Dancer with ALS Performs on Stage Again Through Digital Avatar–WATCH

A photograph from the dancer and her avatar’s December performance at OBA Theater in Amsterdam – credit, Dentsu Lab, supplied to the BBC

Brain interface technology allowed a dancer suffering from ALS to project her imagination onto the stage in the form of a mixed reality avatar that pranced around a theater in Amsterdam as part of a first-of-its-kind performance.

Whether tense or free, timid or morose, the avatar encapsulated all the long years of training that saw Breanna Olson become a successful dancer before the disease confined her to a wheelchair.

The incredible moment gave Olson a chance to dance again, something she had not been able to do for years after being diagnosed with amyotrophic lateral sclerosis, the most common form of motor neuron disease.

“I never dreamed that I would be able to dance on stage again,” she said. “It was just a beautiful and memorable moment I will remember for the rest of my life.”

ALS affects the nerves, brain, and spinal cord, weakening and stiffening them over time which can lead to difficulty talking, breathing, and swallowing. There is no known cure and the disease is fatal.

Breanna Olson – credit, Dentsu Lab

A subsidiary of the Japanese conglomerate Dentsu, Dentsu Lab gave Olson the opportunity to become involved in its “Waves of Will” project that sought to use advanced brain interface technology to help those living with disabilities recapture their personal expression and identity.

Their new brain interface which the Lab developed in concert with a data tech firm NTT uses an electroencephalogram headset that captures the brain activity of Olson, and translates certain electrical signals into dance moves. Olson envisions how she would execute a movement, and the interface delivers it through normal computer instructions to a projected avatar which then performs the motion.

Olson told the BBC that it requires extreme focus and concentration to imagine the precise details of any particular movement for the avatar. She said that it did exactly what the project’s aim was: to give personal expression back to someone like her who had lost that freedom.

In December, at the OBA Theater in Amsterdam, Olson and her Avatar danced around the stage as part of a Waves of Will performance, which Dentsu Labs called the first of its kind anywhere in the world. Dancer with ALS Performs on Stage Again Through Digital Avatar–WATCH

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People can learn to spot AI faces – but the clues are no longer obvious

 

Nightingale / OSF, CC BY

Amy Dawel, Australian National University; Eric Mah, University of Victoria; Jim Tanaka, University of Victoria, and Tanya George, Australian National University

Deepfake faces generated via artificial intelligence (AI) have become so realistic that they routinely fool people, with some research suggesting there may be US$40 billion worth of deepfake-related fraud annually by 2027.

Not only do most people struggle to spot AI faces, but as long ago as 2023 we discovered some AI faces are “hyperreal” – they look more real than actual human faces. We also found people are overconfident they can spot AI faces, with the most confident people making the most errors.

Software-based deepfake detectors do exist, but they can’t really explain the reasons for their detections – and they suffer from serious weaknesses. Some can be fooled simply by converting the image type, such as from png to jpg.

But it turns out most people can learn to spot AI faces with an hour or so of practice. In new research published in PNAS, we show there’s a straightforward way to improve detection of deepfakes, by training people to pick up the tell-tale clues through experience rather than direct instruction.

The difference between human and AI faces

In our early research, we discovered a key difference between AI and human faces. AI faces are hyperaverage.

This means AI faces tend to be more symmetrical, proportional and attractive than human faces. But they’re less expressive and memorable – less likely to stand out in a crowd.

Intriguingly, people can accurately and reliably judge these qualities, but frequently misinterpret the clues. For example, people often think that faces that look a bit odd are AI-generated, when in fact human faces are more likely to have distinctive, unusual features.

 

Compared to real faces (left), AI faces (right) tend to be more symmetrical, attractive and proportional, but less distinctive, memorable and expressive. Nightingale / OSF, CC BY

Although most people struggle to decide whether a face is AI or real, there is one group who are naturally good at picking up on these clues. So-called super-recognisers, who have exceptional human face perception, seem to be attuned to hyperaverageness, making them better at spotting AI faces.

This made us wonder if, for those of us who aren’t super-recognisers, AI detection abilities can be trained like other forms of perceptual expertise.

Learning to spot AI

In our first study, we invited 45 participants into our lab at the Australian National University, and asked them to rate around 100 faces on six qualities that can be used to tell AI faces apart from real ones: distinctiveness, memorability, proportionality, symmetry, attractiveness and expressiveness.

We didn’t tell participants how these clues might help them distinguish an AI face from a real one – they had to figure that part out for themselves.

We told participants which faces were AI and which were human, but we didn’t tell them that the AI faces were more symmetrical or less expressive, for example. They had to learn these clues through experience rather than direct instruction.

Before and after training, we tested participants’ ability to tell AI faces apart from human ones with new faces that were not used in the training.

Training works

In one test, participants were shown three faces – two human and one AI – and asked to select the face that was AI. On this task, average accuracy doubled from 40% before training to 80% afterwards.

Impressively, all participants improved in their AI detection abilities and several achieved close to 100% accuracy. Participants also became faster and more confident in their correct judgements.

 

With training, people get much better at picking AI faces out of a group with human faces. In this example, the middle face is AI generated. Nightingale / OSF, CC BY

To test the robustness of these findings, the Different Minds Lab at the University of Victoria in Canada conducted a replication of the AI detection training with Canadian participants.

The Canadian lab obtained results that were as strong as those reported in the original Australian study. This shows the training is reliable and can work for different groups of people.

The training was also just as effective when it was administered online rather than in person, which suggests it could be a cost-effective remote intervention in deepfake detection.

A promising start

But this doesn’t mean we’ve solved the AI detection problem. Our training used faces produced with one particular generative AI model, called StyleGAN3.

This is one of the most realistic face generators available, but the technology is advancing rapidly and there are many other models.

Our method has potential to adapt to new models by updating the training images and using multimedia, but we don’t yet have evidence that this will work.

The clues we found for spotting AI faces may shift for other models. And other important questions remain: do the training benefits hold up over time? Is the training effective for people of all ages, including older adults or children?

How to improve your chances of spotting AI faces

If you want to get better at recognising AI-generated faces, looking at a lot of examples is a good start. You can see plenty at websites such as Which Face Is Real or This Person Does Not Exist.

While you’re looking, bear in mind the six key factors we identified:

• how distinctive is the face?
• how memorable is it?
• how proportional is it?
• how symmetrical is it?
• how attractive is it?
• how expressive is it?

This exercise may improve your deepfake radar. But the more important takeaway is that AI deepfakes are improving very quickly – they can easily fool us, even if we think we can spot them.

The clues are no longer obvious: they are not based on specific details but on facial impressions which people form rapidly and naturally, but which can be misleading.

At the same time, there is hope. We have shown it is possible to train people to detect AI faces. By combining our human-centred approach with algorithmic detection, we may yet keep up in this cat-and-mouse game of advancing technology.

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Interested in undertaking the AI face detection training? You can register here.

Amy Dawel, Clinical Psychologist and Associate Professor, School of Medicine and Psychology, Australian National University; Eric Mah, Postdoctoral Researcher, Department of Psychology, University of Victoria; Jim Tanaka, Professor of Psychology, University of Victoria, and Tanya George, Research Assistant, Australian National University

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

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Global AI spending expected to surge 47 pc to $2.59 trillion in 2026

(Photo: IANS)

New Delhi, (IANS) Worldwide spending on artificial intelligence is projected to jump 47 per cent year-on-year to $2.59 trillion in 2026, driven largely by rising investments in AI infrastructure and AI-optimised servers as enterprises and cloud providers accelerate adoption of generative AI and agentic workflows, a report said on Tuesday.

The report from Gartner said AI‑optimized infrastructure including AI‑optimized IaaS, servers, network fabric, processing semiconductors and devices will account for over 45 per cent of spending as vendors expand capacity.

“Within this segment, spending on AI‑optimized servers will triple over the next five years to become the largest subsegment, as cloud services providers expand capacity in anticipation of the workloads created by GenAI models and agentic workflows,” said John‑David Lovelock, Distinguished VP Analyst at Gartner.

The firm raised its short‑term outlook for AI models to 110 per cent growth in 2026, adding $6 billion in spending for this year.

Enterprises will expand their use of both the GenAI models embedded in existing software applications and the new AI agents within multiple workflows.

Model consumption will increase through multistep processes and integration into broad suites of tools as enterprises recognize the potential value of agentic automation, the report noted.

The report forecasted spending on AI infrastructure rising from $9,75,581 million in 2025 to $14,31,509 million in 2026, while total AI spending will climb from $17,64,947 million to $25,95,667 million.

Lovelock said that AI spending has primarily been driven by technology companies and hyperscalers till now. Enterprises have yet to really flex their spending potential, which is set to happen in 2026, he added.

The report warned that CIOs face challenges in proving the value from AI investments and demonstrating tangible business outcomes.“Aligning AI initiatives with strategic business objectives is the essential step for success. This incremental approach persists despite AI hype and valuations that reflect aspirations to transform the broader economy,” Lovelock said. Global AI spending expected to surge 47 pc to $2.59 trillion in 2026 | MorungExpress | morungexpress.com

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I used sound waves to make espresso. It could cut coffee‑brewing energy use by 75%

Francisco Trujillo, UNSW Sydney

Most of us think of espresso as a hot, high-pressure ritual. Finely ground coffee goes into a machine, boiling water is forced through it, and in about 30 seconds we get a concentrated shot with crema, aroma, bitterness, body and caffeine.

As someone from Colombia, I like to think coffee is in my blood – and I’m proud to come from a country known for producing some of the best coffee beans in the world.

So perhaps that’s why I have spent a lot of time in my laboratory with my team asking a simple question: does espresso really need hot water?

Our new research suggests the answer may be no.

Low energy, full strength

We have developed what we call an ultrasonic espresso: a room-temperature brewing process that uses high-frequency sound waves to extract the flavour, oils, aroma and caffeine from coffee grounds. The result is an espresso-strength coffee made in under three minutes, but needing far less energy than the conventional method.

Saving up to 75% of energy by not heating the water is a minor benefit for home users or small coffee shops. But for companies making ready-to-drink coffee products at industrial scale, it could be very significant indeed.

A concentrated room-temperature coffee could be used directly in bottled drinks, milk-based beverages or cold coffee products. It can also be shipped as a concentrate and diluted later. This would reduce not only energy use, but potentially processing time as well.

Ultrasound replaces heat

The key to the new process is ultrasound. These are sound waves above the range of human hearing.

In our system, a small metal device called a transducer presses against the side of a traditional espresso basket and makes it vibrate rapidly. Those vibrations move through the water and coffee grounds.

This creates a phenomenon known as acoustic cavitation. Tiny bubbles form and collapse in the liquid.

 

How ultrasonic vibrations are added to a traditional espresso machine. Naliyadhara et al. / Journal of Food Engineering, CC BY

When these bubbles collapse near coffee particles, they produce microscopic jets and forces that act a little like scrubbing brushes. They pit and fracture the surface of the coffee grounds, helping flavour compounds, oils and caffeine move into the water much faster than they normally would at room temperature.

In other words, ultrasound helps us replace heat with mechanical energy.

Water, grind and time

This is not the same as cold brew. Cold brew is usually made by steeping coffee in cold water for 12 to 24 hours. It tends to be smooth, mellow and much less concentrated than espresso. In earlier work, we used ultrasound to speed up cold brew dramatically.

But the challenge in this project was different: could we produce something with the strength, body and intensity of espresso, without heating the water?

To do that, we adjusted several variables. Brew ratio was one of the most important: how much water we used for each gram of coffee. Too much water and the drink becomes diluted; too little and extraction becomes difficult.

Grind size also mattered. Finer grounds allowed us to extract flavour more rapidly. Finally, we tested how long the ultrasound should be applied. We found the sweet spot was about two-and-a-half to three minutes.

The taste test

Of course, making a concentrated coffee in the laboratory is one thing. The real test is whether people want to drink it.

So we ran a blind evaluation with around 100 regular coffee drinkers. They were not trained judges; they were everyday consumers who drink coffee at least once a week.

We served them four coffees in identical cups: traditional espresso, ultrasound-brewed espresso, traditional filter coffee and ultrasound-brewed filter coffee. All were freshly prepared, cooled to the same temperature and presented in random order.

For the espresso samples, participants could not reliably tell the traditional and ultrasonic versions apart. There were no significant differences in aroma, flavour, bitterness or overall liking. For filter coffee, the ultrasound version was actually preferred overall, with participants rating its bitterness more pleasantly.

Those results show espresso may not need to begin with hot water after all. By using sound waves to shake the coffee grounds, we were able to create the same richness, body and intensity, but with far less energy.

Francisco Trujillo, Senior Lecturer, School of Chemical Engineering, UNSW Sydney

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

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Comcast to test Edge AI apps using NVIDIA GPUs

Posted by Harry Baldock | Press Release Comcast today announced a groundbreaking initiative to bring AI processing, using NVIDIA GPUs, closer to customers than ever before to accelerate the development of next-generation AI applications across America. The first-of-its-kind collaboration will test the performance of AI workloads running directly at the edge of Comcast’s network – in regional facilities closer to where customers live and work.

The field trial takes advantage of Comcast’s nationwide, deeply distributed architecture that reaches 65 million homes and businesses and is purpose-built for low-latency, high-bandwidth performance. The goal: show how running AI at the network edge can unlock faster, smarter, more responsive experiences. For consumers and businesses, that translates to quicker apps, more relevant recommendations, smoother gaming, and AI-powered tools that respond instantly.

Comcast’s Edge Architecture: Built for AI

Comcast’s network is designed to put more computing power physically closer to customers, creating one of the largest and most capable platforms in the U.S. for delivering real-time AI inference with significantly reduced latency, power consumption, and cost. With advanced DOCSIS 4.0 FDX nodes, smart amplifiers, and intelligent gateways across its footprint, Comcast can support real-time AI inference at scale – something traditional centralized, fiber-only, or wireless networks cannot match.

As more AI workloads move from distant data centers to local edge locations, Comcast’s architecture positions the company as a key contributor to the emerging AI Grid – a nationwide foundation of distributed compute resources powering the next generation of AI-driven services accelerated by NVIDIA.

Delivering the Next Generation of AI at the Network Edge

Comcast will initially focus on three use cases designed to showcase the benefits of running AI workloads at the network’s edge:

• Personalized Advertising Agent – An advanced ad-delivery engine powered by Decart real-time AI video models. Decart’s technology is capable of customizing video advertisements down to the household level using attributes such as language, content preferences, household size, or other non-sensitive demographic categories – enabling hyper-relevant experiences for viewers while improving efficiency for advertisers.
• Small Business Concierge Agent – Leveraging Personal AI’s small language model (SLM) and memory platform deployed on HPE ProLiant servers to deliver an AI-powered “front desk” service capable of greeting customers, managing appointments, answering questions, and supporting day-today-day operations for small businesses.
• Reducing Latency for Gaming – Delivering ultra-low latency streaming for online gaming, the AI Grid brings GPU resources physically closer to players. This can dramatically improve responsiveness and overall gameplay quality, building on the impact of the low-latency technology Comcast rolled out for NVIDIA GeForce NOW and other applications last year.

Initial testing of these applications demonstrated strong performance in the lab and the field trials now will validate latency improvements, power and cost efficiencies, resiliency, scalability across Comcast’s footprint, and user experience benefits in a live environment.

“The industry is shifting towards a more distributed AI infrastructure and Comcast operates a network that supports it today,” said Elad Nafshi, Chief Network Officer, Comcast. “NVIDIA AI Grid vision requires intelligent infrastructure that reaches all the way to the customer’s doorstep. By bringing NVIDIA GPUs directly into our edge cloud, we can explore what becomes possible when AI inference happens only milliseconds from end users.”

“Distributed AI Grid is the next big opportunity for the telecommunications industry, and Comcast’s nationwide, deeply distributed network is a perfect match for building it,” said Ronnie Vasishta, SVP, AI and Telecoms, NVIDIA. “By bringing intelligent AI inference to the network edge, Comcast can unlock inherent cost efficiencies, while delivering deterministic, low‑latency experiences for customers at massively concurrent scale. This collaboration is powering the next era of hyper personalized experiences that run just milliseconds from users.”

The companies will also explore future opportunities for AI-enhanced advertising, new small-business solutions, premium low-latency gaming tiers, and potential third-party edge compute services. Comcast to test Edge AI apps using NVIDIA GPUs - Total Telecom

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AI demand to push global chip industry revenue past $1.3 trillion in 2026

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New Delhi, ANl  Global semiconductor revenue is projected to exceed $1.3 trillion in 2026, marking the fastest growth in over two decades, according to a report released on Wednesday.

The report by research firm Gartner said the industry is expected to register a 64 per cent growth in 2026 amid strong demand from artificial intelligence (AI) processing, data centre infrastructure and rising memory prices.

"Amid high demand for AI processing, data centre networking and power, and memory price inflation, the semiconductor industry is projected to achieve a third consecutive year of double-digit growth in 2026," said Rajeev Rajput, Senior Principal Analyst at Gartner.

The report further highlighted that total semiconductor revenue is expected to rise from $805.3 billion in 2025 to $1,320.2 billion in 2026, and further to $1,554.5 billion in 2027.

Memory revenue is forecast to nearly triple to $633.3 billion in 2026 from $216.3 billion in 2025, largely due to inflation in memory chip prices.

As per Gartner's analysis, Dynamic Random Access Memory (DRAM) prices are estimated to ncrease by 125 per cent and NAND flash prices by 234 per cent in 2026, with any significant price correction unlikely before late 2027.

The report noted that AI semiconductors are expected to account for around 30 per cent of total industry revenue in 2026 and will remain the primary growth driver.

Spending by hyperscalers on AI infrastructure is projected to grow by more than 50 per cent, boosting demand for AI accelerators, including GPUs and custom chips.

However, the report also cautioned that rising memory prices could delay demand in non-AI segments until 2028."Technology suppliers should prepare for higher prices in the first half of 2026, followed by moderating increases through the rest of the year," Rajput said, advising CIOs and IT leaders to be cautious about long-term supply agreements with unfavourable pricing terms. AI demand to push global chip industry revenue past $1.3 trillion in 2026 | MorungExpress | morungexpress.com

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Indian banks benefit from AI‑driven operating models: Report

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New Delhi, (IANS) Indian banks are benefiting from sustained credit growth, deeper digital public infrastructure and rapid adoption of AI‑driven operating models, and heightened regulatory focus on climate risk, cyber resilience and governance, a report said on Friday.

The report from KPMG International said the sector is at par with global peers scaling from pilots to enterprise AI use, investing in workforce reskilling and strengthening cybersecurity and ESG frameworks to support long‑term resilience.

Based on a survey of 110 global Banking and Capital Markets CEOs, the report found 83 per cent are confident about growth over the next three years and 65 per cent ranked AI as a top investment priority.

Around 70 per cent CEOs said they plan to allocate 10–20 per cent of next‑12‑month budgets to AI, while 59 per cent expect agentic AI to have a transformational impact and 69 per cent expect returns within one to three years.

"Around 83 per cent banking and capital market CEOs are prioritising reskilling for AI; 79 per cent say AI has redefined entry‑level skills whereas 78 per cent warn AI workforce readiness could negatively impact the organisation if not addressed," the report said.

“As global banking leaders respond to rising operational and regulatory costs by pursuing scale and strategic M&A, the same imperative is increasingly shaping the Indian banking sector,” said Sanjay Doshi, Partner and Head, Transaction Services and Financial Services Advisory, KPMG in India.

Doshi said that scale is more than just size for India but a catalyst for expanding distribution, accelerating digital transformation and enhancing cost efficiency.

As banks deepen their investments in technology and modernize their operating models, selective consolidation and partnership‑led growth can unlock new markets, strengthen value propositions and build long‑term competitive resilience, he added.Around 86 per cent CEOs cited cyber insecurity as the top growth threat, 56 per cent cited ethical challenges, and 55 per cent pointed to data readiness and regulatory gaps. Indian banks benefit from AI‑driven operating models: Report | MorungExpress | morungexpress.com

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Operant AI launches ecosystem programme to secure India’s rapidly expanding AI infra

(File Photo/IANS)

New Delhi, (IANS) Operant AI on Tuesday announced the launch of its AI infrastructure ecosystem partnership programme, aiming to strengthen security for large-scale AI systems as India pushes to become a global AI hub.

The company said the new initiative focuses on embedding real-time security directly into the infrastructure that powers enterprise AI applications.

The move comes at a time when the use of AI agents, connected systems, and large-scale deployments is growing rapidly, creating new security challenges.

Many organisations are now running advanced AI systems that can access sensitive data and external tools, often without adequate safeguards.

Operant AI’s platform is designed to address this gap by providing a real-time security layer that works while AI systems are actively processing data.

India’s AI infrastructure is expanding quickly across both government and private sectors. Under the IndiaAI Mission, the government has already deployed tens of thousands of GPUs, with more capacity being added recently at the India AI Impact Summit 2026. Overall, the country now has over 80,000 GPUs in operation, with major investments shaping the future of AI development.

At the same time, data centre companies such as ST Telemedia Global Data Centres, CtrlS, Sify Technologies, Nxtra by Airtel, and Princeton Digital Group are expanding their infrastructure to support AI workloads.

Cloud and GPU providers like Yotta Data Services, NxtGen Datacenter & Cloud Technologies, and Neysa Networks are also building specialised platforms for AI computing.

Operant AI said its new programme will help infrastructure providers offer not just computing power but also secure environments where AI models and agents can run safely.

Vrajesh Bhavsar, Co-founder and CEO of Operant AI, said the rapid growth of AI has outpaced traditional security measures. He noted that as Indian organisations adopt AI across sectors like finance, healthcare, and public services, securing systems at the point where they operate is becoming critical.He added that the company’s goal is to make AI systems more trustworthy and safe to deploy, ensuring that India’s fast-growing AI ecosystem remains secure as it scales. Operant AI launches ecosystem programme to secure India’s rapidly expanding AI infra | MorungExpress | morungexpress.com

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Over $200 billion to be infused in creating AI-related infra in India

New Delhi, March 2 (IANS) Over $200 billion in AI-related investments are expected across infrastructure, foundation models, hardware and applications, as the ‘India AI Impact Summit 2026’ concluded with the adoption of forward-looking commitments and strategic partnerships that advance a shared global vision for responsible, inclusive and development-oriented artificial intelligence, an official statement said on Monday.

While Adani Group announced plans to invest $100 billion by 2035, Reliance Industries pledged $110 billion over seven years towards AI-focused infrastructure.

Tata Group announced a partnership with OpenAI to scale AI-ready data centres. General Catalyst announced a $5 billion investment commitment over five years, while Lightspeed Venture Partners announced $10 billion in investments.

Sundar Pichai, CEO of Alphabet and Google, announced investments including new India–US subsea cable routes and a $15 billion AI hub in Visakhapatnam. Google will train 20 million civil servants, support 11 million students, and expand AI research collaborations, said the statement.

A key announcement at the Summit was the expansion of India’s sovereign compute capacity. In addition to more than 38,000 GPUs already provisioned under the IndiaAI Mission, an additional 20,000 GPUs will be added in the coming weeks, further strengthening national AI infrastructure.

Notably, the Summit witnessed extensive participation, with approximately 6 lakh attendees in person and over 9 lakh cumulative views through live virtual streaming. Delegations from more than 100 countries and 20 international organisations participated in the proceedings.

During the Summit, India achieved a Guinness World Record for the “Most pledges received for an AI responsibility campaign in 24 hours,” with over 2.5 lakh validated pledges reaffirming public commitment towards responsible AI adoption.

Moreover, the ‘India AI Impact Summit Declaration’ was endorsed by 92 countries and international organisations. The Declaration acknowledges the work undertaken by seven thematic working groups during the Summit. The ‘AI Impact Expo’ emerged as one of the largest AI exhibitions globally, with over 850 exhibitors across 10 thematic pavilions. Over $200 billion to be infused in creating AI-related infra in India | MorungExpress | morungexpress.com

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AI is coming to Olympic judging: what makes it a game changer?

Willem Standaert, Université de Liège

As the International Olympic Committee (IOC) embraces AI-assisted judging, this technology promises greater consistency and improved transparency. Yet research suggests that trust, legitimacy, and cultural values may matter just as much as technical accuracy.

The Olympic AI agenda

In 2024, the IOC unveiled its Olympic AI Agenda, positioning artificial intelligence as a central pillar of future Olympic Games. This vision was reinforced at the very first Olympic AI Forum, held in November 2025, where athletes, federations, technology partners, and policymakers discussed how AI could support judging, athlete preparation, and the fan experience.

At the 2026 Winter Olympics in Milano-Cortina, the IOC is considering using AI to support judging in figure skating (men’s and women’s singles and pairs), helping judges precisely identify the number of rotations completed during a jump. Its use will also extend to disciplines such as big air, halfpipe, and ski jumping (ski and snowboard events where athletes link jumps and aerial tricks), where automated systems could measure jump height and take-off angles. As these systems move from experimentation to operational use, it becomes essential to examine what could go right… or wrong.

Judged sports and human error

In Olympic sports such as gymnastics and figure skating, which rely on panels of human judges, AI is increasingly presented by international federations and sports governing bodies as a solution to problems of bias, inconsistency, and lack of transparency. Judging officials must assess complex movements performed in a fraction of a second, often from limited viewing angles, for several hours in a row. Post-competition reviews show that unintentional errors and discrepancies between judges are not exceptions.

This became tangible again in 2024, when a judging error involving US gymnast Jordan Chiles at the Paris Olympics sparked major controversy. In the floor final, Chiles initially received a score that placed her fourth. Her coach then filed an inquiry, arguing that a technical element had not been properly credited in the difficulty score. After review, her score was increased by 0.1 points, temporarily placing her in the bronze medal position. However, the Romanian delegation contested the decision, arguing that the US inquiry had been submitted too late – exceeding the one-minute window by four seconds. The episode highlighted the complexity of the rules, how difficult it can be for the public to follow the logic of judging decisions, and the fragility of trust in panels of human judges.

Moreover, fraud has also been observed: many still remember the figure skating judging scandal at the 2002 Salt Lake City Winter Olympics. After the pairs event, allegations emerged that a judge had favoured one duo in exchange for promised support in another competition – revealing vote-trading practices within the judging panel. It is precisely in response to such incidents that AI systems have been developed, notably by Fujitsu in collaboration with the International Gymnastics Federation.

What AI can (and cannot) fix in judging

Our research on AI-assisted judging in artistic gymnastics shows that the issue is not simply whether algorithms are more accurate than humans. Judging errors often stem from the limits of human perception, as well as the speed and complexity of elite performances – making AI appealing. However, our study involving judges, gymnasts, coaches, federations, technology providers, and fans highlights a series of tensions.

AI can be too exact, evaluating routines with a level of precision that exceeds what human bodies can realistically execute. For example, where a human judge visually assesses whether a position is properly held, an AI system can detect that a leg or arm angle deviates by just a few degrees from the ideal position, penalising an athlete for an imperfection invisible to the naked eye.

While AI is often presented as objective, new biases can emerge through the design and implementation of these systems. For instance, an algorithm trained mainly on male performances or dominant styles may unintentionally penalise certain body types.

In addition, AI struggles to account for artistic expression and emotions – elements considered central in sports such as gymnastics and figure skating. Finally, while AI promises greater consistency, maintaining it requires ongoing human oversight to adapt rules and systems as disciplines evolve.

Action sports follow a different logic

Our research shows that these concerns are even more pronounced in action sports such as snowboarding and freestyle skiing. Many of these disciplines were added to the Olympic programme to modernise the Games and attract a younger audience. Yet researchers warn that Olympic inclusion can accelerate commercialisation and standardisation, at the expense of creativity and the identity of these sports.

A defining moment dates back to 2006, when US snowboarder Lindsey Jacobellis lost Olympic gold after performing an acrobatic move – grabbing her board mid-air during a jump – while leading the snowboard cross final. The gesture, celebrated within her sport’s culture, eventually cost her the gold medal at the Olympics. The episode illustrates the tension between the expressive ethos of action sports and institutionalised evaluation.

AI judging trials at the X Games

AI-assisted judging adds new layers to this tension. Earlier research on halfpipe snowboarding had already shown how judging criteria can subtly reshape performance styles over time. Unlike other judged sports, action sports place particular value on style, flow, and risk-taking – elements that are especially difficult to formalise algorithmically.

Yet AI was already tested at the 2025 X Games, notably during the snowboard SuperPipe competitions – a larger version of the halfpipe, with higher walls that enable bigger and more technical jumps. Video cameras tracked each athlete’s movements, while AI analysed the footage to generate an independent performance score. This system was tested alongside human judging, with judges continuing to award official results and medals. However, the trial did not affect official outcomes, and no public comparison has been released regarding how closely AI scores aligned with those of human judges.

Nonetheless, reactions were sharply divided: some welcomed greater consistency and transparency, while others warned that AI systems would not know what to do when an athlete introduces a new trick – something often highly valued by human judges and the crowd.

Beyond judging: training, performance and the fan experience

The influence of AI extends far beyond judging itself. In training, motion tracking and performance analytics increasingly shape technique development and injury prevention, influencing how athletes prepare for competition. At the same time, AI is transforming the fan experience through enhanced replays, biomechanical overlays, and real-time explanations of performances. These tools promise greater transparency, but they also frame how performances are understood – adding more “storytelling” “ around what can be measured, visualised, and compared.

At what cost?

The Olympic AI Agenda’s ambition is to make sport fairer, more transparent, and more engaging. Yet as AI becomes integrated into judging, training, and the fan experience, it also plays a quiet but powerful role in defining what counts as excellence. If elite judges are gradually replaced or sidelined, the effects could cascade downward – reshaping how lower-tier judges are trained, how athletes develop, and how sports evolve over time. The challenge facing Olympic sports is therefore not only technological; it is institutional and cultural: how can we prevent AI from hollowing out the values that give each sport its meaning?

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Willem Standaert, Associate Professor, Université de Liège

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

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World’s Most Northern Electric Ferry Now Sailing in Frigid -13°F Temps (-25°C)

The M/F Vargsund – credit, Finnmark county administration

An all-electric passenger/car ferry in northern Norway has proven that brain-disabling cold can’t affect its service, despite running entirely on batteries.

Whatever technology was encased within the batteries of our parents’ cars that would see them suffer in the cold is not what powers the M/F Vargsund, which links the two islands of Kvaløya and Seiland in Norway’s Finnmark county.

It couldn’t have been a better acid test as the ferry shot off from the quay at Klokkarøy town on January 1st with its first ever passengers aboard—temperatures had fallen to -13°F, which for our Norwegian readers is -25°C.

While some car ferries operate as far north as M/F Vargsund, none do so powered by electricity.

“This is not just about new ferries. It’s about the future. About showing that green technology works – even in the far north, even in tough conditions,” operator Torghatten said in a statement on Friday after the ferry sailed in regular traffic for two weeks.

Stretching 50 meters prow to stern, she can hold 28 cars and 100 passengers. Torghatten is the country’s largest ferry operator, and as well as helping to reduce the company’s own carbon emissions, executives hope that it will set a standard of ambition and belief in the industry at large.

The firm’s most frequently-used ferry, connecting another pair of towns—Bognes and Lødingen—along the northern reaches of Norway, is also fully electric.A total of 95.9% of all new cars registered in Norway in 2025 were electric vehicles, a result of the the calendar edging closer and closer to long-anticipated government edicts on internal-combustion engines. World’s Most Northern Electric Ferry Now Sailing in Frigid -13°F Temps (-25°C)

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India’s life sciences leaders scaling AI, digital transformation: Report

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New Delhi, Life sciences leaders in India are scaling artificial intelligence (AI) and digital transformation, said a report on Friday.

The report by KPMG, which surveyed 110 global Life Sciences leaders, noted that life sciences CEOs in India are also investing in enhancing workforce skills, and the country is on track for sustainable growth in the sector.

“India’s life sciences sector is moving from aspiration to execution. CEOs are scaling AI and digital transformation, pursuing M&A with strategic clarity, and investing in workforce skills,” said Vijay Chawla, Partner and Head – Life Sciences, KPMG in India.

“By combining innovation with efficiency and leveraging India’s talent base, the industry is positioning itself for sustainable growth and global leadership in a rapidly evolving healthcare and Life Sciences ecosystem,” he added.

Globally, over 80 pc global life sciences CEOs believe AI is crucial for growth, reducing emissions, and improving energy efficiency.

The report showed that leaders are decisively shifting from experimentation to execution, focusing on integrating AI, strategically transforming their businesses through M&A, and reshaping their workforce to unlock a new era of innovation and efficiency.

While 86 per cent of CEOs reported a moderate-to-high appetite for M&A over the next three years, signaling a continued drive for transformation, 80 per cent noted adjusting their growth strategies to navigate interrelated market challenges.

Notably, 73 per cent of global leaders reported plans to allocate between 10 per cent and 20 per cent of their technology budgets specifically to AI initiatives. 65 per cent of CEOs anticipate measurable returns on these investments within the next three years.

However, as AI adoption accelerates, cybersecurity remains a critical concern for 84 per cent of CEOs.

Further, the report said CEOs are making talent development a central focus, acknowledging that 85 per cent believe workforce readiness for AI is crucial to achieving their goals.

For life sciences CEOs, executing ESG initiatives also ranks as the second-most important operational focus over the next three years.About 80 per cent of CEOs believe that AI will play a significant role in reducing emissions and enhancing energy efficiency. As a result, sustainability objectives are increasingly translating into tangible operational improvements and financial gains, signaling a promising path forward for the industry India’s life sciences leaders scaling AI, digital transformation: Report | MorungExpress | morungexpress.com

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Centre sanctions 24 chip design projects in big push to India's semiconductor industry

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New Delhi, (IANS): As many as 24 chip design projects have been sanctioned across areas such as video surveillance, drone detection, energy meters, microprocessors, satellite communications, and broadband and IoT Systems-on-Chip (SoCs) under the Centre's Design Linked Incentive Scheme (DLI) scheme, according to an official statement issued on Sunday.

Additionally, 95 companies have received access to industry-grade Electronic Design Automation (EDA) tools, significantly reducing design and infrastructure costs for Indian chip design startups.

Semiconductor chip design is the main value driver in the supply chain, contributing up to 50 per cent of value addition and 30–35 per cent of global semiconductor sales via the fabless segment.

DLI-supported projects are scaling rapidly, with 16 tape-outs, 6 ASIC chips, 10 patents, 1,000+ engineers engaged, and over 3× private investment having been leveraged, the statement said.

The Design Linked Incentive (DLI) Scheme is being implemented by the Ministry of Electronics and Information Technology (MeitY) with an outlay of Rs 76,000 crore. The programme supports investments in semiconductor and display manufacturing as well as the design ecosystem. The DLI Scheme operates under this programme, ensuring end-to-end backing for design, fabrication and productisation. C-DAC, a premier R&D organisation of the MeitY, is responsible for the implementation of the DLI Scheme as the nodal agency.

The Semicon India Programme aims to catalyse a strong, self-reliant chip design ecosystem by providing financial incentives and access to advanced design infrastructure for domestic startups and MSMEs.

The scheme is now driving the transition from design validation to productisation, enabling start-ups and MSMEs to move toward volume manufacturing, system integration, and market deployment. This evolving ecosystem not only strengthens India’s domestic semiconductor capabilities but also positions the country as a credible player in global chip design and innovation, the statement said.

India’s semiconductor ecosystem is being strengthened through a coordinated institutional framework that combines policy leadership, investment support, capacity building, and indigenous technology development. The key programmes and agencies provide end-to-end backing -- from incentivising chip design and manufacturing to developing skilled talent and fostering open-source microprocessor architectures -- ensuring India’s progression toward a self-reliant and globally competitive semiconductor design ecosystem.

The Chips to Startup (C2S) Programme, being implemented, is an initiative aimed at academic organisations spread across the country to generate 85,000 industry-ready manpower at B.Tech, M.Tech, and PhD levels, specialised in semiconductor chip design.

The DLI scheme aims to offset the existing disabilities in India’s domestic semiconductor design industry. It seeks to help Indian companies move up the semiconductor value chain.Without strong fabless capability, a nation remains dependent on imported core technologies even if electronics are manufactured locally. Building a robust fabless ecosystem, therefore, enables India to own the most critical layer of the value chain, retain intellectual property, reduce imports, attract manufacturing, and establish long-term technological leadership, the statement further said. Centre sanctions 24 chip design projects in big push to India's semiconductor industry | MorungExpress | morungexpress.comn

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Hyundai Motor chief vows AI-driven growth, faster decision-making

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Seoul, (IANS): Hyundai Motor Group Executive Chair Euisun Chung said on Monday the artificial intelligence (AI)-driven industrial transition will create greater growth opportunities for the group, while calling for faster decision-making supported by clear and timely communication.

In a pre-recorded New Year's roundtable with senior executives, Chung said the group's extensive data on "moving physical assets" such as vehicles and robots, along with its manufacturing process data, constitutes a strong competitive edge in the AI era, reports Yonhap news agency.

"This is a powerful advantage that big tech companies cannot easily replicate," Chung said. "For Hyundai Motor Group, which possesses data, capital and manufacturing capabilities, AI is a game in which we have a strong chance of success."

He added that the group must broaden its business ecosystem through collaboration with a wide range of partners in the AI sector amid growing global uncertainties.

The South Korean automaker expects heightened uncertainty in the global business environment this year, driven by the restructuring of global supply chains, slowing economic growth and increasing geopolitical fragmentation.

Commenting on the year ahead, Chung said risks long anticipated by the group are likely to materialise.

To address these challenges, the chairman called for clearer situational awareness and more agile decision-making based on fast and transparent communication.

"Reports should include the reporting employee's own analysis and conclusions and be shared quickly with the right people at the right time," he said.

Chung also underscored the importance of organisational transformation rooted in deep reflection, as competition in the global automotive industry continues to intensify.

As for the group's future direction, Chung said significant growth opportunities lie ahead amid major shifts in artificial intelligence, software-defined vehicles (SDVs) and future mobility.

Previously, Hyundai Motor Group announced plans to invest 125.2 trillion won (US$86.5 billion) over five years through 2030."The investment will not only be used to expand the group's businesses but also to pursue qualitative growth," Chung said. Hyundai Motor chief vows AI-driven growth, faster decision-making | MorungExpress | morungexpress.com

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India emerges as world’s 3rd most competitive AI power

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New Delhi, December 14 (IANS): India has emerged as the world’s third most competitive country in artificial intelligence, according to Stanford University’s Global AI Vibrancy Tool on Sunday.

The latest ranking highlights how India’s fast-growing tech ecosystem and strong talent base are helping the country play a key role in the global AI race.

The Visual Capitalist chart, based on Stanford’s data, shows the United States clearly leading the world in AI competitiveness with a vibrancy score of 78.6.

China is placed second with a score of 36.95, while India ranks third with a score of 21.59. This puts India ahead of several advanced economies, including South Korea, the United Kingdom, Singapore, Japan, Canada, Germany and France.

Stanford’s AI Vibrancy Tool combines several indicators into a single score to measure how developed and competitive a country’s AI ecosystem is.

These indicators include research and development, talent availability, investment and economic impact, infrastructure, public opinion, and policy and governance.

The tool aims to show where innovation and AI talent are growing and how seriously governments are backing artificial intelligence.

The data also shows that income levels play an important role in AI competitiveness. High-income countries dominate the top of the ranking, while upper-middle-income countries such as China and Brazil are steadily closing the gap.

India stands out among lower-middle-income countries, as it is the only one to feature so high on the global list, underlining its unique position in the AI landscape.

In specific areas, different countries lead different metrics. The United States tops categories such as research and development, responsible AI, economy, policy and governance, and infrastructure.

China performs strongly in talent, economy and infrastructure, while India features among the top three in talent -- reflecting its large and skilled workforce in technology and engineering.

The report also flags a broader concern. While the link between national income and AI competitiveness is expected, the growing gap between countries could deepen global inequality if access to AI growth remains uneven.For India, however, the ranking is a major boost. It reflects rising investments in AI, increasing research output, a strong startup ecosystem and a massive pool of engineers and developers. India emerges as world’s 3rd most competitive AI power | MorungExpress | morungexpress.com

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Tongue-Zapping Device Does More in 6 Months Than 4 Years of Normal Stroke Rehabilitation

PoNS Portable Neuro-modulation Stimulator tongue sensor, plugged-in

A fall from a 10 foot ladder triggered a cascade of neurological problems that might have left an Ontario man paralyzed on his right side for life.

But working with a device that delivers an electrical shock through the tongue has allowed him to recover movement and speech, and even has him looking towards a future where he can return to work.

Combined with traditional speech and movement therapy, Mark Foster uses a portable neuro-modulation stimulator, or PoNS device, to deliver a very small electrical current through his tongue to the brain. The current, controlled via a collar worn around the neck, helps form new pathways for existing skills, and can help increase the efficacy of existing therapies.

Foster has been truly impressed, and made more progress in 6 months with the PoNS device than in 4 years of traditional therapy.

“I would say that it has helped an immense amount with confidence,” Foster told London Free Press. “It hasn’t been perfect, don’t get me wrong… but I’m having an easier time getting around.”

Foster, from London, Canada, was laying electrical wire on a build site in Paris (also in Canada), when he fell onto the cement below. He stood up, dusted himself off, and after a moment, concluded he was fine.

Then, at the urging of a coworker, he went to the hospital for a CT scan and MRI. They concluded he had extensive swelling, but also an unusual formation of cells in his brain called a cavernoma that would have pre-dated the fall. A week later, unable to sleep, Foster went for a warm shower, which increased his blood pressure and led to a stroke.

Interestingly enough, he was recovering well from the stroke itself, before his wife Sonia noticed him slurring his speech and having trouble with balance. They returned to the hospital and found the swelling in his brain’s cavernoma area so extreme that the determination was to perform brain surgery immediately, as he was bleeding and experiencing something like a mini-stroke.

It erased both the progress Foster had already made, and most of the hope he had left.

“It took a long time to go from absolutely nothing,” Foster said. “It broke my heart, but at the same time, it gave me a reason to kind of push myself trying to get back into the swing of things.”

The PoNS device, built by Helius Medical Technology, was originally designed to assist in multiple sclerosis recovery, but also, recently and successfully underwent an FDA-overseen Phase 3 clinical trial for stroke rehabilitation.

Over 100 patients undergoing a double-blinded, placebo-controlled trial found significantly increased benefit from physical rehabilitation for both balance and gait when using the PoNS device.

In June, CignaHealth became the fifth major payer to authorize a claim for the PoNS mouthpiece and controller at out-of-network adjusted negotiated list price of $19,161. The average in-network contracted payment rate is $18,350.

GNN has reported on these togue-zapping therapies before, with another such device being designed for use in the curing of tinnitus.WATCH the company press release below… Tongue-Zapping Device Does More in 6 Months Than 4 Years of Normal Stroke Rehabilitation

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Women at forefront of technology, leading with vision: Industry leaders

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New Delhi, (IANS) Leaders from the tech industry on Friday said that women are at the forefront of technology, breaking barriers and leading with vision.

In a statement to IANS, Poonam Puthran, Executive Vice President - Banking at Aurionpro, said that women play a pivotal role in shaping the landscape in the dynamic world of technology.

"With their leadership and resilience, we chart a course towards excellence, driving our organisation forward with purpose and determination," she added.

A report by AIM Research titled 'Women in Tech 2023 (India)' found that out of the total pool of tech professionals across India, the share of women tech professionals is only 29 per cent.

Anjali Sharma, Director, Global Head of L&D, Fulcrum Digital, said the ascent of women in tech in India is both a triumph and a call to action.

"Despite the challenges within the traditionally male-dominated realm, we have witnessed a remarkable shift as women rise through the ranks, particularly in cutting-edge fields like deep tech," she added.According to Sharma, the journey towards gender equality in tech is a shared responsibility, with collaboration needed from individuals, organisations, and policymakers. Women at forefront of technology, leading with vision: Industry leaders | MorungExpress | morungexpress.com

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Driverless Electric Bus Eases Driver Shortages and Congestion In Madrid During Maiden Service

– Courtesy of EMT ©, released to Euronews

After five years of testing, an autonomous electric bus has been deployed to a park in Madrid for one final, real-world experiment in driverless vehicles for public transit.

If it succeeds, the cute little caterpillar-shaped bus may become a mainstay in the Spanish capital, ferrying passengers around Casa de Campo park and beyond.

Developed through a partnership with the Madrid transit authority (EMT) and Automotive Technology Center of Galicia (CTAG), in northern Spain, the driverless bus has been in action between the 15th of September and 24th of October.

It drove in a circuit around Casa de Campo, picking up passengers at 6 stops, and operating for five of the city’s peak hours.

The vehicle is 100% electric, and though many of its body components were manufactured abroad, the brain, eyes, ears, and other software were made at CTAG.

“This bus is one of the best I have ever tested,” César Omar Chacón Fernández, head of the EMT’s Rolling Stock Planning Division, told Euronews. “It behaves very well dynamically. Let’s say that the technology is very well integrated, it doesn’t behave erratically or robotically like other buses.”

The aim of EMT and CTAG is not replacing drivers, but providing a suitable and safe alternative for predictable, shorter routes that can help cities address a current shortage of professional bus drivers.

Though fully autonomous, and capable of detecting pedestrians, cyclists, skateboarders, scooters, animals, crosswalks, stop signs, traffic lights, and roundabouts, and making decisions over when to brake, accelerate, turn, and open/close the doors, a safety officer is always on board just in case.

“The vehicle detects any object, from a bicycle to an animal, and reacts accordingly to avoid collisions,” Chacón said. “It is a fully autonomous line, but we never leave anything to chance.”A cute little thing, it joins a growing number of miniature, electric, European automotive options that fit better into crowds of cyclists, narrow streets, and cramped parking spaces. Driverless Electric Bus Eases Driver Shortages and Congestion In Madrid During Maiden Service

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Telco transformation and the AI efficiency imperative

We caught up with Joaquim Croca, Vice President at global engineering and technology company Cyient, to discuss the pressures facing telcos and their automation journey

For the most part, the 2020s have not been a kind to telco network operators, particularly in Europe. The start of the decade saw them racing to pour billions of euros into their new fibre and 5G networks, betting heavily on the ‘build it and they will come’ mantra made famous by the 1989 film Field of Dreams.

Unfortunately for telcos in 2025, the resulting environment has proven less a field of dreams than a quagmire.

Traditional revenues have remained relatively flat for years in both the fixed and mobile sectors, often bogged down by heavy-handed regulation and fierce competition. At the same time, new revenue streams at scale remain elusive, despite the vastly improved technical capabilities of these new networks.

Against this challenging backdrop, as Cyient’s Senior Vice President Joaquim Croca points out, operational efficiency has never been more important.

“Telco business is fighting not to get fully commoditized,” said Croca. “All of the operators are looking towards becoming more streamlined, more cost-efficient… It’s a matter of survival.”

AI: Unlocking efficiency

At the heart of this drive for efficiency is the rapid evolution of AI in recent years, enabling a level of automation previously unattainable. Today, advanced AI analytics can rapidly compile data from numerous siloes into actionable data points, pre-empting network incidents and triaging problems in real time. Meanwhile, specially designed AI agents can work alongside network engineers, responding to queries in plain language and autonomously making resolving issues.

“AI is no longer just a buzzword; it’s starting to prove its value,” said Croca. “The question of 2025 is really: how can I use AI to release human intelligence to go and do something else?”

“We have been using AI for many years, but now it’s at the forefront of conversations with our customers. They want to know how much AI we are bringing to help overcome their challenges,” he continued.

For Cyient, this concept of freeing engineers to perform more high value tasks is at the core of their VISMON™ platform, a suite of AI-driven tools that enable zero-touch, closed-loop network automation, from planning and deployment through to optimisation and operations. According to the company, this platform is already delivering reductions in network operation time and resources by up to 40% and saving engineers numerous hours every day through autonomous site management.

“Our VISMON platform has been around for 20 years, evolving alongside the industry,” said Croca. “We have a stream of AI-powered scripts that are looking at how the network is performing, what are the issues, what are the fake alerts… ensuring we only call in the network engineer when it’s absolutely necessary.”

Increasing market complexity

In addition to automating network operations directly, part of what makes AI-powered OSS so effective is the smoother integration of these networks within the wider telco operating environment.

In recent years, an increasingly popular monetisation strategy for telcos has been to spin off their infrastructure, aiming to offload some of the operational complexity and serving to attract fresh investment. This, the telcos argue, will allow them to streamline their service operations and better focus on their customers. However, as Croca highlights, this type of fragmentation is making the telco market even more complex.

“We’ve seen a major trend of operators splitting between infracos, netcos, surfcos, etc. It’s all driven by the financial incentive,” explained Croca. “But this approach also creates a more complicated ecosystem. In Europe there are dozens of operators, each of which can divide into two or three different entities, each with their own processes and relationships. It rapidly becomes a very complex world.”

Handling this complexity on an international scale requires careful data management, another area where AI can perform. In a partnership announced this summer, Vodafone is using VISMON for just this purpose, helping to harmonise its network operations across its numerous markets. The platform provides unified network visibility across Vodafone’s markets, enabling them to benchmark configurations, detect anomalies, and track deployments.

The benefits are significant. According to the partners, their collaboration is delivering a 70% reduction in time spent compiling cross-market reports and three times faster decision-making. They also expect to see a 50% decrease in errors caused by inconsistent configuration.

“VISMON provides the strategic foundation to oversee configuration data across all markets, enabling us to harmonize practices, identify best-performing setups, and optimize our networks more effectively than ever,” said Mostafa Noureldien, Manager, Network Development Digital Strategy at Vodafone in the company release.

“We are already deploying AI NOC (Network Operations Centre) agents and rollout agents across two of Vodafone’s operations,” added Croca. “These are fully autonomous and very intelligent. They bring a big gain not just in efficiency but in quality, in terms of First Time Right and First Time Resolved. We’re delivering much faster resolutions to network issues.”

The road towards fully autonomous networks

Of course, the long-term dream for telcos is full network autonomy, requiring the bare minimum of human oversight. For Croca, this goal remains firmly on the horizon due to challenges both technical and philosophical.

“We are still going through existential doubts around how much of the network and its operations can be handed over to AI. There are technical questions and regulatory questions to be addressed, so we will be handing over the reins gradually,” he explained.

Nonetheless, the industry is making blistering progress, with Croca highlighting the need for effective and agile leadership in this rapidly changing landscape.

“To succeed, you need to find the CEOs and CFOs that are really driving new ways of operating, as well as finding some evangelist CTOs that are very keen to look at things in a different perspective,” said Croca. “It’s not just about technological maturity, but our own mindset towards embracing it.”Find out more about Cyient and the journey towards autonomous networks here Telco transformation and the AI efficiency imperativ

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How safe is your face? The pros and cons of having facial recognition everywhere

Joanne Orlando, Western Sydney University

Walk into a shop, board a plane, log into your bank, or scroll through your social media feed, and chances are you might be asked to scan your face. Facial recognition and other kinds of face-based biometric technology are becoming an increasingly common form of identification.

The technology is promoted as quick, convenient and secure – but at the same time it has raised alarm over privacy violations. For instance, major retailers such as Kmart have been found to have broken the law by using the technology without customer consent.

So are we seeing a dangerous technological overreach or the future of security? And what does it mean for families, especially when even children are expected to prove their identity with nothing more than their face?

The two sides of facial recognition

Facial recognition tech is marketed as the height of seamless convenience.

Nowhere is this clearer than in the travel industry, where airlines such as Qantas tout facial recognition as the key to a smoother journey. Forget fumbling for passports and boarding passes – just scan your face and you’re away.

In contrast, when big retailers such as Kmart and Bunnings were found to be scanning customers’ faces without permission, regulators stepped in and the backlash was swift. Here, the same technology is not seen as a convenience but as a serious breach of trust.

Things get even murkier when it comes to children. Due to new government legislation, social media platforms may well introduce face-based age verification technology, framing it as a way to keep kids safe online.

At the same time, schools are trialling facial recognition for everything from classroom entry to paying in the cafeteria.

Yet concerns about data misuse remain. In one incident, Microsoft was accused of mishandling children’s biometric data.

For children, facial recognition is quietly becoming the default, despite very real risks.

A face is forever

Facial recognition technology works by mapping someone’s unique features and comparing them against a database of stored faces. Unlike passive CCTV cameras, it doesn’t just record, it actively identifies and categorises people.

This may feel similar to earlier identity technologies. Think of the check-in QR code systems that quickly sprung up at shops, cafes and airports during the COVID pandemic.

Facial recognition may be on a similar path of rapid adoption. However, there is a crucial difference: where a QR code can be removed or an account deleted, your face cannot.

Why these developments matter

Permanence is a big issue for facial recognition. Once your – or your child’s – facial scan is stored, it can stay in a database forever.

If the database is hacked, that identity is compromised. In a world where banks and tech platforms may increasingly rely on facial recognition for access, the stakes are very high.

What’s more, the technology is not foolproof. Mis-identifying people is a real problem.

Age-estimating systems are also often inaccurate. One 17-year-old might easily be classified as a child, while another passes as an adult. This may restrict their access to information or place them in the wrong digital space.

A lifetime of consequences

These risks aren’t just hypothetical. They already affect lives. Imagine being wrongly placed on a watchlist because of a facial recognition error, leading to delays and interrogations every time you travel.

Or consider how stolen facial data could be used for identity theft, with perpetrators gaining access to accounts and services.

In the future, your face could even influence insurance or loan approvals, with algorithms drawing conclusions about your health or reliability based on photo or video.

Facial recognition does have some clear benefits, such as helping law enforcement identify suspects quickly in crowded spaces and providing convenient access to secure areas.

But for children, the risks of misuse and error stretch across a lifetime.

So, good or bad?

As it stands, facial recognition would seem to carry more risks than rewards. In a world rife with scams and hacks, we can replace a stolen passport or drivers’ licence, but we can’t change our face.

The question we need to answer is where we draw the line between reckless implementation and mandatory use. Are we prepared to accept the consequences of the rapid adoption of this technology?

Security and convenience are important, but they are not the only values at stake. Until robust, enforceable rules around safety, privacy and fairness are firmly established, we should proceed with caution.

So next time you’re asked to scan your face, don’t just accept it blindly. Ask: why is this necessary? And do the benefits truly outweigh the risks – for me, and for everyone else involved?

Joanne Orlando, Researcher, Digital Wellbeing, Western Sydney University

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

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