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Received — 19 July 2026 ⏭

Next UK Prime Minister Drops Digital ID Scheme

✇Slashdot
By: EditorDavid
18 July 2026 at 23:17
Reuters reports: Incoming British prime minister Andy Burnham will scrap the government's troubled plans for a digital ID scheme when he enters office on Monday, a spokesperson for the new Labour Party leader said. Resources devoted to the scheme, deemed a "fiasco" by a cross-party committee of lawmakers, will be redirected to Burnham's priorities, the spokesperson said... "All the time and resource that was going to be spent on a national ID scheme will go instead to where it's most needed, such as helping with the cost of living," Burnham's spokesperson said. In November, the Office for Budget Responsibility watchdog estimated the cost of the digital ID scheme at around £1.8 billion ($2.4 billion) between financial years 2026/27 and 2028/29.

Read more of this story at Slashdot.

Apple Intelligence On-Device Is Winning the AI Race

✇AppleMagazine
By: Ivan Castilho
18 July 2026 at 20:45

Apple Intelligence on-device processing remains Apple’s most convincing argument in artificial intelligence. Rivals may lead in public model rankings, chatbot adoption and the speed of releasing experimental features, but Apple is building AI inside devices people already use for their most personal work.

That distinction changes the definition of winning.

Apple does not need every iPhone owner to open a separate chatbot app. It can place intelligence inside Messages, Mail, Photos, Notes, Shortcuts, Siri and third-party apps. Local models can operate on private information without automatically transferring that information to an external server.

The strategy appeared slow while the AI market measured progress through chatbot launches and model size. It looks more competitive as the industry confronts cloud costs, privacy concerns, unreliable connectivity and the difficulty of turning general-purpose AI into useful daily actions.

In his video “Apple Lost the AI Race,” Marques Brownlee captures the contradiction in one concise line:

“Apple lost the AI race. Actually, Apple won the AI race.”

The apparent reversal describes Apple’s current position well. Apple may have lost the first race for public perception. It is increasingly prepared for the longer contest over where AI runs, how it reaches users and who controls the personal data required to make it useful.

Apple Intelligence On-Device Changes the AI Relationship

Most popular generative AI products began as cloud services. A user sends a request to a remote model, the provider processes it in a data center and the answer returns through an app or browser.

That structure supports enormous models but creates dependence on internet access, server capacity and continuing infrastructure expense. It also means personal prompts leave the device, even when the provider uses strong security and privacy policies.

Apple Intelligence on-device begins from the opposite direction. The iPhone, iPad or Mac first attempts to handle the request locally using Apple Foundation Models optimized for Apple silicon.

That local model does not need to know everything available on the internet. It needs to understand the user’s request, work with relevant personal context and complete common tasks efficiently.

The difference is similar to owning a capable computer rather than renting every calculation from a distant server. Cloud systems remain useful for difficult workloads, but they no longer need to participate in every interaction.

For Apple, local processing is also a product advantage. The company controls the chip, operating system, model architecture, memory system and apps. Each layer can be designed around the others rather than assembled from unrelated services.

A glowing, abstract, multicolored loop forms a star-like shape on a black background, blending orange, yellow, pink, purple, and blue hues with a soft neon effect—perfect as an AI iPhone or iPhone 20 wallpaper.
Image Credit: Apple Inc.

Privacy Becomes a Technical Feature

Technology companies frequently describe privacy as a policy. Apple is trying to make it part of the AI architecture.

When a request runs entirely on an iPhone, the associated data does not need to leave the device. A model can work with messages, appointments, photos or app content without uploading that personal context to a conventional cloud account.

That structure is especially relevant to Siri AI. A truly personal assistant needs access to information that users may never place willingly inside an ordinary chatbot: private conversations, travel plans, health-related details, work documents and relationships between people.

An assistant becomes more useful as it gains context. It also becomes more invasive when the same context is stored, analyzed or used for model training outside the device.

Apple Intelligence on-device allows Apple to expand personal context while maintaining a stronger boundary around it. The assistant can understand information locally, perform an action and discard temporary processing data without building a permanent advertising or training profile.

This is difficult for competitors whose businesses depend on cloud services, large-scale data collection or engagement inside separate AI products. Apple’s hardware business gives it different incentives.

Private Cloud Compute Extends the Model

Not every AI task can fit within the power and memory limits of an iPhone. Apple uses Private Cloud Compute when a request requires a larger model.

Private Cloud Compute is designed to extend the privacy architecture of Apple devices into the cloud. User information is processed only for the request, is not stored and is not made available to Apple for unrelated purposes.

Independent researchers can inspect software images used by the system, creating an unusual level of outside visibility into the cloud environment. Apple has also expanded the architecture while maintaining control over the software and privacy protections used for processing.

This creates a layered system rather than a simple choice between local and cloud AI.

Apple Intelligence first determines whether the device can handle the request. More complex work can move to Private Cloud Compute. External models may be offered separately when the user chooses to involve them.

The user does not need to understand the technical path behind every answer. The system chooses the smallest and most private environment capable of completing the task.

A glowing blue cloud icon is superimposed on a digital circuit board background, symbolizing private cloud computing and technology. Orange lines and nodes indicate data flow, creating a dynamic and futuristic network visualization that elevates your data management strategy.

Apple Silicon Was an AI Investment

Apple’s control of its processors has become one of its largest advantages in the AI era.

The Neural Engine first appeared years before generative AI became a consumer obsession. It supported Face ID, photography, speech recognition and other machine-learning tasks. Apple continued expanding that hardware across the iPhone, iPad and Mac.

Those investments now provide a large installed base of devices designed to run machine-learning workloads locally. Recent Apple chips combine CPU, GPU, Neural Engine and unified memory in one architecture that can move information efficiently between different types of processing.

The same design helps foundation models run without requiring the power consumption or separate memory systems associated with conventional data-center hardware.

Apple also controls the operating systems that schedule those workloads. A model can be optimized for the exact processors, memory limits and energy behavior of supported devices.

Competitors can build excellent models, but they often need to support hardware from several manufacturers with inconsistent capabilities. Apple can decide that an AI feature requires a specific generation of silicon and design the complete experience around it.

Developers Gain a Local Model

The Foundation Models framework extends Apple Intelligence on-device beyond Apple’s own apps. Developers can use the system model through native Swift tools rather than shipping a separate large model or paying for every cloud request.

An app can generate structured content, interpret natural language or use tool calling while keeping supported processing on the device. Apple handles model delivery and system integration.

This can reduce operating costs for developers. A cloud-based AI feature creates an ongoing expense each time a user submits a request. Local inference uses hardware the customer already owns.

It can also improve reliability. Features may remain available without an internet connection and respond without waiting for a remote service.

The framework encourages a quieter form of AI adoption. Apps do not need to add a prominent chatbot tab or redesign themselves around a conversational interface. Intelligence can appear inside existing workflows as a useful capability.

That is where Apple’s strategy may reach scale. Millions of apps can incorporate small AI functions that users experience as normal software behavior rather than as visits to an AI destination.

A rounded square icon with a stylized neural network diagram in the center, featuring interconnected nodes connected by lines, represents Hidden Apple Intelligence on a light gray background.
Image Credit: Apple Inc.

The Installed Base Is Apple’s Distribution Advantage

The AI race is often discussed as a contest between models. Consumer adoption also depends on distribution.

Apple can deliver new intelligence features through operating-system updates across the iPhone, iPad, Mac and Vision Pro. Users do not need to create another account, choose a provider or transfer their personal information into a new service.

Siri already occupies the system interface. Apple Intelligence can work across first-party apps. Developers can connect their own features through Apple frameworks.

The scale is difficult to reproduce. A model provider may have stronger benchmark scores but still need users to download an app and consciously open it. Apple can introduce AI during actions people already perform: replying to a message, locating a photo, organizing a note or completing a shortcut.

This does not guarantee success. Apple must make Siri AI reliable, keep feature availability consistent and prove that local models can deliver enough capability to satisfy users accustomed to powerful cloud assistants.

The distribution advantage only becomes meaningful when the experience works.

Winning Without Building the Loudest Chatbot

Apple has made mistakes during its AI transition. Siri improvements arrived later than promised, regional regulations delayed features and competitors established powerful consumer brands before Apple completed its new system.

Those setbacks explain why the claim that Apple lost the AI race became persuasive.

They do not settle the outcome.

The first stage rewarded companies that released chatbots quickly and demonstrated increasingly large models. The next stage may reward systems that can integrate AI into ordinary life without demanding constant cloud access, high subscription costs or broad collection of personal information.

Apple Intelligence on-device is built for that stage. It uses hardware already in the user’s hand, protects more personal context locally and gives developers access to intelligence without requiring every interaction to become a server transaction.

Apple does not need to win every benchmark or release the most talkative assistant. It needs AI to become a reliable part of the devices people already trust.

Brownlee’s title works because both statements can be true. Apple lost the visible sprint that defined the early generative AI market. By concentrating on local processing, silicon, privacy and operating-system integration, it may already be positioning itself to win the race that follows.

The post Apple Intelligence On-Device Is Winning the AI Race appeared first on AppleMagazine.

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Apple Intelligence on-device processing gives Apple an AI advantage built around privacy, speed, personal context and hundreds of millions of devices.

SpaceX Eager to Support the Pentagon’s Lethal AI Models

✇Futurism
By: Frank Landymore
17 July 2026 at 22:50

After years of being engaged, it looks like SpaceX and the Pentagon are finally ready to send out save-the-dates.

On Friday, the Wall Street Journal reported that Elon Musk’s formerly-just-rockets company is now in talks with the Department of Defense over providing the military access to its data-center capacity for running AI models, tying the knot on their already close relationship.

People familiar with the matter told the newspaper that the arrangement could cost the Pentagon billions of dollars, but stressed the talks are ongoing and could fall apart.

Computing power is a precious commodity during this ever-raging AI boom, and SpaceX has it in spades. Musk has rapidly built data centers in Memphis, Tennessee and a lot of that capacity hasn’t been tapped into, with Bloomberg reporting that his AI startup xAI is only using 11 percent of its total computing power.

Selling this excess compute would help generate the company some much needed revenue, even if it directly benefits its rivals. SpaceX has struggled to be profitable, losing $5 billion last year. And xAI, which was folded into SpaceX before its IPO last month, burned through $6.4 billion over the same period. All the while, Musk has struggled to sell his Grok chatbot to businesses, as its capabilities lag behind leaders like Anthropic and ChatGPT.

The real story, though, is the deepening ties between Musk’s business empire and the Pentagon, which goes way back. SpaceX has worked with the military to deploy a secretive network of spy satellites, for example, and also had a small contract to experiment with building a rocket that could deliver military cargo across the globe.

Those collaborations have gotten way bigger in the past year. In May, it won a $2.29 billion Space Force contract to build a network of satellites that could function as a military internet service, connecting weapon systems across the globe. That same month, it was awarded an even larger $4.16 billion contract to provide the technology to build a system that can track missiles and aircraft from orbit.

To be clear, Musk’s companies are far from the only tech leaders to be getting in bed with the Pentagon. Amazon, Google, Microsoft and Oracle all have similar deals for providing cloud computing capacity to the military, as well as for providing their AI models, the WSJ noted.

But it’s a particularly hypocritical road to be going down for Musk. When Musk cut off Starlink satellite access to the Ukrainian forces in 2023, he reasoned that he didn’t want SpaceX to be “explicitly complicit in a major act of war and conflict escalation.” Fast forward to this June, when a Pentagon official bragged that the military used Grok to fire 2,000 missiles at Iran during Operation Epic Fury, sparking a war that killed thousands of civilians in the country.

More on SpaceX: SpaceX’s Shares Tank After Starship Fails to Launch

The post SpaceX Eager to Support the Pentagon’s Lethal AI Models appeared first on Futurism.

Why More Businesses Are Choosing Cheaper Chinese AI Models

✇eWeek
By: Madeline Clarke
17 July 2026 at 22:03
Chinese AI models are gaining business interest as companies weigh lower costs, open-weight flexibility, performance trade-offs, and enterprise risks.
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