President Donald Trump has just signed a new executive order last 2 June that asks artificial intelligence companies to share advanced AI models with the federal government before public release. The move marks the White House’s latest effort to tighten national security coordination around rapidly advancing AI systems without introducing formal licensing requirements.

The order creates a voluntary framework for AI developers to work with federal agencies on evaluating models with advanced cyber capabilities. Under the proposal, companies could provide government officials with access to certain frontier AI models up to 30 days before wider deployment.

Trump signed the order privately after delaying a planned public event with technology executives several weeks ago. At the time, he told reporters he disliked parts of the original proposal.

The White House framed the directive as a balance between accelerating AI innovation and protecting critical infrastructure from cyber threats. Administration officials repeatedly stressed that the order does not establish mandatory government approval for AI releases.

The executive order directs multiple federal agencies to strengthen cyber defenses within 30 days. The Department of Homeland Security (DHS), through the Cybersecurity and Infrastructure Security Agency (CISA), must issue new operational guidance to protect federal networks and critical infrastructure systems.

The administration also ordered agencies to expand AI-driven cybersecurity programs and improve access to defensive tools for state governments, utilities, hospitals, and community banks.

Another section creates a voluntary AI cybersecurity clearinghouse led by the Treasury Department. The initiative will coordinate vulnerability scanning, software patching, and threat detection efforts alongside private industry partners.

Trump’s order repeatedly positions AI as both a national security asset and a growing cyber risk. The administration argues that advanced AI tools could help defend government systems while also creating new attack surfaces for adversaries.

A central part of the order focuses on so-called "covered frontier models," which refer to advanced AI systems with significant cyber capabilities. Federal agencies, including the National Security Agency (NSA) and the National Institute of Standards and Technology (NIST), must develop a classified benchmarking process within 60 days. That process will assess whether a model meets the threshold for additional government review.

Companies could voluntarily ask the government to evaluate models still under development. Developers may also grant federal agencies early access to those systems before releasing them to outside partners. The order additionally allows the government to collaborate with AI companies on selecting "trusted partners" that receive early model access. However, the White House included language aimed at calming industry concerns over federal overreach.

"Nothing in this section shall be construed to authorize the creation of a mandatory governmental licensing, preclearance, or permitting requirement," the order states. That provision reflects ongoing tension between Silicon Valley and Washington over how aggressively the government should regulate frontier AI systems.

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If you’ve ever been worried about having your iPhone ripped from your hands while walking down the street, Apple is reportedly developing a new feature that will limit the damage that would-be thieves can do once they have your phone.

Reliable sources from 9to5Mac reports that Apple is working on an anti-snatching feature that would lock your iPhone if it detects it’s been stolen, preventing thieves from gaining access to valuable data and personal information.

In a lot of cases, snatchers will run or bike away quickly once they’ve grabbed a phone. If the report is accurate, the new feature would use signals from your phone’s accelerometer and the distance from a paired Apple Watch to decide if your iPhone has been snatched.

Beyond just locking the phone, the feature would work according to the same rules as Stolen Device Protection, restricting access to certain areas of the phone if it detects it’s in an unfamiliar location or connected to an unfamiliar Wi-Fi network.

Apple already provides a pretty robust set of features to protect stolen iPhones and the data on them, but this feature would be the first attempt to automatically detect if one has been physically snatched from you and lock it down. There’s no word on when the feature might be announced or launched, but WWDC 2026 is right around the corner on 8 June 2026. It’s possible it could debut there as part of Apple’s iOS 27 plans.

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Modern computers are often thought of as operating artificial brains. That said, they’re nowhere close to being as complex or energy-efficient as human brains. AI consumes an enormous amount of electricity, and it’s constantly demanding more as it keeps endlessly morphing and advancing.

How can our power supply catch up to a future of neural networks and digitized intelligence? Well, maybe the answer lies in merging living brain cells with a programmable electronic system.

Previous attempts to use actual neurons as the brain of a computer have run into problems. 2D neural cultures—in which the flattened neurons showed abnormal interactions and gene expression — couldn’t survive for long, and these structures were ultimately unable to replicate the connections and activity that occur in vivo.

More advanced in vitro neural networks have tried to compensate for some of those problems by mirroring the structure and function of the brain with organoids. Despite some improvements, brain organoids (clumps of stem cells engineered to turn into neurons) are inconsistent and prone to both hypoxia and necrosis.

Alternative 3D neural networks known as biological neural networks (BNNs) could still be a viable option. Such a system would ideally take the form of an in vitro model that reconstructs the brain’s networks, can be reproduced, and actually lasts. It would also feature both dense and sparse neural connections (not unlike those in the hippocampus) to prevent too much data from moving around at once.

In an effort to create a fusion of biology and machinery, researchers Tian-Ming Fu, James Sturm, and Kumar Mritunjay from Princeton University used electrodes and microscopic metal wires to create a 3D polymer mesh scaffold flexible enough for tens of thousands of living neurons to grow into a network that could operate with minimal energy.

"Understanding the brain’s network structures and working principles could help in the development of general-purpose computing with improved data and energy efficiencies, as well as provide insights into the brain’s physiology and pathology," the researchers said in a study recently published in Nature Electronics.

Fu, Sturm, and Mritunjay began this experiment to gain more insight into other lingering questions about brain function, but soon saw its potential as a biological neural network, and 3D-MIND (3D Micro-Instrumented Neural network Device) was born. Taking inspiration from origami, the researchers initially created the device in two dimensions, embedding precisely enough electronic sensors to match the soma of a neuron before folding it into 3D layers. Neurons were then integrated into the system. While this hasn’t been done with human neurons yet, rat neurons from the hippocampus—which is critical for learning and memory — were extracted and cultured on the scaffold.

Finally, the entire device was covered in a thin gel coating. Protective and practical, the coating contained proteins that would provide extra support for neurons in forming strong connections with glial cells—cells that not only hold these neural structures together, but supply nutrients, perform immune functions, regulate chemistry, produce the fatty insulation for axons known as myelin, and keep the surrounding environment clear for signaling.

Eventually, the researchers observed neurons positioning themselves and forming connections in three dimensions throughout the structure. These neurons were also stable enough to be tracked for extended periods of time, and the team managed to record growth, development, and action potentials—electrical impulses that neurons use to communicate.

The researchers admit that it will be challenging to scale this system up, but it’s definitely promising, especially when compared to current energy-guzzling AI networks.

"The interfaced system can then provide a physiologically relevant understanding of the brain’s 3D network connectivity," the team wrote. "[It has] the ability to track a 3D neural network [and] could be of use in understanding the efficiency and versatility of the brain’s computational capabilities."

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There are numerous benefits of making payments with Apple Pay. Along with making the process of checking out quick and easy, the tool is also a fairly secure payment option. However, even if you already use this form of payment, you might not be leveraging it to its full potential. There are numerous Apple Pay features you might not be using simply because you don't know about them.

Keep in mind that using Apple Pay doesn't just save you time. It can also help you keep your funds and financial information safe in various circumstances. For instance, by using Apple Pay's tap to pay capabilities, you can avoid accidentally sharing your information with gas station pump card skimmers.

That's merely one advantage of embracing this payment method. Just be aware that you'll get even more value from Apple Pay if you familiarize yourself with features you may not have used before.

Learning the status of an order you placed online can involve a few steps. You may need to access your account via the retailer's website or app, then navigate to your "recent orders" page to get the information you seek. Or, you might first have to track down an order confirmation email, then open the link or use the tracking info provided.

The process can be much simpler if you placed an order with Apple Pay. All you have to do is open Apple Wallet and tap on the three dots at the top of the screen representing the "More" option. From there, tap Orders. You should then see a list of qualifying orders you've made using Apple Pay. You can tap an order to get more details about its status. You may also have the option to tap Manage Order, which will bring you to the merchant's website, or you can tap Email This Merchant to get in touch with the retailer.

Be aware that factors like your device and operating system can affect whether certain orders get tracked. Sometimes, Apple Pay tracks orders automatically. In other instances, an order confirmation screen will feature an option to "Track with Apple Wallet." You'll have to choose this option for the order to be added to those you're tracking.

Apple Pay and Apple Wallet aren't just useful as ways of making payments. Apple Wallet and Apple Pay can also store important documents like digital versions of event tickets, boarding passes, and similar documents. How you add these passes will vary depending on their original source. For example, when you purchase an airline ticket via an app or website, once you reach the confirmation screen, you may find an option to add the pass to your Apple Wallet.

While you might already know about this feature, there are some ways to optimize it that users might overlook. For example, on an iPhone, you can go to Settings, then tap iCloud. Next, you can turn on Wallet. This will ensure all passes stored in your Apple Wallet stay updated across your devices.

In addition, via the Settings app on iPhone, you can select either Face ID and Passcode or Touch ID and Passcode. You'll be prompted to enter your passcode. Upon doing so, below where it says Allow Access When Locked, you can turn on Apple Wallet. Doing so allows you to access passes stored in Apple Wallet even when the device is otherwise locked.

These days, it's even possible to store your car keys in Apple Wallet. Naturally, of course, not every vehicle manufacturer offers a digital version of a key that can be stored on a mobile device. You may need to contact your car's manufacturer to find out if this option is available to you.

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Artificial intelligence or AI is already reshaping parts of the commercial aviation industry, and in the coming years, airlines are expected to expand AI systems across operations, pricing, customer service, and flight management as they look to cut costs, increase margins, and improve reliability, efficiency, and performance.

First, modern commercial aircraft generate enormous amounts of operational data through onboard sensors that monitor critical systems. Already, airlines are increasingly using AI systems to analyze that data and identify patterns associated with wear and tear or mechanical failure.

AI could help airlines shift maintenance away from reactive repairs—which usually cause ground delays because they are discovered too late—and toward predictive maintenance. We’ve all been there: Everyone boards the flight and things seem all set to go, only to have the captain come over the intercom and announce that there’s maintenance that needs to be done.

Though maintenance will still need to occur, being able to predict it better in advance could help reduce last-minute delays, which tend to be the most frustrating.

Also, many AI-driven changes on this list could benefit the passenger. However, the outlook remains uncertain when it comes to airlines using AI in its ticket pricing methods.

Airline ticket pricing has long relied on algorithms that result in different prices at different times. But, AI is expected to make pricing systems substantially more individualized, incorporating factors such as booking history, travel timing, route demand, loyalty status, and purchasing behavior to tailor fares to specific travelers.

That could mean two passengers searching for the same flight and the same time receive different ticket prices based on what the airline thinks each will pay, based on the aforementioned criteria, as well as the likelihood of purchasing upgrades, checked bags, or premium seating.

While it is easy to see how such a pricing model could benefit the airlines and their profit margins, the concept of personalized pricing raises concerns about transparency and fairness, particularly if airlines don’t reveal the exact data they are collecting and using to determine prices.

For example, if flight purchase history shows you travel at the end of every month to the same place for work (American Airlines, for example, now makes you select Business or Personal travel when booking), can they increase the price, knowing you have to go?

Lastly, Flight cancellations, weather delays, missed connections, and maintenance disruptions generate enormous customer-service demand, and we all know how that can go: Long lines at customer service desks and tortuous time listening to on-hold music on the phone.

AI might be able to help in some cases. Instead of waiting on hold for a call center representative, passengers may increasingly interact with AI assistants capable of automatically rebooking itineraries, issuing hotel or meal vouchers, translating requests across multiple languages, providing real-time updates during irregular hours, etc. The technology could prove particularly valuable during severe weather events, when airline support systems often become overwhelmed.

Faster rebooking and more proactive communication may help reduce some of the frustration traditionally associated with large-scale travel disruptions, although the reduction in human-to-human contact could create its own frustrations, as anyone who has ever called a 1-800 number and had trouble getting through to a human can understand.

Fuel remains one of the airline industry’s largest operating expenses, often accounting for roughly a quarter or more of total costs. That financial pressure, combined with growing scrutiny over aviation emissions, has made fuel efficiency a major area of AI investment.

Airlines are increasingly using AI to optimize flight routing, cruising altitudes, taxi operations, descent procedures, and aircraft loading to make small efficiency gains that could have significant effects across large fleets operating thousands of flights daily.

In theory, that could save the airlines money and perhaps reduce ticket prices, though it’s fair for consumers to be skeptical of the latter.

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The economics of space operations are unforgiving, and Canadian startup Orbit Robotics just unveiled HELIOS — a four-armed humanoid robot that ditches legs entirely for microgravity operations. This isn’t your typical terrestrial humanoid; it’s purpose-built for environments where walking becomes irrelevant, and efficiency matters most.

Cable-driven architecture prioritizes dexterity over terrestrial walking capabilities.

While Earth-bound humanoids like Boston Dynamics’ Atlas perfect their parkour routines, HELIOS takes a fundamentally different approach. You won’t find heavy torque motors or rigid actuators here. Instead, Orbit reportedly built a lightweight skeletal chassis powered by tendon-driven cables and pulleys—like a marionette designed by aerospace engineers.

The four-arm configuration isn’t just showing off. In microgravity, movement relies on grabbing handholds and surfaces rather than walking. Those extra limbs theoretically mean HELIOS can anchor itself with two arms while manipulating cargo or equipment with the other two. Motors supposedly sit near the shoulders to reduce moving mass in the limbs, while rolling-contact elbow joints promise smooth motion without the backlash that plagues traditional hinged systems.

This design philosophy directly challenges the terrestrial humanoid playbook. Companies like Agility Robotics and Sanctuary AI optimize for warehouse floors and factory lines. HELIOS optimizes for floating through space stations, where your greatest asset isn’t balance—it’s the ability to multitask while staying put.

Maintenance duties and cargo handling represent immediate deployment opportunities.

Orbit isn’t building a robot astronaut — they’re building a robotic assistant targeting specific operational inefficiencies. The company claims current crews spend significant time on maintenance tasks that could potentially be automated.

The company’s IKARUS testbed allegedly demonstrates teleoperation and imitation learning capabilities, suggesting HELIOS might learn tasks by watching human operators rather than requiring complex programming. Think of it as motion capture for space work—astronauts demonstrate procedures once, and the robot handles routine repetitions.

Industry observers suggest that humanoid platforms make sense for human-designed environments. When your workplace has racks, handrails, and hatches built for human bodies, a human-shaped robot offers more flexibility than specialized arms like the ISS’s Canadarm2 or Dextre.

The real test isn’t whether four arms work better than two — it’s whether this approach can deliver meaningful cost savings before the next generation of space stations comes online. If orbital operations become as routine as promised, even modest automation could pay for itself quickly in space’s unforgiving economics with significant benefits for workplace safety.

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