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World’s first AI‑designed vaccine explained
Neil Mabbott, University of Edinburgh
Researchers at the University of Cambridge have developed what they describe as a fundamentally new type of vaccine using artificial intelligence (AI). The vaccine’s key component was designed entirely by AI and has now been tested in people for the first time.
The goal is ambitious: a single vaccine that works not just against all known human coronavirus variants, but against related bat viruses that could jump from animals to humans and cause future pandemics.
Traditional vaccines train our immune system to recognise one specific virus. The problem is that viruses mutate. When they change enough, the vaccine stops working, which is why we need a new flu shot every year and why COVID vaccines have been updated repeatedly since 2021.
AI offers a way around this. By analysing genetic data from thousands of related viruses, it can identify the parts that stay the same across different strains and that are unlikely to change over time. Target those stable features, and you have a vaccine that should work against the whole family, not just the strain you started with.
This is exactly what the Cambridge team did. They used AI to scan viruses from the sarbecovirus family, which includes the viruses that cause both SARS and COVID, as well as a range of animal coronaviruses – looking for shared features that evolution has left largely untouched. Those features became the basis of the vaccine.
DNA vaccines
While many people are familiar with the mRNA shots used during the pandemic, this new vaccine uses DNA. DNA vaccines are generally more stable than mRNA vaccines, making them easier to store and transport. A significant advantage in lower-income countries where “cold-chain” infrastructure is limited.
They can also be administered without needles. A high-pressure stream of liquid delivers the vaccine through the skin, making administration less painful and easier to scale up during an outbreak.
Could it protect against future pandemics?
These practical advantages matter most if the vaccine itself can do something no existing jab can: protect against viruses we haven’t encountered yet.
Broad-spectrum vaccines could change the way the world responds to emerging infectious diseases. By offering much wider protection than traditional vaccines, they could provide rapid immunity against new and emerging viral threats. This would equip public health officials with tools to stop future outbreaks in their tracks before they have a chance to turn into global pandemics.
They could also transform our approach to more familiar diseases. Influenza is a prime target because it exists in many different strains and evolves so rapidly. Scientists have to predict which strains will dominate each flu season, and they guess wrong, vaccine effectiveness can suffer. A universal flu vaccine that targets features shared across multiple strains could eventually end the annual race to keep up with the virus.
And the Ebola virus shows why this matters right now. The recent outbreak in the Democratic Republic of the Congo and Uganda is driven by the Bundibugyo strain, which bypasses existing vaccines. While researchers rush to create a new vaccine specifically for this strain, local communities remain at high risk. A broad-spectrum vaccine designed to cover an entire virus family could transform that picture.
What the trial found
This is the first human trial of an AI-designed vaccine. The results showed that this DNA vaccine was able to stimulate the immune system to produce antibodies that can recognise different types of sarbecoviruses. The technology was found to be safe and well tolerated.
This is an exciting advance because it demonstrates how AI has the potential to design variant-proof vaccines against future pandemic threats. The needle-free delivery system could also make the vaccine easier to administer and distribute worldwide.
However, there is more work to do. Although the results in this study are encouraging, the immune responses following vaccination were modest. It was also uncertain how long the protection lasts and whether further boosters will be required. Larger trials are also needed to determine whether the vaccine can prevent or reduce virus infections in the real world.
A universal vaccine remains a few years away. And any new vaccine must still pass larger trials to prove it is safe, effective and provides lasting protection. But this study shows the goal is getting closer – and AI may help us get there faster.![]()
Neil Mabbott, Personal Chair of Immunopathology, University of Edinburgh
This article is republished from The Conversation under a Creative Commons license. Read the original article.
UN report warns AI could soon use 3% of world’s electricity and more water than we need to drink
Amanda Turnbull-McRae, University of Waikato
One argument often used to quell concerns about the rising energy and resource demand of data centres is that artificial intelligence (AI) models will need less in the future as they improve and become more efficient.
But this seemingly logical thinking is a trap, according to a new United Nations report that quantifies the environmental costs of AI.
The report estimates that by 2030, AI’s energy use could double to consume 3% of the world’s electricity, produce emissions to equal the UK and deplete more water for cooling than the annual drinking water need of the global population.
It also anticipates the use of AI will follow an economic principle known as the “Jevons paradox”, which predicts that when technological improvements increase the efficiency of a resource, it leads to a rise, rather than a fall, in the total consumption of that resource.
The paradox is named after economist William Stanley Jevons who observed this effect with the use of coal in 19th-century England. Efficiency gains did not reduce overall consumption. Instead, the lower costs resulted in expanded use and higher overall demand.
As AI models become cheaper and more attractive, the report expects this to encourage new uses and higher volumes of use, eroding and possibly erasing any savings from efficiency advances.
To avoid falling into this trap, it lays out a roadmap for responsible AI use based on guiding principles of transparency, efficiency by design, equity and justice, lifecycle responsibility, global cooperation and sustainable use.
The scale of the problem
Last year, data centres already consumed as much electricity as Saudi Arabia, which ranks as the world’s 11th largest electricity consumer.
If electricity use doubles as projected by 2030, the associated carbon footprint would require 6.7 billion trees grown over ten years to offset this demand.
Data centres would also require 9.3 trillion litres of water and land nearly ten times the size of Mexico City.
Beyond resource use, the report also underscores the structural inequity at the heart of the AI boom, with only 32 nations hosting AI-specific cloud infrastructure and 90% of that capacity located in the US and China.
It warns of a widening digital divide between nations that build and control AI systems and those that consume them, with the latter often bearing a disproportionate environmental burden caused by mineral extraction and e-waste.
Responsible AI use
Two main forces shape AI’s operational footprint: how much we use it and how we use it.
This involves all tasks AI models perform, from text and code generation to image and video. Each of these tasks requires different levels of computational effort.
The model choice also matters as each AI system performs these task with distinct energy and environmental costs.
The report argues responsible AI requires full value-chain governance, from mineral sourcing to recycling and safe disposal.
It calls for a twinning of capability and environmental stewardship – thinking about both what AI can do for us and the protection of the natural environment.
This would mean making environmental disclosures a routine part of AI development, at both the model and task level, and incorporating projected AI demand in climate and energy planning.
Responsible AI is crucial as countries are promoting and adopting AI across government and the public sector.
In Aotearoa New Zealand, the government has launched a national AI strategy and a public service AI framework.
While the framework was informed by the OECD’s values-based AI principles, including inclusive and sustainable development, there is no requirement for environmental disclosures and no regulator compiling energy use or emissions.
Likewise in Australia, improving public services is part of the national AI plan. For example, the National Film and Sound Archive of Australia has created Bowerbird, a machine learning-enabled mass audio and video transcription engine, to document material. The Department of Veteran’s Affairs has developed a proof-of-concept tool to see whether AI can help speed up the processing of claims.
Both countries take a deliberate “light touch” and principles-based regulatory approach to AI. But this approach risks overlooking the growing environmental cost of AI that can’t be solved by improving it.
The natural environment is foundational to the economy, culture and wellbeing. It should be at the centre of our thinking. It’s time to rethink the AI innovation playbook and shift focus toward a sustainable tech future.![]()
Amanda Turnbull-McRae, Senior Lecturer in Law, University of Waikato
This article is republished from The Conversation under a Creative Commons license. Read the original article.
New AI Glasses for Dementia ‘Sees’ Objects With Labels Projected on Lenses to ‘Significantly’ Improve Lives
Carole Grieg testing the CrossSense AI glasses – SWNS
Warning appears on the lenses of the CrossSense AI glasses (GNN screenshot of SWNS/CrossSense video)
Screenshot of Wispy AI in the midst of interacting with user of theCrossSense AI glasses, discussing care of a houseplant (Still from SWNS video)AI-powered digital stethoscopes show promise in bridging screening gaps
New Delhi, As tuberculosis (TB) continues as the deadliest infectious cause of deaths globally, a new study has shown that artificial intelligence (AI)-enabled digital stethoscopes can help fill critical screening gaps, especially in hard-to-reach areas.
Quantum computers are coming to break our codes faster than anyone expected
Craig Costello, Queensland University of Technology
Online data is generally pretty secure. Assuming everyone is careful with passwords and other protections, you can think of it as being locked in a vault so strong that even all the world’s supercomputers, working together for 10,000 years, could not crack it.
But last month, Google and others released results suggesting a new kind of computer – a quantum computer – might be able to open the vault with significantly less resources than previously thought.
The changes are coming on two fronts. On one, tech giants such as IBM and Google are racing to build ever-larger quantum computers: IBM hopes to achieve a genuine advantage over classical computers in some special cases this year, and an even more powerful “fault-tolerant” system by 2029.
On the other front, theorists are refining quantum algorithms: recent work shows the resources needed to break today’s cryptography may be far lower than earlier estimates.
The net result? The day quantum computers can break widely used cryptography – portentously dubbed “Q Day” – may be approaching faster than expected.
The quantum hardware race
Quantum computers are built from quantum bits, or qubits, which use the counterintuitive properties of very tiny objects to carry out computations in a different and sometimes far more efficient way from traditional computers.
So far the technology is in its infancy, with the major goal to increase the number of qubits that can be connected to work as a single computer. Bigger quantum computers should be much better at some things than their traditional counterparts – they will have a “quantum advantage”.
Late last year, IBM unveiled a 120-qubit chip which it hopes will demonstrate a quantum advantage for some tasks.
Google also recently announced it planned to speed up its move to adopt encryption techniques that should be safe against quantum computers, known as post-quantum cryptography.
Alongside these tech giants, newer approaches are also flourishing. PsiQuantum is using light-based qubits and traditional chip-manufacturing technology. Experimental platforms such as neutral-atom systems have demonstrated control over thousands of qubits in laboratory settings.
In response, standards bodies and national agencies are setting increasingly concrete timelines for moving away from common encryption systems that are vulnerable to quantum attack.
In the United States, the National Institute of Standards and Technology (NIST) has proposed a transition away from quantum-vulnerable cryptography, with migration largely completed by 2035. In Australia, the Australian Signals Directorate has issued similar guidance, urging organisations to begin planning immediately and transition to post-quantum cryptography by 2030.
Algorithms make the lock-picking faster
Hardware is only half the story. Equally important are advances in quantum algorithms – ways to use quantum computers to attack encryption.
Much interest in quantum computer development was spurred by Peter Shor’s 1994 discovery of an algorithm that showed how quantum computers could efficiently find the prime factors of very large numbers. This mathematical trick is precisely what you need to break the common RSA encryption method.
For decades, it was believed a quantum computer would need millions of physical qubits to pose a threat to real-world encryption. This is far bigger than current systems, so the threat felt comfortably distant.
That picture is now changing.
In March 2026, Google’s Quantum AI team released a detailed study showing that far fewer resources may be needed to attack a different kind of encryption which uses mathematical objects called elliptic curves. This is what systems including Bitcoin and Ethereum use – and the study shows how a quantum computer with fewer than half a million physical qubits may be able to crack it in minutes.
That’s still a long way beyond current quantum computers, but around ten times less than earlier estimates.
At the same time, a March 2026 preprint from a Caltech–Berkeley–Oratomic collaboration explores what might be possible using neutral-atom quantum computers. The researchers estimate that Shor’s algorithm could be implemented with as few as 10,000–20,000 atomic qubits. In one design they propose, a system with around 26,000 qubits could crack Bitcoin’s encryption in a few days, while tougher problems like the RSA method with a 2048-bit key would need more time and resources.
In plain terms: the codebreakers are becoming more efficient. Advances in algorithms and design are steadily lowering the bar for quantum attacks, even before large-scale hardware exists.
What now?
So what does this mean in practice?
First, there is no immediate catastrophe – today’s cryptography won’t be broken overnight. But the direction of travel is clear. Each improvement in hardware or algorithms reduces the gap between current capabilities and useful quantum cracking machines.
Second, viable defences already exist. NIST has standardised several post-quantum cryptographic algorithms which are believed to be resistant to quantum attacks.
Technology companies have begun deploying these in hybrid modes: Google Chrome and Cloudflare, for example, already support post-quantum protections in some protocols and services.
Systems that rely heavily on elliptic-curve cryptography – including cryptocurrencies and many secure communication protocols – will need particular attention. Google’s recent work explicitly highlights the need to migrate blockchain systems to post-quantum schemes.
Finally, this is a two-front race. It is not enough to track progress in quantum hardware alone. Advances in algorithms and error correction can be just as important, and recent results show these improvements can significantly reduce the estimated cost of attacks.
Every new headline about reduced qubit counts or faster quantum algorithms should be understood for what it is: another step toward a future where today’s cryptographic assumptions no longer hold.
The only reliable defence is to move – deliberately but decisively – toward quantum-safe cryptography.![]()
Craig Costello, Professor, School of Computer Science, Queensland University of Technology
This article is republished from The Conversation under a Creative Commons license. Read the original article.
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AI could help us more accurately screen for breast cancer – new research
Sasun Bughdaryan/Unsplash
Carolyn Nickson, University of Sydney; The University of Melbourne and Bruce Mann, The University of MelbourneAt least 20,000 Australian women are diagnosed with breast cancer each year. And more than 3,300 die from the disease.
To save women’s lives, we need to detect breast cancer early. Breast screening, which halves women’s risk of dying from breast cancer, is key to that.
A new Australian study published today in The Lancet Digital Health suggests AI could help improve how we screen for breast cancer.
How do we currently screen for breast cancer?
Since 1992, Australia has offered free breast X-rays, known as mammograms, every two years to women aged between 50 and 74. Just over half of eligible women participate.
Of the women found to have cancer, about 25% are diagnosed between the biennial screens. These “interval cancers” are often aggressive and, unfortunately, more likely to be fatal.
In some cases, a more sensitive screening test may have detected them earlier.
The role of AI
Australia’s BreastScreen program was established in response to several major clinical trials conducted between the 1960s and 1980s. The screening technology used by the program has not substantially changed since then.
Researchers are now exploring risk-adjusted screening, which tailors screening to women based on their risk, as a way to detect more cancers earlier. This may include programs offering different technologies for women at higher risk of developing breast cancer.
Currently, we generally assess cancer risk via questionnaires that help identify if a woman has any risk factors associated with breast cancer.
One risk factor is breast density which refers to how much glandular tissue is in the breast. As well as being a risk factor for breast cancer, the higher a woman’s breast density, the harder it is to detect cancer on a mammogram.
We can also use one-off genetic testing to identify women with a higher lifetime risk of developing breast cancer. This involves looking for high-risk gene mutations such as BRCA1 and BRCA2, which are associated with increased breast and ovarian cancer risk. Genetic testing can also help us estimate a person’s lifetime risk of developing breast cancer.
More recently, researchers have been investigating artificial intelligence (AI) as a new approach to assess breast cancer risk. A new Australian study, published in The Lancet Digital Health today, focused on a specific AI tool known as BRAIx.
What did the study involve? And what did it find?
This study used an AI tool, known as BRAIx, trained using BreastScreen Australia data to help radiologists assess mammograms.
The study assessed how well BRAIx predicted women’s risk of developing breast cancer in the next four years, among women who had a clear mammogram.
Of the 95,823 Australian women assessed, 1.1% (1,098) had developed breast cancer in the four years after they received a clear mammogram. Of the 4,430 Swedish women assessed, 6.9% had developed breast cancer within two years of a clear screen.
The study findings show that BRAIx scores were very useful for identifying women who were more likely to develop cancer one to two years after having a clear screen. Findings from the Australian dataset suggest BRAIx scores identified cancers found three to four years later, but with less accuracy.
These findings suggest BRAIx could help identify women who might benefit from additional tests. This may include an MRI (which uses a magnetic field to produce images of organs and tissue) or contrast-enhanced mammography (which uses an iodine dye to improve the visibility of a regular mammogram).
These findings reinforce a 2024 Swedish study that used an AI-based risk assessment to select women for additional testing. The researchers referred 7% of women to have a follow-up MRI, and 6.5% of were found to have cancers missed by mammograms.
Does the study have any limitations?
As with most studies, yes. Here are two.
it’s difficult to compare BRAIx to genetic testing. This is because BRAIx is trained to find missed or emerging cancers over a four year period. In contrast, genetic testing identifies a person’s risk of developing cancer over their lifetime
it might not use the best breast density data. This study found BRAIx more accurately predicts breast cancer risk compared to assessments based on breast density. But this breast density data was collected using a different tool to those used by the Breastscreen program. So this finding should be interpreted carefully.
So, where to from here?
The study adds to a growing body of evidence that AI risk assessment could help breast screening programs find cancers earlier.
BRAIx is now being trialled as part of the BreastScreen Victoria program, to help read mammograms. And other states are already using and evaluating different AI tools for reading mammograms.
So it may be time for Australia to conduct a national, independent review of these new tools. As part of a more risk-adjusted approach to breast screening, they could save lives.![]()
Carolyn Nickson, Principal Research Fellow, Cancer Elimination Collaboration, University of Sydney; The University of Melbourne and Bruce Mann, Professor of Surgery, Specialist Breast Surgeon, The University of Melbourne
This article is republished from The Conversation under a Creative Commons license. Read the original article.
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