Apple is popularly known as a company that prioritizes privacy, and that could be a major pillar of its revamped Siri experience launching with iOS 27 later this year. According to a new report from Bloomberg's Mark Gurman, Apple is planning a feature for the new Siri app that would auto-delete chats after a set period of time, mimicking a feature in the company's Messages app.

The report notes that you'll be able to adjust the settings for the new Siri app to keep chats for 30 days, one year or forever. Similar settings are already available for iMessage chats, reinforcing Apple's focus on making chatting with Siri similar to the experience its users already have with the Messages app.

That's slightly different from how chatbots from other companies work, requiring you to manually delete chats or affirmatively start a chat in an incognito mode that cuts them off from storage and model training.

Gurman also says that the new Siri experience could still launch with a beta label when it ships with iOS 27 later this year. That's reminiscent of Siri's original launch in 2011, which was tagged as beta to signify that the experience would continue to evolve. It's notable this time, however, as the experience has already been delayed for two years since Apple first announced an AI overhaul for Siri's capabilities at WWDC 2024.

Finally, Apple may be looking to boost Genmoji usage with iOS 27. Gurman notes that the company is planning to add a "suggested Genmoji" option on the iOS keyboard. The experience would create suggested Genmoji "created from your photos and your commonly typed phrases," according to the description attached to a new toggle in the iOS 27 keyboard settings.

All of these updates are expected to be revealed first at WWDC 2026, Apple's annual developer conference kicking off on June 8. That's before iOS 27 is expected to launch to customers with this year's new iPhones this fall.

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Apple is planning to launch a bill-splitting feature in iOS 27, and may introduce the feature as soon as next week at its Worldwide Developers Conference, Bloomberg's Mark Gurman reports.

Users will be able to take a photo of a receipt, assign items on it to other people and generate requests for payment, according to the report. The payment service will be linked to the company's peer-to-peer payment system Apple Cash, and will be available via its Wallet app and within Messages.

Payment-splitting will likely compete with peer-to-peer payment apps like Venmo and Cash App, and marks Apple's latest foray into financial services, which have been something of a mixed bag for the company.

While its Apple Wallet and Apple Pay features continue to grow in popularity, its Apple Card credit card, which launched in 2019, was not a strong performer for its banking partner Goldman Sachs.

Earlier this year, Goldman reached a deal with JPMorgan Chase to take over its Apple Card business. And Apple's venture into the buy now, pay later marketplace was short-lived, with the company discontinuing its Apple Pay Later program after less than a year.

Apple will be holding WWDC on 8 June, where it's expected to unveil iOS 27, Siri updates, and more.

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Most leaders believe their teams are using AI as a tool. The research suggests something more consequential is happening. Workers are not just using AI to work faster. They are letting it decide, and in doing so, quietly ceding the human reasoning that determines whether those decisions are any good.

A January 2026 paper from the Wharton School introduced a term for what is now documented and measurable. Researchers Steven Shaw and Gideon Nave call it cognitive surrender.

They define it as adopting AI outputs with minimal scrutiny, thereby overriding both intuition and deliberation. Their framework extends Nobel Prize-winning psychologist Daniel Kahneman's model of fast intuitive thinking and slow deliberate thinking by introducing a third system: artificial cognition that operates entirely outside the brain.

That third system, they argue, can supplement or supplant human reasoning. When it supplants it, AI stops being a thinking partner and starts being the decision-maker.

The performance implications are direct. When workers in the study consulted an AI that was correct, their accuracy rose significantly above what they achieved on their own. When the AI was wrong, their accuracy fell well below the baseline of people who had no AI access at all. The problem is that workers had no reliable way to detect the difference. They accepted incorrect AI answers 80 percent of the time. Their confidence rose either way, whether the AI had helped them or led them astray.

This dynamic is particularly consequential with the large language models now embedded in most workplace tools. LLMs do not retrieve facts. They generate plausible-sounding responses based on patterns in training data, without access to an organization's specific context, strategy, institutional knowledge, or the domain expertise of the person using them. They do not flag uncertainty. They speak with consistent confidence regardless of accuracy.

Getting strong output from an LLM requires a skilled human on the other end: one who validates what it produces, identifies what it missed, expands the ideation beyond the initial response, and applies judgment to decide. Cognitive surrender eliminates every one of those steps.

A Microsoft Research study published in April 2025 found that confidence in AI was among the strongest predictors of whether knowledge workers engaged in critical thinking at all. The higher the trust in the tool, the less scrutiny is applied to what it returned.

As researchers noted in that same study, there is a fundamental irony at the center of automation: when routine cognitive tasks are mechanized and handed to an external system, the human is deprived of the routine practice that builds and sustains judgment. The reps disappear. And so, over time, does the muscle.

A McKinsey "State of Organizations 2026" report, published in February, found that only 23 percent of organizations qualify as AI Pioneers, those actively deploying AI across most departments and functions with a clear understanding of how it will reshape their work. The vast majority are still experimenting, running isolated pilots, or deploying AI in piecemeal ways that have yet to generate measurable enterprise-wide impact.

That gap is the opportunity. Cognitive surrender is not yet the organizational norm. Supplementing human reasoning rather than supplanting it is still a choice leaders can architect into how AI gets deployed. The window to make that choice intentionally, before passive AI reliance becomes the default operating culture, is shorter than most leaders assume.

The structural interventions the research points to are specific. Leaders who build these practices into how AI gets deployed now safeguard their organizations from a culture where AI supplants human reasoning rather than supports it, and from the performance costs that follow.

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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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