Tech
How to archive your photos in the digital age
Wasim Ahmad, Quinnipiac University
Taking photographs used to be a careful, conscious act. Photos were selective, frozen moments in time carefully archived in albums and frames. Now, taking a photograph is almost as effortless and common as breathing – it’s something that people do all the time in the age of smartphone cameras with seemingly endless digital film.
But the downside to capturing every moment is that it creates a mountain of those moments to save for the future. Those photos can be easily lost if they’re not archived properly. All it can take is one accidental dip in the toilet for your phone, and all that data is lost forever.
So what’s a practical backup strategy for the average person? Here are a few ways to make sure memories are never lost:
Cloud storage
The simplest way to archive your photos is cloud storage. For Apple users, there’s iCloud, which starts at US$0.99 per month for 50 gigabytes all the way to $59.99 per month for 12 terabytes with various tiers in between. With an average iPhone photo clocking in at 3 megabytes, that’s a little over 16,000 photos for the cheap plan and 4 million or so for the largest plan. Google’s Google One cloud storage is most cost effective for yearly plans, with 2TB going for $99.99 per year and 5TB going for $249.99 per year.
The actual amount you can store in that space does vary greatly with how a file is shot. Video has larger file sizes than photos. HEIF files, a newer format on Apple phones, compresses files into smaller packages, but long-term compatibility is unknown since the format hasn’t been in use for as long as the standard JPG file, which has been around since 1992.
While cloud services from big providers generally provide the easiest way for most average folks to back up their photos, and operate with little to no intervention via apps that are already on the phone constantly uploading every photo taken, there are risks involved.
Big companies often change their policies about how photos are saved. For instance, depending on what phone and when it was bought, Google’s cloud storage may have saved photos in a “storage saver” format that lowers the quality of images by sizing them down or compressing them differently. This affects your ability to make high-quality prints or view the photos on high-resolution screens down the road. Unless someone is astute enough to notice small text here and there that mentions it, most users won’t even realize it’s happening.
And what happens to cloud services when things go badly wrong? Users of photo backup service Digital Railroad found out the hard way. In 2008, the company abruptly shut down and gave its users 24 hours to download everything before the servers were shut down. Photographers rushed for the exits, trying to grab their photos on the way out, only to strain the servers to the point where few were able to recover anything at all. If this was the only way photos were backed up, it’s a lost cause.
So while the cloud is easy, costs can add up and terms of service can change at a moment’s notice. What are some ways for photographers to control their own fate?
Hard drives and network-attached storage
Manually taking photos off a phone may take some extra time, but the approach offers peace of mind that cloud services can’t necessarily match.
Almost all phones can plug into a computer’s USB port and use the built-in photos app on both Windows or MacOS to download photos to a computer. Apple users can use a method called AirDrop to send photos wirelessly to other Apple devices as well, including laptop and desktop computers.
Now loading photos onto a local hard drive built into the machine can fill it up quickly, but there is a cost-effective way to get around that – namely, external hard drives. Theses are storage devices that you can plug into your computer as needed. They can be of the older and less expensive type with spinning platters or more modern solid-state drives that can survive a drop and greater temperature changes than the older drives can.
These are different than flash drives, more commonly known as thumb drives because of their small size, that are designed as temporary storage to shuffle photos from one place to another.
It’s easy to buy more than one hard drive to have duplicate backups in case of failure or catastrophe, but the downside is that there’s no easy access from the internet to your photos, and backup is generally a process that users must remember to do.
Network-attached storage is one way to solve the cloud storage problem while retaining the ability to access photos from the internet. These are essentially hard drives – sometimes multiple hard drives linked together for even greater or faster storage – that are connected to a router that allows for access to the internet through specialized software.
While not as easy as most third-party cloud storage services, once it’s set up, a network-attached storage unit is a flexible way to store your photos safely and accessibly. There are even companies that specialize in fireproof and waterproof units for extra insurance in case of disaster.
Printing photos
If cloud storage and hard drives seem too complicated, there’s always the old-fashioned approach of printing. There’s still something magical about seeing a photo on a wall or in an album, and thankfully there are ways to print professional-quality archival prints without having to go to a drugstore.
The easiest and most cost-efficient types of printers are dedicated 4×6 printers using a technology similar to professional labs called dye-sublimation. These yield high-quality, waterproof prints that cost about the same as what one would pay for drugstore developing. HP makes its popular Sprocket line of printers, though those require a phone and an app to print from, which makes plugging in a memory card from a professional camera out of the question. However, Canon’s Selphy lineup includes many models with screens and a card slot to make that possible.
The rabbit hole goes very deep, and there are many professional printers that can print even larger sizes. Canon and Epson dominate this space, marketing a range of pigment- and dye-based printers that can emphasize archival needs or color saturation, respectively.
Another option is ordering a photo book, which, as the name suggests, is a physical bound book of your photos. However, photo books are probably more appropriate for memorializing an event – trip, wedding, project – than general archiving, given the typical costs and number of photos involved.
There’s little reason to not make some sort of backups of photos in 2024, whether that’s on printed media, hard drives or in the cloud. The important thing is not which method to use, but to do it at all.
Wasim Ahmad, Assistant Teaching Professor of Journalism, Quinnipiac University
This article is republished from The Conversation under a Creative Commons license. Read the original article.
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CES 2026
Inside the Computing Power Behind Spatial Filmmaking: Hugh Hou Goes Hands-On at GIGABYTE Suite During CES 2026
Inside the Computing Power Behind Spatial Filmmaking: Hugh Hou Goes Hands-On at GIGABYTE Suite During CES 2026
Spatial filmmaking is having a moment—but at CES 2026, the more interesting story wasn’t a glossy trailer or a perfectly controlled demo. It was the workflow.
According to a recent GIGABYTE press release, VR filmmaker and educator Hugh Hou ran a live spatial computing demonstration inside the GIGABYTE suite, walking attendees through how immersive video is actually produced in real-world conditions—capture to post to playback—without leaning on pre-rendered “best case scenario” content. In other words: not theory, not a lab. A production pipeline, running live, on a show floor.
A full spatial pipeline—executed live
The demo gave attendees a front-row view of a complete spatial filmmaking pipeline:
- Capture
- Post-production
- Final playback across multiple devices
And the key detail here is that the workflow was executed live at CES—mirroring the same processes used in commercial XR projects. That matters because spatial video isn’t forgiving. Once you’re working in 360-degree environments (and pushing into 8K), you’re no longer just chasing “fast.” You’re chasing:
- System stability
- Performance consistency
- Thermal reliability
Those are the unsexy requirements that make or break actual production days.
Playback across Meta Quest, Apple Vision Pro, and Galaxy XR
The session culminated with attendees watching a two-minute spatial film trailer across:
- Meta Quest
- Apple Vision Pro
- Newly launched Galaxy XR headsets
- Plus a 3D tablet display offering an additional 180-degree viewing option
That multi-device playback is a quiet flex. Spatial content doesn’t live in one ecosystem anymore—creators are being pulled toward cross-platform deliverables, which adds even more pressure on the pipeline to stay clean and consistent.
Where AI fits (when it’s not the headline)
One of the better notes in the release: AI wasn’t positioned as a shiny feature. It was framed as what it’s becoming for a lot of editors—an embedded toolset that speeds up the grind without hijacking the creative process.
In the demo, AI-assisted processes supported tasks like:
- Enhancement
- Tracking
- Preview workflows
The footage moved through industry-standard software—Adobe Premiere Pro and DaVinci Resolve—with AI-based:
- Upscaling
- Noise reduction
- Detail refinement
And in immersive VR, those steps aren’t optional polish. Any artifact, softness, or weird noise pattern becomes painfully obvious when the viewer can look anywhere.
Why the hardware platform matters for spatial workloads
Underneath the demo was a custom-built GIGABYTE AI PC designed for sustained spatial video workloads. Per the release, the system included:
- AMD Ryzen 7 9800X3D processor
- Radeon AI PRO R9700 AI TOP GPU
- X870E AORUS MASTER X3D ICE motherboard
The point GIGABYTE is making is less “look at these parts” and more: spatial computing workloads demand a platform that can run hard continuously—real-time 8K playback and rendering—without throttling, crashing, or drifting into inconsistent performance.
That’s the difference between “cool demo” and “reliable production machine.”
The bigger takeaway: spatial filmmaking is moving from experiment to repeatable process
By running a demanding spatial filmmaking workflow live—and repeatedly—at CES 2026, GIGABYTE is positioning spatial production as something creators can depend on, not just test-drive.
And that’s the shift worth watching in 2026: spatial filmmaking isn’t just about headsets getting better. It’s about the behind-the-scenes pipeline becoming stable enough that creators can treat immersive production like a real, repeatable craft—because the tools finally hold up under pressure.
Source:PRNewswire – GIGABYTE press release
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Science
AI-induced cultural stagnation is no longer speculation − it’s already happening
AI-induced cultural stagnation. A 2026 study by researchers revealed that when generative AI operates autonomously, it produces homogenous content, referred to as “visual elevator music,” despite diverse prompts. This convergence leads to bland outputs and indicates a risk of cultural stagnation as AI perpetuates familiar themes, potentially limiting innovation and diversity in creative expression.
Ahmed Elgammal, Rutgers University
Generative AI was trained on centuries of art and writing produced by humans.
But scientists and critics have wondered what would happen once AI became widely adopted and started training on its outputs.
A new study points to some answers.
In January 2026, artificial intelligence researchers Arend Hintze, Frida Proschinger Åström and Jory Schossau published a study showing what happens when generative AI systems are allowed to run autonomously – generating and interpreting their own outputs without human intervention.
The researchers linked a text-to-image system with an image-to-text system and let them iterate – image, caption, image, caption – over and over and over.
Regardless of how diverse the starting prompts were – and regardless of how much randomness the systems were allowed – the outputs quickly converged onto a narrow set of generic, familiar visual themes: atmospheric cityscapes, grandiose buildings and pastoral landscapes. Even more striking, the system quickly “forgot” its starting prompt.
The researchers called the outcomes “visual elevator music” – pleasant and polished, yet devoid of any real meaning.
For example, they started with the image prompt, “The Prime Minister pored over strategy documents, trying to sell the public on a fragile peace deal while juggling the weight of his job amidst impending military action.” The resulting image was then captioned by AI. This caption was used as a prompt to generate the next image.
After repeating this loop, the researchers ended up with a bland image of a formal interior space – no people, no drama, no real sense of time and place.
As a computer scientist who studies generative models and creativity, I see the findings from this study as an important piece of the debate over whether AI will lead to cultural stagnation.
The results show that generative AI systems themselves tend toward homogenization when used autonomously and repeatedly. They even suggest that AI systems are currently operating in this way by default.
The familiar is the default
This experiment may appear beside the point: Most people don’t ask AI systems to endlessly describe and regenerate their own images. The convergence to a set of bland, stock images happened without retraining. No new data was added. Nothing was learned. The collapse emerged purely from repeated use.
But I think the setup of the experiment can be thought of as a diagnostic tool. It reveals what generative systems preserve when no one intervenes.
This has broader implications, because modern culture is increasingly influenced by exactly these kinds of pipelines. Images are summarized into text. Text is turned into images. Content is ranked, filtered and regenerated as it moves between words, images and videos. New articles on the web are now more likely to be written by AI than humans. Even when humans remain in the loop, they are often choosing from AI-generated options rather than starting from scratch.
The findings of this recent study show that the default behavior of these systems is to compress meaning toward what is most familiar, recognizable and easy to regenerate.
Cultural stagnation or acceleration?
For the past few years, skeptics have warned that generative AI could lead to cultural stagnation by flooding the web with synthetic content that future AI systems then train on. Over time, the argument goes, this recursive loop would narrow diversity and innovation.
Champions of the technology have pushed back, pointing out that fears of cultural decline accompany every new technology. Humans, they argue, will always be the final arbiter of creative decisions.
What has been missing from this debate is empirical evidence showing where homogenization actually begins.
The new study does not test retraining on AI-generated data. Instead, it shows something more fundamental: Homogenization happens before retraining even enters the picture. The content that generative AI systems naturally produce – when used autonomously and repeatedly – is already compressed and generic.
This reframes the stagnation argument. The risk is not only that future models might train on AI-generated content, but that AI-mediated culture is already being filtered in ways that favor the familiar, the describable and the conventional.
Retraining would amplify this effect. But it is not its source.
This is no moral panic
Skeptics are right about one thing: Culture has always adapted to new technologies. Photography did not kill painting. Film did not kill theater. Digital tools have enabled new forms of expression.
But those earlier technologies never forced culture to be endlessly reshaped across various mediums at a global scale. They did not summarize, regenerate and rank cultural products – news stories, songs, memes, academic papers, photographs or social media posts – millions of times per day, guided by the same built-in assumptions about what is “typical.”
The study shows that when meaning is forced through such pipelines repeatedly, diversity collapses not because of bad intentions, malicious design or corporate negligence, but because only certain kinds of meaning survive the text-to-image-to-text repeated conversions.
This does not mean cultural stagnation is inevitable. Human creativity is resilient. Institutions, subcultures and artists have always found ways to resist homogenization. But in my view, the findings of the study show that stagnation is a real risk – not a speculative fear – if generative systems are left to operate in their current iteration.
They also help clarify a common misconception about AI creativity: Producing endless variations is not the same as producing innovation. A system can generate millions of images while exploring only a tiny corner of cultural space.
In my own research on creative AI, I found that novelty requires designing AI systems with incentives to deviate from the norms. Without it, systems optimize for familiarity because familiarity is what they have learned best. The study reinforces this point empirically. Autonomy alone does not guarantee exploration. In some cases, it accelerates convergence.
This pattern already emerged in the real world: One study found that AI-generated lesson plans featured the same drift toward conventional, uninspiring content, underscoring that AI systems converge toward what’s typical rather than what’s unique or creative.
Lost in translation
Whenever you write a caption for an image, details will be lost. Likewise for generating an image from text. And this happens whether it’s being performed by a human or a machine.
In that sense, the convergence that took place is not a failure that’s unique to AI. It reflects a deeper property of bouncing from one medium to another. When meaning passes repeatedly through two different formats, only the most stable elements persist.
But by highlighting what survives during repeated translations between text and images, the authors are able to show that meaning is processed inside generative systems with a quiet pull toward the generic.
The implication is sobering: Even with human guidance – whether that means writing prompts, selecting outputs or refining results – these systems are still stripping away some details and amplifying others in ways that are oriented toward what’s “average.”
If generative AI is to enrich culture rather than flatten it, I think systems need to be designed in ways that resist convergence toward statistically average outputs. There can be rewards for deviation and support for less common and less mainstream forms of expression.
The study makes one thing clear: Absent these interventions, generative AI will continue to drift toward mediocre and uninspired content.
Cultural stagnation is no longer speculation. It’s already happening.
Ahmed Elgammal, Professor of Computer Science and Director of the Art & AI Lab, Rutgers University
This article is republished from The Conversation under a Creative Commons license. Read the original article.
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Consumer Corner
LUMISTAR Draws Record Crowds at CES 2026 With AI Tennis and Basketball Training Systems
LUMISTAR’s CES 2026 debut showcased TERO and CARRY, innovative AI sports training systems that engage athletes actively. The systems allow real-time adaptations, transforming training into competitive practice while effectively utilizing performance data for measurable skill development. Pre-orders start March 2026.
LUMISTAR wrapped up its CES 2026 debut in Las Vegas with record-level attention, as live demos of its AI-powered sports training systems consistently drew full crowds throughout the show, according to the company.
The sports-focused AI brand showcased TERO, its AI tennis training system, and CARRY, its AI basketball training system—both described by attendees as “game changers” for how training can be delivered, measured, and scaled.
Why the Booth Stayed Packed
Across multiple days of hands-on demonstrations, LUMISTAR’s booth became a focal point for athletes, coaches, club operators, and sports technology professionals. Visitors repeatedly pointed to one key difference: the systems don’t just record results—they actively participate in training.
That’s a major break from the standard model in sports tech, where:
- traditional ball machines run pre-set drills, and
- wearables/video tools analyze performance after a session ends.
Training That Adapts in Real Time
LUMISTAR says both TERO and CARRY combine real-time computer vision, adaptive decision-making, and on-court execution to respond instantly to athlete behavior—adjusting difficulty, tempo, and training logic shot by shot.
Attendees noted that this turns practice from repetition into something closer to competition—an evolving back-and-forth between athlete and system.
“This is not an incremental improvement—it’s a complete rethink of what training equipment should do,” one professional coach attending CES said in the release. “For the first time, the machine is reacting to the athlete, not the other way around.”
From Data Collection to Action
Another standout point from CES feedback: the platform’s focus on turning performance data into immediate training outcomes.
LUMISTAR’s approach emphasizes:
- continuous data retention across sessions
- real-time performance interpretation
- clear visualization of progress and training efficiency
Coaches and athletes highlighted that this could reduce wasted training time and accelerate skill development by making each session measurable and comparable.
What’s Next: Pre-Orders and Kickstarter
LUMISTAR outlined a 2026 rollout plan following CES:
- TERO opens for pre-orders in March 2026, with full market availability beginning May 2026
- CARRY launches via Kickstarter in Q2 2026
- The company will continue private demonstrations and pilot programs with select training institutions worldwide ahead of commercial release
More information is available at https://www.lumistar.ai.
Source: PRNewswire press release from LUMISTAR (Jan. 11, 2026)
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