By Ana-Maria Stanciuc Google’s announcement that its Gemini app now writes music for you isn’t just one of those “blowing my mind” product updates. It feels like a symbolic surrender to a long-standing refrain from Big Tech: creative work is now just another checkbox for a machine.  If you don’t know what I am talking about, yesterday Google […] This story continues at The Next Web

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Enterprises struggling with the cost and complexity of long-term data archival could soon have a new option: a piece of glass.

New research published on Wednesday suggests that a borosilicate glass plate 120mm square and just 2mm thick can store 4.8TB of data across 301 layers with accelerated aging tests, indicating that the data would remain intact for at least 10,000 years.

“Glass is a permanent data storage material that is resistant to water, heat, and dust,” Microsoft researchers wrote in a paper published in the science and technology journal, Nature. “We have unlocked the science for parallel high-speed writing and developed a technique to permit accelerated aging tests on the written glass, suggesting that the data should remain intact for at least 10,000 years.”

Previous versions of the technology required fused silica, a high-purity glass available from only a handful of manufacturers. The new findings show the system works equally well with borosilicate — widely manufactured and significantly cheaper — bringing the technology a step closer to commercial viability, the paper added.

The timing is significant.

The global datasphere is doubling approximately every three years, according to Seagate research cited in the paper, yet “most digital archive systems rely on media that degrade” well short of the multi-decade retention timescales that legal, financial, and regulatory obligations increasingly demand, the authors noted.

Magnetic tape, the most widely deployed archival medium today, reflects those constraints. An LTO-10 (Linear Tape-Open) cartridge, the current enterprise benchmark, holds 30TB to 40TB native at 400MB/s, but its rated shelf life is just 30 years. It requires climate-controlled storage between 16°C and 25°C and migration roughly every five to ten years.

That operational overhead, analysts say, is the real cost of tape — not the media. “Archival estates rarely fail because cartridges chemically degrade on schedule,” said Sanchit Vir Gogia, chief analyst at Greyhound Research. “They fail because compatibility windows close, drive generations evolve, firmware support sunsets, and robotics require refresh.”

Tape-as-a-service models have shifted some of that burden, noted Vishesh Divya, principal analyst at Gartner, moving hardware lifecycle management to providers under defined service-level assurances.

“LTO tape remains the benchmark for enterprise cold storage,” he said. “The media cost per terabyte remains low, the ecosystem is mature, and enterprises have decades of operational experience managing refresh cycles,” Divya said.

Sony’s Optical Disc Archive — the main optical alternative at 5.5TB per cartridge with a 100-year rated shelf life — was discontinued in March 2025, leaving no comparable product on the market.

How data is written and read from the glass

Project Silica, Microsoft’s glass-based storage initiative, uses femtosecond laser pulses to encode data as three-dimensional structures called voxels inside the glass, at 25.6 megabits per second per beam and an energy cost of 10.1 nanojoules per bit.

The paper describes two encoding methods. The first method, birefringent voxels, modifies the polarization properties of the glass. The team reduced the laser pulses required to a pseudo-single-pulse technique — one pulse split to simultaneously begin one voxel and complete another — enabling faster beam scanning.

The second method, phase voxels, is a new invention that modifies the phase properties of the glass instead and requires only a single pulse per voxel. Crucially, it works in borosilicate glass, where the birefringent approach did not. “Much higher levels of three-dimensional inter-symbol interference in phase voxels can be mitigated with a machine learning classification model,” the researchers wrote.

Earlier Project Silica readers required three or four cameras to retrieve data; the updated system requires one, completing what the researchers described as the first fully demonstrated end-to-end glass archival system, from writing and storage through to retrieval.

Longevity verified through accelerated aging

The longevity claim is backed by a nondestructive optical method the team developed to measure voxel degradation in place, combined with accelerated aging techniques applied to written borosilicate samples. “Accelerated ageing tests on written voxels in borosilicate suggest data lifetimes exceeding 10,000 years,” the researchers noted.

For enterprise buyers, longevity alone will not make the case.

“A realistic TCO comparison must be modelled across multi-decade lifecycle horizons, not procurement cycles,” said Gogia. “Glass storage reframes the economic curve by potentially eliminating migration cycles — reducing labour, reconciliation overhead, and operational disruption.” Write speeds remain materially slower than tape, however, making glass better suited to ultra-cold, low-ingestion estates.

Compliance adds a further dimension. Data encoded as permanent optical modifications cannot be overwritten, reducing ransomware exposure. But “compliance is a system property, not a substrate property,” Gogia cautioned. “Enterprises must still ensure encryption key rotation, metadata indexing, and audit trail completeness. A 10,000-year medium does not remove the obligation to demonstrate governance discipline.”

No commercial product yet

Microsoft said in a separate blog post that the research phase of Project Silica is now complete. “We are continuing to consider learnings from Project Silica as we explore the ongoing need for sustainable, long-term preservation of digital information,” the company said, without disclosing a commercialization roadmap. If commercialized, glass storage is unlikely to displace tape.

“It is more likely to emerge as a specialized ultra-long retention rather than a replacement for tape-based cold storage,” said Gartner’s Divya. “Any new medium would have to compete on the full-stack equation — economics, hardware, software, and operational model — not just on media longevity.”

Source:: Computer World

By Shikhar Mehrotra Meta could launch an AI-powered smartwatch in 2026, marking a return to wearables after scrapping earlier plans amid metaverse-focused restructuring and cost-cutting efforts.
The post Meta could launch a smartwatch in 2026, years after killing its original plans appeared first on Digital Trends.

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By Hisan Kidwai It’s no secret that smartphone launches in 2026 have been plagued by rising costs, thanks in…
The post I Used the vivo V70 for a Month — And It’s the Best V-Series Yet appeared first on Fossbytes.

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By Ana-Maria Stanciuc Plato, a Berlin-based startup, has raised $14.5 million in seed funding to bring generative AI into wholesale distribution, a massive industry that rarely makes tech headlines but quietly moves a significant share of the world’s goods. The round was led by Atomico, with Cherry Ventures, Discovery Ventures, and D11Z joining in. Wholesale distribution accounts for […] This story continues at The Next Web

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>More than half of the software purchased by companies today could eventually be replaced by AI, Mistral AI CEO Arthur Mensch said in an interview with CNBC. The statement comes at a time when software stocks are under pressure due to concerns that AI will undermine SaaS business models.

According to Mensch, a major shift is already happening, with companies increasingly choosing to build their own AI-based applications instead of purchasing traditional SaaS services. He estimated that more than 50% of today’s SaaS spending could shift to AI solutions.

“We are also seeing with our customers that we can create fully customized applications within a few days to run a workflow — for example, a purchasing workflow or supply chain workflows — in a way that, five years ago, would have required a vertical SaaS solution,” Mensch said. “This platform change is a great opportunity for us, as we now have more than 100 corporate customers who are also turning to us with a desire to perhaps change and modernize their IT systems — for example, getting rid of things they bought 20 years ago and which are starting to become quite expensive.”

Even so, Mensch believes that basic business systems that store and structure company data will remain in place and become an important basis for AI applications.

Mistral AI recently purchased Koyeb, a Paris-based cloud startup.

Source:: Computer World

In 2019, Gartner analyst Dave Cappuccio issued the headline-grabbing prediction that by 2025, 80% of enterprises will have shut down their traditional data centers and moved everything to the cloud.

A lot has gone down since 2019, and Gartner’s latest guidance on the topic comes from John-David Lovelock, vice president analyst,who says, “It’s not as though the data center is going away. The enterprise data center is here to stay. There’s still enough spending by enterprises on servers, licensed software, and the skill sets they need to maintain and operate the environment that currently exists.”

Continue reading on CIO.com

Source:: Computer World

By Ana-Maria Stanciuc If you tuned in to China’s 2026 CCTV Spring Festival Gala looking for traditional lion dances and nostalgic tunes, you may have done a double-take when what greeted you was a squad of humanoid robots performing kung fu, synchronized moves, and comedy sketches with more precision than most of us manage during family reunions. It […] This story continues at The Next Web

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By Ana-Maria Stanciuc A Paris-based venture firm that has quietly shaped the quantum technology scenery is now making a much louder statement about Europe’s role in the future of computing, materials science, and sensing. Quantonation Ventures has today announced the successful close of its second flagship fund at €220 million, more than double the size of its first […] This story continues at The Next Web

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Stockholm-based Strawberry is launching its “self-driving” AI-powered browser in open beta after a year in closed testing. Strawberry is a browser with built-in AI agents that can surf, click, and perform real tasks on behalf of the user, even on login-protected sites.

The idea is to make AI agents available to non-technical users such as salespeople, recruiters, and analysts, without them needing to be able to write advanced prompts or code. A key new feature is a personalized onboarding process that maps the user’s role and workflow. With the user’s consent, the system builds a profile and then suggests relevant tasks to reduce the barrier to getting started.

According to Strawberry’s own tests, the browser outperforms competing AI tools, including Perplexity’s Comet and Open AI’s ChatGPT Atlas, in practical workflows. The browser also scores around 78% in the GAIA agent benchmark.

Strawberry is free to download and try. Access to its full functionality costs $20 per month.

Source:: Computer World

By Ana-Maria Stanciuc When European tech observers talk about AI ambition, the narrative often splits neatly in two: models and infrastructure. On one side are the clever bits of code that can write, reason, and generate text or images. On the other is the gritty reality of making those bits run reliably, at scale, and in production. Today, […] This story continues at The Next Web

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By Nadeem Sarwar Apple has reportedly sped up efforts to develop an AI-powered pendant equipped with a camera that could launch next year.
The post Apple racing to launch an AI pendant to serve as your iPhone’s eyes and ears appeared first on Digital Trends.

Source:: Digital Trends

February is Heart Month, so it’s appropriate to speak with the team that built the recently introduced hypertension notifications system for watchOS 26 and Apple Watch. 

I spoke with Apple’s Steve Waydo, director for health sensing, and Dr. Rajiv Kumar, physician-researcher, who offered a glimpse into the science and decisions behind their lengthy project to give smartwatch users an actionable and reliable tool to track this aspect of heart health.

Waydo led the long development of the hypertension notification feature. “The idea goes way back to not all that long after we launched the first Apple Watch,” he said. “We had this device collecting physiological data on users all the time. This hadn’t existed before. We saw it as a new and unique opportunity.”

But first, Apple needed to develop new sensor capabilities, assemble world-class technical and clinical expertise, and build accurate and effective machine learning tools. Apple wanted its solutions to be grounded in science, so it also launched a large-scale heart health study with the University of Michigan. 

Understanding Hypertension

Hypertension is a state of chronic high blood pressure. Each time your heart beats, it moves blood out of the heart and into your blood vessels. When blood pressure is high, there’s a lot of back pressure so the heart must beat harder than usual to get the blood out. That, over time, is called hypertension. The problem with this condition is that it’s totally asymptomatic, which is why it is seen as a silent killer. 

More than 1 billion people have this condition, and nearly half of all US adults suffer from it. Yet, around half of them don’t know they have it. 

That’s why Apple’s tool is important; it could help people identify the condition and take steps to manage it. “So much of our health is invisible even to ourselves, and one of the biggest barriers to better health is just simply not knowing what’s going on,” said Waydo.

Apple’s plan is to harness the power of wearable data to help surface conditions such as this one, which otherwise might not be easily managed by any of us.

Machine learning, data, and context

The data an Apple Watch provides differs from most test data because the device is worn all day, almost daily. That means the information it gathers changes over time, which helps identify deep health insights. What makes the information more actionable is artificial intelligence, which helps the device itself surface useful insights based on the data it can track. 

Kumar explained how Apple developed a machine learning system to combine that personal data with real world information drawn from the Apple Heart study. The latter helped Apple understand, “what the signals look like, what they look like across a person’s life and in a variety of circumstances and break the raw sensor data down into thousands of independent factors that we can quantify.”

Apple also leaned into supervised learning data, in this case information derived from both sensor data and ground truth. This is the kind of information generated by Apple’s work with the University of Michigan. The beauty of the combination is that Apple can see how sensor data correlates with scientific data. Machine learning models can then analyze the personal data and contrast it with sensor data across thousands of factors to identify a person’s hypertensive status.

You can learn more about how Apple’s system works by reading the company’s extensive white paper on the topic.

Apple Watch, a wearable doctor

“These machine learning tools are a key enabling technology, because with something like hypertension, the way it manifests in our signals is extremely subtle,” said Waydo. “It’s really subtle features of the actual shape of the signal that we get off the sensors…. We’re looking at much more subtle signals that correlate with high blood pressure, because those signals tell us something about how your blood vessels respond every time your heart beats. So, we apply these machine learning techniques to millions of data segments.”

“I’ve been at Apple for 13 years, so I’ve been here along this whole journey,” says Waydo. “And these same kinds of tools make it possible for your watch to track your activity, understand if you’re walking, or swimming in a pool, estimate how long you spend in any sleep stage, identify when you take a fall, so that it can connect you with emergency services. So, we’re using machine learning tools all over the place.”

In each case, Apple finds that it is important to look at how a person’s data evolves over a long period of time, as opposed to just giving a notification based on one moment.

The art of noise

The phrase “garbage in, garbage out” does a lot of work in the AI age, but Apple’s experts had interesting insights into the nature of data noise. “You know, we are processing vast amounts of data to develop these features,” says Waydo. That means the algorithms must figure out how to grade the data they pick up. 

Apple, which supplements the data with research acquired from large-scale, real-world studies, found that building in support for “messiness” can make for better results. “Having that data set that actually that has messiness and realism to it is very important for coming up with signals that are more than a research curiosity and can really apply to, you know, actual people using our devices in the world,” Waydo said.

In AI, failure builds success

Getting the system to work was a long process of iteration and repetition. Apple’s teams built bigger and better data sets, revised their algorithms, and kept improving what they had built until it became ready to roll it out into the world.  “We rinse and repeat that process for weeks or months or years until we have something we’re happy with,” said Waydo.

Apple

The team also gets excited when things don’t work. “We may find use cases or particular kinds of users or particular scenarios where we get a lot of false positives or where we get no true positives. And that tells us where to go in order to improve the algorithm and iterate on the algorithm, and usually that means getting more data that captures those use cases that we can incorporate into our machine learning training.”

As part of the work, Apple’s teams also looked closely at demographics. The intention is to ensure that age, sex, or race don’t impact the performance of the systems Apple provides. Apple is a global company that ships products to a lot of people. It’s solutions have to work for everyone.

What it isn’t

The feature isn’t intended to be a complete replacement for regular check-ups. Recent reporting that not every case of hypertension will be picked up is correct, and reflects the balance the developers had to reach to create a system they could ship. That’s because the team realized that training the algorithm to be more sensitive would diagnose more cases, but at the cost of more false positives. 

The danger of false positives is that people stop listening. After all, if you are given health notifications by your device you must be able to trust its accuracy. No one wants to be given false information.

Waydo explained the conundrum: Should Apple aim for 100% sensitivity when it means the system will have a lot of false positives? Or should it aim to build a system that minimizes those? That’s why Apple had to achieve a balance.

“We weigh our work very heavily towards trying to manage false positives and trying to make sure that when we do notify someone of a potential issue, whether it’s hypertension or a regular rhythm or any of these other things, that notification is really trustworthy. And sometimes that means that there are cases that we can’t catch, because if we catch this additional set of cases, we’re also going to end up catching a bunch of people who don’t need the notification. And that undermines the utility of the whole thing,” Waydo said.

Apple recommends that any Apple Watch user receiving a hypertension notification check and log their blood pressure and visit their doctor.

Fan mail

Apple’s rock solid commitment to privacy means its teams can’t track how successful its systems are in the field because it never sees that kind of information coming from personal devices. But the team does get letters from users, medical practitioners, and others who have been affected

“I love hearing from clinicians who say they met someone who otherwise wouldn’t have known or who wouldn’t have come in, and it’s really changed their lives,” said Dr. Kumar. “Each of our features, whether it be in women’s health, hearing health, or heart health — they’re all based on science, must be actionable and absolutely built with privacy at the core.”

And each time someone gains better insight into their own health, they become better equipped to improve the health decisions they take in the future.

Please follow me on Twitter, or join me in the AppleHolic’s bar & grill and Apple Discussions groups on MeWe. Also, now on Mastodon.

Source:: Computer World

By Thomas Cuvelier Venture capital has long avoided ‘hard’ sectors such as government, defence, energy, manufacturing, and hardware, viewing them as uninvestable because startups have limited scope to challenge incumbents. Instead, investors have prioritised fast-moving and lightly regulated software markets with lower barriers to entry. End users in these hard industries have paid the price, as a lack […] This story continues at The Next Web

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By Hisan Kidwai To strengthen its appeal to fitness enthusiasts, Garmin has announced a new retail partnership with Giant…
The post Garmin Partners With Giant Bicycles India to Bring Cycling Tech to Retail Stores appeared first on Fossbytes.

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By Hisan Kidwai To streamline servicing for users, ASUS has announced a new after-sales initiative in India called ASUS…
The post ASUS Launches PUR Doorstep Pickup & Return Service for Accessories in India appeared first on Fossbytes.

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By Pranob Mehrotra A new report suggests that Apple may not launch its advanced AR glasses with built-in displays until 2028.
The post You might have to wait until 2028 for Apple’s rumored AR smart glasses appeared first on Digital Trends.

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By Ana-Maria Stanciuc If you checked X today expecting the usual stream of hot takes, memes, and AI spats, you probably saw… nothing. A widespread outage hit the platform today, leaving feeds blank, timelines unresponsive, and users staring at the digital equivalent of an empty room. Outage trackers such as Downdetector logged a dramatic surge in problem reports […] This story continues at The Next Web

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The AI bubble isn’t just hype — it’s real and could create many corporate casualties if or when it bursts. The companies that will succeed will be the ones solving real-world problems and engaging clients, according to tech industry execs and analysts.

AI startup valuations have skyrocketed, creating the fear of an AI bubble that’s drawn parallels to the dot-com bubble of the early 2000s; in the aftermath of that internet hype cycle, many once-promising companies went under.

Lofty AI valuations have economists concerned about a market correction if the investments don’t improve productivity or produce real-world results. Trade, geopolitical and tariff concerns are also raising further alarms and uncertainty.

Even though the discussion of a potential bubble is ubiquitous, what’s going on is more nuanced than simple boom-and-bust chatter, said Francisco Martin-Rayo, CEO of Helios AI.

“What people are really debating is the gap between valuation and real-world impact. Many companies are labeled ‘AI-driven,’ but only a subset are delivering measurable value at scale,” Martin-Rayo said.

Founders confuse fundraising with progress, which comes only when they are solving real problems for real clients, said Nacho De Marco, founder of BairesDev. “Fundraising gives you dopamine, but real progress comes from customers,” De Marco said. “The real value of a $1B valuation is customer validation.”

The economic impact of AI was a big part of the conversation at last month’s World Economic Forum (WEF), where De Marco participated in a panel discussion called “How High Can Unicorns Fly.”  He stressed that AI lowers the financial and operational barrier of entry for founders starting their businesses.

“You can build something massive without outside capital, but only if your unit economics work. When you bootstrap, your north star is payroll, not burn rate,” De Marco said.

The AI shakeout has already started, and the tenor at WEF “feels less like peak hype and more like the beginning of a sorting process,” Martin-Rayo said.

There are fewer foundational models and more verticalized applications, and companies that can’t translate impressive demos into durable revenue will fall, Martin-Rayo said.

Companies that survive the coming shakeout will be those willing to rebuild operations from the ground up rather than throwing AI into existing workflows, said Jinsook Han, chief agentic AI officer at Genpact. ”It’s not about just bolting some AI into your existing operation,” Han said. “You have to really build from ground up — it’s a complete operating model change.”

Foundational models are becoming more mature and can do more of what startups sell. As a result, AI providers that don’t offer distinct value will have a tough time surviving, Han said.

“There are a lot of companies that just are leveraging foundational models. And I think those will go away. And if we want to call that a bubble, I think that definitely is,” Han said.

In talks with clients, Han has found that many are confused. Tech demos look great, but clients aren’t sure whether AI fits their operating model. “Does it work in my environment? Does it protect me? So that’s where we are. I think there’s that part of bubble for sure,” Han said.

AI’s fundamental unresolved problems, such as hallucinations, are still underappreciated as companies chase valuations, said Deepak Seth, director analyst for Gartner. “Organizations need to be future aware, but they have to be grounded in their past also. You cannot just be chasing the shiny object all the time,” Seth said.

Source:: Computer World

By Shikhar Mehrotra Gemini now delivers AI audio summaries in Google Docs, offering natural voices, playback controls, and hands-free listening for Workspace subscribers on the web.
The post Gemini can now create audio summaries of your Google Docs appeared first on Digital Trends.

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