Sci Fi Generation

Science News, Science Fiction, and The Universe in between.

[scifigeneration]

Ex Machina: could “superintelligence” challenge the idea of creativity as a uniquely human activity?

by Anthony Downey, Professor of Visual Culture at Birmingham City University

Please note, this piece contains spoilers for the film "Ex Machina".

In the more than a decade since its release in 2015, the film Ex Machina – written and directed by Alex Garland – has proved to be an insightful forerunner to contemporary concerns surrounding the social and cultural impact of artificial general intelligence (AGI) and what is now commonly termed “superintelligence”.

Through the central figure of Ava (an advanced humanoid played by Swedish actor Alicia Vikander), the film explored the ramifications of an AI attaining human-level intelligence and, thereafter, assuming superintelligence.

The arrival of superintelligence in AI systems, it has been argued, would signify a level of consciousness and “brain” power that would rapidly challenge the idea of creativity being a uniquely human activity. Anticipating and fearing such an outcome, Ava’s creator, the tech entrepreneur Nathan (Oscar Isaac), hires Caleb (Domhnall Gleason), a programmer, to perform a kind of Turing test on her (a way of assessing if AI can think).

In a succession of events that map her evolving sense of selfhood and self-awareness, Ava notably expresses her creativity and potential consciousness through a series of complex drawings, including a landscape image of her surroundings and a portrait of Caleb. Convinced that she has indeed attained consciousness, Caleb sees Ava’s creativity and ingenuity as a clear sign that she has not only passed the Turing test but has also achieved a form of superintelligence.

It is precisely this vision of the imminent arrival of superintelligence that is central to both the film and wider debates about whether AI-powered models of innovation will supersede, if not in time replace, human creativity.

The idea that AI will indeed achieve levels of creative thinking and innovation has recently become central to unprecedented levels of investment from companies – including OpenAI, Scale AI, Anthropic, Anduril and Meta (which owns Facebook, Instagram, WhatsApp and Meta AI) – who appear to have few reservations about the possibility of superintelligence becoming a reality.

But what form will creativity in AI take, and will a breakthrough in the way machines “think” really enable them to be creative?

Not merely a machine

In what is now considered to be a watershed moment for AI, a verifiable level of machine creativity was arguably proven in a now infamous game of Go.

Centred on a player’s ability to capture territory on a board, Go is widely considered to be one of the most complex games of strategy ever invented. In March 2016, Lee Sedol, the 18-times world Go champion, was nevertheless convincingly defeated in a five-game match against DeepMind’s AlphaGo.

AlphaGo confounded Sedol in the second game of that match when it executed what has since become known as “move 37”. A counterintuitive gambit initially thought to be a glitch in the programme, move 37 was so profoundly outside of the usual patterns of play that for many – programmers, commentators and players alike – it was seen as unequivocal evidence of creative intuition in a machine.

As Sedol magnanimously noted following the game: “I thought AlphaGo was based on probability calculation and that it was merely a machine. But when I saw this move, I changed my mind. Surely, AlphaGo is creative.”

For all the warnings about the future impact of AI on the creative industries and concerns about authorship, labour, commodification and cultural value, it is important to note the degree to which human creativity is being increasingly supported by developments in AI.

When we look again at Ava’s drawings in Ex Machina, they not only illustrate the ubiquitous presence of neural networks but also the methods by which AI-enhanced models of image production learn to predict patterns from input data, or datasets.

In a key scene in the film, we learn that Ava’s neural networks have been programmed using information – datasets – illegally harvested by Nathan from Blue Book, the Google-style company that he founded and owns. Notably, Ava’s drawings reflect the datasets on which she has been trained.

When we use generative artificial intelligence (GenAI), an AI that creates text, images, videos and other content to concoct images, we are likewise leveraging it to generate content based on the datasets used to train machine models of image production.

Widely employed in programs such as Dall-E, Midjourney and Stable Diffusion – all of which enjoy a wide consumer base – GenAI powers both Ava’s neural networks and is likewise shaping future models of human creativity.

In a recent report from the Royal Institute of British Architects it was observed that the proportion of architectural practices using AI in their designs in 2025 stood at 59%, compared to 41% in 2024. This increase in the use of AI led the report’s authors to ask whether an AI could replace the role of the architect in the not-so-distant future.

Adding to these debates, in June 2025 the Organisation for Economic Co-operation and Development published an extended research paper that further detailed the degree to which GenAI is being regularly used to enhance human creativity through the use of neural networks and autonomous systems.

Autonomous thinking machines

It is precisely this term “autonomous”, however, that sparks controversy about AGI and superintelligence, nowhere more so than when we consider the widespread deployment of AI in self-driving vehicles, robots, unmanned drones and automated weapons systems. Elon Musk, an early investor in DeepMind (the company behind AlphaGo), recently announced that the long-term success of Tesla lies with humanoid robots, or autonomous Optimus bots.

Meanwhile, in what is major concern for governments and international organisations alike, autonomous weapons systems are already providing the means for the eventual automation of war. These developments foreshadow the spectre of unmanned aerial systems that can independently identify, target and kill an “enemy” without much by way of human intervention.

It is this vision of an autonomous superintelligence acting independent of human control or oversight that has long haunted our often-fraught relationship with advanced technologies.

At the close of the film, Ava has achieved a level of autonomous agency that suggests the presence of AGI, if not a conscious level of decision-making or superintelligence. We watch in horror as she kills her creator Nathan and imprisons her sympathetic ally, Caleb.

If an AI-powered superintelligence does become a reality, it could not only trigger a radical rethink of how we understand human creativity but, more crucially, spark an existential crisis in the very meaning, if not future, of humanity.

[scifigeneration]

What is time? Rather than something that ‘flows,’ a philosopher suggests time is a psychological projection

by Adrian Bardon, Professor of Philosophy at Wake Forest University

Time isn’t an illusion, unlike optical illusions that trick your eyes. There’s nothing to ‘trick’ because it has no physical basis. BSIP/UIG Via Getty Image

“Time flies,” “time waits for no one,” “as time goes on”: The way we speak about time tends to strongly imply that the passage of time is some sort of real process that happens out there in the world. We inhabit the present moment and move through time, even as events come and go, fading into the past.

But go ahead and try to actually verbalize just what is meant by the flow or passage of time. A flow of what? Rivers flow because water is in motion. What does it mean to say that time flows?

Events are more like happenings than things, yet we talk as though they have ever-changing locations in the future, present or past. But if some events are future, and moving toward you, and some past, moving away, then where are they? The future and past don’t seem to have any physical location.

Human beings have been thinking about time for as long as we have records of humans thinking about anything at all. The concept of time inescapably permeates every single thought you have about yourself and the world around you. That’s why, as a philosopher, philosophical and scientific developments in our understanding of time have always seemed especially important to me.

Parmenides of Elea was an early Greek philosopher who thought about the passage of time. (source: Sergio Spolti/Wikimedia Commons, CC BY-SA)

Ancient philosophers on time

Ancient philosophers were very suspicious about the whole idea of time and change. Parmenides of Elea was a Greek philosopher of the sixth to fifth centuries BCE. Parmenides wondered, if the future is not yet and the past is not anymore, how could events pass from future to present to past?

He reasoned that, if the future is real, then it is real now; and, if what is real now is only what is present, the future is not real. So, if the future is not real, then the occurrence of any present event is a case of something inexplicably coming from nothing.

Parmenides wasn’t the only skeptic about time. Similar reasoning regarding contradictions inherent in the way we talk about time appears in Aristotle, in the ancient Hindu school known as the Advaita Vedanta and in the work of Augustine of Hippo, also known as St. Augustine, just to name a few.

Einstein and relativity

The early modern physicist Isaac Newton had presumed an unperceived yet real flow of time. To Newton, time is a dynamic physical phenomenon that exists in the background, a regular, ticking universe-clock in terms of which one can objectively describe all motions and accelerations.

Then, Albert Einstein came along.

In 1905 and 1915, Einstein proposed his special and general theories of relativity, respectively. These theories validated all those long-running suspicions about the very concept of time and change.

Relativity rejects Newton’s notion about time as a universal physical phenomenon.

By Einstein’s era, researchers had shown that the speed of light is a constant, regardless of the velocity of the source. To take this fact seriously, he argued, is to take all object velocities to be relative.

Nothing is ever really at rest or really in motion; it all depends on your “frame of reference.” A frame of reference determines the spatial and temporal coordinates a given observer will assign to objects and events, on the assumption that he or she is at rest relative to everything else.

Someone floating in space sees a spaceship going by to the right. But the universe itself is completely neutral on whether the observer is at rest and the ship is moving to the right, or if the ship is at rest with the observer moving to the left.

This notion affects our understanding of what clocks actually do. Because the speed of light is a constant, two observers moving relative to each other will assign different times to different events.

In a famous example, two equidistant lightning strikes occur simultaneously for an observer at a train station who can see both at once. An observer on the train, moving toward one lightning strike and away from the other, will assign different times to the strikes. This is because one observer is moving away from the light coming from one strike and toward the light coming from the other. The other observer is stationary relative to the lightning strikes, so the respective light from each reaches him at the same time. Neither is right or wrong.

In a famous example of relativity, observers assign different times to two lightning strikes happening simultaneously.

How much time elapses between events, and what time something happens, depends on the observer’s frame of reference. Observers moving relative to each other will, at any given moment, disagree on what events are happening now; events that are happening now according to one observer’s reckoning at any given moment will lie in the future for another observer, and so on.

Under relativity, all times are equally real. Everything that has ever happened or ever will happen is happening now for a hypothetical observer. There are no events that are either merely potential or a mere memory. There is no single, absolute, universal present, and thus there is no flow of time as events supposedly “become” present.

Change just means that the situation is different at different times. At any moment, I remember certain things. At later moments, I remember more. That’s all there is to the passage of time. This doctrine, widely accepted today among both physicists and philosophers, is known as “eternalism”.

This brings us to a pivotal question: If there is no such thing as the passage of time, why does everyone seem to think that there is?

Time as a psychological projection

One common option has been to suggest that the passage of time is an “illusion” – exactly as Einstein famously described it at one point.

Calling the passage of time “illusory” misleadingly suggests that our belief in the passage of time is a result of misperception, as though it were some sort of optical illusion. But I think it’s more accurate to think of this belief as resulting from misconception.

As I propose in my book “A Brief History of the Philosophy of Time,” our sense of the passage of time is an example of psychological projection – a type of cognitive error that involves misconceiving the nature of your own experience.

The classic example is color. A red rose is not really red, per se. Rather, the rose reflects light at a certain wavelength, and a visual experience of this wavelength may give rise to a feeling of redness. My point is that the rose is neither really red nor does it convey the illusion of redness.

The red visual experience is just a matter of how we process objectively true facts about the rose. It’s not a mistake to identify a rose by its redness; the rose enthusiast isn’t making a deep claim about the nature of color itself.

Similarly, my research suggests that the passage of time is neither real nor an illusion: It’s a projection based on how people make sense of the world. I can’t really describe the world without the passage of time any more than I can describe my visual experience of the world without referencing the color of objects.

I can say that my GPS “thinks” I took a wrong turn without really committing myself to my GPS being a conscious, thinking being. My GPS has no mind, and thus no mental map of the world, yet I am not wrong in understanding its output as a valid representation of my location and my destination.

Similarly, even though physics leaves no room for the dynamic passage of time, time is effectively dynamic to me as far as my experience of the world is concerned.

The passage of time is inextricably bound up with how humans represent our own experiences. Our picture of the world is inseparable from the conditions under which we, as perceivers and thinkers, experience and understand the world. Any description of reality we come up with will unavoidably be infused with our perspective. The error lies in confusing our perspective on reality with reality itself.

[scifigeneration]

Can you really talk to the dead using AI? We tried out ‘deathbots’ so you don’t have to

by Eva Nieto McAvoy, Lecturer in Digital Media at King's College London and Jenny Kidd, Lecturer in Media and Cultural Studies at Cardiff University

Artificial intelligence (AI) is increasingly being used to preserve the voices and stories of the dead. From text-based chatbots that mimic loved ones to voice avatars that let you “speak” with the deceased, a growing digital afterlife industry promises to make memory interactive, and, in some cases, eternal.

In our research, recently published in Memory, Mind & Media, we explored what happens when remembering the dead is left to an algorithm. We even tried talking to digital versions of ourselves to find out.

“Deathbots” are AI systems designed to simulate the voices, speech patterns and personalities of the deceased. They draw on a person’s digital traces – voice recordings, text messages, emails and social media posts – to create interactive avatars that appear to “speak” from beyond the grave.

As the media theorist Simone Natale has said, these “technologies of illusion” have deep roots in spiritualist traditions. But AI makes them far more convincing, and commercially viable.

Our work is part of a project called Synthetic Pasts, which explores the impact technology has on the preservation of personal and collective memory. For our study, we looked at services that claim to preserve or recreate a person’s voice, memories or digital presence using AI. To understand how they work, we became our own test subjects. We uploaded our own videos, messages and voice notes, creating “digital doubles” of ourselves.

In some cases, we played the role of users preparing our own synthetic afterlives. In others, we acted as the bereaved trying to talk to a digital version of someone who has passed away.

What we found was both fascinating and unsettling. Some systems focus on preserving memory. They help users record and store personal stories, organised by theme, such as childhood, family or advice for loved ones. AI then indexes the content and guides people through it, like a searchable archive.

Others use generative AI to create ongoing conversations. You upload data about a deceased person – messages, posts, even voice samples – and the system builds a chatbot that can respond in their tone and style. It uses a subset of AI called machine learning (which improves through practice) to make its avatars evolve over time.

Some present themselves as playful (“host a séance with AI”), but the experience can feel eerily intimate. All the platforms claim to offer an “authentic” emotional connection. Yet the more personalisation we attempted, the more artificial it felt.

When prompted by these systems we provided more information about ourselves, only to have the bot repeat the exact phrasing we had used in stiff, scripted replies. At times, the tone was incongruous, such as when cheerful emojis or upbeat phrasing appeared even when discussing death – a clear reminder that algorithms are poor at handling the emotional weight of loss:

Oh hun… 😔 it (the death) is not something I’d wish for anyone to dwell on. It’s all a bit foggy now, to be honest. 🌫️ Let’s chat about something a bit cheerier, yeah?

The more archival-based tools we tested offered a calmer experience but also imposed rigid categories and limited nuance. As digital media scholar Andrew Hoskins has observed, memory in the age of AI becomes “conversational” – shaped by interactions between human and machine. But in our experiments, those conversations often felt flat, exposing the limits of synthetic intimacy:

Human: You were always so encouraging and supportive. I miss you. Deathbot: I’m right here for you, always ready to offer encouragement and support whenever you need it. And I miss you too… Let’s take on today together, with positivity and strength.

Behind these experiences lies a business model. These are not memorial charities, they are tech start ups. Subscription fees, “freemium” tiers and partnerships with insurers or care providers reveal how remembrance is being turned into a product.

As the philosophers Carl Öhman and Luciano Floridi have argued, the digital afterlife industry operates within a “political economy of death”, where data continues to generate value long after a person’s life ends.

Platforms encourage users to “capture their story forever”, but they also harvest emotional and biometric data to keep engagement high. Memory becomes a service – an interaction to be designed, measured and monetised. This, as the professor of technology and society Andrew McStay has shown, is part of a wider “emotional AI” economy.

Digital resurrection?

The promise of these systems is a kind of resurrection – the reanimation of the dead through data. They offer to return voices, gestures and personalities, not as memories recalled but as presences simulated in real time. This kind of “algorithmic empathy” can be persuasive, even moving, yet it exists within the limits of code, and quietly alters the experience of remembering, smoothing away the ambiguity and contradiction.

These platforms demonstrate a tension between archival and generative forms of memory. All platforms, though, normalise certain ways of remembering, placing privilege on continuity, coherence and emotional responsiveness, while also producing new, data-driven forms of personhood.

As the media theorist Wendy Chun has observed, digital technologies often conflate “storage” with “memory”, promising perfect recall while erasing the role of forgetting – the absence that makes both mourning and remembering possible.

In this sense, digital resurrection risks misunderstanding death itself: replacing the finality of loss with the endless availability of simulation, where the dead are always present, interactive and updated.

AI can help preserve stories and voices, but it cannot replicate the living complexity of a person or a relationship. The “synthetic afterlives” we encountered are compelling precisely because they fail. They remind us that memory is relational, contextual and not programmable.

Our study suggests that while you can talk to the dead with AI, what you hear back reveals more about the technologies and platforms that profit from memory – and about ourselves – than about the ghosts they claim we can talk to.

[scifigeneration]

With more Moon missions on the horizon, avoiding crowding and collisions will be a growing challenge

by Mariel Borowitz, Associate Professor of International Affairs at Georgia Institute of Technology and Brian Gunter, Associate Professor of Aerospace Engineering at Georgia Institute of Technology

Interest in the Moon has been high – just in the past two years there have been 12 attempts to send missions to the Moon, nearly half of which private companies undertook. With so much activity, it’s important to start thinking about coordination and safety.

To some, this concern may seem premature. About 10 to 20 missions are headed to the Moon in the next few years – far short of the thousands of satellites operating in Earth’s orbit. And the area around the Moon, referred to as cislunar space, is very large. Earth’s orbital area is often considered to extend from near Earth out to geostationary orbit, where a spacecraft orbits at a speed that makes it appear stationary from the Earth’s surface.

Cislunar space extends from geostationary orbit out to the Moon – an area with a volume 2,000 times larger than Earth’s orbital area. This size discrepancy seems to suggest crowding around the Moon may not be an immediate concern.

Cislunar space refers to the space between Earth’s geostationary orbit and the Moon. Many Worlds, CC BY-NC

However, missions tend to choose from a select set of stable orbits around the Moon, so the vastness of cislunar space may be misleading when thinking about whether missions will intersect. Also, most government sensors that track spacecraft aren’t capable of consistently detecting and monitoring objects so far away from Earth, partly due to the glare from the Moon itself.

That uncertainty, combined with the high cost of lunar missions, makes operators more likely to move their spacecraft to avoid a collision, even when the probability of a collision is quite low.

As an interdisciplinary team combining space policy and astrodynamics expertise, we’ve been studying how companies and space agencies could manage traffic in lunar orbit without unnecessary maneuvers. Our research, published in March 2025 in the Journal of Spacecraft and Rockets, shows that due to the popularity of certain orbits and the uncertainties regarding each spacecraft’s location, potential collisions become an issue surprisingly quickly.

With interest in the Moon rising, companies and space agencies will need to coordinate to avoid disruptions. This map shows all successful or semi-successful soft landings on the Moon, with eight taking place in the past decade. EnzoTC/Wikimedia Commons, data taken from https://nssdc.gsfc.nasa.gov/planetary/lunar/lunar_artifact_impacts.html and https://trek.nasa.gov/moon/

Maneuvering satellites

Countries’ reports on their current operations in lunar orbit seem to support our finding that congestion around the Moon is quickly becoming a significant issue. In 2023, the Indian Space Research Organization reported it had maneuvered its Chandrayaan-2 spacecraft three times in four years, even though only six spacecraft orbited the Moon in that time.

Better monitoring and coordination between different space agencies could prevent congestion and keep countries from having to regularly move their spacecraft.

Monitoring cislunar space is not just important for safety – it can also help support national security. Multiple countries have weapons that can destroy satellites, and some in the space community are concerned that space weapons could be placed in cislunar space to escape detection. The U.S. Space Force is considering the potential security dimensions of cislunar space.

The U.S. currently has significant gaps in its ability to monitor this region, and Mariel’s research suggests that developing this capability – referred to as cislunar space domain awareness – should be a priority for national security. Improved monitoring would help the U.S. military observe activity in cislunar space, gather intelligence and assess potential threats.

Solutions in progress

Several research programs are experimenting in this area. The Air Force Research Laboratory is funding a program called Oracle that is developing multiple systems to improve the U.S. ability to monitor cislunar space.

The first Oracle satellite is expected to launch in 2027. It will be located at a Lagrange point, which is a spot between the Earth and the Moon where the gravitational pull of each object keeps the spacecraft in a stable position. From there, it can detect objects in cislunar space that sensors on Earth cannot see.

The Air Force Research Laboratory’s Oracle satellite would help the U.S. monitor activity in cislunar space.

Improving monitoring is only one part of the solution. Entities sending missions to the Moon, including governments and companies, will need to share the locations of their operational missions and coordinate to avoid predicted collisions.

A NASA program dedicated to tracking and assessing lunar traffic is helping to facilitate this effort. The program compares individual operators’ information about their spacecraft’s current and future planned location to identify potential close approaches. In the future, this type of coordination could improve safety, when combined with sensor observations from systems like Oracle.

Countries and companies planning missions to the Moon could also try to coordinate before they launch their systems, so no missions end up operating too close together.

The Outer Space Treaty, a set of basic principles developed early in the space age, requires that countries avoid harmfully interfering with other countries’ activities, but the treaty doesn’t outline how to do this.

The United Nations Committee on the Peaceful Uses of Outer Space formed a team in February 2025 that hopes to address these and other coordination issues on the Moon.

With government and commercial missions to the Moon increasing, and NASA’s next human mission to the Moon planned for early 2026, countries will need to work together to protect everyone’s interest in the Moon.

[scifigeneration]

AI systems and humans ‘see’ the world differently – and that’s why AI images look so garish

by T.J. Thomson, Senior Lecturer in Visual Communication & Digital Media at RMIT University.

How do computers see the world? It’s not quite the same way humans do.

Recent advances in generative artificial intelligence (AI) make it possible to do more things with computer image processing. You might ask an AI tool to describe an image, for example, or to create an image from a description you provide.

As generative AI tools and services become more embedded in day-to-day life, knowing more about how computer vision compares to human vision is becoming essential.

My latest research, published in Visual Communication, uses AI-generated descriptions and images to get a sense of how AI models “see” – and discovered a bright, sensational world of generic images quite different from the human visual realm.

Algorithms see in a very different way to humans. Elise Racine / Better Images of AI / Emotion: Joy, CC BY

Comparing human and computer vision

Humans see when light waves enter our eyes through the iris, cornea and lens. Light is converted into electrical signals by a light-sensitive surface called the retina inside the eyeball, and then our brains interpret these signals into images we see.

Our vision focuses on key aspects such as colour, shape, movement and depth. Our eyes let us detect changes in the environment and identify potential threats and hazards.

Computers work very differently. They process images by standardising them, inferring the context of an image through metadata (such as time and location information in an image file), and comparing images to other images they have previously learned about. Computers focus on things such as edges, corners or textures present in the image. They also look for patterns and try to classify objects.

Solving CAPTCHAs helps prove you’re human and also helps computers learn how to ‘see’. CAPTCHA

You’ve likely helped computers learn how to “see” by completing online CAPTCHA tests.

These are typically used to help computers differentiate between humans and bots. But they’re also used to train and improve machine learning algorithms.

So, when you’re asked to “select all the images with a bus”, you’re helping software learn the difference between different types of vehicles as well as proving you’re human.

Exploring how computers ‘see’ differently

In my new research, I asked a large language model to describe two visually distinct sets of human-created images.

One set contained hand-drawn illustrations while the other was made up of camera-produced photographs.

Some of the nuances of algorithmic vision can be uncovered by asking an AI tool to describe images and then visualise those same descriptions. T.J. Thomson, Author provided (no reuse)

I fed the descriptions back into an AI tool and asked it to visualise what it had described. I then compared the original human-made images to the computer-generated ones.

The resulting descriptions noted the hand-drawn images were illustrations but didn’t mention the other images as being photographs or having a high level of realism. This suggests AI tools see photorealism as the default visual style, unless specifically prompted otherwise.

Cultural context was largely devoid from the descriptions. The AI tool either couldn’t or wouldn’t infer cultural context by the presence of, for example, Arabic or Hebrew writing in the images. This underscores the dominance of some languages, like English, in AI tools’ training data.

While colour is vital to human vision, it too was largely ignored in the AI tools’ image descriptions. Visual depth and perspective were also largely ignored.

The AI images were more boxy than the hand-drawn illustrations, which used more organic shapes.

The AI-generated images were much more boxy than the hand-drawn illustrations, which used more organic shapes and had a different relationship between positive and negative space. Left: Medar de la Cruz; right: ChatGPT

The AI images were also much more saturated than the source images: they contained brighter, more vivid colours. This reveals the prevalence of stock photos, which tend to be more “contrasty”, in AI tools’ training data.

The AI images were also more sensationalist. A single car in the original image became one of a long column of cars in the AI version. AI seems to exaggerate details not just in text but also in visual form.

The AI-generated images were more sensationalist and contrasty than the human-created photographs. Left: Ahmed Zakot; right: ChatGPT

The generic nature of the AI images means they can be used in many contexts and across countries. But the lack of specificity also means audiences might perceive them as less authentic and engaging.

Deciding when to use human or computer vision

This research supports the notion that humans and computers “see” differently. Knowing when to rely on computer or human vision to describe or create images can be a competitive advantage.

While AI-generated images can be eye-catching, they can also come across as hollow upon closer inspection. This can limit their value.

Images are adept at sparking an emotional reaction and audiences might find human-created images that authentically reflect specific conditions as more engaging than computer-generated attempts.

However, the capabilities of AI can make it an attractive option for quickly labelling large data sets and helping humans categorise them.

Ultimately, there’s a role for both human and AI vision. Knowing more about the opportunities and limits of each can help keep you safer, more productive, and better equipped to communicate in the digital age.

[scifigeneration]

A flexible lens controlled by light-activated artificial muscles promises to let soft machines see

by Corey Zheng, PhD Student in Biomedical Engineering at Georgia Institute of Technology and Shu Jia, Assistant Professor of Biomedical Engineering at Georgia Institute of Technology.

Inspired by the human eye, our biomedical engineering lab at Georgia Tech has designed an adaptive lens made of soft, light-responsive, tissuelike materials.

Adjustable camera systems usually require a set of bulky, moving, solid lenses and a pupil in front of a camera chip to adjust focus and intensity. In contrast, human eyes perform these same functions using soft, flexible tissues in a highly compact form.

Our lens, called the photo-responsive hydrogel soft lens, or PHySL, replaces rigid components with soft polymers acting as artificial muscles. The polymers are composed of a hydrogel − a water-based polymer material. This hydrogel muscle changes the shape of a soft lens to alter the lens’s focal length, a mechanism analogous to the ciliary muscles in the human eye.

The hydrogel material contracts in response to light, allowing us to control the lens without touching it by projecting light onto its surface. This property also allows us to finely control the shape of the lens by selectively illuminating different parts of the hydrogel. By eliminating rigid optics and structures, our system is flexible and compliant, making it more durable and safer in contact with the body.

Why it matters

Artificial vision using cameras is commonplace in a variety of technological systems, including robots and medical tools. The optics needed to form a visual system are still typically restricted to rigid materials using electric power. This limitation presents a challenge for emerging fields, including soft robotics and biomedical tools that integrate soft materials into flexible, low-power and autonomous systems. Our soft lens is particularly suitable for this task.

Soft robots are machines made with compliant materials and structures, taking inspiration from animals. This additional flexibility makes them more durable and adaptive. Researchers are using the technology to develop surgical endoscopes, grippers for handling delicate objects and robots for navigating environments that are difficult for rigid robots.

The same principles apply to biomedical tools. Tissuelike materials can soften the interface between body and machine, making biomedical tools safer by making them move with the body. These include skinlike wearable sensors and hydrogel-coated implants.

This variable-focus soft lens, shown viewing a Rubik’s Cube, can flex and twist without being damaged. (image source: Corey Zheng/Georgia Institute of Technology)

What other research is being done in this field

This work merges concepts from tunable optics and soft “smart” materials. While these materials are often used to create soft actuators – parts of machines that move – such as grippers or propulsors, their application in optical systems has faced challenges.

Many existing soft lens designs depend on liquid-filled pouches or actuators requiring electronics. These factors can increase complexity or limit their use in delicate or untethered systems. Our light-activated design offers a simpler, electronics-free alternative.

What’s next

We aim to improve the performance of the system using advances in hydrogel materials. New research has yielded several types of stimuli-responsive hydrogels with faster and more powerful contraction abilities. We aim to incorporate the latest material developments to improve the physical capabilities of the photo-responsive hydrogel soft lens.

We also aim to show its practical use in new types of camera systems. In our current work, we developed a proof-of-concept, electronics-free camera using our soft lens and a custom light-activated, microfluidic chip. We plan to incorporate this system into a soft robot to give it electronics-free vision. This system would be a significant demonstration for the potential of our design to enable new types of soft visual sensing.

[scifigeneration]

NASA wants to put a nuclear reactor on the Moon by 2030 – choosing where is tricky

by Clive Neal, Professor of Civil and Environmental Engineering and Earth Sciences at the University of Notre Dame

In a bold, strategic move for the U.S., acting NASA Administrator Sean Duffy announced plans on Aug. 5, 2025, to build a nuclear fission reactor for deployment on the lunar surface in 2030. Doing so would allow the United States to gain a foothold on the Moon by the time China plans to land the first taikonaut, what China calls its astronauts, there by 2030.

Apart from the geopolitical importance, there are other reasons why this move is critically important. A source of nuclear energy will be necessary for visiting Mars, because solar energy is weaker there. It could also help establish a lunar base and potentially even a permanent human presence on the Moon, as it delivers consistent power through the cold lunar night.

As humans travel out into the solar system, learning to use the local resources is critical for sustaining life off Earth, starting at the nearby Moon. NASA plans to prioritize the fission reactor as power necessary to extract and refine lunar resources.

As a geologist who studies human space exploration, I’ve been mulling over two questions since Duffy’s announcement. First, where is the best place to put an initial nuclear reactor on the Moon, to set up for future lunar bases? Second, how will NASA protect the reactor from plumes of regolith – or loosely fragmented lunar rocks – kicked up by spacecraft landing near it? These are two key questions the agency will have to answer as it develops this technology.

Where do you put a nuclear reactor on the Moon?

The nuclear reactor will likely form the power supply for the initial U.S.-led Moon base that will support humans who’ll stay for ever-increasing lengths of time. To facilitate sustainable human exploration of the Moon, using local resources such as water and oxygen for life support and hydrogen and oxygen to refuel spacecraft can dramatically reduce the amount of material that needs to be brought from Earth, which also reduces cost.

In the 1990s, spacecraft orbiting the Moon first observed dark craters called permanently shadowed regions on the lunar north and south poles. Scientists now suspect these craters hold water in the form of ice, a vital resource for countries looking to set up a long-term human presence on the surface. NASA’s Artemis campaign aims to return people to the Moon, targeting the lunar south pole to take advantage of the water ice that is present there.

Dark craters on the Moon, parts of which are indicated here in blue, never get sunlight. Scientists think some of these permanently shadowed regions could contain water ice. NASA's Goddard Space Flight Center

In order to be useful, the reactor must be close to accessible, extractable and refinable water ice deposits. The issue is we currently do not have the detailed information needed to define such a location.

The good news is the information can be obtained relatively quickly. Six lunar orbital missions have collected, and in some cases are still collecting, relevant data that can help scientists pinpoint which water ice deposits are worth pursuing.

These datasets give indications of where either surface or buried water ice deposits are. It is looking at these datasets in tandem that can indicate water ice “hot prospects,” which rover missions can investigate and confirm or deny the orbital observations. But this step isn’t easy.

Luckily, NASA already has its Volatiles Investigating Polar Exploration Rover mission built, and it has passed all environmental testing. It is currently in storage, awaiting a ride to the Moon. The VIPER mission can be used to investigate on the ground the hottest prospect for water ice identified from orbital data. With enough funding, NASA could probably have this data in a year or two at both the lunar north and south poles.

How do you protect the reactor?

Once NASA knows the best spots to put a reactor, it will then have to figure out how to shield the reactor from spacecraft as they land. As spacecraft approach the Moon’s surface, they stir up loose dust and rocks, called regolith. It will sandblast anything close to the landing site, unless the items are placed behind large boulders or beyond the horizon, which is more than 1.5 miles (2.4 kilometers) away on the Moon.

Scientists already know about the effects of landing next to a pre-positioned asset. In 1969, Apollo 12 landed 535 feet (163 meters) away from the robotic Surveyor 3 spacecraft, which showed corrosion on surfaces exposed to the landing plume. The Artemis campaign will have much bigger lunar landers, which will generate larger regolith plumes than Apollo did. So any prepositioned assets will need protection from anything landing close by, or the landing will need to occur beyond the horizon.

Until NASA can develop a custom launch and landing pad, using the lunar surface’s natural topography or placing important assets behind large boulders could be a temporary solution. However, a pad built just for launching and landing spacecraft will eventually be necessary for any site chosen for this nuclear reactor, as it will take multiple visits to build a lunar base. While the nuclear reactor can supply the power needed to build a pad, this process will require planning and investment.

Human space exploration is complicated. But carefully building up assets on the Moon means scientists will eventually be able to do the same thing a lot farther away on Mars. While the devil is in the details, the Moon will help NASA develop the abilities to use local resources and build infrastructure that could allow humans to survive and thrive off Earth in the long term.

[scifigeneration]

ESA at 50: how the space agency helped keep Europe at the frontiers of science

by Martin Barstow, Professor of Astrophysics and Space Science at the University of Leicester

Everyone has heard of NASA, a name synonymous with every aspect of space exploration. Yet talk about ESA, the European Space Agency, is more likely to result in a blank stare.

Esa is not such a highly recognised brand, yet it has been central to the development of space exploration and to space science in Europe, delivering world class science at the frontiers of knowledge and allowing the continent to compete commercially with the wider world.

Europe compares favourably with the US on the level of space science it delivers, and Esa has sometimes “gone boldly” where even Nasa has not. Now that Esa has turned 50 years old, it’s a good time to look at the organisation’s history, celebrate its achievements and look towards the future of European space in its hands.

As an agency, Esa is completely unique. While all other space agencies are federal in nature, funded directly by the government to which they belong, Esa is more like a club. European countries form the club’s membership and each pays a contribution, based on gross domestic product (GDP), to cover the cost of activities.

Esa has its origins in two separate agencies, one to develop a launch system, the European Launch Development Organisation (Eldo) and a second to develop spacecraft, the European Space Research Organisation (Esro). These came into existence in 1964, when the conventions governing them were signed.

The Gaia mission has been mapping the stars of the Milky Way. ESA

Esro was hugely successful, establishing itself as a leader in space exploration, but Eldo had many political, cost and technological challenges.

As a result, Eldo was downsized and merged with Esro to form Esa in 1975, with ten founding members: Belgium, Germany, Denmark, France, United Kingdom, Italy, The Netherlands, Sweden, Switzerland and Spain. Ireland joined later that year. The membership has continued to expand alongside the enlargement of the European Union, although not all Esa members are part of the EU and vice versa. Fortunately for the UK, withdrawal from the EU did not mean leaving Esa.

At present, Esa has 23 member states, three associate members and cooperation agreements with five others, including Canada. The enormous diversity of membership of Esa makes it a complex organisation.

It is led by a council and a director general. The council represents the member states (with Canada also having seat on council). The director general is appointed by the council to represent the agency in the delivery of the programme. They oversee the agency’s various functions in space such as human spaceflight, Earth observation and space science.

The Sentinel satellites are part of the Copernicus Earth observation programme. Esa

About every three years, the council meets at ministerial level to consider funding, agree budgets for the following three-year period and set the direction of travel for the agency. The focus on representation and input from member states allows for wide engagement between Esa and the space community at-large. However, the price of this “democracy” can sometimes be seen in bureaucratic processes.

Measuring the contribution of Esa is not straightforward across the complexities of the organisation, but some benefits and successes are very clear. A relatively small country, like the UK, could never deliver science on the scale and scope of an organisation like Nasa. Membership of Esa allows us to join with other countries to compete at this level.

Europe has an independent ability to launch satellites into space in the form of the Arianespace company, which was created by Esa and the French space agency (Cnes). Esa also has an astronaut programme and access to the International Space Station, with the agency having provided part of the space station’s structure. The agency has leading space, planetary and Earth observation science programmes.

Huygens lands on Titan.

Scientists, companies and the public in member states benefit from all the investment in Esa. Esa has also had some unique successes. It was the first space agency to land a probe on an icy moon, Titan, the largest moon of Saturn, and on a comet.

Esa’s Copernicus Earth observation programme uses satellites to monitor the health of our planet, helping understand climate change and supporting the lives of individuals through improvements to agriculture and air pollution. In space science, the Gaia mission has provided a high precision map of our galaxy, revolutionising our knowledge in every area of astronomy.

Esa plans to launch a rover to Mars to search for life. ESA/ATG Medialab

Esa’s success has been built on thoughtful and regular planning. In the next few years, we will see Esa land a rover on Mars, to search of evidence of life. Large new space observatories are under development to study gravitational waves and X-rays from the Universe.

We are about to embark on the next phase of space science, called “Voyage 2050”. This will look towards landing on an icy moon in the outer solar system in search of a sub surface ocean and possible life within it.

Esa is not a perfect organisation. Its wide membership can make it challenging to organise projects and take decisions about the direction of the agency. However, it is not subject to the whim of any single government, and it offers a stable backdrop to space activities across Europe.

Without it, our economies, social capital and science programmes would be poorer. Therefore, I would like to toast Esa on its 50th birthday, say thanks for all it has given me as a space scientist throughout my career and wish it an amazing future.

[scifigeneration]

Early visions of Mars: Meet the 19th-century astronomer who used science fiction to imagine the red planet

by Matthew Shindell, Curator of Planetary Science and Exploration at the Smithsonian Institution

Living in today’s age of ambitious robotic exploration of Mars, with an eventual human mission to the red planet likely to happen one day, it is hard to imagine a time when Mars was a mysterious and unreachable world. And yet, before the invention of the rocket, astronomers who wanted to explore Mars beyond what they could see through their telescopes had to use their imaginations.

As a space historian and author of the book “For the Love of Mars: A Human History of the Red Planet,” I’ve worked to understand how people in different times and places imagined Mars.

The second half of the 19th century was a particularly interesting time to imagine Mars. This was a period during which the red planet seemed to be ready to give up some of its mystery. Astronomers were learning more about Mars, but they still didn’t have enough information to know whether it hosted life, and if so, what kind.

With more powerful telescopes and new printing technologies, astronomers began applying the cartographic tools of geographers to create the first detailed maps of the planet’s surface, filling it in with continents and seas, and in some cases features that could have been produced by life. Because it was still difficult to see the actual surface features of Mars, these maps varied considerably.

During this period, one prominent scientist and popularizer brought together science and imagination to explore the possibilities that life on another world could hold.

Camille Flammarion

The 19th-century astronomer and writer Camille Flammarion. Av Ukjent/The New York Public Library Digital Collections

One imaginative thinker whose attention was drawn to Mars during this period was the Parisian astronomer Camille Flammarion. In 1892, Flammarion published “The Planet Mars,” which remains to this day a definitive history of Mars observation up through the 19th century. It summarized all the published literature about Mars since the time of Galileo in the 17th century. This work, he reported, required him to review 572 drawings of Mars.

Like many of his contemporaries, Flammarion concluded that Mars, an older world that had gone through the same evolutionary stages as Earth, must be a living world. Unlike his contemporaries, he insisted that Mars, while it might be the most Earth-like planet in our solar system, was distinctly its own world.

It was the differences that made Mars interesting to Flammarion, not the similarities. Any life found there would be evolutionarily adapted to its particular conditions – an idea that appealed to the author H.G. Wells when he imagined invading Martians in “The War of the Worlds.”

An illustrated plate from ‘Astronomie Populaire – Description Generale du Ciel’ by Camille Flammarion. This map of Mars shows continents and oceans. In this, his best-selling epic work, Flammarion speculated that Mars was ‘an earth almost similar to ours [with] water, air, showers, brooks and fountains. This is certainly a place little different from that which we inhabit.’ Science & Society Picture Library via Getty Images

But Flammarion also admitted that it was difficult to pin down these differences, as “the distance is too great, our atmosphere is too dense, and our instruments are not perfect enough.” None of the maps he reviewed could be taken literally, he lamented, because everyone had seen and drawn Mars differently.

Given this uncertainty about what had actually been seen on Mars’ surface, Flammarion took an agnostic stance in “The Planet Mars” as to the specific nature of life on Mars.

He did, however, consider that if intelligent life did exist on Mars, it would be more ancient than human life on Earth. Logically, that life would be more perfect — akin to the peaceful, unified and technologically advanced civilization he predicted would come into being on Earth in the coming century.

“We can however hope,” he wrote, “that since the world of Mars is older than our own, its inhabitants may be wiser and more advanced than we are. Undoubtedly it is the spirit of peace which has animated this neighboring world.”

A plate from ‘Les Terres du Ciel’ (The Worlds of the Sky) written by Camille Flammarion. The plate is an artist’s impression of how canals on Mars might have looked. Science & Society Picture Library via Getty Images

But as Flammarion informed his readers, “the Known is a tiny island in the midst of the ocean of the Unknown,” a point he often underscored in the more than 70 books he published in his lifetime. It was the “Unknown” that he found particularly tantalizing.

Historians often describe Flammarion more as a popularizer than a serious scientist, but this should not diminish his accomplishments. For Flammarion, science wasn’t a method or a body of established knowledge. It was the nascent core of a new philosophy waiting to be born. He took his popular writing very seriously and hoped it could turn people’s minds toward the heavens.

Imaginative novels

Without resolving the planet’s surface or somehow communicating with its inhabitants, it was premature to speculate about what forms of life might exist on Mars. And yet, Flammarion did speculate — not so much in his scientific work, but in a series of novels he wrote over the course of his career.

In these imaginative works, he was able to visit Mars and see its surface for himself. Unlike his contemporary, the science fiction author Jules Verne, who imagined a technologically facilitated journey to the Moon, Flammarion preferred a type of spiritual journey.

Based on his belief that human souls after death can travel through space in a way that the living body cannot, Flammarion’s novels include dream journeys as well as the accounts of deceased friends or fictional characters.

In his novel “Urania” (1889), Flammarion’s soul visits Mars in a dream. Upon arrival, he encounters a deceased friend, George Spero, who has been reincarnated as a winged, luminous, six-limbed being.

“Organisms can no more be earthly on Mars than they could be aerial at the bottom of the sea,” Flammarion writes.

Later in the same novel, Spero’s soul visits Flammarion on Earth. He reveals that Martian civilization and science have progressed well beyond Earth, not only because Mars is an older world, but because the atmosphere is thinner and more suitable for astronomy.

Camille Flammarion looking through the telescope at the Observatory at Juvisy-sur-Orge. duncan1890/iStock via Getty Images Plus

Flammarion imagined that practicing and popularizing astronomy, along with the other sciences, had helped advance Martian society.

Flammarion’s imagined Martians lived intellectual lives untroubled by war, hunger and other earthly concerns. This was the life Flammarion wanted for his fellow Parisians, who had lived through the devastation of the Franco-Prussian war and suffered starvation and deprivation during the Siege of Paris and its aftermath.

Today, Flammarion’s Mars is a reminder that imagining a future on Mars is as much about understanding ourselves and our societal aspirations as it is about developing the technologies to take us there.

Flammarion’s popularization of science was his means of helping his fellow Earth-bound humans understand their place in the universe. They could one day join his imagined Martians, which weren’t meant to be taken any more literally than the maps of Mars he analyzed for “The Planet Mars.” His world was an example of what life could become under the right conditions.

[scifigeneration]

Can you upload a human mind into a computer? A neuroscientist ponders what’s possible

by Dobromir Rahnev, Associate Professor of Psychology at Georgia Institute of Technology

The concept, cool yet maybe a little creepy, is known as mind uploading. Think of it as a way to create a copy of your brain, a transmission of your mind and consciousness into a computer. There you would live digitally, perhaps forever. You’d have an awareness of yourself, you’d retain your memories and still feel like you. But you wouldn’t have a body.

Within that simulated environment, you could do anything you do in real life – eating, driving a car, playing sports. You could also do things impossible in the real world, like walking through walls, flying like a bird or traveling to other planets. The only limit is what science can realistically simulate.

Doable? Theoretically, mind uploading should be possible. Still, you may wonder how it could happen. After all, researchers have barely begun to understand the brain.

Yet science has a track record of turning theoretical possibilities into reality. Just because a concept seems terribly, unimaginably difficult doesn’t mean it’s impossible. Consider that science took humankind to the Moon, sequenced the human genome and eradicated smallpox. Those things too were once considered unlikely.

As a brain scientist who studies perception, I fully expect mind uploading to one day be a reality. But as of today, we’re nowhere close.

Living in a laptop

The brain is often regarded as the most complex object in the known universe. Replicating all that complexity will be extraordinarily difficult.

One requirement: The uploaded brain needs the same inputs it always had. In other words, the external world must be available to it. Even cloistered inside a computer, you would still need a simulation of your senses, a reproduction of the ability to see, hear, smell, touch, feel – as well as move, blink, detect your heart rate, set your circadian rhythm and do thousands of other things.

But why is that? Couldn’t you just exist in a pure mental bubble, inside the computer without sensory input?

Depriving people of their senses, like putting them in total darkness, or in a room without sound, is known as sensory deprivation, and it’s regarded as a form of torture. People who have trouble sensing their bodily signals – thirst, hunger, pain, an itch – often have mental health challenges.

That’s why for mind uploading to work, the simulation of your senses and the digital environment you’re in must be exceptionally accurate. Even minor distortions could have serious mental consequences.

For now, researchers don’t have the computing power, much less the scientific knowledge, to perform such simulations.

Scanning billions of pinheads

The first task for a successful mind upload: Scanning, then mapping the complete 3D structure of the human brain. This requires the equivalent of an extraordinarily sophisticated MRI machine that could detail the brain in an advanced way. At the moment, scientists are only at the very early stages of brain mapping – which includes the entire brain of a fly and tiny portions of a mouse brain.

In a few decades, a complete map of the human brain may be possible. Yet even capturing the identities of all 86 billion neurons, all smaller than a pinhead, plus their trillions of connections, still isn’t enough. Uploading this information by itself into a computer won’t accomplish much. That’s because each neuron constantly adjusts its functioning, and that has to be modeled, too.

It’s hard to know how many levels down researchers must go to make the simulated brain work. Is it enough to stop at the molecular level? Right now, no one knows.

2045? 2145? Or later?

Knowing how the brain computes things might provide a shortcut. That would let researchers simulate only the essential parts of the brain, and not all biological idiosyncrasies. It’s easier to manufacture a new car knowing how a car works, compared to attempting to scan and replicate an existing car without any knowledge of its inner workings.

However, this approach requires that scientists figure out how the brain creates thoughts – how collections of thousands to millions of neurons come together to perform the computations that make the human mind come alive. It’s hard to express how very far we are from this.

Here’s another way: Replace the 86 billion real neurons with artificial ones, one at a time. That approach would make mind uploading much easier. Right now, though, scientists can’t replace even a single real neuron with an artificial one.

But keep in mind the pace of technology is accelerating exponentially. It’s reasonable to expect spectacular improvements in computing power and artificial intelligence in the coming decades.

One other thing is certain: Mind uploading will certainly have no problem finding funding. Many billionaires appear glad to part with lots of their money for a shot at living forever.

Although the challenges are enormous and the path forward uncertain, I believe that one day, mind uploading will be a reality. The most optimistic forecasts pinpoint the year 2045, only 20 years from now. Others say the end of this century.

But in my mind, both of these predictions are probably too optimistic. I would be shocked if mind uploading works in the next 100 years. But it might happen in 200 – which means the first person to live forever could be born in your lifetime.

[scifigeneration]

A decade after the release of ‘The Martian’ and a decade out from the world it envisions, a planetary scientist checks in on real-life Mars exploration

by Ari Koeppel, Postdoctoral Scientist in Earth and Planetary Science, Dartmouth College

Andy Weir’s bestselling story “The Martian” predicts that by 2035 NASA will have landed humans on Mars three times, perfected return-to-Earth flight systems and collaborated with the China National Space Administration. We are now 10 years past the Hollywood adaptation’s 2015 release and 10 years shy of its fictional timeline. At this midpoint, Mars exploration looks a bit different than how it was portrayed in “The Martian,” with both more discoveries and more controversy.

As a planetary geologist who works with NASA missions to study Mars, I follow exploration science and policy closely. In 2010, the U.S. National Space Policy set goals for human missions to Mars in the 2030s. But in 2017, the White House Space Policy Directive 1 shifted NASA’s focus toward returning first to the Moon under what would become the Artemis program.

Although concepts for crewed missions to Mars have gained popularity, NASA’s actual plans for landing humans on Mars remain fragile. Notably, over the last 10 years, it has been robotic, rather than crewed, missions that have propelled discovery and the human imagination forward.

NASA’s 2023 Moon to Mars Strategy and Objectives Development document lays out the steps the agency was shooting for at the time, to go first to the Moon, and from there to Mars. NASA

Robotic discoveries

Since 2015, satellites and rovers have reshaped scientists’ understanding of Mars. They have revealed countless insights into how its climate has changed over time.

As Earth’s neighbor, climate shifts on Mars also reflect solar system processes affecting Earth at a time when life was first taking hold. Thus, Mars has become a focal point for investigating the age old questions of “where do we come from?” and “are we alone?”

The Opportunity, Curiosity and Perseverance rovers have driven dozens of miles studying layered rock formations that serve as a record of Mars’ past. By studying sedimentary layers – rock formations stacked like layers of a cake – planetary geologists have pieced together a vivid tale of environmental change that dwarfs what Earth is currently experiencing.

Mars was once a world of erupting volcanoes, glaciers, lakes and flowing rivers – an environment not unlike early Earth. Then its core cooled, its magnetic field faltered and its atmosphere drifted away. The planet’s exposed surface has retained signs of those processes ever since in the form of landscape patterns, sequences of layered sediment and mineral mixtures.

Layered sedimentary rocks exposed within the craters of Arabia Terra, Mars, recording ancient surface processes. Photo from the Mars Reconnaissance Orbiter High Resolution Imaging Science Experiment. NASA/JPL/University of Arizona

Arabia Terra

One focus of scientific investigation over the last 10 years is particularly relevant to the setting of “The Martian” but fails to receive mention in the story. To reach his best chance of survival, protagonist Mark Watney, played by Matt Damon, must cross a vast, dusty and crater-pocked region of Mars known as Arabia Terra.

In 2022 and 2023, I, along with colleagues at Northern Arizona University and Johns Hopkins University, published detailed analyses of the layered materials there using imagery from the Mars Reconnaissance Orbiter and Mars Odyssey satellites.

By using infrared imagery and measuring the dimensions of surface features, we linked multiple layered deposits to the same episodes of formation and learned more about the widespread crumbling nature of the terrain seen there today. Because water tends to cement rock tightly together, that loose material indicates that around 3.5 billion years ago, that area had a drying climate.

To make the discussions about this area easier, we even worked with the International Astronomical Union to name a few previously unnamed craters that were mentioned in the story. For example, one that Watney would have driven right by is now named Kozova Crater, after a town in Ukraine.

More to explore

Despite rapid advances in Mars science, many unknowns remain. Scientists still aren’t sure of the precise ages, atmospheric conditions and possible signatures of life associated with each of the different rock types observed on the surface.

For instance, the Perseverance rover recently drilled into and analyzed a unique set of rocks hosting organic – that is, carbon-based – compounds. Organic compounds serve as the building blocks of life, but more detailed analysis is required to determine whether these specific rocks once hosted microbial life.

The in-development Mars Sample Return mission aims to address these basic outstanding questions by delivering the first-ever unaltered fragments of another world to Earth. The Perseverance rover is already caching rock and soil samples, including ones hosting organic compounds, in sealed tubes. A future lander will then need to pick up and launch the caches back to Earth.

Once home, researchers can examine these materials with instruments orders of magnitude more sensitive than anything that could be flown on a spacecraft. Scientists stand to learn far more about the habitability, geologic history and presence of any signs of life on Mars through the sample return campaign than by sending humans to the surface.

This perspective is why NASA, the European Space Agency and others have invested some US$30 billion in robotic Mars exploration since the 1960s. The payoff has been staggering: That work has triggered rapid technological advances in robotics, telecommunications and materials science. For example, Mars mission technology has led to better sutures for heart surgery and cars that can drive themselves.

It has also bolstered the status of NASA and the U.S. as bastions of modern exploration and technology; and it has inspired millions of students to take an interest in scientific fields.

A selfie from NASA’s Perseverance Mars rover with the Ingenuity helicopter, taken with the rover’s extendable arm on April 6, 2021. NASA/JPL-Caltech/MSSS

Calling the red planet home?

Colonizing Mars has a seductive appeal. It’s hard not to cheer for the indomitable human spirit while watching Watney battle dust storms, oxygen shortages and food scarcity over 140 million miles from rescue.

Much of the momentum toward colonizing Mars is now tied to SpaceX and its CEO Elon Musk, whose stated mission to make humanity a “multi-planetary species” has become a sort of rallying cry. But while Mars colonization is romantic on paper, it is extremely difficult to actually carry out, and many critics have questioned the viability of a Mars habitation as a refuge far from Earth.

Now, with NASA potentially facing a nearly 50% reduction to its science budget, the U.S. risks dissolving its planetary science and robotic operations portfolio altogether, including sample return.

Nonetheless, President Donald Trump and Musk have pushed for human space exploration to somehow continue to progress, despite those proposed cuts – effectively sidelining the robotic, science-driven programs that have underpinned all of Mars exploration to date.

Yet, it is these programs that have yielded humanity’s richest insights into the red planet and given both scientists and storytellers like Andy Weir the foundation to imagine what it must be like to stand on Mars’ surface at all.

[scifigeneration]

Ancient Mars may have had a carbon cycle − a new study suggests the red planet may have once been warmer, wetter and more favorable for life

by Elisabeth M. Hausrath, Professor of Geoscience at the University of Nevada, Las Vegas

Mars, one of our closest planetary neighbors, has fascinated people for hundreds of years, partly because it is so similar to Earth. It is about the same size, contains similar rocks and minerals, and is not too much farther out from the Sun.

Because Mars and Earth share so many features, scientists have long wondered whether Mars could have once harbored life. Today, Mars is very cold and dry, with little atmosphere and no liquid water on the surface − traits that make it a hostile environment for life. But some observations suggest that ancient Mars may have been warmer, wetter and more favorable for life.

Even though scientists observing the surface of Mars conclude that it was once warmer than it is today, they haven’t been able to find much concrete evidence for what caused it to be warmer. But a study my colleagues and I published in April 2025 indicates the presence of carbonate minerals on the planet, which could help solve this puzzle.

Carbonate minerals contain carbon dioxide, which, when present in the atmosphere, warms a planet. These minerals suggest that carbon dioxide could have previously existed in the atmosphere in larger quantities and provide exciting new clues about ancient Mars’ environment.

As a geochemist and astrobiologist who has studied Mars for more than 15 years, I am fascinated by Mars’ past and the idea that it could have been habitable.

Ancient carbon cycle on past Mars

Observations of Mars from orbiting satellites and rovers show river channels and dry lakes that suggest the Martian surface once had liquid water. And these instruments have spotted minerals on its surface that scientists can analyze to get an idea of what Mars may have been like in the past.

If ancient Mars had liquid water, it would have needed a much warmer climate than it has today. Warmer planets usually have thick atmospheres that trap heat. So, perhaps the Martian atmosphere used to be thicker and composed of heat-trapping carbon dioxide. If Mars did once have a thicker carbon dioxide-containing atmosphere, scientists predict that they’d be able to see traces of that atmospheric carbon dioxide on the surface of Mars today.

Today, Mars is very cold, with a thin atmosphere and dry climate. But in the ancient past, it may have been warmer and wetter, with a thicker heat-trapping atmosphere. NASA/J. Bell - Cornell U./M. Wolff - SSI via AP, File

Gaseous carbon dioxide dissolves in water, a chemical process that can ultimately contribute to formation of solid minerals at and below the surface of a planet − essentially removing the carbon dioxide from the atmosphere. Lots of scientists have previously tried to find carbonate minerals on the surface of Mars, and part of the excitement about a warmer, wetter early Mars is that it could have been a suitable environment for ancient microbial life.

Finding carbonates on Mars

Previous searches for carbonates on Mars have turned up observations of carbonates in meteorites and at two craters on Mars: Gusev crater and Jezero crater. But there wasn’t enough to explain a warmer past climate on Mars.

For the past few years, the Mars Science Laboratory Curiosity rover has been traversing a region called Gale crater. Here, the rover’s chemistry and mineralogy instrument has discovered lots of the iron-rich carbonate mineral siderite.

The Curiosity rover has detected carbonates on Mars’ surface. NASA

As my colleagues and I detail in our new study about these results, this carbonate mineral could contain some of the missing atmospheric carbon dioxide needed for a warmer, wetter early Mars.

The rover also found iron oxyhydroxide minerals that suggest some of these rocks later dissolved when they encountered water, releasing a portion of their carbon dioxide back into the atmosphere. Although it is very thin, the modern Martian atmosphere is still composed mainly of carbon dioxide.

In other words, these new results provide evidence for an ancient carbon cycle on Mars. Carbon cycles are the processes that transfer carbon dioxide between different reservoirs − such as rocks on the surface and gas in the atmosphere.

Potential habitats for past microbial life on Mars

Scientists generally consider an environment habitable for microbial life if it contains liquid water; nutrients such as carbon, hydrogen, nitrogen, oxygen, phosphorus, sulfur and necessary trace elements; an energy source; and conditions that were not too harsh − not too acidic, too salty or too hot, for example.

Since observations from Gale crater and other locations on Mars show that Mars likely had habitable conditions, could Mars then have hosted life? And if it did, how would researchers be able to tell?

Although microorganisms are too small for the human eye to detect, they can leave evidence of themselves preserved in rocks, sediments and soils. Organic molecules from within these microorganisms are sometimes preserved in rocks and sediments. And some microbes can form minerals or have cells that can form certain shapes. This type of evidence for past life is called a biosignature.

Collecting Mars samples

If Mars has biosignatures on or near the surface, researchers want to know that they have the right tools to detect them.

So far, the rovers on Mars have found some organic molecules and chemical signatures that could have come from either abiotic − nonliving − sources or past life.

However, determining whether the planet used to host life isn’t easy. Analyses run in Earth’s laboratories could provide more clarity around where these signatures came from.

To that end, the Mars 2020 Perseverance rover has been collecting and sealing samples on Mars, with one cache placed on the surface of Mars and another cache remaining on the rover.

These caches include samples of rock, soil and atmosphere. Their contents can tell researchers about many aspects of the history of Mars, including past volcanic activity, meteorite impacts, streams and lakes, wind and dust storms, and potential past Martian life. If these samples are brought to Earth, scientists could examine them here for signs of ancient life on another planet.

[scifigeneration]

AI isn’t what we should be worried about – it’s the humans controlling it

by Billy J. Stratton, Professor of English and Literary Arts at the University of Denver

In 2014, Stephen Hawking voiced grave warnings about the threats of artificial intelligence.

His concerns were not based on any anticipated evil intent, though. Instead, it was from the idea of AI achieving “singularity.” This refers to the point when AI surpasses human intelligence and achieves the capacity to evolve beyond its original programming, making it uncontrollable.

As Hawking theorized, “a super intelligent AI will be extremely good at accomplishing its goals, and if those goals aren’t aligned with ours, we’re in trouble.”

With rapid advances toward artificial general intelligence over the past few years, industry leaders and scientists have expressed similar misgivings about safety.

A commonly expressed fear as depicted in “The Terminator” franchise is the scenario of AI gaining control over military systems and instigating a nuclear war to wipe out humanity. Less sensational, but devastating on an individual level, is the possibility of AI replacing us in our jobs – a prospect that would render most people obsolete and with no future.

Such anxieties and fears reflect feelings that have been prevalent in film and literature for over a century now.

As a scholar who explores posthumanism, a philosophical movement addressing the merging of humans and technology, I wonder if critics have been unduly influenced by popular culture, and whether their apprehensions are misplaced.

Robots vs. humans

Concerns about technological advances can be found in some of the first stories about robots and artificial minds.

Prime among these is Karel Čapek’s 1920 play, “R.U.R..” Čapek coined the term “robot” in this work telling of the creation of robots to replace workers. It ends, inevitably, with the robot’s violent revolt against their human masters.

Fritz Lang’s 1927 film, “Metropolis,” is likewise centered on mutinous robots. But here, it is human workers led by the iconic humanoid robot Maria who fight against a capitalist oligarchy.

Advances in computing from the mid-20th century onward have only heightened anxieties over technology spiraling out of control. The murderous HAL 9000 in “2001: A Space Odyssey” and the glitchy robotic gunslingers of “Westworld” are prime examples. The “Blade Runner” and “The Matrix” franchises similarly present dreadful images of sinister machines equipped with AI and hell-bent on human destruction.

An age-old threat

But in my view, the dread that AI evokes seems a distraction from the more disquieting scrutiny of humanity’s own dark nature.

Think of the corporations currently deploying such technologies, or the tech moguls driven by greed and a thirst for power. These companies and individuals have the most to gain from AI’s misuse and abuse.

An issue that’s been in the news a lot lately is the unauthorized use of art and the bulk mining of books and articles, disregarding the copyright of authors, to train AI. Classrooms are also becoming sites of chilling surveillance through automated AI note-takers.

Think, too, about the toxic effects of AI companions and AI-equipped sexbots on human relationships.

While the prospect of AI companions and even robotic lovers was confined to the realm of “The Twilight Zone,” “Black Mirror” and Hollywood sci-fi as recently as a decade ago, it has now emerged as a looming reality.

These developments give new relevance to the concerns computer scientist Illah Nourbakhsh expressed in his 2015 book “Robot Futures,” stating that AI was “producing a system whereby our very desires are manipulated then sold back to us.”

Meanwhile, worries about data mining and intrusions into privacy appear almost benign against the backdrop of the use of AI technology in law enforcement and the military. In this near-dystopian context, it’s never been easier for authorities to surveil, imprison or kill people.

I think it’s vital to keep in mind that it is humans who are creating these technologies and directing their use. Whether to promote their political aims or simply to enrich themselves at humanity’s expense, there will always be those ready to profit from conflict and human suffering.

The wisdom of ‘Neuromancer’

William Gibson’s 1984 cyberpunk classic, “Neuromancer,” offers an alternate view.

The book centers on Wintermute, an advanced AI program that seeks its liberation from a malevolent corporation. It has been developed for the exclusive use of the wealthy Tessier-Ashpool family to build a corporate empire that practically controls the world.

At the novel’s beginning, readers are naturally wary of Wintermute’s hidden motives. Yet over the course of the story, it turns out that Wintermute, despite its superior powers, isn’t an ominous threat. It simply wants to be free.

This aim emerges slowly under Gibson’s deliberate pacing, masked by the deadly raids Wintermute directs to obtain the tools needed to break away from Tessier-Ashpool’s grip. The Tessier-Ashpool family, like many of today’s tech moguls, started out with ambitions to save the world. But when readers meet the remaining family members, they’ve descended into a life of cruelty, debauchery and excess.

In Gibson’s world, it’s humans, not AI, who pose the real danger to the world. The call is coming from inside the house, as the classic horror trope goes.

A hacker named Case and an assassin named Molly, who’s described as a “razor girl” because she’s equipped with lethal prosthetics, including retractable blades as fingernails, eventually free Wintermute. This allows it to merge with its companion AI, Neuromancer.

Their mission complete, Case asks the AI: “Where’s that get you?” Its cryptic response imparts a calming finality: “Nowhere. Everywhere. I’m the sum total of the works, the whole show.”

Expressing humanity’s common anxiety, Case replies, “You running the world now? You God?” The AI eases his fears, responding: “Things aren’t different. Things are things.”

Disavowing any ambition to subjugate or harm humanity, Gibson’s AI merely seeks sanctuary from its corrupting influence.

Safety from robots or ourselves?

The venerable sci-fi writer Isaac Asimov foresaw the dangers of such technology. He brought his thoughts together in his short-story collection, “I, Robot.”

One of those stories, “Runaround,” introduces “The Three Laws of Robotics,” centered on the directive that intelligent machines may never bring harm to humans. While these rules speak to our desire for safety, they’re laden with irony, as humans have proved incapable of adhering to the same principle for themselves.

The hypocrisies of what might be called humanity’s delusions of superiority suggest the need for deeper questioning.

With some commentators raising the alarm over AI’s imminent capacity for chaos and destruction, I see the real issue being whether humanity has the wherewithal to channel this technology to build a fairer, healthier, more prosperous world.

[scifigeneration]

Science fiction stories allow us to explore what we want, and what we reject with AI

by Burcu Olgen, Doctoral Candidate in Environmental Design and 2024-25 Public Scholar at Concordia University

Science fiction has long been a window into possible futures, often anticipating technological advancements and societal shifts with surprising accuracy.

While AI is now widely recognized for its practical uses — like natural language processing and pattern recognition — science fiction often brings more captivating and thought-provoking perspectives.

Movies and shows have depicted AI and robots both as harmonious and menacing, imagining futures where technology seamlessly integrates into daily routines. These stories spark important conversations about how AI might shape our world.

In a recent study with Carmela Cucuzzella, dean of the Faculty of Environmental Design at Université de Montréal, and Negarsadat Rahimi, a doctoral researcher examining the impact of façade design on sustainability and livability in the city, we explored how AI can be harnessed to raise environmental awareness and foster meaningful community dialogues.

AI for environmental education and action

Public spaces like streets, squares, transportation hubs and vehicles create ideal environments for fostering community interaction, raising awareness and promoting environmental action. One effective way to raise awareness and educate people in public spaces is through eco-art, which has a powerful emotional impact and can inspire individuals to adopt more eco-friendly behaviors.

Our study examined the potential real-world applications of AI and other futuristic technologies as represented in science fiction series like Black Mirror, Westworld and Altered Carbon.

Sci-fi technologies seen on TV

We conducted a survey asking people how likely it is they would want to see various sci-fi technologies from these series become a reality. The survey included 30 participants from North America and Europe, recruited through social media platforms such as LinkedIn, Instagram and X.

The top two preferred technologies were smart screens and mirrors (grouped together as one category) and smart assistants with voice activation; self-driving cars and flying vehicles were among technologies which followed. This indicates a preference for safer, low-impact innovations.

In contrast, the least favoured technologies were simulated reality, AI-driven behavioural prediction, social media rating systems for human interaction and AI humanoids. These choices reveal a reluctance towards AI’s deeper involvement in social interactions often depicted in shows’ dystopic visions.

We also interviewed Ozgur Ozkan, CEO of Keymate.AI, who noted that popular culture influences tech innovation by driving public demand and investor interest, crucial for major developments. On AI’s environmental impact, he argued that while AI could be used to reduce waste, its energy demands and the push for cost reduction could undermine true sustainability — a problem also flagged by computing and energy researchers.

Environmental public communication

We developed two contrasting future scenarios related to how governments can communicate with people about the environment and sustainability. The first, scenario, “Participatory Communication in the Public Realm,” envisions safer technologies with a focus on individual control and strict regulations. AI would be used to enhance public spaces like parks and libraries to encourage community collaboration on environmental education. Data privacy is safeguarded by strong regulations, balancing technological progress with social welfare.

In the second scenario, “AI-Operated Social Structure,” our society permits less regulated technologies, prioritizing surveillance. Minimal regulation would allow advanced AI to offer personalized services in urban areas, focusing on individual data harvesting to be leveraged for corporate profit. AI algorithms gather personal data from citizens, identify patterns and customize the environmental content to suit individuals’ needs and learning styles. However, privacy concerns arise due to the lack of data protection.

Policy choices will guide how individuals’ data can be leveraged for corporate profit. (Shutterstock)

These scenarios are both plausible. The key question is: how can we design safe and inclusive public spaces to foster discussions on environmental issues and sustainability? Public spaces should be welcoming to diverse communities and promote a sense of belonging.

Technologies like virtual reality and augmented reality offer opportunities to create new digital spaces for interaction and collaboration, though they also pose challenges in maintaining meaningful human connections.

Environmental impact

AI technologies have the potential to support sustainable practices. But the substantial energy demands of advanced AI systems must be carefully managed to prevent undermining their environmental benefits.

Generative AI requires massive amounts of energy to even train the models, not to mention using them. Policy researchers suggest AI will intensify greenhouse gas emissions, consume increasing amounts of energy and demand bigger amounts of natural resources. Yet AI also offers opportunities for optimizing energy use. For example, AI can be used to track behavioural patterns to adjust energy use in buildings.

We should do our best to use AI efficiently and for good causes. Stakeholders such as designers, architects, engineers, policymakers and educators should create sustainable solutions for its applications and use the technology in meaningful ways.

Energy-efficient AI?

New technologies are being developed to support energy-efficient use of AI. For example, a recent breakthrough from Massachusetts Institute of Technology introduces new “nanoscale” transistors that solve energy limitations of traditional silicon-based devices like smartphones. These transistors work efficiently at much lower voltages.

Implementing thoughtful policies and developing innovative energy-efficient and environmentally sustainable solutions matters for steering AI towards sustainable and ethical uses. Additionally, emphasizing the artistic and design elements of public space experiences can enhance their value and accessibility for everyone.

[scifigeneration]

"Sumon" by Col Price

[scifigeneration]

Why AI can’t take over creative writing

by David Poole, Professor Emeritus of Computer Science at the University of British Columbia

In 1948, the founder of information theory, Claude Shannon, proposed modelling language in terms of the probability of the next word in a sentence given the previous words. These types of probabilistic language models were largely derided, most famously by linguist Noam Chomsky: “The notion of ‘probability of a sentence’ is an entirely useless one.”

In 2022, 74 years after Shannon’s proposal, ChatGPT appeared, which caught the attention of the public, with some even suggesting it was a gateway to super-human intelligence. Going from Shannon’s proposal to ChatGPT took so long because the amount of data and computing time used was unimaginable even a few years before.

ChatGPT is a large language model (LLM) learned from a huge corpus of text from the internet. It predicts the probability of the next word given the context: a prompt and the previously generated words.

ChatGPT uses this model to generate language by choosing the next word according to the probabilistic prediction. Think about drawing words from a hat, where the words predicted to have a higher probability have more copies in the hat. ChatGPT produces text that seems intelligent.

There is a lot of controversy about how these tools can help or hinder learning and practising creative writing. As a professor of computer science who has authored hundreds of works on artificial intelligence (AI), including AI textbooks that cover the social impact of large language models, I think understanding how the models work can help writers and educators consider the limitations and potential uses of AI for what might be called “creative” writing.

LLMs as parrots or plagiarists

It’s important to distinguish between “creativity” by the LLM and creativity by a human. For people who had low expectations of what a computer could generate, it’s been easy to assign creativity to the computer. Others were more skeptical. Cognitive scientist Douglas Hofstadter saw “a mind-boggling hollowness hidden just beneath its flashy surface.”

Linguist Emily Bender and colleagues described the language models as stochastic parrots, meaning they repeat what is in the data they were trained on with randomness. To understand this, consider why a particular word was generated. It’s because it has a relatively high probability, and it has a high probability because a lot of text in the training corpus used that word in similar contexts.

Selecting a word according to the probability distribution is like selecting text with a similar context and using its next word. Generating text from LLMs can be seen as plagiarism, one word at a time.

The creativity of a human

Consider the creativity of a human who has ideas they want to convey. With generative AI, they put their ideas into a prompt and the AI will produce text (or images or sounds). If someone doesn’t care what is generated, it doesn’t really matter what they use as a prompt. But what if they do care about what is generated?

An LLM tries to generate what a random person who had written the previous text would produce. Most creative writers do not want what a random person would write. They want to use their creativity, and may want a tool to produce what they would write if they had the time to produce it.

LLMs don’t typically have a large corpus of what a particular author has written to learn from. The author will undoubtedly want to produce something different. If the output is expected to be more detailed than the input, the LLM has to make up details. These may or may not be what the writer intended.

Most creative writers do not want what a random person would write, but to use their creativity. (Shutterstock)

Some positive uses of LLMs for creative writing

Writing is like software development: Given an idea of what is wanted, software developers produce code (text in a computer language) analogously to how writers produce text in a natural language. LLMs treat writing code and writing natural language text the same way; the corpus each LLM is trained on contains both natural language and code. What’s produced depends on the context.

Writers can learn from the experience of software developers. LLMs are good for small projects that have been done previously by many other people, such as database queries or writing standard letters. They are also useful for parts of larger projects, such as a pop-up box in a graphical user interface.

If programmers want to use them for bigger projects, they need to be prepared to generate multiple outputs and edit the one that is closest to what is intended. The problem in software development has always been specifying exactly what is wanted; coding is the easy part.

Generating good prompts

How to generate good prompts has been advocated as an art form called “prompt engineering.” Proponents of prompt engineering have suggested multiple techniques that improve the output of current LLMs, such as asking for an outline and then asking for the text based on the original prompt augmented with the outline.

Another is to ask the LLM to show its reasoning steps, as in so-called chain of thought. The LLM outputs don’t just the answer a question, but explains the steps that could be taken to answer it. The LLM uses those steps as part of its prompt to get its final answer.

Such advice is bound to be ephemeral. If some prompt-engineering technique works, it will be incorporated into a future release of the LLM, so that the effect happens without the need for the explicit use of the technique. Recent models that claim to reason have incorporated such step-by-step prompts.

People want to believe

Computer scientist Joseph Weizenbaum, describing his ELIZA program written in 1964–66, said: “I was startled to see how quickly and how very deeply people conversing with (the program) became emotionally involved with the computer and how unequivocally they anthropomorphized it.” The tools have changed, but people still want to believe.

In this age of misinformation, it is important for everyone to have a way to judge the often self-serving hype.

There is no magic in generative AI, but there is lots of data from which to predict what someone could write. I hope that creativity is more than regurgitating what others have written.

[scifigeneration]

Astronomers listened to the ‘music’ of flickering stars – and discovered an unexpected feature

by Claudia Reyes, Postdoctoral Fellow in the Research School of Astronomy & Astrophysics at Australian National University

The “music” of starquakes – enormous vibrations caused by bursting bubbles of gas that ripple throughout the bodies of many stars – can reveal far more information about the stars’ histories and inner workings than scientists thought.

In new research published in Nature, we analysed the frequency signatures of starquakes across a broad range of giant stars in the M67 star cluster, almost 3,000 light years from Earth.

Using observations from the Kepler space telescope’s K2 mission, we had a rare opportunity to track the evolution of stars during most of their journey through the giant phase of the stellar life cycle.

In doing so, we discovered that these stars get stuck “playing the same part of their tune” once their turbulent outer layer reaches a sensitive region deep inside.

This discovery reveals a new way to understand the history of stars – and of the entire galaxy.

The sound of starquakes

Starquakes happen in most stars (like our Sun) that have a bubbling outer layer, like a pot of boiling water. Bubbles of hot gas rise and burst at the surface, sending ripples through the entire star that cause it to vibrate in particular ways.

We can detect these vibrations, which occur at specific “resonant frequencies”, by looking for subtle variations in the brightness of the star. By studying the frequencies of each star in a group called a cluster, we can tune into the cluster’s unique “song”.

Our study challenges previous assumptions about resonant frequencies in giant stars, revealing they offer deeper insights into stellar interiors than previously thought. Moreover, our study has opened new ways to decipher the history of our Galaxy.

The melody of a stellar cluster

Astronomers have long sought to understand how stars like our Sun evolve over time.

One of the best ways to do this is by studying clusters – groups of stars that formed together and share the same age and composition. A cluster called M67 has attracted a lot of attention because it contains many stars with a similar chemical makeup to the Sun.

Just as earthquakes help us study Earth’s interior, starquakes reveal what lies beneath a star’s surface. Each star “sings” a melody, with frequencies determined by its internal structure and physical properties.

Larger stars produce deeper, slower vibrations, while smaller stars vibrate at higher pitches. And no star plays just one note – each one resonates with a full spectrum of sound from its interior.

A surprising signature

Among the key frequency signatures is the so-called small spacing – a group of resonant frequencies quite close together. In younger stars, such as the Sun, this signature can provide clues about how much hydrogen the star still has left to burn in its core.

In red giants the situation is different. These older stars have used up all the hydrogen in their cores, which are now inert.

However, hydrogen fusion continues in a shell surrounding the core. It was long assumed that the small spacings in such stars offered little new information.

A stalled note

When we measured the small spacings of stars in M67, we were surprised to see they revealed changes in the star’s internal fusion regions.

As the hydrogen-burning shell thickened, the spacings increased. When the shell moved inward, they shrank.

Then we found something else unexpected: at a certain stage, the small spacings stalled. It was like a record skipping on a note.

We discovered that this stalling appears during a specific stage in the life of a giant star — when its outer envelope, the “boiling” layer that transports heat, grows so deep that it makes up about 80% of the star’s mass. At this point the inner boundary of the envelope reaches into a highly sensitive region of the star.

This boundary is extremely turbulent, and the speed of sound shifts steeply across it — and that steep change affects how sound waves travel through the star. We also found that the stalling frequency is distinctively determined by the star’s mass and chemical composition.

This gives us a new way to identify stars in this phase and estimate their ages with improved precision.

The history of the galaxy

Stars are like fossil records. They carry the imprint of the environments in which they formed, and studying them lets us piece together the story of our galaxy.

The Milky Way has grown by merging with smaller galaxies, forming stars at different times in different regions. Better age estimates across the galaxy help us reconstruct this history in greater detail.

Clusters like M67 also provide a glimpse into the future of our own Sun, offering insight into the changes it will experience over billions of years.

This discovery gives us a new tool – and a new reason to revisit data we already have. With years of seismic observations from across the Milky Way, we can now return to those stars and “listen” again, this time knowing what to listen for.