned out to be an ideal system for exploring this question.

Unlike speech, our ability to laugh is shared with all the great apes. Chimpanzees, bonobos, gorillas and orangutans all produce laughter-like vocalisations during play. Although these sound quite different from our familiar “ha ha ha”, they serve similar social functions, signalling playful intentions and strengthening social bonds.

Human laughter is much more diverse, however. We can produce loud belly laughs, quiet chuckles, giggles, snorts or almost silent laughter. We laugh when we are amused but also when we are embarrassed, nervous or trying to ease social tension. This flexibility makes laughter much more than a simple emotional reflex for us.

Human laughter is highly context-dependent. We laugh differently when we are genuinely amused, when we engage in polite social interactions, when we feel awkward, or when we share an inside joke.

Together with colleagues, I analysed 140 laughter bouts from humans and the four other great ape species. Instead of asking whether laughter sounded similar across species, we examined its rhythm: how quickly each laugh element followed the previous one, and how consistent that timing was. Our results revealed a clear evolutionary pattern.

When laughing, humans show greater temporal variability than our closest relatives. Chimpanzees and bonobos produce laughter at a slower rhythm than humans, while gorillas and orangutans laugh at an even slower pace.

In contrast, human laughter consists of more rapidly repeated elements – successive vocal bursts that together form a laugh. This increased flexibility may be one of the evolutionary changes associated with the emergence of finer voluntary control over vocal production.

In other words, humans’ ability to “laugh fast” may reflect one of the key innovations that eventually made speech possible.

The link between laughter and speech

The evolution of speech may not simply have involved producing new sounds, but changing the very tempo at which vocal actions are organised. Our findings suggest traces of this key evolutionary transition are still preserved in one of our most familiar activities: laughing.

While humans and chimpanzees share a common evolutionary history, we will never hear how our ancient ancestors laughed. Fossils preserve bones not vocalisations, so the only way to reconstruct the evolution of laughter is by comparing living species and inferring the changes that occurred along the hominin lineage.

Because we found that chimpanzee and bonobo laughter differs from human laughter in rhythmic structure and temporal variability, we can use these living species to infer that early hominins may have exhibited laughter patterns which were already distinct from those of other great apes during the evolutionary transition towards modern human laughter.

Three-to-four million years ago, early hominins may already have shown some of the foundations of the flexible laughter patterns seen in humans today. Our findings suggest that increased temporal variability in laughter evolved along the hominin lineage. This may have enabled laughter to become increasingly adapted to different social contexts, from playful interactions to maintaining social bonds and signalling affiliation.

Every time we compare ourselves with our closest great ape relatives, we discover that the gap between humans and other apes is smaller than was once imagined. Yet we also uncover subtle differences that may have had profound evolutionary consequences.

Laughter is one of them. It may seem like a simple expression of joy, but hidden within its rhythm is a record of millions of years of evolution – and, perhaps, one of the clues to how our human ancestors eventually found their voice.

Chiara De Gregorio 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.

Nasa is about to launch a space observatory that could help settle some of the biggest mysteries in modern astronomy. The Nancy Grace Roman space telescope will study how the universe began, what it is made of and how it has evolved over time.

The observatory is named after Nasa’s first chief of astronomy and its first female executive. Nancy Grace Roman, who died in 2018 aged 93, made important contributions to our understanding of stars and played a crucial role in shaping Nasa’s space-based astronomy programme.

The telescope is undergoing final preparations ahead of a launch set to take place from Florida’s Kennedy Space Center in late August. It is eight months ahead of its original schedule, and under budget – a rare feat for a Nasa flagship mission.

Unlike the James Webb space telescope, which is designed to look at individual objects in extraordinary detail, Roman is built to survey the sky at scale. It will take in vast areas of the cosmos, allowing astronomers to build up a much broader picture of the universe and how it changes.

Roman has an unusual history. It began life as a “spare” spy satellite, donated to Nasa by the US government agency responsible for gathering intelligence from space.

Roman carries two main instruments, added by Nasa. These are the wide field instrument and the coronagraph. The wide field instrument will detect infrared light coming from the universe. Infrared light has wavelengths that are slightly longer than those of visible light. This allows the instrument to see through cosmic dust and look deeper into the cosmos.

Its field of view – how much sky it sees when pointed in a given direction – is about 100 times that of the Hubble space telescope’s infrared camera, but with similar sharpness. The wide field instrument’s capabilities also mean it can survey the cosmos about 1,000 times faster than Hubble. Observations that would take Hubble centuries could be completed by Roman in a fraction of the time.

The coronagraph is designed to block the overwhelming glare of nearby stars. The idea is a bit like placing a hand in front of the Sun to make it easier to see something faint beside it.

By suppressing starlight, the coronagraph will allow astronomers to test methods for directly imaging faint planets and discs of dust and gas around other stars.

Over the course of its mission, Roman is expected to detect billions of galaxies and tens of billions of stars. Roman’s combination of scale and precision gives the telescope its power. It will see enough of the sky to reveal broad cosmic patterns, while measuring galaxies and stars accurately enough to detect small but important effects.

Cosmic evolution

Modern cosmology – the study of how the universe originated and evolved – rests on a remarkably successful model. According to this model of the universe, ordinary matter – the stuff that makes up stars, planets and people — accounts for just a small fraction of everything out there. The rest of the cosmos is made up of two unseen, mysterious components.

The first is dark matter, which makes up about 25% of the universe. This form of matter neither reflects nor absorbs light, yet its gravitational pull helps hold galaxies together. The mission could help shed light on the nature of dark matter by tracking how it has shaped the formation of galaxies across different epochs of cosmic history.

The second unseen component is dark energy, which makes up around 70% of the universe. Its exact nature remains elusive, but dark energy is causing the expansion of the universe to accelerate. For decades, astronomers assumed that it is constant, or uniform. However, recent results hint that dark energy could be changing over time. Roman will put these competing ideas to the test.

If dark energy is constant, it points to a universe that keeps expanding forever. If dark energy changes, it could point to new physics beyond our current theories.

Roman may help address a longstanding puzzle: why we observe the universe at a moment when the densities of matter and dark energy are roughly comparable. In the early universe, matter dominated.

In the far future, dark energy is expected to dominate completely. The present era appears to be a transition between the two – a cosmic intermission lasting billions of years, but brief in the lifespan of the universe.

This could be a simple coincidence or a clue to new insights about the cosmos. For example, it could be that dark energy is changing over time, that gravity behaves differently on cosmic scales or that some ingredient is missing from our model of the universe. Distinguishing between these scenarios would require observations across huge spans of cosmic time – exactly the kind of data that Roman will provide.

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Read more: Dark energy: could the mysterious force seen as constant actually vary over cosmic time?

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Beyond cosmology

Roman’s science extends well beyond dark energy. It will carry out the largest survey yet of planets beyond our solar system, detecting thousands of new worlds using a technique called gravitational microlensing.

Microlensing happens when the gravity of a foreground star briefly bends and magnifies the light from a more distant background star – acting like a natural magnifying lens passing between us and a distant object. If the foreground star has a planet, that planet can create a small extra brightening in the signal, revealing its presence.

Many of these exoplanets would be difficult to find with the two most widely used techniques to discover exoplanets – the transit and radial velocity methods. Using microlensing, Roman could find planets in wide orbits, low mass planets and even free floating worlds that are not bound to any star.

Roman is also part of a new era of survey astronomy. Its wide-field capabilities place it alongside other major missions. The European Space Agency’s Euclid space mission is mapping billions of galaxies to study dark matter and dark energy. The Vera C. Rubin Observatory, built on a mountaintop in Chile, will repeatedly scan the southern sky.

Together, these observatories will help astronomers map the universe across different wavelengths, distances and timescales.

Roman will also work together with the James Webb space telescope, identifying large-scale patterns and unusual objects across the sky, which Webb can then study in detail. This combination of breadth and depth is increasingly important in modern astronomy.

Roman’s greatest impact may come from the unexpected. With repeated observations of large areas of sky, it will detect rare events and previously unseen phenomena. For astronomers, this is one of the mission’s most exciting aspects.

By mapping the universe in unprecedented detail, the Roman telescope will provide a powerful test of our current understanding of cosmology – and potentially point the way to new physics.

Noelia Noël 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.

Hearing that a new primate species has been named is exciting news for scientists because primates across the world are in decline. According to the IUCN Red list, a global inventory of animal extinction risk, 44.4% of primate species are classed as endangered or critically endangered (versus 13.2% of all mammals).

In most cases when the number of primate species increases, it is because a known species has been upgraded from the status of subspecies to species, or two populations that were previously considered the same species are re-classified into two separate ones. The African monkey Likweli (Colobus congoensis), however, is a completely new addition to the primate family tree, which makes this news particularly interesting.

Likweli is a colobus monkey. The word colobus stems from the ancient Greek word κολοβός, meaning truncated, because they don’t have much of a thumb. It has an orange mouth, lives in the Democratic Republic of Congo and is part of the large subfamily colobines. Colobines are found in both Asia and Africa, but not in the Americas.

Three colobine genera are found in Africa. The olive colobus genus, has one species (a small monkey with drab olive-brown fur and a grey-black face). The red colobus genus and the black-and-white colobus genus, that Likweli belongs to. What is interesting about Likweli is that its closest relative is the black colobus (Colobus satanas), which has two subspecies – the critically endangered Bioko black colobus on the Island Bioko (20 miles off the coast of Cameron) and the Gabon black colobus on the nearby mainland. Their nearest range is 1,200km away from the range of Likweli monkeys.

Colobines have specialist digestive adaptations, including multi-chambered stomachs, that allow them to digest high fibre leaves and toxic seeds. To accommodate their large gut, many colobines are relatively large for monkeys. Likweli is smaller than the other colobus species though, and more comparable to some Asian colobines (like langurs and leafmonkeys). African colobines depend on forests and rarely come to the ground, but they can survive in small forest fragments and mangroves.

Likweli’s orange tinted mouth resembles that of some Asian langurs. But members of the Colobus genus have all black faces, making it almost impossible for human observers to recognise individuals based on their facial features. This also makes it hard to see their facial expressions. Scientists believe these monkeys’ more subtle facial expressions are related to the relatively low levels of competition within groups and less stringent social hierarchies.

Relationships between groups, however, can involve threats, loud roars and sometimes fights. Colobus males produce a loud roar to warn group members of danger, scare off predators, or during encounters with other groups of the same species. Likweli monkeys produce a similar roar although the researchers were unable to determine the sex of the callers. Likweli’s roar resembles that of the black colobus, but is distinctly different from that of other nearby colobus species.

It is not surprising that the central African region is providing new scientific discoveries. The area has been politically unstable for decades and a difficult place to conduct scientific work in. Working in these forests as a scientist means a risk of running into rebels, soldiers and professional poachers.

But the area is not only under-studied, it also represents a birthplace for many new species because the region has diverse soil including one of the largest peatlands on the globe. Plus, the landscape has been shaped and reshaped over centuries by a combination of tectonic plate movement, climatic changes, human influences and changes in its wetlands and rivers. Current climate change forecasts suggest that the area will remain suitable for colobus species in the future.

Some forests in this region contain around 20 species of primate, a value only matched by some Amazonian regions.

Most of those species are cercopithecids, the sister sub-family of the colobines. Likweli also shares its range with the striking Angola colobus, Colobus angolensis and the Lomami red colobus (Piliocolobus parmentieri). Likweli, like many red colobus species, has often been found mingled in with groups of other primates (up to six species), like guenons and sometimes Angola colobus or Lomami red colobus.

There are also apes (gorillas, chimpanzees and bonobos) in the region. Chimpanzees in other regions are well known to hunt red and black-and-white colobus monkeys for meat.

Likweli lives in small groups (a mean of six but this can range from one to 20). All Colobus species typically live in units of multiple females together with one male and their offspring. Multi-male groups are rare. Some Colobus species, like the Angola colobus, can form large aggregations of over 200 monkeys, consisting of multiple smaller one-male units travelling together.

The Likweli is only the fifth new African monkey species to have been described since 1975, one of which was a local guenon, the Lesula (Cercopithecus lomamiensis). Meanwhile, Miss Waldron’s red colobus has probably gone extinct as there have been no conclusive sightings since 1978, despite intense searching after some unconfirmed sightings in 2000 and 2001.

The Likweli is already considered endangered and protection of this hotspot for biodiversity is urgently needed.

There may well be more undescribed primates (although another monkey species is unlikely) hiding in these forests. It is nice to imagine that these African forests still contain hidden secrets.

Amanda Korstjens 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.

Around 80 million years ago, a stretch of tropical coastline in what is now north-eastern Italy was lashed by monsoon rains and tropical cyclones. Those storms swept dinosaur carcasses, crocodiles, plants and river mud onto a tidal flat, where the tides and a film of microbes sealed everything away with extraordinary preservation.

My colleagues and I have published a new study that reconstructs the violent tropical climate behind the Villaggio del Pescatore Lagerstätte near Trieste and reveals it preserves just four decades of life, recorded almost day by day.

The Villaggio del Pescatore site near Trieste is one of the most important Late Cretaceous fossil localities in the Mediterranean. It is the only known site in the region to preserve large land animals, including dinosaurs such as Tethyshadros insularis, from the final 15 million years or so of the age of dinosaurs.

Tethyshadros insularis was a plant-eating, duck-billed dinosaur that lived in what is now north-eastern Italy around 80 million years ago. It was initially interpreted as a dwarf island species, shaped by the “island rule” – that large animals evolve smaller bodies in environments with limited space and resources.

However, the discovery of larger specimens challenged this idea. Up to 11 individuals are potentially preserved in this succession of rocks, with at least three other fairly complete skeletons still waiting to be extracted from the hard (and expensive to cut) limestone rock. Geological evidence has also revealed that the site was about 10 million years older than previously thought and was not part of a tiny, isolated island. Instead, it lay near a larger landmass connected through the ancient Mediterranean region.

The fossils of tiny crocodilians named Acynodon (with shorter muzzles than modern crocodilians) have also been found at this site.

After years of research, Professor Federico Fanti of the University of Bologna and I led a multidisciplinary team of palaeontologists, physicists and geologists to determine exactly how this fossil treasure trove formed. Our findings overturn decades of competing interpretations.

In a study published in Global and Planetary Change, we show that the site was not a quiet, isolated lake or sinkhole, as previously suggested. Our results suggest it was a storm-battered tidal flatland at the edge of a tropical sea.

Villaggio del Pescatore is not simply a place where fossils happen to be beautifully preserved. It is also a record of the weather that shaped the site. We can identify individual storms and floods in the rocks, almost day by day. Very few fossil sites on Earth allow us to do that.

Reading the rock like a calendar

The fossils at Villaggio del Pescatore are concentrated in a finely layered carbonate rock, marked by thousands of paper-thin light and dark bands. For years these were interpreted as annual layers (like tree rings) laid down season by season.

Our new analysis tells a different story. Using high-resolution X-ray scans at the Elettra Synchrotron in Trieste, alongside detailed geochemical and microscopic analyses, we showed that the bands were not annual but daily. A synchrotron accelerates electrons (particles with a negative charge) to almost the speed of light. This produces powerful X-rays that can penetrate dense materials such as rock. This allowed us to examine structures inside the rocks and bones at a resolution of fractions of a millimetre.

The scans revealed that the layers were deposited by ocean tides, two pairs each day. These tides varied according to the familiar fortnightly cycle of stronger and weaker tides that still happen today. We could count these tidal layers, thanks to this unparalleled resolution.

The entire fossil-bearing rock pile, our results calculate, accumulated in only about 40 years, a single human lifetime, frozen in stone. Most fossil deposits containing several individuals average together thousands or millions of years. Here, we’re looking at a few decades.

Monsoons, cyclones and microbes

To explain why storms were so central to the preservation of the fossils, the team ran climate simulations. The models consistently placed the site at around 29° north, inside a powerful, summer-dominated monsoon belt. This was a tropical regime of intensely wet summers and recurring tropical cyclones, sustained by the greenhouse climate of the age.

Monsoon-driven floods and cyclone surges created pulses of sediment, nutrients, plant debris and animal carcasses onto the tidal flat. The tides trapped this material, and mats of microbes rapidly stabilised it. This encouraged the sediment to harden right after the carcass was deposited, before it started rotting or predators could scavenge its dead tissues. These storms were the main agents behind this preservation.

The site’s three most important Tethyshadros insularis dinosaurs show a range of fates: one buried so quickly that soft-tissue outlines survive, another bearing the cracked, sun-dried bones of a carcass that lay exposed inland before being washed in by a flood. Crocodilians such as Acynodon adriaticus, fish and shrimps were also local residents, entombed close to where they lived and died.

We propose that this combination of “ingredients”, monsoon floods, tidal trapping and microbial sealing, represents a recipe for creating exceptionally preserved fossil sites in hot, tropical greenhouse climates – precisely the kind of setting in which rapid decay would normally destroy organic remains.

The study also offers an ancient perspective on how intensifying monsoons and cyclones can reshape coastlines, ecosystems and what becomes preserved along tropical shores.

Villaggio del Pescatore has been studied for more than four decades, yet it still has new stories to tell. Understanding how extreme storms transformed this ancient coastline may also help us think about how modern coasts could respond to a warmer and more turbulent climate fuelled by high levels of CO₂.

Alfio Alessandro Chiarenza works for University College London, Department of Earth Sciences. He receives funding from The Royal Society as a Newton International Research Fellow.

The obituaries were being written before the body was cold. Within hours of Prime Minister Andy Burnham confirming that Whitehall’s responsibilities for science and technology would be absorbed into a new Department for Business, Innovation, Science and Trade (DBIST), Sir Paul Nurse, president of the Royal Society, was on Radio 4 warning that “science isn’t a slave to business”. The trade association TechUK declared it “the wrong change at the wrong time”.

You might imagine that something ancient, ineffable and essential to British science had been ripped away. In fact, the Department for Science, Innovation and Technology (DSIT) was only born in February 2023 – a surprise late tweak to the machinery of government under Rishi Sunak’s faltering premiership.

Science has never enjoyed a settled home in Whitehall. For most of the period from 1995 to 2023, it sat precisely where Burnham has put it now: inside a business super-department. Notably, the single most important protection UK science secured in that era – a ring-fenced budget through the George Osborne austerity years – was won from inside the Business, Innovation and Science department, under then-business secretary Vince Cable.

The historian David Edgerton has long argued that Britain’s fondness for tinkering with the machinery of government is a form of displacement activity. What actually shapes the fortunes of research is money, people and demand for what they produce, not the nameplates on London’s Victoria Street.

On that test, the outlook for science is far calmer than some reactions to this latest move would suggest. Under plans published last October, the UK’s total research and development budget is locked in at £58.5 billion over the four-year spending review to 2030 – a real-terms increase.

Machinery changes move budget lines across departments; they don’t unravel them. As yet, nothing we’ve heard from the Burnham government suggests the settlement for UK Research and Innovation (UKRI) is at risk.

A US wrecking ball

If we want to be reminded what real damage to a science system looks like, we only need to glance across the Atlantic. While London rearranges the departmental furniture and dishes out new email addresses, Washington is taking a wrecking ball to the foundations of its public science system.

A 2027 budget request will halve the size of the US National Science Foundation, if passed. A draft White House rule, due to take effect in October, will hand political appointees the power to vet federal research grants against “the president’s priorities” and terminate awards mid-stream.

A predictable exodus has begun. The Nobel laureate Omar Yaghi has just left the University of California, Berkeley for Tsinghua University in China.

Set against political commissars patrolling the borders of knowledge, the UK’s science soap opera feels deeply provincial – a debate about which secretary of state sponsors oversight of a gradually swelling budget.

And the institutional layer beneath this is in better shape than doomsayers allow. UKRI’s new strategy has set a clear five-year path: priority programmes in AI, quantum and clean energy, faster funding decisions, and a welcome seriousness about experimentation with the modes and means by which we manage public investment in research.

Not everyone is happy with UKRI’s approach to prioritisation. But a funder with a published strategy, agreed budget and hard targets is a more durable asset than a three-year-old ministry. The research councils have outlasted every department that ruled over them, and UKRI will outlast this too.

Nor should the science community be spooked by closer proximity to business. When I leave my office at UCL and stroll down the Euston Road through London’s Knowledge Quarter – dubbed “the new square mile” – I pass by the Wellcome Trust, the Crick and Turing institutes, the British Library, Anthropic and Google DeepMind. Day in, day out, this dense weave of labs, firms and cultural institutions delivers what the triple helix of government, industry and academia is all about.

And we can find versions of the same story unfolding in Manchester, Sheffield, Cambridge and Glasgow. Rather than science being subordinate or “enslaved”, the UK’s world-leading universities have spent the past decade aligning themselves with agendas for regional growth and frontier technologies.

But there’s a harder truth lurking here too. Public money accounts for less than a third of UK research and development investment. The rest is financed and largely performed by the private sector.

UK science’s chronic weakness has never been the quality of publicly funded research. It is the R&D intensity of UK firms, and their ability to convert discoveries into products, services and revenue streams generated and owned in the UK.

If yoking science to industrial strategy in a single department reminds Whitehall to treat these as a single problem, some good may come of Burnham’s reorganisation.

AI policy concerns

With DSIT’s disappearance, a particular source of concern has been the future of AI policy. This at a time when the radical possibilities and risks of AI frontier models, fluctuating US export controls, and the latest “Sputnik moment” jolt from China’s Kimi K-3 demand sustained and technically fluent policy attention.

We are also seeing a sharp acceleration in the ways AI is transforming science itself – whether by cracking mathematical problems that stumped humans for the past 87 years, or collapsing decades-old models of grant writing and peer review.

Splitting AI from science just as they converge in practice would have been the least defensible element of this reorganisation. So it is reassuring that cross-government oversight of AI, including the AI Security Institute and AI public sector adoption, is moving to a new “AI taskforce” in the Cabinet Office – alongside the promotion of Kanishka Narayan to the status of AI minister attending Cabinet. Much now rests on his shoulders.

Government departments come and go. Those of us who have long advocated for research policies built on evidence rather than gut can allow ourselves a wry smile at the fuss created by this latest reorganisation – and then get back to work.

DSIT is dead, but UK science will be fine. Keep your eyes on the AI prize – and be thankful we are arguing about organograms, not survival.

James Wilsdon receives funding from UKRI and the Wellcome Trust, and a wider consortium of international research funders who partner in the Research on Research Institute (RoRI).