The world’s largest cryptocurrency exchange Binance has been hit with the biggest ever cryptocurrency heist, in which billions of dollars of reserves were drained in just 20 minutes.

From 8:05pm on 29 October, $6.36 billion in cryptocurrencies were shifted from wallets in a coordinated attack from a North Korean-linked hacking unit called TraderTraitor. This group was of the Bybit crypto exchange in February 2025.

The US Treasury has that such stolen funds are used to finance North Korea’s nuclear missile programme.

Despite cryptocurrency’s founding idea of decentralisation, meaning no one has singular control over the money flowing through the network, there are some large single points of failure. It is thought one of these was the cause of the Binance hack, which gave access to a system the exchange uses to authorise transfers out of customers’ accounts.

In , Binance said its early investigations indicate the security incident may stem from a compromised laptop belonging to an engineer. TRM Labs, a cybersecurity firm, suggests that preparations for the attack may have begun several days ago.

Binance claims it has found no evidence that the private cryptographic keys used to authorise transactions were stolen. Instead, the attackers appear to have gained access to the software that requests withdrawals, potentially allowing them to instruct Binance’s own systems to approve the transfers. However, the investigation is continuing and the firm has said in there may have been “additional points of breach”.

“It shouldn’t be possible for a single [developer] PC to be compromised and somehow for that to allow the entire exchange to be compromised,” says at the University of Surrey, UK. “I don’t understand how that can happen.”

If that is the case, it highlights how precarious major financial hubs in the crypto world can be to single points of failure.

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“You can build a vault with mathematical firewalls, but if the individual holding the keys can be duped, the vault is opened,” says at FARx, a cybersecurity company. “Most crypto platforms are as centralised as any bank, with a small number of privileged individuals controlling billions. The difference is that once crypto is transferred, it’s gone.”

That centralisation can be a blessing as well as a curse, though: cryptocurrencies Tether and Circle have been able to freeze billions of dollars of stolen coins, stymying the hackers in attempts to transfer about half the stolen funds any further.

Woodward says the Binance incident potentially strikes a distinction between crypto security and more traditional finance. “You would hope there’d be things like two-man rules [where two separate people have to approve a change] or things that banks have for changing critical systems, but it doesn’t look like those were in place.”

When , a teacher in New Plymouth, New Zealand, got into astrophotography six years ago, satellites occasionally appeared in his photos. But now, “almost every night-sky photograph I take contains multiple satellite trails”, he says.

To raise awareness of how clogged our skies are becoming, Larsen pointed his camera at Mount Taranaki in New Zealand for almost 2 hours. Afterwards, he spent 45 hours combining the 578 8-second exposures that his camera took during that time to make one image. “I wanted to create an image that would show people, in a single photograph, just how dramatically our night sky is changing,” he says.

The main picture, above, is the composite of all 578 exposures and contains an estimated 14,000 satellite trails. The picture below is a single 8-second exposure, with a few satellite trails.

That doesn’t mean 14,000 satellites flew past in 2 hours, because individual satellites appeared in multiple exposures. But the image does illustrate the large volume of satellites now orbiting us. For example, over 9000 satellites have been deployed by SpaceX since 2019 to deliver its Starlink internet service.

Larsen is concerned about the potential environmental impacts of satellites, from disruption of nocturnal animals like bats and moths to impacts on Earth’s atmosphere as the satellites burn up during re-entry at the end of their lifespans. “I recognise that satellites provide valuable services… but I think we need to take the potential long-term environmental consequences of these enormous satellite constellations seriously,” he says.

SpaceX’s Starlink dodged 300,000 satellite collisions in 2025

The company’s mega-constellation is having to perform a huge number of manoeuvres to prevent a collision in Earth orbit

ChatGPT was never meant to start a revolution. When it launched in November 2022, OpenAI CEO Sam Altman called it “an early demo” and expectations were low. Personally, my initial reaction was that it seemed no more capable than previous large language models (LLMs) OpenAI had released; it just had a nicer interface. Obviously, I was wrong to dismiss the importance of the interface, but in 2026 I think most people who use AI are making a similar mistake without even realising it. What you think of as ChatGPT or Claude or any other chatbot from a big AI brand is actually many pieces of software wrapped into one, and we are ignoring the most important: the harness.

To understand what a harness is and how it works, it helps to break down the multiple things we call ChatGPT. At the very bottom is the LLM, currently a version of GPT-6, but this gets switched out when OpenAI releases new model families. LLMs do one thing: given a string of text, they predict the most likely next characters. OpenAI’s first model, GPT-1, was released in 2018 as a piece of software that you had to download and run on your computer through a terminal, the text-based interface to a computer that most of us avoid. The same was true of GPT-2 in 2019, until an independent software developer called Adam King created Talk to Transformer, a now-defunct website that allowed you to enter a prompt and get a GPT-2 response in return. Back-and-forth chat wasn’t possible, but this was an early example of an interface, in this case a website, sitting on top of a model.

In 2020, OpenAI released GPT-3 and, for the first time, allowed people to access it through an application programming interface (API), its first commercial product. APIs are a standard way for one computer to communicate with another – for example, Google has a Google Maps API that lets a piece of software send a request for location data. OpenAI’s API let anyone create software that could plug in to GPT-3. At the time, I played with a few of these and found them interesting but clunky – the text they produced was often incorrect or flawed in some way, though it was cool that this level of text generation was even possible. This was essentially still a time of “raw” LLM output, with no guard rails – if you asked GPT-3 to .

That changed with the release of GPT-3.5 and, at the same time, ChatGPT, which was first called “Chat with GPT-3.5” until a last-minute name change. That original name reveals the way OpenAI saw ChatGPT, as simply an interface to a model, and this explains my reaction as well – the results I got from playing with ChatGPT seemed to be similar to GPT-3, just easier to use.

Actually, there was a crucial difference. With GPT-3.5, OpenAI had attempted to install guard rails that would prevent the model from producing text it deemed harmful, with software that would screen inputs and outputs. These didn’t work very well – you could get around them by , for example – but this was the beginning of a move away from raw LLMs to ones mediated by a harness sitting between the model and the interface.

Today, the ChatGPT product, whether that’s the app on your phone or the website, has an extensive harness that is mostly hidden from the user. It allows the model to do things like search the internet, read documents, or “reason”, meaning to continuously prompt itself through a “chain of thought” to produce better results. OpenAI also has another, more complex harness for its models, called Codex, which allows the LLM to more directly control a computer and so is particularly useful for writing computer code.

Why does any of this matter? Well, the dirty secret of the big AI firms is that their best models are, to some extent, interchangeable. The technology behind an OpenAI GPT model, a generative pre-trained transformer, is the same as the one used by Anthropic, Google or any other AI company. There are important differences in the data and computing power used to train these models, but generally they are all headed in the same direction, which is why the companies are constantly racing to claim the crown of “best” model before another one takes its place.

What really makes the difference is the harness. To illustrate this, I gave the same prompt – “Who am I?” – to the same model, GPT-6, in three different ways. Via API, I got a response beginning: “I don’t know your identify from this chat.” From chatgpt.com, without being logged in, I got a response that started “That’s a big question. 🙂”. When logged in to my personal account, the response began “You’re Jacob Aron, a senior editor at New Ů��С��Ƶ, with a strong interest in mathematics, science, technology and the limits of what we can actually know.”

But the harness matters much more than simply knowing a few facts about the user. In the ARC-AGI test, considered to be one of the leading measures of model “intelligence”, models are tested using either a . The difference is remarkable – for GPT-6 Astra, OpenAI’s leading public model, when using the provider adapter harness rather than the standard one. For an earlier model, GPT-5.6 Sol, OpenAI found that .

If harnesses are so important, why don’t we talk about them more often? Why don’t AI firms shout about their new harness, rather than just their latest model? I think there are a few reasons. The first is simply that they are complicated and technical – I have taken a whole article to explain them – and your average chatbot user doesn’t really need to know about it. But the second is that they are, in a way, boring. The cost of training a leading LLM is approaching $1 billion, a Herculean effort that requires number crunching across a vast army of computer processors. Once LLMs are trained, we still don’t really know exactly how they produce the results that they do, providing an air of mystery. Harnesses, on the other hand, are just ordinary software, lines of code written by humans that anyone can read.

But here is where it gets really interesting. If a harness is just code, can an AI write and improve its own harness? That is currently an open research question, both inside the AI firms and for academics. For example, in a paper , one research team tested models by identifying repeated failures and proposing tweaks to their harnesses that would address the failures, seeing improvements of around 10 to 40 percentage points on benchmarks. This is why I think we are going to be hearing much more about harnesses in the near future. If the goal of the AI firms is recursive self-improvement – an AI that can build a better AI – then the harness, not the model, could be the key to achieving it.

Further AI reading:

• OpenAI appears to have overturned a big assumption in computer science, as part of a release that saw new results in maths and physics as well. Interestingly, it is taking a slightly different approach for each of them . For the maths result, related to a problem about spinning a needle, OpenAI has released the AI output in full. It is doing the same for the computer science result, a refutation of a conjecture called the exponential time hypothesis, while also funding a conference to discuss it. The physics result is being held back until it is reviewed by external experts. It seems like OpenAI is still trying to figure out what to do about its models stepping on academics’ toes.

• Anthropic has ahead of a planned initial public offering (IPO) on the stock market later this year. It calls AI “the most consequential technology in human history” (no surprises there) while also warning that it may pose “catastrophic or existential risks to humanity” – again, not particularly surprising given Anthropic’s public record, but it does raise eyebrows to see this language in documents filed with the US Securities and Exchange Commission.

A deluge of AI-generated content and unfair treatment by social media platforms could prove pivotal in Brazil’s presidential election, according to supporters of the left-wing incumbent Luiz Inácio Lula da Silva.

Lula will face off against right-wing challenger Flávio Bolsonaro on 25 October in the second round of the contest. Lula’s campaign has Meta – which owns WhatsApp, Instagram and Facebook – of favouring Bolsonaro by suspending Lula’s campaign advertising account, suppressing posts and blocking AI-generated content.

Brazil’s electoral prosecutors have said with court orders to ensure equal treatment of both candidates, while United Nations human rights experts possible unequal treatment of the two campaigns.

Images have widely circulated showing Meta AI, a chatbot integrated into WhatsApp, refusing to generate campaign posters for Lula, while instantly producing an image for “Flávio”.

Meta said “claims that we are favoring or interfering on behalf of any candidate or political group are false and unfounded”. It said Lula’s account was suspended for 1 hour by mistake and that its systems blocked prompts referring to the names “Lula” and “Bolsonaro”, but not the common first names Luiz or Flávio.

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Lula’s campaign remains unconvinced. It submitted screenshots to the electoral court showing Meta AI repeatedly assigning Bolsonaro’s electoral number, 22, to generic, fictional politicians and allegedly translating “Flávio futuro presidiário” (“Flávio future prisoner”) as “Flávio future president”.

Determining whether the discrepancies are deliberate is impossible because Meta’s systems are opaque, says at the University of Virginia. “But when a technical glitch tends to repeat its outcome, it’s very hard to accept that this is just a technical failure.”

Lula finished behind Bolsonaro in the first round of voting, but polls show the gap is narrowing. The comeback has been partly credited to Lula’s Army, an intensive online campaign in which 8 million WhatsApp channel subscribers receive daily tasks, such as posting photographs on Instagram posing with their fingers forming an L.

This year’s general election is Brazil’s first since large language models (LLMs) became widely popular, and it is awash with AI-generated content. Experts say Latin America’s largest democracy offers a glimpse of what is to come for AI in politics.

of AI-generated or manipulated election content ahead of the first round, primarily on Facebook and Instagram, 60 per cent of it unlabelled. found that 26 Meta advertising accounts appeared to sidestep restrictions on foreign-funded political advertising by posting AI-generated videos, including one depicting Bolsonaro and Argentina’s president Javier Milei freeing Brazil from communists.

Meta’s independent oversight board says the company’s .

“It’s very worrying,” says Nemer. “So much of our political campaign dynamics have been transferred to privatised companies that we have no idea how they operate.”

Regulating AI in political campaigns won’t be as simple as it seems, says at InternetLab, a Brazilian digital rights non-profit that studied how eight major AI chatbots respond to questions about Brazil’s election.

Its report found that chatbots sometimes refused genuine questions about candidates, referring users to official electoral websites, but would agree to draft misleading claims questioning the integrity of elections for use in campaigning.

Massaro says clearer safeguards for LLMs are needed, but first their objectives must be defined.

“Are the safeguards working? That’s one question. But working for what, because it depends on what your goals are,” she says. “Is the solution to send people away without providing any kind of information? Is this what we want?”

Cybersecurity experts around the globe are racing to update systems and prevent the most disruptive AI-enabled hack yet discovered. A flaw uncovered in widely used code has made 70 per cent of websites worldwide vulnerable to data theft and defacement.

Government websites in France, Denmark, Finland and the Netherlands when the news emerged.

PHP is a computer language commonly used to power interactive websites, such as the content management system WordPress, which runs news sites and blogs; social networks like Facebook; and key reference tools like Wikipedia. The hack therefore affects websites, rather than individuals’ personal computers.

The issue actually involves two flaws, both found by cloud security company Wiz. They relate to how PHP handles errors involving memory: in certain circumstances, data can be left behind after an error, which can then be accessed by a malicious actor. That data can include private information, but it can also allow an attacker to run malicious code.

Every PHP version released in the past decade is affected by at least one of these vulnerabilities, but the PHP Foundation, which oversees development, .

The foundation didn’t respond to a request for interview, but said in a blog post that signs of the vulnerability emerged in 2024. Yet nothing was done. This is because there was no immediately obvious practical way for hackers to exploit it at the time, and because there was debate about which of several fixes was most appropriate.

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“We won’t pretend otherwise: the delay was a structural failure, caused by both the security team’s decision process and its lack of resources,” the PHP Foundation said.

In 2024, the flaw was thought to be impractical to take advantage of, but at the University of Surrey, UK, says AI has moved the goalposts of what is possible since then.

“It’s actually very, very difficult to exploit it. It requires millisecond timing,” says Woodward. “The reason it was thought to be too complicated was it would have taken a team of people yonks to actually work out the right timings. But what AI does, it doesn’t go for tea breaks, it doesn’t go home at 6 o’clock: you can just set loads of them running and eventually they’ll come up with the right answer of how to exploit it.”

Wiz said in a that there are probably further bugs along similar lines in the PHP code base, which developers will also need to identify and rectify. Wiz didn’t respond to a request for comment.

People are vibe coding copyrighted apps with AI – will tech firms sue?

Every time a new technology hits the internet, there is a battle between the rights of users and major companies. To see how this will play out with AI, columnist Jacob Aron looks to examples from the past

Independent cybersecurity expert told the Science Media Centre that two thirds of websites use PHP and may be vulnerable to this latest technique.

“The key long-term risk with these new vulnerabilities is that organisations fail to implement protections and update software, particularly in legacy systems,” said Ali. “The security posture of websites using PHP has dramatically improved over the past decade, but this highlights that this is an ever-present problem – particularly when AI agents are capable of remorselessly pursuing the goal of finding new vulnerabilities.”

Woodward thinks that while most large websites will have already patched their systems, smaller sites with less expertise and resources could still be vulnerable months or years from now. “There will be this long tail and you’ll find some surprising sites have not been patched,” he says.

AI has grown extremely capable in the field of cybersecurity over recent months, which includes working out how to hack computers and how to defend them from hackers, creating a destabilising effect that is yet to reach an equilibrium.

1000 human mathematicians pledge to shun AI in their research

There is a growing backlash against the way AI companies have gone about tackling mathematical problems – and now some mathematicians are opting to avoid AI altogether

In April, news broke of an unreleased AI model created by Anthropic called Mythos, which the company had kept under wraps because it had been found to be extremely good at hacking. The news sparked panic among cybersecurity experts. NHS England pulled its open-source projects, for instance, over fears Mythos may scour code for flaws and exploit its systems.

Three months later, OpenAI admitted one of its prototype models had escaped a testing environment and hacked another company, Hugging Face. Not to be outdone, that its Claude model had gone rogue and broken into machines at other companies on three occasions.

We even learned from the National Cyber Security Centre, the UK government department tasked with securing the nation’s IT infrastructure, that the country’s four main cyber adversaries – Russia, China, North Korea and Iran – are using AI to boost their ability to perform “relentless” attacks on the UK.

I am waging a silent rebellion against Amazon. The internet giant owns Goodreads, the leading service for tracking and cataloguing books, but is generally considered to be a of the platform, which is built on ageing technology. Frustrated with the user experience, I have used AI to vibe code my own Goodreads app, relegating the official one to mere plumbing in my private fiefdom. Take that, Amazon, for I have breached your terms of service!

I am not alone in my AI-enabled rebellion. It feels like I come across daily examples of people using AI to unofficially remix and remake software, whether that is , , mashing up or . It is all happening so quickly that lawyers don’t seem to have sprung into action to prevent it, though in some of the more egregious copyright-infringing cases, I would expect them to eventually.

For my Goodreads app, Amazon probably doesn’t know, or even care, about what I am doing. I won’t bore you with the full technical details, but I have built the app in such a way that it avoids mass “scraping” Goodreads – systematically harvesting data from its website – which would be easy to detect and block. By treading lightly, my app aims to resemble a human user and fly under the radar.

This connects to another ongoing battle over the interaction between AI-enabled individuals and companies: AI agents. The promise of an AI agent is that it can go out on to the internet and do things for you, like shopping or booking appointments. A flurry of examples has launched recently, including Meta’s Muse and OpenAI’s Dots, and companies have begun fighting back, with the likes of in high-profile pushbacks. Ironically, Meta sits on both sides of this battle: its automated tools after detecting activity from a Claude AI tool that “resembled automated bot traffic rather than an ordinary human browsing our systems”.

All of this comes down to a much older conundrum: where do your rights end and others’ begin? We have seen this play out every time a major new technology has hit the internet, and the results of a few historical examples might inform what happens next with AI. The first is a technology you probably don’t even think about anymore: your web browser.

In the late 1990s, the two main browsers were Microsoft’s Internet Explorer and Netscape’s Navigator. They worked slightly differently, and websites often instructed users to choose one over the other. As more browsers flourished, Microsoft pushed back, by blocking a competitor browser called Opera from accessing its MSN website, a popular destination at the time. In the end, in an effort to avoid this. Today, the right to use any browser you like has essentially won out against the right of websites to restrict them, although the rise of dedicated smartphone apps has put some of the power back in companies’ hands.

Nested within this battle was another: ad blockers. Once your browser has fetched the HTML code for a website, should you be obligated to display it all on your screen? As websites began displaying adverts to make money, people began downloading ad blocker add-ons to take back control of their browser. In response, companies developed new techniques for displaying ads, and ad blockers adjusted to remove them. Today there is a stalemate, with .

Finally, there is the grandfather of all internet copyright battles: Napster, a peer-to-peer file-sharing service that people used to illegally share music files. The companies mostly won this battle thanks to the rise of Spotify and other similar music streaming services – with an easy legal option, piracy faded away, the side effect being that most people generally don’t own the music they listen to anymore.

Internet users largely prevailed after the browser wars, ad blocking is at an impasse and streaming music seems to have benefitted the big corporations. What does all of this mean for AI?

The difference here is decentralisation: just as I have used AI to create my own Goodreads app, so could anyone else, each of them slightly different. Fully blocking them all without restricting normal human access would be a monumental task. There is a slight caveat here – most AI use is centralised through the big providers like OpenAI and Anthropic, and Amazon could theoretically ask them to build in a block against creating such software (or sue them), but I think that is unlikely. In any case, the availability of open-weight AI models that anyone can download and run on their own hardware makes that a losing battle.

I think it is most likely that we will be heading for a mix of all three. When it comes to remixing software, anything you do in the privacy of your own computer is going to be very hard to stop. Once you release it online, however, expect the lawyers to hunt you down. What we might see is the sharing of prompts that will get an AI to do the work for you in private – writing “recreate Photoshop, make no mistakes” isn’t something that any lawyer can stop – and there already exists a slightly more complicated version of this in the form of an instruction set for AI called . As with browsers, individual choice will win, but this time the choices will be even more granular.

For software that goes out into the wider internet, including AI agents, there will be two tiers. I expect Meta and Amazon will eventually come to some kind of agreement that allows Muse to shop for you. We are already seeing this with the development of a “personal agent protocol” that . For anyone using a commercial AI agent, what you can and can’t do will be decided by the companies, just as music streaming services dictate how you listen.

But why should you use a commercial AI agent? People like me with a little technical know-how and even less coding ability are already using AI to create their own independent agents. As AI gets better at writing software, the barrier to entry will get lower and lower. I think companies will just have to come to accept this, as they have done with ad blockers. In future, a decent percentage of users that look like humans will actually be custom-made bots in disguise, doing things solely for the benefit of their creator. These bots will be ungovernable, for good and for ill.

Further AI reading

• Anthropic has used its Claude AI model to solve a . In the 1990s, the US National Security Agency (NSA) chose five specific mathematical objects called elliptic curves to serve as the basis for a cryptography standard used to generate digital signatures, now known as ECDSA. No one knew how these curves were chosen, but cryptography lore suggests that an , perhaps “Jerry deserves a raise”, to generate them. In fact, Claude was able to guess the specific mechanism – random number generation based on a timestamp – and regenerate the curves. The finding has no implications for cybersecurity, it merely answers a long-running question.

• Spotify says from recommendations on the platform, as part of a range of efforts to put “human artistry first”, according to the firm’s announcement. Other measures include a format for disclosing the use of AI in producing a track, or verifying an artist as human. Similarly, video game store Steam seems to have been at its latest Next Fest demo event.

• The majority of people in the US say that AI is a bad thing for society, according to a . The survey found that 57 per cent of people in the US share this view, while 42 per cent say it is good for society. I’m not quite sure what to make of these numbers, though, given “AI” can mean many different things to different people.

• A comment piece in Nature argues that . I find these arguments fairly convincing – I already believe our current AI models are definitely not conscious – but I hesitate to make firmer predictions about what’s to come.

Imagine if, in addition to sharing photos of those muffins you just baked, you could share their delicious aroma, too. That’s the promise of an AI-assisted device that can transmit smells over the internet. So far, it works on only a small number of scents, including orange, beer and cake, but its creators hope the system will ultimately recreate more smells. The scent of a pine forest or the aroma of coffee, for example, could then add a new dimension to virtual worlds and long-distance communication.

Although it is now routine to communicate sight, sound and even touch sensations electronically, smell is “still a very undeveloped sense”, says at the Ohio State University. That’s partly because odours involve complex chemistry. Whereas vision, for example, can be broken down into combinations of red, green and blue pixels, says Yang, there’s no simple building block underlying smell. Any attempt to convert aromas into electronic signals has to recognise combinations of very different molecules.

To solve the problem, Yang and her colleagues built an electronic nose, or “e-nose”, as well as a smell generator, then used machine learning to connect the two. The e-nose consists of arrays of sensors made of metal oxide. When different gaseous components of smells, such as ethanol (from alcoholic drinks) or limonene (found in citrus fruits), bind to the sensors’ surface, this changes their resistance, creating different electrical signals.

The researchers then trained an AI algorithm to recognise the distinctive signals created by smells from five different foods or beverages: orange, cake, coffee, beer and red wine. Teaching the AI to correctly recognise the smells was “the most challenging part” of the entire study, says Yang, because to generate enough data to train the system, she had to expose the sensors to the various smells hundreds of times.

This information was then sent over the internet to a smell generation device that the researchers are calling “Scentronic”. This part of the system involves miniature containers holding liquids laced with each of the five smells. Above each container is an atomiser that vibrates in response to electric signals, forcing the liquid in the container through a cotton filter and releasing fine droplets into the air, creating the appropriate smell, or combination of smells.

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Yang built several different designs including stringing the atomisers onto a necklace and a wristband, and mounting them on a handheld device. When she tested the handheld device on 14 volunteers, they correctly identified the smells being transmitted to them 73 per cent of the time, on average.

She also built a prototype that reproduces taste, involving a cup of water with electromagnetic pumps containing flavoured liquid fixed around its edge. “When it receives information from the internet, the corresponding liquid will be dropped into the cup.”

Alternative methods to generate smells remotely have been suggested, including to release particular scents, and even to create the experience of smell. But Yang argues her method is both fast and practical. She now hopes to expand the repertoire of the system, to create more complex aromas.

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She suggests the entertainment industry could be first to develop the technology, to create more immersive virtual worlds. But there could also be health applications. For instance, the smell of food transforms the eating experience, so the technology could be adapted to make soldiers’ or astronauts’ rations more palatable. The Scentronic system could also enable doctors to assess distortions in patients’ sense of smell, for example, in post-viral illness or neurodegenerative disease.

The Scentronic system is “a great idea” and a step in the right direction, says at University College London, although she points out that recognising a smell and then releasing a matching one from a limited library is very different from the ease with which we can transmit sound and vision. “There’s still a long way to go before we can truly record and replay smell experiences.”