The first Artificial Intelligence vaccine

 

World’s first AI‑designed vaccine explained

Sooksaard/Shutterstock.com

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.

DNA and RNA viruses explained.

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.

Artificial Intelligence Part 9 - specific industry impacts - retail marketing, advertising and fashion

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Part 8 of this series on AI explored the impact of the new technology on the retail sector. This posting will further consider AI's effect on specific retail functions. 

Marketing and advertsiing in retail

AI has most impact with -

• Performance marketing: Google and Meta's advertising platforms are increasingly self-optimising with informed automation. The previous large teams of digital marketing specialists who managed bid strategies, audience targetting and creative testing are shrinking.  The platforms can carry out the operations that previously employees would carry out.
• Personalised communications: AI can and does generate individualised email, push notifications and SMS content at scale replacing or reducing human campaign teams.
• Market research and consumer insight: AI can synthesise customer data, social media listening, consumjer feedback and sales patterns into insight reports thus reducing analyst headcount.

Fashion retail specifically

Fashion sits at the intersection between the retails and creative indsutries making it doubly exposed -

• Trend forecasting: traditionally carried out by high-cost specialist agencies and in-house teams, AI can now analyse social media, runway coverage and sales data to predict trends with considerable accuracy.
• Design assistance: Ai tools can and do generate design concepts, colourway combinations and print patterns. This places junior design assistant roles under pressure in a very cost conscious industry.
• Fit and sizing: AI fit technology reduces return rates thus threatening the customer service infrastructure built around managing returns.
• Wholesale and buying: the buyer role which requires relationship-building with suppliers and market intuition is more protected from AI encroachment. The analytical support underneath these roles is much less so.

At this stage AI appplication in fashion is more of an augmenting role rather than displacement of jobs but increasingly this may change.

Astronomy - alien visits to Earth unlikely

Aliens might exist. But there are three reasons why they’re not visiting us

Steven Spielberg’s new film, Disclosure Day, explores the idea of extraterrestrial life. Universal

Carol Oliver, UNSW Sydney

The United States government’s recent release of hundreds of previously classified Unidentified Anomalous Phenomena (UAPs) cases spanning the 1940s to the present, along with the new Steven Spielberg movie, Disclosure Day, about extraterrestrial life, has fuelled the idea that aliens are visiting Earth.

In fact, polls in Australia, the US and elsewhere indicate around a third of the public believes aliens are here.

However, while what we know about the universe suggests aliens may exist, there are three compelling reasons why they probably aren’t visiting us.

Space is big – very big

To begin with, space is vast – beyond our imagination.

Proxima Centauri, the nearest star to our Sun, is about 40 trillion kilometres away, 268,000 times farther than the Sun is from Earth. That’s 4.3 light years as astronomers measure it. A light year is the distance light travels in one year at 300,000km per second.

We can only travel across space at a fraction of the speed of light with current technology. Even our fastest spacecraft, the Parker Solar Probe, travels at a top speed of roughly 191 kilometres per second – 0.064% the speed of light.

At that speed, it would take about 6,650 years to reach Proxima Centauri, and that’s just in our local stellar neighbourhood. So interstellar travel within human lifespans would require much higher velocities.

Let’s assume we did have the means to travel close to the speed of light. That introduces the first problem with travelling at that velocity. Albert Einstein demonstrated that time is relative; the rate of time flow is not the same everywhere in the universe. The faster a spaceship travels from Earth, the slower time will pass for its passengers. This is called time dilation.

For example, when NASA astronaut Scott Kelly arrived back on Earth from a year on the International Space Station, he was milliseconds younger than his identical twin because time moves more slowly for objects in motion, and the International Space Station travels at roughly 28,150 kilometres per hour.

This difference was negligible for the Kelly twins. But for any aliens cartwheeling through our skies, it would be significantly more because of the journey to Earth and back from a distant star system at a necessarily higher speed. They would go home to a planet much older than the one they left – perhaps by a century or more. They would be time exiles.

A photograph from the Apollo 17 mission in December 1972. NASA

Unimaginably high energy requirements

Then there’s the unimaginably high energy requirement for interstellar travel.

The mass of the spaceship increases with velocity, so an increasing amount of energy is required to accelerate it.

At the speed of light, the ship becomes infinitely massive, requiring an infinite amount of energy. This is clearly impossible.

Another significant issue is that space is a vacuum – but not completely. There are just enough particles to worry about. They can potentially cause fatal radiation for passengers and the instruments of a high velocity spacecraft, or destroy it. Sparsely spread hydrogen atoms turn into intense radiation at near light speed, and the heat that is generated would ablate and eventually destroy the hull.

Faster-than-light travel, according to physicist Miguel Alcubierre, is possible, but it comes with its own set of issues and a currently impossible energy requirement.

That raises the question of why spend all this energy to travel to Earth? Anything we have, an advanced civilisation (as they would have to be to get here) would be able to make on their planet.

A unique biosphere

Yet another issue is our biosphere, unique to Earth as far as scientists know.

Life and the planet co-evolved. Complex life would not exist on Earth if cyanobacteria, a type of single-celled microbe, had not pumped oxygen into our mostly nitrogen atmosphere 2.4 billion years ago.

It’s therefore not toxic for us, but oxygen is reactive and could be highly corrosive for aliens. And while they could wear protective suits like humans do when going to inhospitable environments, reports of visiting aliens do not include any descriptions of spacesuits.

So, are aliens out there?

If aliens are not here, are they out there?

It’s an interesting question, scientifically and philosophically. Scientists do not have enough information yet, but they are working on the question.

About 6,200 exoplanets have been found in more than 4,700 solar systems, though none are like Earth or our Solar System.

Most stars could have at least one planet, and there are more than 100 billion stars in our galaxy alone. The number of planets is therefore astronomical, and some may be habitable.

Closer to home, there are worlds with potential for microbial life either past or present – Mars, Europa (a moon of Jupiter), and Enceladus and Titan (moons of Saturn). If we discover life began twice in our Solar System, that will increase the odds of life elsewhere.

Since 1960, we’ve had the capability to look for intelligence elsewhere, piggybacking on normal radio astronomy. The biggest search for alien life projects are carried out by the SETI Institute in California and the Breakthrough Listen project based at Oxford University in the United Kingdom.

Nothing has been found across all the searches made. Finding intelligence in our time frame – about a hundred years – in the 13.8-billion-year history of the universe is challenging.

However, as a 1959 Nature paper noted, while it’s difficult to estimate the chance of success, if we don’t search, the chance drops to zero.

Carol Oliver, Professor in Science Communication and Astrobiology, UNSW Sydney

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Sydney Film Festival 2026

 
The Sydney Film Festival (SFF) is currently running until 14 June 2026 with up to 18 separate screen venues in operation. With a mix of  all genres of film and including major releases from the international film festivals, this blog will provide a series of film reviews from the SFF over coming days.

Climate Change - cooling cities using trees

 

Trees and greenery can cool cities by as much as 18°C – but only if they’re the right type

You Le/Unsplash

Mohammad A Rahman, The University of Melbourne

Cities around the world are planting more trees to cope with rising urban heat. But our research shows trees alone are often not enough. In some cases, the wrong kind of greening can even make streets feel less comfortable on a hot day.

We compared field measurements from Melbourne, Munich and Hong Kong to test how different kinds of urban planting changed the heat people experience outdoors.

The results showed layered vegetation – where trees are combined with shrubs and ground cover – often cooled cities more effectively than trees alone. We also found local climate and street design strongly shaped whether greening worked well.

These findings matter because urban greening is no longer just about aesthetics. As cities spend billions adapting to extreme heat, planting design may matter as much as planting quantity.

Cities are getting hotter

Cities trap heat. Roads, buildings and asphalt absorb solar energy during the day and slowly release it back into the air, especially at night.

This “urban heat island” effect, combined with climate change, is making heatwaves more intense and more dangerous in our cities.

Trees are one of the most popular responses because they provide shade and reduce the amount of heat absorbed by surrounding surfaces. But outdoor comfort depends on more than air temperature alone.

People experience heat through sunlight, reflected heat, humidity and airflow. A shaded street can still feel uncomfortable if humidity is high or if wind cannot move through the space.

That is why a “one-size fits all” greening strategy can fail. A planting design that works well in Melbourne may behave very differently in Hong Kong or Munich.

What we found

To better understand how urban vegetation affects heat stress, we did field measurements in three cities with different climates: temperate Melbourne, cooler Munich and humid subtropical Hong Kong.

Rather than relying only on computer models, we measured real conditions in streets and green spaces during summer.

We compared open urban spaces (with no plantings), sites with trees only, and layered planting (which means trees, shrubs and ground cover together).

Importantly, we did not just measure air temperature. We also measured “mean radiant temperature”, which captures the heat radiating from roads, walls and other surfaces onto the human body.

In Melbourne, street trees reduced radiant heat absorbed by pedestrians by more than 18°C, compared with open streets. Even where air temperatures changed only slightly, shaded streets felt substantially cooler.

Munich showed the strongest benefits from layered planting. There, streets and green spaces containing trees, shrubs and ground cover reduced afternoon heat stress by almost 8°C compared with more open spaces.

Hong Kong also benefited from vegetation, especially through shade created by overlapping tree canopies. But the results there were more mixed because the humid climate changed how cooling worked (more on that later).

Across all three cities, one finding stood out: vegetation structure matters.

Combining trees with shrubs and ground cover often performed better than trees alone, but the benefits depended on how the planting interacted with the local environment.

Why some greening can fail

The study showed that more vegetation is not automatically better.

In Hong Kong, dense vegetation sometimes increased humidity enough to reduce some of the cooling benefit. Plants release water vapour into the air through transpiration, which can help to cool dry climates. But in already humid cities, extra moisture can make outdoor spaces feel sticky and uncomfortable because sweat evaporates less efficiently.

In some Munich streets, dense vegetation reduced airflow through narrow urban corridors, trapping warm air and slowing the movement of vehicle pollution away from pedestrians.

These findings highlight why cities cannot rely on generic canopy targets copied from elsewhere. Climate, street width and airflow all shape whether vegetation improves comfort or creates unintended side effects.

Designing cooler cities

The solution is not to stop planting trees. It is to design urban greening more carefully.

Cities need planting strategies tailored to local conditions rather than universal greening formulas. In parks and open green spaces, layered vegetation can provide strong cooling while also supporting biodiversity. In dense streets, planners may need to balance shade with ventilation.

The findings also suggest cities should move beyond measuring success through tree numbers alone. The arrangement, density and type of vegetation matter just as much as canopy cover.

Designing for local conditions

Our research shows urban vegetation can reduce heat stress, but the benefits depend on how and where cities plant it.

Melbourne demonstrated the strong cooling effect of street trees on radiant heat, Munich showed the added value of layered vegetation, and Hong Kong revealed how dense planting can sometimes backfire in humid conditions.

Cities need climate-smart green spaces designed for local conditions, airflow and human comfort to remain liveable as temperatures rise.

Mohammad A Rahman, Senior Lecturer in Urban Horticulture, The University of Melbourne

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Artificial intelligence Part 8: specific industry impacts - retail sector

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The retail industry has managed several significant disruptions, particularly with technology, over many decades. Retail in this definition, spans both the physical and digital environments with multiple different segments and collectively has the highest overall level of employment. The impact of AI occurs differently across the segments as discussed below -

E-commerce and digital retail

Disruption caused by AI is most advanced in this segment of retail -

• Merchanidising and product curation: AI is already used online to determine what products are shown, to which potential purchasers at what price and in which order. The human merchandiser role that once required deep product knowledge and market intuition is being replaced by AI. The result is buying teams are being reduced in headcount.
• Pricing and promotions: AI can operate dynamic pricing in real timer across millions of stock keeping units (SKUs) based on demand, competitor pricing and inventory levels. The pricing analyst role has been largely automated.
• Demand forecasting and inventory management: this is a large team function traditionally in retail requiring significant human judgement (which sometimes was unsuccessful with some product lines), however now AI is a dominant force. Inventory planners are increasingly deployed to exception management only.
• Product descriptions, copywriting and promotional content: once the function of large copy teams for catalogues, AI now generates product listings at scale for example, at Amazon, ACOS and Zalando. 
• Customer service: Tier 1 customer service in e-commerce had become largely automated with chatbots and AI agents now capable of handling item returns, order queries, and complaints at scale. Human agents are increasingly handling ecalated issues only.
• Searches and recommendations: the entire discovery layer of e-commerce is AI-driven thus reducing the need for manual curation teams.

Physical Retail - Shops, boutiques bricks and mortar stores

The physical retail segment has a much stronger protective layer being the embodied, social experience of shopping and the overall 'retail therapy' of personal service, expertise and the physical handling of possible items for purchase. Luxury retail, specialist retailers together with experience-led formats have led to an investment in more knowledgeable human staff. Consumers expect such interaction, expertise and better service. Retailers whom do not offer higher level services for more expensive products often find public criticism including negative consumer ratings online are the result. Nonetheless there is AI intrusion and pressure in this segment -

• Checkout and payment: the increasing use of self-checkout from stores and the replacement of human checkout counters has been the proverbial thin-edge-of-the-wedge for the past two decades. For example, Amazon Go and similar checkout models eliminate checkout operator roles entirely. This process started in gocery stores but has expanded across a large part of the retail industry however not without some caveats - staff are needed to supervise and monitor self-checkout store sections, increased surveillance for fraud and theft has become essential and human operator checkout aisles have needed to be retained in smaller form. 
• Stock management and replenishment: computer operated vision and AI inventory systems can detect shelf gaps and product depletion thus triggering replenishment action without human stock checkers. Warehouse stock picking has increasingly become robotic.
• Loss prevention: AI camera systems are increasindly replacing human loss prevention officers for monitoring and surveillance in-store. Human response to any alerts however remains essential.
• In-store customer assistance: customer assistance where genuinely helpful product knowledge  roles are needed means there positions are much more protected from AI encroachment. Generic customer greeting roles are much less so.
• Visual merchandising: AI tools can optimise store layouts using foot traffic data and correction with sales information. This reduces the creative and analytical work of visual merchandisers.

Grocery and Fast Moving Consumer Goods (FMCG) Retail

Grocery and fast moving consumer goods is another segment when AI operates removing more of the operational back office roles. For now it cannot do the physical work of storage, presentation and despatch however systems operation is custom made for AI.

• Category management: the analytical heavy lifting that category planners (planogram design, range rationalisation, promotion assessment and evaluation) are all in the process of being automated.
• Supply chain coordination: AI optimises routing, supplier ordering and waste reduction. The impact is the reduction of operational planning team headcount.
• Fresh food management: AI waste reduction tools are already deployed in major supermarkets. This is a direct threat to manual stock management roles but not to actual physical movement of materials.

A further post in this blog will cover other aspects of the retail industry where AI is impacting marketing, advertising and retail fashion.

Climate change - the option of algae instead of biofuels ?

 

Many biofuels haven’t panned out. Could algae make the clean diesel and aviation fuel Australia needs?

Peter Ralph, University of Technology Sydney; Alexandra Thomson, University of Technology Sydney, and Martin Lloyd, University of Technology Sydney

Diesel is critical to Australia. Any supply disruption has immediate and widespread consequences, given Australia imports almost 80% of its liquid fuels. As the energy shocks of the Iran war ripple out, Australia’s leaders have scrambled to shore up supplies of fuel – especially diesel and aviation fuel.

Disruptions to fuel supplies have happened before, such as in 2008 and 2022. This disruption won’t be the last.

What should policymakers do? One option is to ramp up local production of biofuels made not from crude oil but from natural oils such as canola, animal fats – or algae.

As algae researchers, we believe these humble organisms are worth exploring. Making biodiesel and sustainable aviation fuel from these fast growing organisms can be done with much less land than other crops. Technological advances mean the fuel could scale up.

Many biofuels come with trade-offs

Biofuels have gained traction worldwide as efforts to reduce dependence on fossil fuels and meet climate targets accelerate.

The Australian biofuel sector is relatively small. Farmers exported about 6 million tonnes of canola in 2023–24 to be turned into biofuels overseas.

The Australian government last year announced A$1.1 billion in incentives to boost low-carbon fuels such as biofuels.

Biofuels from corn, soybean, canola and palm oil have boosted fuel security in some nations. Brazil produces 22% of its own transport fuel from biofuels, while biofuels account for 6% of the fuel used in the United States.

The problem is, biofuels often come at an environmental cost. A third of all US corn is used to make ethanol for fuel.

What’s so good about algae?

The type we’re interested in are microalgae, single-celled organisms, not macroalgae such as kelp and other types of seaweed.

These small organisms can grow exceptionally rapidly and hold high concentrations of oils. Many microalgae species can double their weight every day. Nannochloropsis and Chlorella are the two main types used to make oil.

Traditionally, algae was grown in large, shallow outdoor pools called “raceways”. They’re now increasingly grown in high-efficiency algae bioreactors.

Algae can be processed using proven technologies such as hydrothermal liquefaction to produce biodiesel able to be used in existing trucks and machinery. It can also produce sustainable aviation fuel.

Compared to crop-based biofuels, algae has several advantages. It doesn’t compete with food production and it can be grown on non-arable land or in industrial facilities. Some species can grow in saltwater or even treat wastewater while using it for growth. If algal facilities are located near heavy industry, carbon emissions can be captured and used for algal growth in a form of carbon storage.

Algal fuels needs much less land than conventional biofuels. A hectare of algae can yield more than 58,000 litres of oil per year. By contrast, a hectare of corn produces just 172 litres.

What are the barriers?

Interest in algal fuel dates back many decades. Oil shocks in the 1970s and 1990s drove significant research into algae-based fuels. But when oil prices fell, algal biofuels were no longer cost-competitive.

Since the 1990s, technologies have matured and policy settings become more favourable. Efforts to reduce fossil fuel use have put an implicit or explicit price on carbon. Mandates to increase output of sustainable aviation fuel are emerging in the European Union.

Fossil fuel price shocks in 2022 and 2026 have nudged authorities to seriously explore alternatives. Sovereign fuel security has become a strategic priority. Both the United Arab Emirates and the US are exploring algal fuels as a long-term strategic asset.

Algae for Australia?

Australia would be well placed to explore the potential of algal fuels. It has plenty of non-arable land, abundant sunlight and some of the world’s best algae research capabilities. Plus, it depends very heavily on imported diesel and aviation fuel.

Our research group and many others have been systematically working to overcome previous limitations of algal biofuels. We now know how to produce high-quality algal fuels and scale up production at costs low enough to challenge fuels derived from crude oil.

The first step would be to invest in pilot projects to prove the technology can work at scale under real-world conditions. Overseas, similar pilots have been set up next to industry to test the use of carbon capture, or alongside research partners.

If this is successful, the next step would be to build facilities in regional locations where fossil diesel is in demand and expensive to transport – and where algae can offer a dual benefit by treating wastewater or capturing carbon.

Over time, the versatile technology could be expanded, as algae can produce not only biodiesel but also other useful products such as edible protein for animal feed and biochar, highly porous charcoal able to soak up pollutants such as heavy metals.

Researchers have been working to boost yields and scale up oil production from algae. mayaluana, CC BY-NC-ND

Algae deserves our attention

Many previous efforts to scale up biofuels have run into problems over environmental impact or cost.

It’s important to be sceptical of claims of the next big thing. But it’s also important not to overlook the potential of humble technologies such as making fuel from algae.

As leaders look for ways to bolster fuel security, algae deserves a closer look.

Peter Ralph, Distinguished Professor of Marine Biology and Executive Director of the Climate Change Cluster, University of Technology Sydney; Alexandra Thomson, Industry Engagement Manager, Climate Change Cluster, University of Technology Sydney, and Martin Lloyd, Strategic Lead, Research Translation, University of Technology Sydney

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Artificial intelligence Part 7: specific industry impacts - corporate governance and risk management

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The impact of artificial intelligence on governance and risk management is nuanced. There are some tasks that AI can do, but other functions must be undertaken by human beings. As a general description of work functions, governance teams usually produce documentation such as board briefing papers, governance reports, policy updates, compliance documentation and meeting summariess. These are all processes open to AI. This posting will provide an indication of changes coming to roles in corporate governance and risk management due to AI. 

Corporate Governance

• Board reporting and briefing papers preparation: AI can synthesise management informaiton, financial data and identify risks into baord-ready material reducing the administrative teams that currently do this work.
• Corporate secretariat functions: the various documents produced such as records of minutes, compliance tracking, regulatory filings are all highly structured functions that are automatable and suited to AI.
• ESG reporting: Environmental, Social and Governance (ESG) reporting requirements have increased in recent years creating an additional compliance burden. AI can offset the increase through tools that aggregate and report sustainability data.

Risk Management

• Credit risk modelling: Credit risk modelling is a heavily quantitative task suited to AI thus reducing the analyst layers that do the manual model running and reporting. 
• Operational risk assessment: risk assessment identifies and quantifies risk across various buinsess processes. The assessment often produces a risk assessment table which rates and grades the risk including mitigation method. These processes are suited to be augmented by AI at the data gathering and first-analysis stage.
• Market risk monitoring: real-time AI surveillance is replacing/reducing some human monitor headcounts for this task.

Compliance and regulation

• KYC (Know Your Customer) and Anti-Money Laundering (AML): AI is transforming these tasks from labour-intensive manual processes to AI-monitored exception-handling workflows. The impact is the reduction in the size of large compliance teams. 
• Regulatory change management: the tracking and interpreting of new regulations can be augmented by AI for monitoring and summarising only. The interpretation of ambiguous regulatory language remains an essential human function not AI.
• Audit: the largest 4 accounting firms are deploying AI to analyse entire transation populations rather than the traditional scope of only sampling. This change provides a better quality audit but it requires fewer junior auditors.

Prudential Regulation (Central Banks and Regulators)

• Supervisory data anlysis: regulators such as APRA (in Australia), FCA (US) and Central Banks are using AI to monitor systemic risk across institutional data.
• Examination and inspection of institutions: the inspection teams face efficiency improvements and possibly headcount pressure.
• Policy and rule-making: roles in these functions are more protected however the judgement and accountability requires is high for human decision making.

In terms of employment, senior risk professionals are still needed for a range of actions such as - determining acceptable risk levels, advising executives on risk management, assessing complex of emerging risks, balancing regulatory, financial and reputational considerations. 

Physical inspection teams are still needed and remain human-based for purposes such as: construction safety inspections, infrastructure maintenance checks, environmental site visits, equipment integrity inspections. 

In the future, the workforce changes are more likely to be a reduction in the entry-level and junior operational roles with a structure as shown -

Before
Chief risk/governance officer

Senior specialists

Large analyst and reporting teams

After

Chief risk/governance officer

Senior specialists (fewer)

Small team supervising AI monitoring systems

The AI systems in use include: Microsoft Copilot, IBM Watson, Palantir Technologies and SAS Institute.

AI example - star series - black hole - animation using html code

 

Health - Hantavirus explanation

 

What is hantavirus, the disease that has killed 3 cruise ship passengers?

AFP/Getty

Thomas Jeffries, Western Sydney University

Three people have died after a suspected outbreak of hantavirus on a cruise ship in the middle of the Atlantic ocean. At least one other passenger is in intensive care in South Africa.

The World Health Organization announced the deaths in a social media statement on Monday, along with one confirmed case of the rare disease. Authorities are investigating another five suspected cases among passengers travelling on the MV Hondius.

So, what is hantavirus? And why can it be so deadly?

As the investigation unfolds, here’s what we know.

What is hantavirus?

Hantavirus is a rare but severe respiratory illness that can cause severe bleeding, fever and even death.

The virus is spread by rodents, such as mice and rats, mainly through the urine and droppings of infected animals.

Hantavirus does not typically spread from person to person. However, in rare cases it may spread between people.

Globally, there are an estimated 150,000 to 200,000 cases of hantavirus each year.

It is less contagious than airborne viruses such as COVID and influenza, as it typically does not spread from person to person.

What makes it so deadly?

There are two main types of hantavirus, each with different symptoms.

Hantavirus pulmonary syndrome, which affects the lungs, is mainly found in the United States. If a person becomes infected with this type of hantavirus, within days they will likely experience coughing and shortness of breath.

As the illness progresses, they can develop symptoms such as fatigue, fever and muscle aches. They may also get headaches, dizziness, nausea, vomiting and abdominal pain. This is the most deadly kind of hantavirus. Tragically, about 38% of people who develop these symptoms die from the disease.

Hemorrhagic fever with renal syndrome is mainly found in Europe and Asia, but the strain known as the Seoul virus has spread around the world. This form of hantavirus mainly affects the kidneys.

People usually develop symptoms within two weeks of being exposed to this virus. Early symptoms include severe headaches, abdominal pain, nausea and blurred vision. More advanced symptoms include low blood pressure, internal bleeding and even acute kidney failure. This disease can be caused by different viruses and some are more deadly than others, meaning between 1% and 15% of cases can be fatal.

Unfortunately, there is no specific treatment or cure for either type of hantavirus. However, early medical treatment may increase a person’s chance of survival. This can include using respirators, oxygen therapy and dialysis.

Authorities are still investigating which type of hantavirus the passengers were exposed to.

How did it get on a cruise ship?

In a closed environment such as a cruise ship, there are two possible ways passengers could have contracted hantavirus.

One is being exposed to the virus while on a shore excursion.

The other possibility is that rodents may have entered the ship on cargo, and then spread the disease to passengers through their infected urine or droppings. Other factors such as hygiene standards and food storage practices may have caused the infection to spread more quickly.

To contain this suspected outbreak, authorities must first ensure any rodents are safely contained and removed from the ship. They should then monitor all passengers for hantavirus symptoms. The virus is diagnosed with a PCR test, similar to those used to diagnose viruses such as COVID.

Given there is no specific treatment for the disease, authorities must help any infected passengers manage their symptoms. This involves checking that they are breathing normally and their kidneys are functioning properly.

So, how worried should we be?

Although alarming, cases of hantavirus remain are extremely rare. But it can look similar to other respiratory illness, so you should always get symptoms checked. If you’ve been in regions where the virus is found and experience shortness of breath, fever or any other flu-like symptoms, see your GP.

Thomas Jeffries, Senior Lecturer in Microbiology, Western Sydney University

This article is republished from The Conversation under a Creative Commons license. Read the original article.

AI example - star series - neutron star - Pulsar animation using html code

Artificial intelligence Part 6: specific industry impacts - healthcare

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The impact of AI on healthcare is more nuanced and varied than most other white collar sectors. Healthcare is more complex due to need to retain a strong physical human presence in the medical and care functions that cannot be automated or replaced by digital technology. Healthcare has a complex set of regulations, legal liability and an irreducable human dimension with doctors, nurses, allied health professionals required for direct patient face-to-face contact inclusive of the use of tele-health services.

In contrast, administrative and diagnostic supportive functions are highly exposed. A summary is provided below and is not exhaustive -

Clinical diagnosis and decision support

• Radiology already has AI systems that match or exceed radiologists in detecting certain cancers (breast, lung, skin). The aspect of concern is potential over diagnosis due to the sensitivity of the digital systems used. The radiologist role is shifting towards oversight, complex cases handling and AI exception management. The potential risk in this field being the volume of radiologists needed to undertake radiology functions may reduce.
• Pathology has a similar pattern to radiology as AI can analyse tissue samples at scale. The role of pathologists however is not removed at this time but is being augmented.
• Diagnostic support to General Practitioners can be provided through AI tools that synthesise patient medical history, symptoms and test results. These AI tools are being deployed already however the intention is to support the medical service provided by doctors to patients not substitute it. A secondary intention is to reduce the need for specialist referrals however this has yet to be achieved.
• Dermatology and ophthalmology are two specialties that are heavily dependent on pattern recognition and will face some AI encroachment, however as with other diagnositic tools it may be a supportive function not a medical role replacement one. 

Clinical administrative functions and documentation

• Medical transcription is already largely automated with voice-to-text using clinical AI being widely used.
• Clinical note writing is being addressed by ambient AI scribes such as Nuance DAX. Documentation can consume 30-40% of physician time and assists medical practitioners to achieve quality of life improvement however it reduces medical transcription services significantly, if not in some cases, entirely.
• Prior authorisation, coding and billing  are very large cost centres and are being progressively automated and threatening large administrative workforces in hospitals and insurance companies.

Nursing and Allied Health

• Triage and patient monitoring: AI can monitor patient vital signs, identity and alert to deterioriation and prioritise nursing care. AI provides service augmentation but not replacement of front line nursing care which must be physically provided.
• Care coordination roles do face pressure from AI that can track patient journeys, identify gaps and schedule follow-ups. At this time however this remains an augmentation tool rather than a job replacement one. 
• Bedside care, emotional support and physical nursing are strongly human services and cannot be replaced by AI. It remains one of the most protected areas across all industries. 

Pharmaceuticals and medical science/research

• Drug discovery timelines are being compressed by AI which reduces some research roles but creates new roles in AI-guided drug design. An example of AI impact is Alphaford's protein structural predictions which transformed structural biology.
• Clinical trial design and patient matching is being assisted by AI but not replaced by it. 

In healthcare, it is the administrative organisational pyramid that is being compressed with headcount reduction. Clinical roles continue with signifcantly increased volumes of patients possibly over time.

ANZAC Day 2026

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ANZAC Day continues to have strong public support in recognition of the service of men and women during time of war. This special commemorative day has been held for 110 years on the 25th April and was originally intented to honour the members of the Australian and New Zealand Army Corps who served in the Gallipoli campaign in 1915 (World War I). Since that time, it has expanded to include other conflicts and peace keeping operations until the present time. On this day, those who lost their lives as a result of their service are particularly remembered.

Lest we forget

Science: the mind and imagination

 

How does imagination really work in the brain? New theory upends what we knew

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Thomas Pace, University of the Sunshine Coast and Roger Koenig-Robert, University of Technology Sydney; UNSW Sydney

Your brain is currently expending about a fifth of your body’s energy, and almost none of that is being used for what you’re doing right now. Reading these words, feeling the weight of your body in a chair – all of this together barely changes the rate at which your brain consumes energy, perhaps by as little as 1%.

The other 99% is used on the activity the brain generates on its own: neurons (nerve cells) firing and signalling to each other regardless of whether you’re thinking hard, watching television, dreaming, or simply closing your eyes.

Even in the brain areas dedicated to vision, the visuals coming in through your eyes shape the activity of your neurons less than this internal ongoing action.

In a paper just published in Psychological Review, we argue that our imagination sculpts the images we see in our mind’s eye by carving into this background brain activity. In fact, imagination may have more to do with the brain activity it silences than with the activity it creates.

Imagining as seeing in reverse

Consider how “seeing” is understood to work. Light enters the eyes and sparks neural signals. These travel through a sequence of brain regions dedicated to vision, each building on the work of the last.

The earliest regions pick out simple features such as edges and lines. The next combine those into shapes. The ones after that recognise objects, and those at the top of the sequence assemble whole faces and scenes.

Neuroscientists call this “feedforward activity” – the gradual transformation of raw light into something you can name, whether it’s a dog, a friend, or both.

In brain science, the standard view is that visual imagination is this original seeing process run in reverse, from within your mind rather than from light entering your eyes.

So, when you hold the face of a friend in mind, you start with an abstract idea of them – a memory or a name, pulled from the filing cabinet of regions that sit beyond the visual system itself.

That idea travels back down through the visual sequence into the early visual areas, which serve as your brain’s workshop where a face would normally be reconstructed from its parts – the curve of a jawline, the specific shade of an eye. These downward signals are called “feedback activity”.

A signal through the static

However, prior research shows this feedback activity doesn’t drive visual neurons to fire in the same way as when you actually see something.

At least in the brain regions early in the vision process, feedback instead modulates brain activity. This means it increases or decreases the activity of the brain cells, reshaping what those neurons are already doing.

Even behind closed eyes, early visual brain areas keep producing shifting patterns of neural activity resembling those the brain uses to process real vision.

Imagination doesn’t need to build a face from scratch. The raw material is already there. In the internal rumblings of your visual areas, fragments of every face you know are drifting through at low volume. Your friend’s face, even now, is passing through in pieces, scattered and unrecognised. What imagining does is hold still the currents that would otherwise carry those pieces away.

All that’s needed is a small, targeted suppression of neurons that are pulled by brain activity in a different direction, and your friend’s face settles out of the noise, like a signal carving its way through static.

Steering the brain

In mice, artificially switching on as few as 14 neurons in a sensory brain region is enough for the animal to notice it and lick a sugar-water spout in response. This shows how small an intervention in the brain can be while still steering behaviour.

While we don’t know how many neurons are needed to steer internal activity into a conscious experience of imagination in humans, growing evidence shows the importance of dampening neural activity.

In our earlier experiments, when people imagined something, the fingerprint it left on their behaviour matched suppression of neuronal activity – not firing. Other researchers have since found the same pattern.

Other lines of evidence strengthen our theory, too. About one in 100 people have aphantasia, which means they can’t form mental images at all. One in 30 form these images so vividly they approach the intensity of images we actually see, known as hyperphantasia.

Research has found that people with weaker mental imagery have more excitable early visual areas, where neurons fire more readily on their own. This is consistent with a visual system whose spontaneous patterns are harder to hold in shape.

Taking all this together, the spontaneous activity reshaping hypothesis – our new theory that imagination carves images out of the steady stream of ongoing brain activity – explains why imagination usually feels weaker than sight. It also explains why we rarely lose track of which is which.

Visual perception arrives with a strength and regularity the brain’s own internal patterns don’t match. Imagination works with those patterns rather than against them, reshaping what is already there into something we can almost see.

Thomas Pace, Researcher and Lecturer at the Thompson Institute, University of the Sunshine Coast and Roger Koenig-Robert, Senior Research Fellow, Graduate School of Health, University of Technology Sydney; UNSW Sydney

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Earth Day 2026

 

Artificial intelligence Part 5: specific industry impacts - finance and banking

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AI is particularly suited to back office operations in banks and financial institutions analysing large amounts of financial data. Typically banks and financial institutions employ large numbers of people to undertake functions such as compliance documentation, fraud review, transaction monitoring, credit analysis and financial reporting. AI systems designed by Palantir Technologies and SAS Institute for example, can review financial data and identify anomalies much faster than manual teams. The impact of AI on specific industry segments is summarised as follows -

Investment banking and capital markets

• Analyst roles are vulnerable to AI systems. The business folklore of junior bankers working 100 hour weeks using Excel models, pitch books and risk management due diligence is already under severe pressure as these tasks are highly structured and can be executed by AI. Examples already known include Goldman Sachs and JPMorgan deploying AI for financial modelling, earnings analysis and report generation.
• Equities market research has been transformed as AI can monitor thousands of stocks, synthesise earnings and generate initial research notes faster than any human team. 

Asset management

• Quantative analysis and factor modelling can be easily augmented by AI and is increasingly occuring already. This situation is leading to a changing and evolving role for quantitative analysts.
• Portfolio reporting and client communication is increasingly being automated with AI at the commodity end.
• Active investment funds management comes under further pressure as passive funds are now better guided by AI-driven strategies.
• Compliance reporting which is a very large cost centre in financial markets is being substantially automated with AI. The use of automation was an existing trend for many years but AI enables a faster rate of uptake. 

Retail and commercial banking

• Loans underwriting is already largely algorithmic and automated for retail consumers and the SME business level already. AI does not alter the trend but merely further reduces the remaining human review layer.
• Customer service and branch banking continues a long decline with face-to-face service reduction. This situation however is subject to fluctuations due to community pressure and increasing consumer preferences for personal interaction for specific services. AI's influence is limited in this line of business activity.
• Fraud detection and ani-money laundering (AML) monitoring is already within the AI-dominated sphere. Human reviewers have been shifting to exception handling only.
• Financial advice at the mass market level  already has limited use of robo-advisers. This segment is however subject to regulation and government oversight and the requirement for financial advice licenses, accountability and legal liability. The use of robo-advisers beyond limited information provision and recommendations for the mass retail market has not yet occured. High-net individuals particularly prefer human advisers and personal banking managers rather than an automated service. Various financial advice scandals in the sector may also limit the use of AI for the time being.

As with all industries, the use of AI in finance and banking is most easily implemented in large data analysis, administrative and reporting tasks. It is not well suited to client relationships and regulatory, legal and compliance responsibilities.

Artificial intelligence Part 4: specific industry impacts - graphic arts and visual design

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Graphic arts and visual design are another industry that is heavily exposed to AI particularly with impacts such as hierarchical pyramid compression. Tasks and projects that once needed a team of junior artists can now be completed by a single art director using AI tools. AI image systems can now produce concept art, advertising visuals, books covers, storyboards and marketing graphics. Specific industry segments affected are discussed as follows -

Commercial illustration and stock art

• Stock photography and illustration is already heavily impacted. Companies such as Shutterstock, Getty Images and Adobe all now offer AI image generation. The market for generic commercial illustration has largely collapsed for independent artists.
• Illustrators who designed books covers, editorial art and advertising assets, mainly mid-tier commercial work, now face severe income compression. This blog uses AI generated images having once held accounts with commercial image suppliers such as Shutterstock.

Advertising and brand design

• Mood boards, concept art and campaign mockups are increasingly AI-generated at the brief stage.
• The jobs of junior designers whose purpose is to execute pixel-perfect images under senior creative direction are now heavily at risk as these tasks are automatable.

UI/UX design

•  AI tools: Such as Figma AI can automate layout generation, component creation and user flow suggestions. Junior UI designers who develop wirseframes face significant automation pressure.
• UX research such as interviews, synthesis and insight generation remain more protected however even parts of these processes such as synthesis and pattern recognition can be managed through AI.

The multi-part series covering AI, published in this blog, has been researched and compiled using Claude ai (Anthropic), ChatGPT (OpenAI), and Grok (Xai). 

Climate change - Antarctic Emperor Penguins and fur seals now endangered species

 

The beloved emperor penguin and Antarctic fur seal are now officially endangered. Here’s what can be done

The Conversation, CC BY-ND

Mary-Anne Lea, University of Tasmania; Jane Younger, University of Tasmania, and Noemie Friscourt, University of Tasmania

In 1902, British explorer Robert Falcon Scott spotted a large group of large black and white birds at Ross Island, Antarctica. This was among the many milestones of Scott’s famous Discovery expedition: the first breeding colony of emperor penguins.

Now, only 124 years since this penguin colony was discovered, emperor penguins have officially been listed as endangered, along with the Antarctic fur seal. As the world warms, Antarctic krill are shifting southwards and sea ice is shrinking at record levels. And these unprecedented changes are having a domino effect on these species.

These are the first penguin and pinniped – marine mammals that have front and rear flippers – to be given this conservation status in the Southern Ocean. Their perilous situation is a critical turning point, and shows how rapidly the Antarctic environment is changing.

At the same time, the spread of highly contagious avian influenza, or bird flu, adds a new and immediate threat to Southern Ocean wildlife, compounding the pressures of climate change on stressed species.

Antarctic fur seal with pups at Sailsbury Plain on South Georgia. The number of fur seals has dropped by over 50% since 1999. Posnov/Getty

Dramatic declines linked to climate change

The first emperor penguin breeding colony was discovered at Cape Crozier, on Ross Island, during Robert Falcon Scott’s Discovery expedition in 1902. A decade later, Scott’s Terra Nova expedition returned, in part to collect emperor penguin eggs. It was an ill-fated expedition, immortalised in Apsley Cherry-Garrard’s famous book, The Worst Journey in the World.

In the 1960s, Scott’s son, Sir Peter Scott, one of the founders of modern conservation, helped establish the International Union for the Conservation of Nature’s Red List. Just 124 years after those early discoveries at Cape Crozier, that same framework has now been used to classify emperor penguins as endangered. The swift arc from discovery to extinction risk is a striking reminder of how quickly the species’ fortunes have changed.

Over nine years, between 2009 and 2018, emperor penguin numbers fell by 10%. Their numbers are expected to halve by 2073.

Southern elephant seals are now officially listed as vulnerable. Mary-Anne Lea, CC BY-ND

The decline is more pronounced for Antarctic fur seals. Hunted to the brink of extinction in the early 1880s, by 1999 their numbers had rebounded to an estimated 2.1 million mature seals. But since then, the global population has decreased by more than 50%, to about 944,000 mature individuals.

In just a decade, they have been reclassified on the IUCN’s Red List, going from of “least concern” – those species that are widespread and at low risk of extinction – to “endangered”. The IUCN’s red list is the comprehensive information source on the extinction risk status of species. This shows the remarkable speed at which these seals are declining.

Climate change and bird flu

Both of these dramatic declines are linked to climate change. Warming ocean temperatures and a reduction in sea ice affect the availability of the Antarctic fur seal’s key prey, Antarctic krill. Krill are shifting southwards and moving deeper, potentially making them less accessible to some predators. Competition with a growing population of whales has also increased.

Emperor penguins, by contrast, are completely dependent on sea ice. They use it as a stable platform for courtship, incubating their eggs and rearing chicks. But as sea ice declines and becomes less reliable, their breeding success is increasingly threatened. If the ice breaks up before chicks are fully developed, many are unable to survive.

At the same time, the spread of highly contagious bird flu adds a new and immediate threat to Southern Ocean wildlife. High mortality associated with avian influenza has also caused the uplisting of the southern elephant seal to “vulnerable” this week.

Some elephant seal populations have experienced more than 90% of pups dying, alongside sharp declines in breeding adults. These represent tens of thousands of animals lost, with many Antarctic fur seals also dying as a result of bird flu outbreaks.

Emperor penguin chicks at Cape Crozier. Mary-Anne Lea, CC BY-ND

We need to know more

Emperor penguins, Antarctic fur seals and southern elephant seals are three of the more widely researched Southern Ocean predators. But there is still a lot we don’t know, because of the remote location and the difficulty of sustaining research over time. And there are many species we know far less about. Antarctic ice seals, including Weddell seals, crabeater seals, leopard seals, and Ross seals, have “unknown” population trends on the IUCN red list, meaning there is not enough data to know if numbers are declining.

These recent listings make clear the urgent and ongoing need for improved, real-time monitoring. We need to know much more about wildlife health and population trends, the Antarctic environment and sea ice quality.

Human-driven threats facing Antarctic wildlife are many, and cumulative. To respond, we need to better protect Antarctic habitat and the species that live there. We need to reduce the interaction of marine species with industrial fishing. And we must improve how we assess current and suspected threats in Antarctica, when there is growing evidence of impacts.

Defining these animals as endangered is a stark reminder of how quickly Antarctica is changing before our eyes. Without a rapid reduction in greenhouse gas emissions and sustained conservation action, these species may be lost forever.

Mary-Anne Lea, Professor in Marine/Polar Predator Ecology, University of Tasmania; Jane Younger, Senior Lecturer in Southern Ocean Vertebrate Ecology, Institute for Marine and Antarctic Studies, University of Tasmania, and Noemie Friscourt, Research Associate, Institute for Marine and Antarctic Studies, University of Tasmania

This article is republished from The Conversation under a Creative Commons license. Read the original article.