OpenAI has just introduced GPT-5.6, a new family of large language models led by its flagship Sol model, alongside Terra and Luna variants built for different performance and cost requirements. However, the company is limiting the initial rollout to a small group of trusted U.S.-based partners after a request from the U.S. government.

The GPT-5.6 series introduces a new naming system, with Sol representing the highest capability tier, Terra offering GPT-5.5-level performance at half the cost, and Luna targeting lower-cost, faster AI applications. OpenAI said the models will become generally available through ChatGPT, Codex, and its API in the coming weeks.

GPT-5.6 Sol also introduces a new maximum reasoning mode that gives the model more time to solve complex tasks. OpenAI is also launching an Ultra mode that uses subagents to tackle sophisticated workflows beyond the capabilities of a single AI agent.

The company said GPT-5.6 Sol delivers its strongest performance yet in coding, biology, and cybersecurity while introducing its "most robust safety stack to date."

According to OpenAI, GPT-5.6 Sol achieved a new state of the art on TerminalBench 2.1, a benchmark for command-line coding workflows. In biology, the company said the model outperformed GPT-5.5 on GeneBench v1 while using fewer output tokens.

OpenAI also highlighted gains in cybersecurity. On ExploitBench, GPT-5.6 Sol matched the performance of Anthropic’s Mythos Preview while using roughly one-third of the output tokens. On ExploitGym, developed by researchers at UC Berkeley with OpenAI and other frontier AI labs, all three GPT-5.6 models showed improved cyber capabilities as reasoning increased.

Despite those gains, OpenAI said GPT-5.6 Sol does not cross the Cyber Critical threshold under its Preparedness Framework.

"GPT-5.6 Sol is better at helping people find and fix vulnerabilities than reliably carrying out end-to-end attacks," the company said.

The company also introduced a layered safety system that combines model-level protections, real-time misuse detection, account-level monitoring, differentiated access, and extensive automated and human red-teaming. OpenAI said it dedicated more than 700,000 A100-equivalent GPU hours to automated red-teaming to uncover jailbreak techniques before release.

Unlike previous launches, GPT-5.6 will initially be available only to a select group of trusted partners.

"As part of our ongoing engagement with the U.S. government, we previewed our plans and the models’ capabilities ahead of today’s launch," OpenAI said.

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There was a time when it is easy to tell when a face was generated with artificial intelligence (AI). Whether it was a distinctive uncanny sheen, impossibly smooth skin, eyes that didn't quite make sense or a conspicuous third ear, older AI models' facsimiles of human faces were simple to spot and easy to dismiss. That's just not true anymore.

Now, AI image generators can produce portraits so convincing that even careful observers struggle to distinguish fact from figment. That's why apps such as Zoom and Tinder allow their users to submit biometric identification, such as retinal scans, to help prove that a real person exists behind a profile picture. But a new study suggests you can train your brain to get better at spotting fakes.

Past attempts to teach people to spot AI faces have focused on training viewers to look for visual glitches or statistical fingerprints left behind by a particular image generator, such as a wonky ear or an eye with two pupils. The problem is that those clues can disappear with a software update or by simply using a different prompt.

"The AI is getting too good," said Amy Dawel, an associate professor at Australian National University and the lead author on the study, in a press release. "And fraudsters may avoid using pictures with obvious flaws anyway." The result is an endless technological arms race humanity seems destined to lose.

Instead, the researchers taught the participants how to recognize broader patterns in how AI systems generate images. "Our training directs people's attention to global qualities that differ between AI and human faces," Dawel said.

Current AI image generators are themselves trained on datasets composed of millions of images. When prompted to generate a face, they don't copy specific faces, but instead compose a new face that is based in part on the mathematical patterns shared across the faces in that data set—these allow the AI to construct a "typical" human face.

The result is that AI-generated faces often drift toward statistical averages. They're not overly unrealistic, so much as a little too balanced, a little too generic, and a little too conventional. Individually, none of these traits are necessarily suspicious. But together, the whole is blander than the sum of its parts—a subtle banality humans can often implicitly sense.

"Even relatively short training sessions helped participants improve their accuracy," says Tanya George, a student researcher at Australian National University who trained the study's participants. "Research like this can help people navigate increasingly complex online environments."

Compared with real faces, AI-generated faces tend to be more symmetrical, more proportional, and more attractive—while also being less expressive, less distinctive and significantly less memorable. When the researchers trained participants to look for these six markers instead of fleeting artifacts like malformed ears or mismatched jewelry, their ability to spot the AI face almost doubled.

In other words, AI gravitates to the center. Real people do not. Our faces are shaped by countless small deviations from the norm—our subtle asymmetries, distinctive features, and expressions make us memorable. Those imperfections are not flaws. They are our signature.

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There's a new update available for all iPhone users today, but don't expect any big new features. Rather, this update is focused on fixing lots of vulnerabilities, mostly having to do with the WebKit engine for your web browser. The version number is iOS 26.5.2, and it's available to download now.

In total, there are 29 security fixes with today's update, and you can find the full list of them in Apple's official documentation.

As Apple notes, these fixes were originally included with the iOS 26.6 betas, but they've been released on their own with this update. Most of the vulnerabilities patched in this update prevent malicious websites from exploiting your device.

The iPhone update isn't the only one rolling out from Apple's servers today. You'll also find a similar set of fixes available for both iPadOS and macOS, both of which also carry the 26.5.2 version number. You should be able to download them on your Mac and iPad now.

While Apple still may have minor updates coming, including iOS 26.6, you shouldn't expect any big new features until iOS 27 lands this fall. That update will feature a revamped Siri and lots of new Apple Intelligence features, along with some UI tweaks and more. It should launch alongside Apple's iPhone 18 (and folding iPhone) lineup later this year.

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Most Robots can now pick up boxes, sort packages, and screw in bolts without breaking a sweat. Some of them can even walk and run like humans. Hand one a floppy, slippery piece of raw salmon, though, and everything starts falling apart.

A team at the Norwegian University of Science and Technology set out to solve that problem. The result is the Sashimi-Bot, a three-armed robot that can prepare sashimi from a raw salmon loin without a chef in sight.

It divides the job neatly between its three arms. The first arm stabilizes and positions the salmon on the cutting board. The second holds a chef's knife and slices. The third picks up each finished slice with chopsticks and transfers it to a serving tray.

What makes this more than just clever arm arrangement is how the robot learned to do it. Lead researcher Sverre Herland and the team trained it using deep reinforcement learning inside a virtual simulation.

The technology let the robot practice thousands of movements and learn through trial and error, without any practice on real fish.

The knife arm also carries a GelSight tactile sensor, a soft gel surface with an embedded camera that tells the robot exactly when it has reached the cutting board.

During testing, the robot cut 34 slices of salmon. It successfully grasped 26 of the 28 slices that fell onto the cutting board with chopsticks. An additional six slices that had stuck to the knife blade were retrieved directly from it.

Each cut cycle averaged 27.9 seconds. The study is published in npj Robotics (via TechXplore). While most robots do best with rigid, predictable objects, the Sashimi-Bot is more significant than its culinary application suggests.

It is an example of robots handling delicate, irregular materials by making real-time movements and adjustments.

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Retail is moving into a new phase where artificial intelligence no longer just advises, but actually acts on it. The rise of agentic AI marks a shift from systems that recommend products or insights to systems that can make decisions and complete tasks on their own within set limits.

In simple terms, agentic AI refers to AI systems that can carry out multi-step actions without constant human prompting.

In simple terms, agentic AI refers to AI systems that can carry out multi-step actions without constant human prompting.

In retail, this includes updating stock levels, adjusting prices, managing supply chain steps, and even completing parts of a customer journey such as product selection or checkout.

The key change is autonomy. AI is moving from being a support tool to becoming an operational actor.

This shift is closely tied to advances in large language models, which now serve as the foundation for more complex AI agents. These systems are increasingly being tested across e-commerce platforms, logistics networks, and marketing operations.

While many applications are still in pilot stages, the direction is consistent across the industry: more tasks are being handed over to automated decision-making systems.

For years, AI in retail has mainly focused on prediction and recommendation. It suggested what customers might buy, or helped businesses forecast demand. Agentic AI goes further by acting on those predictions.

In practical terms, an AI agent can monitor inventory levels across multiple warehouses and trigger restocking when thresholds are reached. It can adjust product listings based on real-time demand signals.

It can also support dynamic pricing, where prices shift in response to supply, demand, and competitor activity.

These systems are being explored in both online and physical retail environments. The goal is to reduce delays between insight and action. Instead of a manager reviewing data and making a decision, the system can execute the response directly, within predefined rules.

Marketing teams are also beginning to use agentic systems to manage campaign performance. AI can test different versions of adverts, adjust targeting, and reallocate budgets based on performance data.

This reduces manual workload and allows faster responses to changing customer behaviour.

One of the most important applications of agentic AI is in supply chain management. Retail supply chains involve many moving parts, including suppliers, warehouses, transport networks and stores. Small delays or forecasting errors can quickly become costly.

Agentic AI systems can track these moving parts in real time. They can identify risks such as low stock, delayed shipments or sudden spikes in demand. In some cases, they can automatically reorder products or reroute logistics flows to reduce disruption.

This type of automation is particularly valuable for global retailers operating across multiple markets. It allows them to respond more quickly to local demand changes and reduce reliance on manual coordination between teams.

However, most retailers are still in early stages of adoption. Current use is typically limited to specific functions rather than full end-to-end autonomy. The focus is on improving accuracy and efficiency while keeping human oversight in place.

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Modern smartphones are often packed with camera lenses, referred to as a camera cluster (or bump). This is why the camera on your phone isn't flat: the lens needs space between the sensor. The more lenses, the more space needed.

But why in the world do we need multiple lenses in our smartphones to begin with?

Well, that's simple. Each lens performs a specific job, such as a main (wide) lens, an ultrawide lens, a telephoto lens, and/or a macro lens. Macro is handy for close-up shots, and telephoto helps capture objects from a distance while taking more of the landscape. You'll use the main lens for other kinds of shots, like portraits. Through software, these photos can be further polished or even combined to create shots through computational photography.

Essentially, smartphones offer a selection of cameras and lenses to give you, the user, the choice of how to take your photos. Your main lens will likely take in the most light for standard use. This is known as aperture, and it's measured on F-stops. The lower the F-stop, the more light your lens lets in, so make sure to check which aperture each lens in your camera phone offers.

Generally, ultrawide lenses offer a slightly higher aperture as less light is taken in to afford a much higher depth of field, so you can shoot large landscapes. As for macro, many manufacturers use the ultrawide to capture macro shots as it can also focus on subjects from a short distance. Camera bumps exist to give us options so our phones can handle any shooting situation.

One of the main features to consider when purchasing a smartphone is the type of camera lenses it offers. Typically, smartphones offer three lenses in the back, though some camera clusters that once offered a dedicated macro lens have switched to a zoomed ultrawide for a similar effect. You can see this on devices from players like OnePlus, which uniquely uses a monochrome lens in its cluster.

Competitors like Samsung and Google also pack an ultrawide that can zoom for macro instead of a dedicated macro lens, ensuring to offer a choice for the best budget and expensive phones for photographers, no matter your brand or shooting preference.

A smartphone's camera quality is seen as a major selling point, and rightfully so, given that 91 percent of phone users regularly use their smartphones for pictures. Cameras are a very commonly used feature.

For comparison, only 80 percent of smartphone users actually use their phones to make calls. So, not only are the cameras built into our phones a normalized feature these days, but they are more important than these gadgets original use. How good these cameras are can easily make or break a phone; that's simply how important the tech is.

So, if you ever wondered why just about every smartphone has a silly-looking camera cluster on the back, there's a very good reason for it. Consumers absolutely demand killer cameras in their smartphones, which is why Google, Apple, and Samsung have all been battling neck and neck for over a decade to offer the very best and most popular smartphone cameras to the public.

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