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 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

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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.

Artificial intelligence Part 3: specific industry impacts - film and television

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The impact of AI is the most pronounced in the film and television industry with a variety of occupations impacted by the technology. The WGA and SAG-AFTRA union strikes in the United States in 2023 highlighted the concerns of people employed in the creative industries. Breaking down the various subsectors in the film and television industry, the role of AI can be easily defined -

CGI and VFX production

AI now covers environmental generation, crowd simulation, rotoscoping, motion cleanup, texture creation, background characters. 

• Rotoscoping, cleanup and compositing are traditionally large pools of junior labour and these tasks are being automated rapidly. Mid-tier VFX companies are under existential pressure with work bifurcating toward very high-end boutique work but the commodity work is fully AI generated. The roles that are disappearing are junior asset builders, repetitive compositing roles and the large teams that produce background elements.
• AI tools are Unreal Engine, Blender, Runway, Sora and similar programs.

Acting and performance

AI can and is already producing synthetic actors to create digital doubles and AI-generated crowds.

• Background artists are already displaced due to AI generated crowds and extras in a limited manner. This displacement of extras, crowd performers and minor background roles is expected to increase.
• Voice acting is severely threatened as synthetic voices are increasingly now near indistinguishable from real human voices and can be used for minor characters, video games, commericals and dubbing. Studios can licence a voice and use it indefinately.
• AI tools are Nvidia and Runway AI

Writing

AI can already operate to develop plot structure, dialogue drafts, storyboarding, episode outlines, alternate scene ideas. Writers room teams that once had 6-12 junior writers now only require a headwriter, 2-3 senior writers and AI-assisted drafting tools. A showrunner with AI-assistance may need only 2-3 senior writers rather than a full room.

Localisation and dubbing is already occuring using AI replacing human translators and lip sync dubbing artists at scale.

The reality is that with time and patience, AI will enable very small teams to produce cinema-quality films. Early versions of AI films can already be found on YouTube however many of these projects suffer from continuity failures and many technical deficiencies in storytelling structure.

The safest roles in the AI-era are those positions with creative authority, not basic production. Examples could be roles such as showrunner, art director, creative director, lead animator, production designer. These are decision-making roles and decide what should exist rather than merely producing it.