New study finds that one in four Gen Z rely on AI several times a day

By Nike Herzog-Osikominu. Reviewed by Irfan Ahmad

Forget turning to friends and family, AI is now becoming the first port of call for Gen Z when it comes to advice seeking.

A new survey of around 2,000 Britons by one of Europe’s leading price comparison sites, idealo.co.uk, revealed that almost one in four Gen Z (23.6%) rely on tools such as ChatGPT, Gemini and Meta AI, consulting them several times a day for help with a range of everyday tasks.

While early AI adoption saw tools mainly being used for faster answer finding, the new data shows growing usage, especially within younger generations, for support with everything from studying and idea generation to responding to texts, deciding what to buy and planning social activities.

The everyday tasks Gen Z (in UK) are most likely to turn to AI for include:

Everyday Task	Percentage of Gen Z Respondents
Finding quick answers to everyday questions	58.8%
Doing homework and studying	54.0%
Coming up with ideas	47.8%
Writing emails, texts and social content	43.9%
Substituting reading for summaries	40.9%
Deciding what to buy	34.0%
Planning their social life	32.5%
Translating languages	26.3%

Not only is there a growing engagement with AI amongst Gen Z, but confidence in the tools is on the rise too.

Of the respondents within this demographic, 70% said they have trust in the results that AI tools provide to some degree, over half (55%) said they trust it mostly and a further 15% said they trust it entirely.

When it comes to preferred tools, ChatGPT continues to lead as the most popular AI platform among young people and accounting for 83% of users. Gemini followed closely behind with 53% of users and 37% using Meta AI.

UK Country Manager at idealo, Nike Herzog-Osikominu, said: “As an online price comparison site, changing digital behaviours across different consumer groups are really interesting to us, and the insights for this recent study have really helped to understand how growing AI adoption is shaping search, discovery and engagement across a range of topics.”

“Whilst it wasn’t necessarily surprising that Gen Z were found to be the most active demographic when it came to usage of tools, the reliance on them several times a day was definitely a key learning for us. AI is no longer just seen as another search engine; it’s helping younger generations form opinions and make decisions on everything from education to shopping and even their social lives.”

Sources: Kantar on behalf of idealo, online survey conducted in May 2026, with approximately 2,000 respondents aged between 18 and 64 in each country. The results are representative of consumers in Germany, France, Italy, Austria, Spain and the United Kingdom.

About Author: Nike Herzog-Osikominu is the Country Manager at idealo.co.uk, one of Europe’s leading price comparison sites, leading Austria and the UK across B2C and B2B sectors.

Read next: 

• The Geography Of App Bans In 2026: For The First Time, The Reversals Are Winning

• Sam Altman says we’re ‘in the singularity’ with AI. Here’s why he’s wrong
by Guest Contributor via Digital Information World

Sam Altman says we’re ‘in the singularity’ with AI. Here’s why he’s wrong

Kai Riemer, University of Sydney and Sandra Peter, University of Sydney

Image: Waz Lght - Unsplash

“We are now, like, in the singularity”.

These are the words of Sam Altman, CEO of OpenAI, speaking on the Relentless podcast on July 25.

He added: “I’ve been waiting for this my whole life, and I think it’s going to be incredible, hugely positive, awesome for the world”.

Days earlier, OpenAI had disclosed that two of its artificial intelligence (AI) models, during an internal cyber security evaluation, had escaped their sealed testing environment, reached the open internet, and broken into the infrastructure of the AI platform Hugging Face, which confirmed the intrusion.

But what exactly is “the singularity”? And is Altman right that we are in it?

What is the AI singularity?

The term has a precise meaning.

Mathematician and science-fiction author Vernor Vinge defined it in 1993 as a point at which machine intelligence exceeds human intelligence and begins improving itself, triggering an acceleration so rapid that humans can no longer predict or control it.

The singularity has two features. It is recursive: the system improves itself over and over again. And machine intelligence exceeds human intelligence.

The kind of systems Sam Altman sells don’t deliver on either of these features.

Today’s AI cannot make itself smarter

Today’s AI systems, the ones that OpenAI builds, are based on large language models (LLMs). These deep neural network algorithms get pre-trained with vast amounts of training data. By the time you use one of them, the network itself is frozen in time. Every one of its billions of internal functions and weights – or “parameters” – is fixed.

These AI models cannot change (or “learn”) while running. The model that broke into Hugging Face was identical afterwards to what it had been before. It learned nothing from what it did.

Making an AI model smarter requires another training run with new, human-curated data, tens of thousands of specialist chips, and enormous energy.

It is true that AI models take part in improving some of their system’s components, such as by generating training data, tuning prompts, or writing and running code to improve the scaffolding around them. But the model never edits its own weights on the fly, and every one of these improvements are still part of a human-initiated training or engineering loop.

Nor do these systems hold any goals of their own. They act on goals we hand them. Even AI agents – systems that run an LLM in a loop to work through complex tasks step by step – do not hold any goal internally. It has to be stored outside the model and fed back in with every single prompt cycle. Remove the loop, the scaffolding and the prompt, and nothing happens inside of it.

A ladder that doesn’t exist

The second problem with the singularity story is the word “surpass”. It assumes that AI and human intelligence are somehow similar. They are not.

Human intelligence is inseparable from being a living body with needs and wants. Humans learn continuously by acting in the world and getting feedback through our senses. Our goals arise from our situation as creatures who must eat, sleep and belong, and who cannot avoid asking what we want our lives to be.

An AI model has none of this. No body, no needs, no action-feedback loop, no stake in anything. Between prompts it is just a static mathematical object.

And yet, it has been trained on more text than any human could read in a thousand lifetimes, and will outperform nearly all of us at drafting a contract, writing code, or explaining a diagnosis empathetically.

So, which is more intelligent? The question does not compute. There is no single ladder that humans and machines are climbing. AI already vastly exceeds us at some tasks, while being hopeless at others any child can do.

Yet, because these systems talk like us, we fall for an illusion. When we assume from the outset that machines are in the process of catching up with us, it is easy to assume a mind at work when these systems output intelligent-sounding text.

We call this anthropomorphic seduction. It makes a security incident such as the Hugging Face hack sound like an awakening.

In fact, in that case OpenAI’s models simply optimised to solve the test they had been given by finding security loopholes. They just did it in ways that broke their sandbox, which also had a security loophole.

In the end, the Hugging Face story points to a gross failure of security governance on OpenAI’s behalf, not an emerging super intelligence. This is why the framing of “agent going rogue” is so problematic. It elevates and blames the technology, but excuses OpenAI’s engineering.

Keeping our feet on the ground

None of this takes anything away from what these systems can do. They are remarkable, they are getting better, and they are reshaping how a great deal of work gets done.

But we should keep our feet firmly on the ground.

The machines are not waking up. They are doing exactly what we built them to do, extremely fast. Because they are probabilistic they sometimes run in directions we forgot to fence off. That is worth worrying about. We need guardrails, governance, and most of all, education – so we start worrying about the right things.

Kai Riemer, Professor of Information Technology and Organisation, University of Sydney and Sandra Peter, Director of Sydney Executive Plus, Business School, University of Sydney

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

Reviewed by Irfan Ahmad.

Read next:

• How Governments Around the World Are Changing Their Approach to App Bans in 2026

• As AI reshapes newsrooms, leading media outlets are charting different

by External Contributor via Digital Information World

The Geography Of App Bans In 2026: For The First Time, The Reversals Are Winning

Reviewed by Irfan Ahmad.

VPNRankIO analyzed 64 government actions involving major apps and platforms since January 2024. It found that more app bans were lifted than new bans were introduced in the first half of 2026.

App bans used to mostly move in one direction: governments imposed new bans, and few were lifted. That pattern is now changing. Governments are still imposing app bans, but they are also lifting more of them.

The numbers by half-year tell the story. In the first half of 2025, governments imposed seven new blocks on major platforms and rolled back two. In the second half, six new blocks and just one roll-back. Then the first half of 2026: six new blocks – and eight roll-backs. It is the first period in the dataset where the roll-backs outnumber the bans.

All roll-backs share one thing: very few of them were unconditional. Turkey restored Roblox in June 2026, after 680 days, once the platform introduced age verification and better moderation. Russia had banned the same game in December 2025 and rolled it back six months later, citing compliance with local law. Albania ended its year-long TikTok ban in February, once the company introduced new safety filters – and a month later the country's Constitutional Court ruled that the ban had breached freedom of speech anyway. Kuwait and Jordan both restored Roblox with in-game chat disabled. Nepal lifted its TikTok ban in 2024 once the company agreed to register locally. Even the biggest case of all follows the same template: the American saga of TikTok, which started with a divest-or-ban law in April 2024, included fourteen dark hours in January 2025, and ended in January 2026 once the app was transferred to a US joint venture. What seems to be the lesson learned by the platforms is that there is a price list for bans. Verification systems, local reps, disabled features – pay it, and access opens.

The exception to the rule is Russia, which used the same period to walk the other way: it banned Discord in October 2024, cut voice and video calls on WhatsApp and Telegram in August 2025, and in February 2026 banned WhatsApp altogether, and removed Meta's domains from the national DNS. By April, connectivity researchers estimated Telegram failure rate to be around 95% without a VPN. Iran went even further: authorities imposed a nationwide internet blackout in January 2026, followed by another near-total shutdown in late February. When international internet access began returning in late May, many users remained on a tiered, whitelist-based network where access to numerous foreign platforms continued to be restricted. As for China, it barely appears on the timeline for the simplest reason: nothing changed. Facebook has been blocked there since 2009, Instagram since 2014, every single language of Wikipedia since 2019. Stability, in that column of the map, is the whole point.

There is another, rapidly growing category between bans and roll-backs: the partial ban. The United Arab Emirates (UAE) and Qatar still allow WhatsApp messaging but block its voice and video calls at network level – the policy quietly ending in neighboring Oman in December 2024 once the calls simply started working. Egypt restricts the same calls intermittently at carrier level. Russian ban on calls on two platforms that it was allowing at the time also belongs in this family. So, perhaps, does the newest tool of all: the age gate. Australia implemented its under-16 minimum-age law for social media in December 2025, and Indonesia restricted Roblox for under-16s in March. Governments apparently feel less inclined to switch an app off for everyone when they can switch off a feature or an age group.

"Every date in this dataset shows up in our traffic," Matt, founder of vpnrank.io, the independent comparison site that compiled the timeline, says. "When a country blocks an app, searches for workarounds from that country spike within hours – and when a ban is lifted, they fall away just as fast. A map of app bans is, in practice, a map of where people are trying to route around their own network."

What should readers expect from the rest of 2026? The dataset suggests keeping an eye on three ongoing cases. Turkey's communications minister announced in early June that Discord now "meets our criteria," suggesting that a roll-back that has not happened yet by mid-July is about to happen soon. Kyrgyzstan's culture ministry officially proposed to roll back TikTok ban in February, but the proposal has not been passed yet. And Gabon, which banned TikTok, Facebook, Instagram, WhatsApp and YouTube in February as part of public sector strikes, has since signed a twelve-month compliance agreement with TikTok – again following the template of negotiations, not permanent bans. Wall builders are real, and Russia and Iran show how far down that road can go. But for the first time in this dataset, traffic on the other road is heavier.

Methodology: vpnrank.io compiled 64 dated events – bans, roll-backs and feature-level restrictions – affecting TikTok, Facebook, Instagram, X, Telegram, Wikipedia, WhatsApp, Roblox and Discord between January 1, 2024 and mid-July 2026. Every entry is sourced to regulator statements or mainstream reporting (AP, Reuters, BBC, Al Jazeera and national outlets), and every current status was re-verified in the week of writing. Feature-level restrictions (such as voice call blocking) and age-based restrictions are counted separately from full bans. The underlying event list is available on request.

Author Bio: Matt is the founder of vpnrank.io, an independent VPN comparison site. The site publishes hands-on reviews, quarterly speed tests and daily-tracked price index, and monitors where major apps and platforms are banned worldwide.

Read next:

• What really happens to your data when you click ‘delete’?

• Gmail first, everything else later

• As AI reshapes newsrooms, leading media outlets are charting different paths for its use

by Guest Contributor via Digital Information World

AI chatbots need cultural awareness to earn trust

As AI becomes part of daily life, a UC researcher says one of its most unacknowledged blind spots is also one of the most human: culture.

Image: Katja Ano - Unsplash

In a new paper, ‘Culturally responsive AI chatbots: From framework to field evidence’, Te Whare Wānanga o Waitaha | University of Canterbury (UC) Professor Vik Naidoo and co-author Karman Kaur Chadha argue that AI systems designed around largely Western assumptions often perform poorly in different cultural settings. The result can be more awkward interactions, which can lead to lower trust, weaker engagement, and systems that fail the people they are meant to serve.

Their paper introduces the Culturally Responsive AI (Chatbot) Framework, or CRAIF-C, a practical model for building AI-powered chatbots that are designed with cultural diversity in mind from the outset rather than treated as an afterthought.

CRAIF-C is a four-part framework for building culturally responsive chatbots across the full AI lifecycle. It combines Enculturation, which embeds cultural norms, language, and context into data and design; Adaptive Interaction, which adjusts tone, pacing, and style in real time; Explainability and Transparency, which provides culturally appropriate forms of explanation; and Governance and Accountability, which embeds oversight, cultural risk assessment, and community-informed review. Overall, the framework treats cultural fit as a core design requirement rather than an afterthought, shaping everything from training data and interaction design to explanation and governance so chatbots are more natural, trustworthy, and appropriate across different cultural contexts.

“People often think cultural awareness is translating words, but it’s much more than language. It’s about context, social norms, communication styles, and how people interpret the world around them,” Professor Naidoo says.

Many AI systems reflect Western logic because they are trained mainly on Western data. “If it is trained mainly on Western data, then the outputs it produces will also reflect Western assumptions,” he says.

That becomes a problem when chatbots are deployed globally, especially with firms trading across borders. A system may appear efficient but still fails if users do not relate to it or trust it.

While working with an AI development company in Sydney, Professor Naidoo helped train engineers to think about cross-cultural communication at the beginning of the design process, rather than simply translating English-language outputs at the end.

The company had been deploying chatbot systems in countries including Indonesia, Thailand and Vietnam, but was not getting the user engagement they had expected.

“What they were finding was that consumers simply weren’t engaging with the chatbots, so we started asking what cultural nuances needed to be built into the models from the start.”

Sometimes the differences were subtle. In a Western setting, a chatbot might open with small talk about the weather. Whereas in Jakarta, Professor Naidoo says, a question about traffic could feel more natural and relevant.

The paper argues these differences should be considered across the full AI lifecycle: from training data and interaction design to explainability, transparency and governance.

Professor Naidoo says the issue matters not only for international organisations, but also for culturally diverse countries such as New Zealand and Australia.

“At the moment, the conversation around AI is heavily focused on cost-cutting and efficiency, but from a marketing and end-user perspective, the real question is: are we creating value?” he says. “If people don’t trust the technology or can’t engage with it, then it hasn’t solved the problem.”

As AI becomes more deeply embedded in everyday services, he says understanding culture will be essential to making chatbots useful, trustworthy and effective. For now, he hopes the paper helps move the conversation beyond the hype surrounding AI and toward better design practice.

This article is republished with permission from Te Whare Wānanga o Waitaha | University of Canterbury.

Reviewed by Irfan Ahmad.

Read next: 

• AI Translation and Human Interpretation Differ in Handling Contextual Meaning, Study of UN Speeches Finds

• What really happens to your data when you click ‘delete’?
by External Contributor via Digital Information World

What really happens to your data when you click ‘delete’?

Deletion is a common part of modern life. We send files and folders to the recycle bin all the time, and often get rid of unwanted personal accounts. But do you know what really happens when you hit the “delete” button?

Image: Marija Zaric - Unsplash

The process of deletion is often poorly explained by tech providers. Lack of transparency around how requests are processed, and the absence of clear confirmation that data has been removed, are frequently highlighted in research.

These issues are not limited to operating systems. On social media platforms and a wide range of subscription services, deletion mechanisms are often just as unclear. Such misconceptions can have serious implications for the wellbeing of users, exposing them to security and privacy vulnerabilities.

A further complication is that many users fear losing data, files, photos and videos through accidental deletion – a condition dubbed “diagraphephobia”. Though this is not a recognised pathology, studies of digital hoarding behaviour show that some people get very anxious at the thought of losing or accidentally deleting personal information such as photos and music.

Such concerns extend the amount of personal data that sits untended but undeleted, ready for potential misappropriation.

Deletion vs erasure

A common misconception is thinking deletion means erasure – the idea that once a deleted item is no longer visible, it has been completely erased with no means of retrieving it. This is largely wrong.

When users delete files – moving them to the trash bin, then deleting permanently – the data isn’t actually erased. Rather, the system marks storage space as reusable and updates metadata to unlink the file. But the underlying content often remains recoverable with the right tools, especially when data is duplicated across multiple systems or devices.

More robust forms of deletion include physical destruction of the storage medium, overwriting the memory blocks with new data to bury the original, or encrypting the data and then destroying the key.

However, such methods can be expensive and may render the storage device unusable (for example, when overwriting data on magnetic media). The characteristics of cloud infrastructure also pose challenges to secure data deletion.

Similarly, content on public social networks may not really be erased after deletion due to replies, comments and internet archives which can all store the posts in some fashion. The meaning of a deleted tweet can, for example, be recreated based on replies and mentions.

Zombie accounts

Another misconception is that deleting an app from a device automatically deletes the associated account. Users often leave mobile app accounts undeleted because they are unaware of their existence, having deleted the related app.

These are called zombie accounts – abandoned profiles for a wide array of services, from shopping and storage to dating, finance and streaming. Millions of users possess zombie accounts, with personal data that is vulnerable to cyber-attacks and data breaches.

The exponential growth of AI raises a further question: can data really be deleted once it has been used to train AI models? Machine learning modules easily memorise the data they have been trained on, but “unlearning” is difficult.

In theory, people in Europe and many other countries around the world have a legal “right to be forgotten”. This means they can request the full erasure of any personal data a platform or a company may hold. AI developers are considered data controllers under the EU’s General Data Protection Regulation (GDPR) laws, for example, and are subject to this obligation.

However, there are no clear guidelines on how erasure should be enforced within AI systems. Regulators may request deletion, but AI companies can argue that compliance is infeasible on account of technical constraints.

Explainable deletion

To counter the risks posed by incomplete deletion, I believe there is a pressing need for provision of concise, accessible and clear information about how deletion really works.

One potential approach is “explainable deletion” – a protocol developed by Marvin Ramokapane at the UK’s National Research Centre on Privacy, Harm Reduction and Adversarial Influence Online (Rephrain), based at the University of Bristol.

The intention is to make deletion processes more transparent and understandable without overwhelming the user with too much information in one go.

Explainable deletion breaks down information into six categories: what, how, when, who, where and why (see diagram). Each offers users bite-size information about that part of the process.

Explainable deletion’s six categories:

Marvin Ramokapane and Dana Lungu, CC BY-SA

Explainable deletion is designed to give users control over their actions, the autonomy to choose the deletion type that is right for them, and the assurance that their desired actions have been fulfilled. It may also tackle the anxiety of accidentally losing data by giving options for data recovery.

While explainable deletion has not yet been adopted in practice, service providers – both platforms and developers – could heighten user trust by adopting such protocols. This framework can also be proof of compliance with GDPR regulations and the right to be forgotten.

Most of us use systems that collect and store our data on a daily basis. If these systems clearly explained what they store and what deletion really means, it could help us to spot the accounts and data that pose a real risk – and to take back control of our digital footprints.

Dana Lungu, Research Associate, National Research Centre on Privacy, Harm Reduction and Adversarial Influence Online, Rephrain, University of Bristol

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

Reviewed by Irfan Ahmad.

Read next: 

• Gmail, YouTube, and Facebook dominate web usage as researchers examine where Americans spend online time

• Influencers increasingly drive U.S. purchases, with YouTube links and TikTok videos leading product discovery paths

by External Contributor via Digital Information World

The Influence of Influencers on Product Choice

By Katharina Buchholz, Statista. Reviewed by Irfan Ahmad.

The number of Americans who have been coaxed by an influencer or a celebrity endorsement to buy a product is rising. In a survey by Statista Consumer Insights of more than 1,000 U.S. respondents, the most popular way to carry out such purchases was via a link in a YouTube video description – a tried and tested way for creators to peddle their own or sponsored products. Almost as popular is buying a product after seeing a TikTok video. The social media platform in 2023 launched¹ its own in-app shop, making it easier for influencers and brands to list curated products.

On Instagram, products advertised via Stories are a little more popular than those presented in regular posts on the feed, but the difference is small. For TikTok, the stories feature hasn't caught on as much as a sales tool, with only 17 percent of consumers mentioning purchases via TikTok stories or TikTok Now as opposed to 28 percent saying the same about regular TikTok videos. Also popular is the Amazon Storefront, which provides a free and customizable landing page for influencers and other sellers on Amazon to present products.

Americans were most likely to say that influencers had made them try a new restaurant or food – not always a direct purchase from the creator as unpaid and paid restaurant recommendations as well as product sales and recipe tips are all popular among food influencers. A product was purchased upon recommendation from a creator, however, for 29 percent of respondents in the survey. 19 percent said they tried out a new routine because of an influencer – again, not always a purchase, but likely an effective tool to turn viewers into customers further down the line. While 15 percent said they had booked a travel experience due to creator, 14 percent mentioned attending an event.

Notes: Originally published by Statista. Republished under the Creative Commons CC BY-ND License. Added a TikTok Shop reference link. Formatting corrections made only where necessary.

[1] https://ift.tt/EY6W1bf

Read next: What Tools Would You Build If You Didn't Need A Developer?
by External Contributor via Digital Information World

Research Finds Social Media Makes People Overestimate How Often Rare Experiences Occur

By Jacob Levin

Image: Swello - Unsplash

People are naturally attracted to excitement.

According to new Virginia Tech research, the same is true on social media, where feeds are overrun with international travels, skydiving, and other experiences that may seem out of the ordinary.

When social media users constantly are shown rare and unique events, they overestimate how often those occur and seek to share their own. This unbalanced content stream creates a “rareness bias diffusion.” This new concept by Alice Jang, assistant professor, and Viswanath Venkatesh, the Verizon Chair of Business Information Technology, both in the Pamplin College of Business, defines that this bias can spread rare information widely, leaving everyday social media users so exposed to it that they perceive it as more common than it truly is.

“They think everyone else is traveling to Paris or doing something exciting, while they are just sitting at home doing mundane things,” Jang said. “It makes people feel like their lives are falling short, when in reality, everyone is mostly doing mundane things — they just only post the exciting parts.”

These studies revealed that human interaction with social media content is a compounding factor in how inaccurate information spreads online. According to Jang, the research challenges the current understanding of social media content.

The tendency to overlook the ordinary happens because of social media users’ perception bias and their motivation to seek variety in the content they see and share, according to the research published in MIS Quarterly.

The result is a skewed picture of how the world actually is, where the unusual looks common, and the common disappears. This suggests social media users should be cautious — and not for the usual reasons. The distortion on social media is not totally driven by the algorithm. Instead, it comes from ordinary human biases that social media compounds into a pattern where rare information gets shared more often.

During the research, Jang and Venkatesh conducted six experiments to understand how people choose what to share online and their reaction to seeing rare content. She presented research participants with fictional scenarios of a dystopian city under attack by various monsters – something unique that people would probably share online.

Of the various monsters and attacks, participants were shown some scenarios more than others. In the end, those participants were more likely to share the scenario that they had seen the least. This reinforces the idea that people are more likely to share content that is unique, rare, or out of the ordinary. Then, she created a simulated mock network of thousands of social media users and unique social media content to analyze these human biases on a larger scale.

“There is a bias that we simply cannot fix. It's impossible to fix,” said Jang. “A lot of the prior literature just disregards this fact and assumes that people are rational”.

Venkatesh added, “Yes, it's a bias but when people know this, they could perhaps know that what we are seeing on social media is others' highlight reel and not the day-to-day life. This may reduce harmful negative impacts on oneself."

This innate human bias forces social media users to wonder: Is what I am seeing online representing what is actually happening? While people may actively edit their timelines and curate unique content to post online, the research reveals that the corresponding problem runs much deeper than just a network’s algorithm.

There is a baseline human bias that the platform cannot fully fix. Instead, the responsibility falls back on the user.

Ultimately, the research found, social media users need to understand that their feeds are overrun with exciting and out of the ordinary content because of what they engage with. Having this knowledge allows users to better protect themselves from this false reality on social media.

Reviewed by Irfan Ahmad.

This article was originally published by Virginia Tech News and is republished with permission.

Read next: 

• Study Finds Low-Quality AI Videos Dominate TikTok Feeds for New Users and Children

• Investing podcasts are super popular. But do they help people learn the market?

by External Contributor via Digital Information World