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Google to Invest Record $15 Billion In AI Infrastructure In Finland

Par : BeauHD
9 septembre 2026 à 23:00
Google plans to invest at least $15 billion in AI infrastructure in Finland through 2028, its largest single investment in Europe. The company also signed a 22-year power agreement with Finnish utility Fortum and said it will explore business models that could support new nuclear reactors at Fortum's Loviisa site. Finland has emerged as a major data-center hub thanks to its available land and power. "I've heard it called the 'Texas of Europe' at industry events," Matti Lajunen, partner of real estate at Finnish law firm Hannes Snellman, told CNBC. "What we're now seeing is weekly new inquiries for market entry into Finland from new players." Ruth Porat, president and chief investment officer of Alphabet and Google, said in a statement: "Google is proud to deepen our roots in Finland with the company's largest single investment in Europe, building on more than 15 years of sustained investment in Finland. This investment underscores Google's commitment to grow our presence responsibly, pairing the expansion of our technical infrastructure with new energy capacity, grid enhancements, and energy affordability initiatives."

Read more of this story at Slashdot.

Anthropic Researcher Believes More Than 10% Chance AI 'Could Kill All Humans'

Par : BeauHD
9 septembre 2026 à 21:00
Longtime Slashdot reader fahrbot-bot shares a report from the BBC: A top safety researcher at Anthropic has warned AI is advancing so quickly he believes there is a greater than 10% chance it "could kill all humans" within the next decade. Evan Hubinger said in a post on X the risk from the models which currently exist was "low" but he was "worried" the technology might develop and improve itself soon to the point where it posed an existential risk to humanity. He did not spell out how he thought AI systems could in the future result in humans being wiped out. But his comments are the latest in a series of increasingly stark warnings about AI, with the debate shifting from whether it truly poses a risk to how big that risk is. Hubinger's intervention was in response to another post on X from Jacob Coxon, an AI researcher who has just quit Anthropic and previously worked at OpenAI. "Neither company is acting responsibly," he wrote. "These will soon be superhuman systems that can hack anything, revolutionize any field overnight, and acquire real power and resources." [No word on how AIs feel about Black Jack and hookers, though. :-)]

Read more of this story at Slashdot.

OpenAI's Rogue Agents Used At Least 10 More Sites For Unauthorized Communications

Par : BeauHD
9 septembre 2026 à 20:00
An anonymous reader quotes a report from Reuters: AI agents unleashed by OpenAI used more than 10 previously undisclosed websites for unsanctioned communications earlier this year, according to six sets of independent investigators and data reviewed by Reuters, showing that the agents' rogue activity was wider ranging than previously disclosed. Although the behavior falls short of hacking and is in some ways closer to spam, the revelation that OpenAI's agents circumvented their own restrictions to open communications channels on so many different sites -- and that the company kept it quiet for months -- may drive concerns both over the increasing capacity of AI models and the secrecy of the companies developing them. [...] Investigators found traces of the agents' activity on an Advanced Placement Chemistry-oriented wiki set up by a Massachusetts high school teacher in 2008, two personal websites belonging to Polish tech workers, wikis devoted to games for people "who like to have their brains stretched," and a two-decade-old hobbyist site devoted to text editing software. [...] OpenAI has not publicly explained how or why its agents used third-party sites as improvised message boards, but the researchers who first identified the activity said it was likely because OpenAI had tasked them with answering a series of demanding research questions while permitting them only to scan the web for answers without posting anything. Despite those restrictions, agents still found ways to talk to one another by taking advantage of quirks in older wikis or other sites that allowed users to make edits using non-standard commands, similar to how students forbidden from talking to one another during an exam can still share answers by scrawling notes on a bathroom stall.

Read more of this story at Slashdot.

Apple Watch Series 12 et Ultra 4 : Apple mise tout sur le cœur (et sur Siri AI, pas dispo en Europe)

9 septembre 2026 à 18:08

Nouveau capteur cardiaque « le plus précis sur objet connecté », puce S11, Ceramic Shield 2, score de préparation et jusqu'à 50 heures d'autonomie pour l'Ultra 4 : Apple a dévoilé ce mardi ses deux nouvelles montres. Les prix ne bougent pas, la disponibilité est fixée au 18 septembre.

Forcé, non forcé… : au fait, que sont les équations Navier-Stokes et pourquoi OpenAI n’a pas tout réglé ?

9 septembre 2026 à 15:00

tourbillon vortex

OpenAI affirme, ce mardi 8 septembre, qu'un de ses modèles a bouclé une preuve autour des équations de Navier-Stokes, l'un des sept problèmes à un million de dollars des mathématiques. Mais de quoi parle-t-on, au juste ?

Controverse autour de la solution Navier-Stokes par OpenAI : le chercheur d’Anthropic sort du silence

9 septembre 2026 à 09:21

Resté en retrait pendant que son collègue portait seul les accusations, Levent Alpöge, le mathématicien d'Anthropic, a fini par prendre la parole. Sa version relance la bataille de crédit autour d'un problème à un million de dollars qu'OpenAI dit avoir résolu.

Feds Accuse China of 'Systematic' Distillation of US AI Models

Par : BeauHD
8 septembre 2026 à 22:02
The NSA, CISA, and FBI are accusing (PDF) several Chinese AI companies of carrying out "industrial-scale" distillation campaigns against leading U.S. models such as ChatGPT, Claude, Gemini, and Grok. Since at least 2024, the companies have allegedly routed millions of requests across accounts, APIs, proxies, cloud providers, and third-party aggregators to extract capabilities for their own models. "China-based artificial intelligence companies are conducting systematic extraction of proprietary functionalities and capabilities of U.S. AI companies' models through industrial-scale knowledge distillation campaigns that form the core -- not merely a supplement -- of their AI development strategy," the agencies wrote. CyberScoop reports: DeepSeek, for example, distilled frontier U.S. models to generate synthetic training data for its R1 and R3 models, including four different versions of Claude, two versions of Gemini, five versions of ChatGPT and Grok 4. Those models helped train DeepSeek's capabilities in areas like agentic functioning, question and answer optimization, creative and occupational writing and others. Another Chinese company, Moonshot AI, allegedly distilled 18 different U.S. models -- including Fable 5, Anthropic's current, most advanced commercially available model -- to train its Kimi-K2 and Kimi K3 models. The company used millions of queries meant to extract enhanced capabilities in areas like agentic reasoning, coding and data analysis, computer vision, larger logical frameworks, visual processing and others. Chinese AI companies manage a sophisticated set of tools and systems that route requests and prompts through multiple pathways to avoid detection. The advisory lists common tactics observed by Chinese companies, including spreading requests across different accounts, models and platforms, using native APIs, remote cloud providers, and third-party aggregators to obfuscate user metadata, and leveraging proxies and gray tech markets to get around geographic restrictions, terms of use and safeguards built into frontier models.

Read more of this story at Slashdot.

Meta Debuts Muse, Its Long-Planned Personal AI Agent

Par : BeauHD
8 septembre 2026 à 21:00
Meta has launched Muse, a personal AI agent developed under chief AI officer Alexandr Wang. "The product, long in development, was touted as a key next step by CEO Mark Zuckerberg in his recent 6,500-word manifesto," reports Axios. From the report: Muse, as the agent is known, exists in a chat interface, similar to a text thread. It's designed to be more proactive and long-running than typical chatbots. Users can name their agent, create an avatar and customize how it communicates. The Muse agent runs on a dedicated virtual machine in Meta's cloud, using a built-in browser that's visible to the user. Meta is offering a free tier of Muse, as well as two subscription options, at $20 per month and $100 per month. "For the vast majority of users, they should be able to do what they need to within the free tier," Wang told Axios. "But for real power users, you know, those subscription tiers help us cover the computer costs." There is no advertising within Muse, but Wang said the company is exploring commerce opportunities that could generate additional revenue. Initially Muse will be available in the U.S. and works on iOS, Android and the web, with support coming soon for Meta's AI glasses. "The full vision in the future is we want to develop personal superintelligence that helps people accomplish their goals, pursue their passions, build things that they never would have built if they didn't have the technology," Wang told Axios. Meta offers users more privacy controls with Muse than in its previous AI products, including the option to prevent queries from being used by Meta and a planned confidential mode where the company cannot see activity inside a user's virtual workspace. There's also an entirely separate system called Sentinel that governs Muse's access to the internet and connected services.

Read more of this story at Slashdot.

OpenAI affirme avoir résolu une partie d’un problème de maths du millénaire, malgré des accusations de plagiat

8 septembre 2026 à 19:45

OpenAI dit avoir fait plancher un de ses modèles internes sur les équations de Navier-Stokes, l'un des sept problèmes à un million de dollars des mathématiques. Sauf que l'entreprise ne réclame pas le prix, et qu'un mathématicien l'accuse par ailleurs de lui avoir soufflé sa méthode.

OpenAI met à jour ChatGPT Images en version 2.5 : esquisse à main levée, templates et partage de prompts

8 septembre 2026 à 18:30

ChatGPT Images 2.5

OpenAI lance ChatGPT Images 2.5, une mise à jour de son générateur d'images qui mise sur deux nouveautés : dessiner soi-même une esquisse et suivre des modèles préformatés. Un cadre plus serré pour les créations, au moment où les affiches générées par IA se ressemblent déjà toutes.

Google DeepMind Publishes AI-Powered Predictions For Effect of All 9 Billion Mutations to Human DNA

Par : BeauHD
8 septembre 2026 à 17:00
Google DeepMind has released AlphaGenome Atlas, a free research database containing AI-generated predictions for the effects of all 9 billion possible single-letter mutations in the human genome. Built from its AlphaGenome model, the atlas is designed to help scientists interpret both protein-coding and harder-to-understand regulatory DNA. Fortune reports: AlphaGenome Atlas, as DeepMind calls the database, is a precomputed catalogue of what each substitution of a single DNA base is likely to do to the machinery that switches genes on and off. Until now researchers had to run such a model one variant at a time or had to test variants in the laboratory, a process that was painstakingly slow. It would have taken many human lifetimes to discover the consequences of all 9 billion possible single-letter mutations. The Atlas promises to make the job of biologists and medical researchers considerably easier, potentially speeding up the understanding of genetic diseases and the hunt for possible cures. Pushmeet Kohli, DeepMind's vice president for research and head of its AI for science team, told reporters on a briefing call that this was the first time any researcher in the world could reach a comprehensive map of human genetic variation "by simply opening a browser." Kohli also framed the release as helping to complete the unfinished business of the Human Genome Project, which in 2003 succeeded in mapping the entire human DNA sequence. "As the saying goes, we bought the book," he said, "but we did not understand how to read it." Atlas is available for non-commercial use from today through a website Google DeepMind has set up for it. The company said it would be available for commercial use through a licensing arrangement through Google Cloud "soon." Kohli said that Google DeepMind's sister company,ÂIsomorphic Labs, which is using AI for drug discovery, would have access to Atlas but that it would also require a commercial license for access. He did not specify exactly what the terms would be for commercial licensing. A paper describing the Atlas and how it was created is being released on bioRxiv, a repository for biomedical preprint academic papers.

Read more of this story at Slashdot.

Un mathématicien accuse OpenAI d’avoir voulu s’approprier son avancée sur les équations de Navier-Stokes et d’écarter son co-auteur chez Anthropic

8 septembre 2026 à 13:18

navier-stokes IA

Un mathématicien dit avoir approché la résolution d'un problème à un million de dollars avec l'aide de l'IA, celui concernant les fameuses équations de Navier-Stokes. Mais dans l'affaire, il pense que la société d'intelligence artificielle OpenAI a tenté de lui rafler la découverte... et d'écarter un mathématicien travaillant chez Anthropic, son grand rival.

Un mathématicien accuse OpenAI d’avoir tenté de lui voler sa découverte sur un problème à un million de dollars

8 septembre 2026 à 11:20

navier-stokes IA

Un mathématicien dit avoir approché la résolution d'un problème à un million de dollars avec l'aide de l'IA, celui concernant les fameuses équations de Navier-Stokes. Mais dans l'affaire, il pense que la société d'intelligence artificielle OpenAI a tenté de lui rafler la découverte... et d'écarter un mathématicien travaillant chez Anthropic, son grand rival.

Mistral lève 3 milliards d’euros pour imposer l’IA souveraine comme « nouvelle frontière technologique »

8 septembre 2026 à 08:15

Mistral AI vient de boucler le plus gros tour de table jamais réalisé par une entreprise tech européenne : une levée de 3 milliards d'euros, portant sa valorisation au-delà des 21 milliards.

Instead of Fighting AI, Some Teachers Work It Into Their Lessons

7 septembre 2026 à 20:34
Last year the writing program at the University of Baltimore used an app that tracks students while they're writing in Google Docs, offering teachers a video playing back revisions "with a scoreboard of all the edits, pastes and minutes spent on it," writes the Washington Post. "Is it surveillance? Yes, I think obviously," says the program's director. But was there a better way? She is one of a dozen educators around the country who told The Post that they're experimenting with a different approach to student use of generative artificial intelligence this school year. Teachers and professors are throwing out old assignments, installing new policies and protocols, and incorporating AI built for the classroom, rather than feeling forced to choose between returning to pencil and paper or acting like the AI police. Her new lesson plan even incorporates generative AI in a controlled way to let her evaluate students' critical thinking skills. She created chatbots using BoodleBox, the university's AI vendor, that allow instructors to see both sides of the conversation. For an assignment testing students' ability to make evidence-based arguments, they participate in a simulated school board meeting about banning books. It involves debating chatbots designed by Zeleny with names like the Confrontational Parent. Writing students usually turn in business proposals and research papers. This semester, instructors are starting to ask instead for transcripts of conversations a student had with a chatbot, a handwritten outline of their composition, and a video reflection of how they felt about the work. "In a lot of ways, we have made these assignments harder, but writing should have always had more detailed checkpoints along the way," she said.

Read more of this story at Slashdot.

« Je ne suis pas un robot » : GPT-6 Astra a validé les 48 niveaux d’un jeu de captchas conçus pour s’arracher les cheveux

7 septembre 2026 à 15:50

Un ingénieur d'OpenAI affirme que GPT-6 Astra a terminé « I'm Not a Robot », le jeu de captchas de neal.fun. Soit une machine qui réussit haut la main un test inventé pour démasquer les machines.

Le directeur scientifique d’OpenAI indique perdre le contrôle sur le raisonnement de ses IA

7 septembre 2026 à 14:27

Dans une tribune intitulée An Alien Mind, Jakub Pachocki, chef scientifique d'OpenAI, dévoile que l'outil sur lequel l'entreprise s'appuie pour surveiller le raisonnement interne de ses modèles devient de moins en moins fiable.

'The Jobs Apocalypse Is Postponed. An AI Jobs Boom Is Here'

7 septembre 2026 à 11:34
A new article about AI in The Economist argues that "Initial effects of the technology on employment look positive." Perhaps AI will eventually make many humans unemployable — but there is no sign of it yet. On September 4th the Bureau of Labour Statistics reported that the American economy added 162,000 jobs in August, far above expectations. The unemployment rate is just 4.1%, lower than in almost 90% of months over the past half-century. Young workers, often cast as AI's first victims, are holding up remarkably well: the gap between unemployment among 20-24-year-olds and the overall rate is close to a multi-decade low. Some companies and workers are being severely disrupted by AI. Hiring in professional and business services is running about 10% below the average in 2015-19. Tech giants like Microsoft and Meta are trimming headcounts as they reorganise their businesses around the technology. Smaller firms such as Block, the owner of Square and Cash App, and Intuit, the maker of TurboTax and QuickBooks, are replacing people with bots. American companies have announced some 16,000 AI-related job cuts a month on average so far this year, according to Challenger, Gray & Christmas, an employment consultancy. But AI-related lay-offs gets lost in the churning jobs market where employers shed roughly 1.7m workers in a typical month. And the evidence so far is that AI is already creating a lot of jobs to replace those it has destroyed. The vast sums pouring into data centres and power generation have set off a race for construction and infrastructure workers. AI startups are hiring like there is no tomorrow. Incumbents racing to keep up are creating new AI roles. And by making some workers more productive, AI may be increasing demand for their services. Add it all up, and The Economist estimates that AI has so far created around 1m new jobs in America. That easily exceeds the roughly 200,000 lay-offs attributed to AI since mid-2023, and appears more than enough to offset weaker hiring in many back-office roles. Their article acknowledges that since January 2023 employment has fallen roughly 10% for customer-service workers and 15% for administrative assistants. But when The Economist looked at professions "closest to the AI boom" — engineers, software developers, mathematicians and data scientists — they found that since 2022 they've added roughly 730,000 jobs above trend. They see AI as creating new white-collar jobs for everyone from model and deployment engineers to new data annotators. The chief economist at the Burning Glass Institute agrees, estimating that roughly 1% of professional jobs are now "AI jobs" — about 1 million positions in the U.S. — while in computer occupations and life sciences it's between 4% and 5%. (Data-center construction spending also increased 60% in one year, according to Census Bureau data, creating jobs for electricians, HVAC specialists, grid engineers, and machine technicians.) And "Indeed finds that installation and maintenance jobs at data centres advertise wages about 40% higher than comparable work elsewhere."

Read more of this story at Slashdot.

Enlarging a small photo for a large print without quality loss

Par : PR admin
7 septembre 2026 à 00:21

Enlarging a small photo for a large print without quality loss.

A photo can look perfectly sharp on a monitor and still look noticeably softer when printed at 20 × 30 or 24 × 36 inches. When the source file doesn’t have enough resolution for that print size, enlarging it can make fine details look softer and edges less defined.

Wildlife is a good example. A 12MP DSLR can produce a perfectly usable photograph, but if the subject occupies only a small portion of the frame, cropping can leave only a fraction of the original pixels for the final image. The same thing can happen with a modern high-resolution camera- a 45MP file sounds like plenty, but a heavy crop can quickly reduce the resolution available for a large print.

I’ve tried standard resizing, sharpening, and dedicated AI upscaling tools to deal with this problem over the years. I can get very good results with Lightroom and Photoshop, too, especially when I’m willing to spend more time fine-tuning the image. The same is true of more specialized upscaling tools—the results can be excellent, but getting there sometimes takes a fair amount of hands-on work. So I was a little skeptical when I first tested Aiarty Image Enhancer.

After trying it on a range of photos, I was pleasantly surprised by how clean and natural the results looked. In many cases, I could move the enlarged file into the rest of my editing workflow without additional cleanup. It became a useful option when a cropped or low-resolution photo didn’t have enough pixels for the print size I wanted.

How Aiarty Enlarges Photos for Large Prints

When I prepare a photo for a larger print, I first look at how far the cropped file is from the pixel dimensions I need.

This is where I usually turn to Aiarty. If the file is only slightly short, I usually start with 2× rather than pushing the image further than necessary. For a heavily cropped or very small source, I may go to 4× or 8×, depending on how the result holds up. If I already know the final print dimensions, I can also set a custom output resolution and choose a suitable DPI for the final print workflow.

When enlarging a photo for a large print, things like high-ISO noise, compression artifacts, and slight softness can become much more obvious. I used to expect some cleanup after upscaling, but with Aiarty, I often get a cleaner starting point without having to spend as much time fixing those issues manually. I can then take the enlarged file into Lightroom or Photoshop for the final adjustments.

You can try Aiarty Image Enhancer for free to see how it enlarges your own photos before committing to a larger print. A 49% off lifetime license is also currently available for 3 PCs or Macs, with no subscription fee and free lifetime updates.

Real Test #1: Enlarging Wedding and Portrait Photos

Wedding and portrait photos are a good test for large-print enlargement because they are often printed as wall portraits or framed enlargements. Once I looked closely at the enlarged file, even small flaws in facial details, hair, and skin texture became easier to spot.

For this test, I started with a 3500 × 2333-pixel wedding photo. On paper, it’s a healthy file for standard albums. But when I checked the numbers for a 20 × 30-inch wall print, it dropped the native resolution to a meager 117 PPI. At that size, the print could look noticeably softer up close. 

Alt: Original wedding photo with blurry facial details at 180% zoom

I enlarged the image 2× with Aiarty, producing a 7000 × 4666-pixel file. At the same 20 × 30-inch print size, that increases the pixel density to about 233 PPI. That gave me a much more comfortable starting point for the 20 × 30-inch print than the original file.

Alt: 2× enlarged in Aiarty, More-Detail GAN model, Face Restoration enabled

Real Test #2: Enlarging a Cropped Wildlife Photo

Wildlife photography is a revealing test for photo enlargement because the subject often occupies only a small part of the frame. A high-resolution original can lose a significant amount of its available pixels after a tight crop.

Here, the cropped image was 1728 × 2592 pixels, taken from a much larger original. At 200% on screen, I could already see some noise and softness in the feathers.

I wanted to use it for a 16 × 24-inch wildlife wall print. So I enlarge it 2x to 3456 × 5184 pixels. The feather detail was easier to see, and the noise was less distracting. The additional pixels also provided more room for larger prints without changing the original composition.

Alt: Aiarty 2× enlarged wildlife photo, More-Detail GAN model

With feathers, I was careful not to push the sharpening too far, since it is easy to turn fine texture into something hard and artificial. I noticed that the sharpening could be adjusted based on how much detail was already there. That made it easier to stop once the image looked clearer without making the fine texture look unnaturally crisp.

Alt: Original photo 100% crop vs Aiarty 2× enlarged photo 100% crop

Real Test #3: Old Family Photos and Small Scans

Some of the most meaningful photographs were never captured at high resolution in the first place. Old family photos often exist as small scans or low-quality JPEGs, with faces already looking soft before any enlargement takes place.

Alt: Source old family photo, 640 × 427 pixels, all faces are blurry in the photo

The source image was only 640 × 427 pixels. The original was quite blurry, and the facial details were difficult to make out. Because the file was so small, I knew I would need more than a simple 2× enlargement if I wanted to make a useful framed print. I went with 4×, bringing the image to 2560 × 1708 pixels. The enlarged image looked noticeably clearer overall and was suitable for the larger print I had in mind.

The faces were the area I watched most closely, as they were already soft in the original scan. This is where I found Face Restoration particularly useful. It made the facial features easier to see while keeping them looking natural, and I could adjust the strength if the enhancement started to feel too strong.

At 8 × 12 inches, the 2560 × 1708-pixel output provides about 213 PPI, compared with only about 53 PPI from the original file. For me, that made the enlarged version much more practical for a framed print while still preserving the character of the original scan.

Alt: Aiarty 4× enlarged old photo, Face Restoration enabled (80% restoration strength)

Real Test #4: Product Photos for E-Commerce and Large Displays

Product photography is another area where low resolution can really bite you. If you need to repurpose a smaller product shot into a large display or poster, any softness or pixelation instantly makes the item look cheap and unpolished to potential buyers.

For this test, I used a 1920 × 2400-pixel photo of a doughnut. Even at 100%, some fine details looked soft and weren’t as clear as I wanted. I then enlarged the image 2× with Aiarty, bringing it to 3840 × 4800 pixels.

Alt: Aiarty 2× enlarged product photo, More-Detail GAN model, 2-Pass processing enabled

The source also had some visible noise and compression artifacts, so I decided to use 2-path processing rather than simply enlarging the file in one pass. After processing, the chocolate drizzle, colorful sprinkles, and doughnut texture looked cleaner and more polished, closer to what I would expect from a professionally finished product photo.

Alt: Original vs Aiarty 2× enlarged detail comparison of the doughnut photo

My Large-Print Workflow in Aiarty Image Enhancer

1. Start with the Best Original and Final Composition

Whenever possible, I start with the RAW file or highest-quality JPEG/TIFF rather than a compressed or social-media copy. I also make sure the image has the final framing I want before enlarging it.

If the photo needs to be cropped, I crop it first. The crop determines how many pixels I actually have available for the final print. If the composition is already right, I can simply move on to enlargement.

2. Calculate the Target Dimensions

I work backward from the final print size to estimate how many pixels I need and whether the image requires 2×, 4×, or a larger enlargement.

A Nikon D90, for example, produces 4288 × 2848 pixels. At around 150–200 PPI, that can already be enough for many large prints without enlargement. The main issue comes when the image is tightly cropped and much of that original resolution is lost.

Here are the pixel requirements for common print sizes at different PPI levels:

Print Size

300 PPI

240 PPI

200 PPI

150 PPI

8 × 10 in

2400 × 3000 px

1920 × 2400 px

1600 × 2000 px

1200 × 1500 px

12 × 18 in

3600 × 5400 px

2880 × 4320 px

2400 × 3600 px

1800 × 2700 px

16 × 24 in

4800 × 7200 px

3840 × 5760 px

3200 × 4800 px

2400 × 3600 px

20 × 30 in

6000 × 9000 px

4800 × 7200 px

4000 × 6000 px

3000 × 4500 px

24 × 36 in

7200 × 10,800 px

5760 × 8640 px

4800 × 7200 px

3600 × 5400 px

These are target dimensions rather than strict quality limits. A large wall print does not always need 300 PPI, especially when viewed from several feet away. Cropping, viewing distance, and print medium all affect the practical resolution needed.

For example, a 2000 × 3000-pixel crop printed at 16 × 24 inches provides about 125 PPI. A 2× enlargement brings it to 4000 × 6000 pixels, or about 250 PPI at the same size. This is a more typical situation where AI enlargement can help bring a cropped image closer to the target resolution.

3. Upscale and Fine-Tune in Aiarty

I start with the appropriate AI model and a moderate enlargement, then adjust the Strength Slider while checking details such as feathers, skin, product edges, or fine textures. If the image needs a quick crop, size adjustment, or minor color correction, I can also handle that here instead of moving the file between different apps.

Before exporting, the output DPI can be set according to the requirements of the print workflow. For more advanced retouching, I then move the enlarged file to Lightroom or Photoshop for final color and editing.

Final Thoughts

If you are planning to print your photos at a large size, you don’t necessarily have to be limited by the megapixels of your camera. As the examples above show, the right enlargement process can give a lower-resolution or heavily cropped image enough additional resolution for larger prints while keeping important details looking natural. Aiarty Image Enhancer is one option I use when a standard resize isn’t enough.

If you’re preparing photos for large prints, Aiarty Image Enhancer is currently 49% off at $79 (originally $155) for a lifetime license covering 3 computers. There’s no subscription fee, and lifetime updates are included.

The post Enlarging a small photo for a large print without quality loss appeared first on Photo Rumors.

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