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.Storing your photos in a cloud service like iCloud is probably the easiest method. Chris Messina/Flickr, CC BY-NC
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.
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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.Desktop photo printers are a way to bring those digital photos into the physical world, ready for organizing in photo albums. Leksey/Wikimedia
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.
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View from the driver’s seat of the gear shift lever in a car with an automatic transmission and climate control panel. Black-gray car interior
CVT Transmissions Explained: Model Years to Avoid, Reliability Issues, and Maintenance Tips
Continuously Variable Transmissions — better known as CVTs — are now common in compact cars, hybrids, and fuel-efficient vehicles. They promise smoother driving and better gas mileage, but their reputation has been uneven, depending heavily on brand, design, and model year.
Here’s what CVTs are, which vehicles have had the most trouble, and how owners can protect themselves from costly repairs.
What Is a CVT?
A CVT (Continuously Variable Transmission) doesn’t use traditional fixed gears like a 6-speed or 8-speed automatic. Instead, it relies on two variable-diameter pulleys connected by a steel belt or chain. As the pulleys change size, the transmission seamlessly adjusts the gear ratio.
Smooth acceleration
No noticeable gear shifts
Improved fuel efficiency
This design is why CVTs are especially common in hybrids, where efficiency and smooth power delivery matter more than outright performance.
Illustration credit: Samarins.com
Why CVTs Are Popular in Hybrids
Most hybrid systems use a variation called an eCVT, which is mechanically different — and generally more reliable — than belt-driven CVTs found in many gas-only cars.
Manufacturers like Toyota and Honda favor eCVTs because they:
Reduce mechanical complexity
Eliminate traditional belts under high stress
Integrate seamlessly with electric motors
Deliver long-term durability with minimal maintenance
This is why hybrid CVTs tend to have far fewer failure complaints than early gasoline-only CVTs.
CVT Model Years to Avoid (Buyer Beware)
Not all CVTs are created equal. Some manufacturers — most notably Nissan — experienced widespread issues during certain production years.
Nissan CVT Model Years With Higher Failure Rates
Nissan Altima: 2007–2012, 2013–2018
Nissan Sentra: 2012–2017
Nissan Rogue: 2014–2018
Nissan Pathfinder: 2013–2014
Common issues reported included:
Shuddering and hesitation
Overheating
Whining noises
Premature belt or pulley failure
Complete transmission replacement well before 100,000 miles
These problems were serious enough to result in extended warranties and class-action settlements in some cases. Newer Nissan CVTs (2019 and newer) show improvement, but long-term reliability data is still developing.
How Other Brands Compare
Toyota & Honda: Generally strong CVT reliability, especially in hybrids
Subaru: Mixed results; early Lineartronic CVTs had complaints, later versions improved
Mitsubishi: Some issues in budget models, fewer reports overall than Nissan
In short, design, torque limits, and cooling systems matter more than the CVT label alone.
How to Extend the Life of a CVT
Despite the myth of “lifetime fluid,” most transmission specialists agree that maintenance is critical.
Change CVT fluid every 30,000–50,000 miles
Use only manufacturer-specified CVT fluid
Avoid aggressive acceleration and heavy towing
Watch for early warning signs like whining, slipping, or shuddering
Keep the vehicle’s cooling system in good condition
Verify service records before buying a used CVT vehicle
Neglecting fluid service is one of the fastest ways to shorten a CVT’s lifespan.
CVT vs Dual-Clutch Transmission
Feature CVT Dual-Clutch (DCT) Gear changes Continuous Fixed gears Driving feel Smooth, no shifts Fast, sporty shifts Fuel economy Often better Good, performance-focused Reliability Varies by brand/year Can be complex or jerky
Final Takeaway
CVTs aren’t inherently bad — but early designs and poor maintenance gave some brands a lasting reputation problem. Buyers should focus on:
Specific model years
Service history
Driving habits
Whether the CVT is a traditional belt-driven unit or a hybrid eCVT
When properly designed and maintained, a CVT can deliver excellent efficiency and long service life — especially in modern hybrids.
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Girls and boys solve math problems differently – with similar short-term results but different long-term outcomes
Girls and Boys: New research finds girls and women more often use step-by-step algorithms, while boys and men use shortcuts. Accuracy is similar short-term, but algorithm use links to weaker performance on complex problems and may help explain gaps on high-stakes tests and in math-intensive careers.
Girls and boys solve math problems differently – with similar short-term results but different long-term outcomes
Sarah Lubienski, Indiana University; Colleen Ganley, Florida State University, and Martha Makowski, University of Alabama Among high school students and adults, girls and women are much more likely to use traditional, step-by-step algorithms to solve basic math problems – such as lining up numbers to add, starting with the ones place, and “carrying over” a number when needed. Boys and men are more likely to use alternative shortcuts, such as rounding both numbers, adding the rounded figures, and then adjusting to remove the rounding. But those who use traditional methods on basic problems are less likely to solve more complex math problems correctly. These are the main findings of two studies our research team published in November 2025. This new evidence may help explain an apparent contradiction in the existing research – girls do better at math in school, but boys do better on high-stakes math tests and are more likely to pursue math-intensive careers. Our research focuses not just on getting correct answers, but on the methods students use to arrive at them. We find that boys and girls approach math problems differently, in ways that persist into adulthood.
A possible paradox
In a 2016 study of U.S. elementary students, boys outnumbered girls 4 to 1 among the top 1% of scorers on a national math test. And over many decades, boys have been about twice as likely as girls to be among the top scorers on the SAT and AP math exams. However, girls tend to be more diligent in elementary school and get better grades in math class throughout their schooling. And girls and boys across the grades tend to score similarly on state math tests, which tend to be more aligned with the school curriculum and have more familiar problems than the SAT or other national tests. Beyond grades and test scores, the skills and confidence acquired in school carry far beyond, into the workforce. In lucrative STEM occupations, such as computer science and engineering, men outnumber women 3 to 1. Researchers have considered several explanations for this disparity, including differences in math confidence and occupational values, such as prioritizing helping others or making money. Our study suggests an additional factor to consider: gender differences in approaches to math problems. When older adults think of math, they may recall memorizing times tables or doing the tedious, long-division algorithm. Memorization and rule-following can pay off on math tests focused on procedures taught in school. But rule-following has its limits and seems to provide more payoff among low-achieving than high-achieving students in classrooms. More advanced math involves solving new, perplexing problems rather than following rules.Math can be creative, not rote.AP Photo/Jacquelyn Martin
Differing strategies
In looking at earlier studies of young children, our research team was struck by findings that young boys use more inventive strategies on computation problems, whereas girls more often use standard algorithms or counting. We wondered whether these differences disappear after elementary school, or whether they persist and relate to gender disparities in more advanced math outcomes. In an earlier study, we surveyed students from two high schools with different demographic characteristics to see whether they were what we called bold problem-solvers. We asked them to rate how much they agreed or disagreed with specific statements, such as “I like to think outside the box when I solve math problems.” Boys reported bolder problem-solving tendencies than girls did. Importantly, students who reported bolder problem-solving tendencies scored higher on a math problem-solving test we administered. Our newer studies echo those earlier results but reveal more specifics about how boys and girls, and men and women, approach basic math problems.
Algorithms and teacher-pleasing
In the first study, we gave three questions to more than 200 high school students: “25 x 9 = ___,” “600 – 498 = ___,” and “19 + 47 + 31 = ___.” Each question could be solved with a traditional algorithm or with a mental shortcut, such as solving 25 x 9 by first multiplying 25 x 8 to get 200 and then adding the final 25 to get 225. Regardless of their gender, students were equally likely to solve these basic computation items correctly. But there was a striking gender difference in how they arrived at that answer. Girls were almost three times as likely as boys – 52% versus 18% – to use a standard algorithm on all three items. Boys were far more likely than girls – 51% versus 15% – to never use an algorithm on the questions. We suspected that girls’ tendency to use algorithms might stem from greater social pressure toward compliance, including complying with traditional teacher expectations. So, we also asked all the students eight questions to probe how much they try to please their teachers. We also wanted to see whether algorithm use might relate to gender differences in more advanced problem-solving, so we gave students several complex math problems from national tests, including the SAT. As we suspected, we found that girls were more likely to report a desire to please teachers, such as by completing work as directed. Those who said they did have that desire used the standard algorithm more often. Also, the boys in our sample scored higher than the girls on the complex math problems. Importantly, even though students who used algorithms on the basic computation items were just as likely to compute these items correctly, algorithm users did worse on the more complex math problems.
Continuing into adulthood
In our second study, we gave 810 adults just one problem: “125 + 238 = ___.” We asked them to add mentally, which we expected would discourage them from using an algorithm. Again, there was no gender difference in answering correctly. But 69% of women, compared to 46% of men, reported using the standard algorithm for their mental calculation, rather than using another strategy entirely. We also gave the adults a more advanced problem-solving test, this time focused on probability-related reasoning, such as the chances that rolling a seven-sided die would result in an even number. Similar to our first study, women and those who used the standard algorithm on the computation problem performed worse on the reasoning test.
The importance of inventiveness
We identified some factors that may play a role in these gender differences, including spatial-thinking skills, which may help people develop alternate calculation approaches. Anxiety about taking tests and perfectionism, both more prevalent among women, may also be a factor. We are also interested in the power of gender-specific social pressures on girls. National data has shown that young girls exhibit more studious behavior than do boys. And the high school girls we studied were more likely than boys to report they made a specific effort to meet teachers’ expectations. More research definitely is needed to better understand this dynamic, but we hypothesize that the expectation some girls feel to be compliant and please others may drive teacher-pleasing tendencies that result in girls using algorithms more frequently than boys, who are more socialized to be risk-takers. While compliant behavior and standard math methods often lead to correct answers and good grades in school, we believe schools should prepare all students – regardless of gender – for when they face unfamiliar problems that require inventive problem-solving skills, whether in daily life, on high-stakes tests or in math-intensive professions.Sarah Lubienski, Professor of Mathematics Education, Indiana University; Colleen Ganley, Professor of Developmental Psychology, Florida State University, and Martha Makowski, Assistant Professor of Mathematics, University of Alabama This article is republished from The Conversation under a Creative Commons license. Read the original article.
Head in the Clouds: Cloud is no longer just storage—it’s the intelligent core of modern business. Explore how “cognitive cloud” blends AI and cloud infrastructure to enable real-time, self-optimizing operations, improve customer experiences, and accelerate enterprise modernization.
(Family Features) You approve a mortgage in minutes, your medical claim is processed without a phone call and an order that left the warehouse this morning lands at your door by dinner. These moments define the rhythm of an economy powered by intelligent cloud infrastructure. Once seen as remote storage, the cloud has become the operational core where data, AI models and autonomous systems converge to make business faster, safer and more human. In this new reality, the smartest companies aren’t looking up to the cloud; they’re operating within it. Public cloud spending is projected to reach $723 billion in 2025, according to Gartner research, reflecting a 21% increase year over year. At the same time, 90% of organizations are expected to adopt hybrid cloud by 2027. As cloud becomes the universal infrastructure for enterprise operations, the systems being built today aren’t just hosted in the cloud, they’re learning from it and adapting to it. Any cloud strategy that doesn’t account for AI workloads as native risks falling behind, holding the business back from delivering the experiences consumers rely on every day. After more than a decade of experimentation, most enterprises are still only partway up the curve. Based on Cognizant’s experience, roughly 1 in 5 enterprise workloads has moved to the cloud, while many of the most critical, including core banking, health care claims and enterprise resource planning, remain tied to legacy systems. These older environments were never designed for the scale or intelligence the modern economy demands. The next wave of progress – AI-driven products, predictive operations and autonomous decision-making – depends on cloud architectures designed to support intelligence natively. This means cloud and AI will advance together or not at all.
The Cognitive Cloud: Cloud and AI as One System
For years, many organizations treated migration as a finish line. Applications were lifted and shifted into the cloud with little redesign, trading one set of constraints for another. The result, in many cases, has been higher costs, fragmented data and limited room for innovation. “Cognitive cloud” represents a new phase of evolution. Imagine every process, from customer service to supply-chain management, powered by AI models that learn, reason and act within secure cloud environments. These systems store and interpret data, detect patterns, anticipate demand and automate decisions at a scale humans simply cannot match. In this architecture, AI and cloud operate in concert. The cloud provides computing power, scale and governance while AI adds autonomy, context and insight. Together, they form an integrated platform where cloud foundations and AI intelligence combine to enable collaboration between people and systems. This marks the rise of the responsive enterprise; one that senses change, adjusts instantly and builds trust through reliability. Cognitive cloud platforms combine data fabric, observability, FinOps and SecOps into an intelligent core that regulates itself in real time. The result is invisible to consumers but felt in every interaction: fewer errors, faster responses and consistent experiences.
Consumer Impact is Growing
The impact of cognitive cloud is already visible. In health care, 65% of U.S. insurance claims run through modernized, cloud-enabled platforms designed to reduce errors and speed up reimbursement. In the life sciences industry, a pharmaceuticals and diagnostics firm used cloud-native automation to increase clinical trial investigations by 20%, helping get treatments to patients sooner. In food service, intelligent cloud systems have reduced peak staffing needs by 35%, in part through real-time demand forecasting and automated kitchen operation. In insurance, modernization has produced multi-million-dollar savings and faster policy issuance, improving both customer experience and financial performance. Beneath these outcomes is the same principle: architecture that learns and responds in real time. AI-driven cloud systems process vast volumes of data, identify patterns as they emerge and automate routines so people can focus on innovation, care and service. For businesses, this means fewer bottlenecks and more predictive operations. For consumers, it means smarter, faster, more reliable services, quietly shaping everyday life. While cloud engineering and AI disciplines remain distinct, their outcomes are increasingly intertwined. The most advanced architectures now treat intelligence and infrastructure as complementary forces, each amplifying the other.
Looking Ahead
This transformation is already underway. Self-correcting systems predict disruptions before they happen, AI models adapt to market shifts in real time and operations learn from every transaction. The organizations mastering this convergence are quietly redefining themselves and the competitive landscape. Cloud and AI have become interdependent priorities within a shared ecosystem that moves data, decisions and experiences at the speed customers expect. Companies that modernize around this reality and treat intelligence as infrastructure will likely be empowered to reinvent continuously. Those that don’t may spend more time maintaining the systems of yesterday than building the businesses of tomorrow. Learn more at cognizant.com. Photo courtesy of Shutterstock SOURCE:Cognizant
Sarah Lubienski, Professor of Mathematics Education, Indiana University; Colleen Ganley, Professor of Developmental Psychology, Florida State University, and Martha Makowski, Assistant Professor of Mathematics, University of Alabama This article is republished from The Conversation under a Creative Commons license. Read the original article.
