CONSUMER

Why iPhone photography is Apple's AI blueprint

Nothing has transformed iPhone photography more over the past decade than machine learning and artificial intelligence. 

The combined power of software and Apple silicon has enabled tiny photography lenses and minuscule camera sensors to take photos that defy the laws of physics. While early iPhone cameras were good for wide-angle shots of nearby subjects in full daylight, over the years Apple has advanced the cameras to take more and more shots traditionally reserved for professional DSLRs and mirrorless cameras: from low-light night shots to zoom photography to macros to full-bokeh portraits. And nearly all of it is due to ML and AI. 

In fact, iPhone photography is arguably Apple's greatest AI success story, and it's a clue for how Apple will likely continue to implement AI in the future: by quietly making features you use every day a lot better. 

As we prepare for the unveiling of the next iPhone this week, here's a quick recap of the AI and ML features that have launched on the iPhone throughout the years:

• 2016: iPhone 7 Plus, iOS 10, Photos app - Portrait Mode with ML-powered subject separation; AI-powered people, object, and scene recognition as Memories in the Photos app

• 2017: iPhone X (2017) - Portrait Lighting, ML-powered front-camera Portrait Mode

• 2018: iPhone XS (2018) - Smart HDR, ML depth segmentation, adjustable Depth Control

• 2019: iPhone 11 (2019) - Night mode, Deep Fusion, ML-powered Smart HDR

• 2020: iPhone 12 Pro (2020) - Smart HDR 3, expanded Deep Fusion and Night mode

• 2021: iPhone 13, iOS 15, Photos app (2021) - Photographic Styles, Smart HDR 4, Cinematic mode, Live Text, Visual Look Up

• 2022: iPhone 14, iOS 16, Photos app (2022) - Photonic Engine, subject lifting from photos

• 2023: iPhone 15 (2023) - Automatic portrait detection, next-gen Smart HDR

• 2024: iPhone 16, iOS 18, Photos app (2024) - Next-generation Photographic Styles, Clean Up, natural-language search, create your own movies in Memories from prompts

• 2025: iPhone 17 - AI-powered Center Stage to automatically reframe group selfies, updated Photonic Engine that uses ML to improve detail, noise, and color

It's important to note that Apple approaches AI and ML from a different lens than competitors like Samsung, Google, and Chinese manufacturers like Huawei. While competitors use generative AI more broadly to add things to an image, such as adding yourself to a group shot, putting a different sky in the image, or making a partially eaten cupcake look uneaten, Apple is more of a purist when it comes to what is or isn't in the photo. 

Apple's VP of camera software engineering, Jon McCormack, has stated in multiple interviews over the years that Apple sees a photograph as a celebration of a moment that happened, and that it deserves our respect as such. 

This year, in iOS 27 with Spatial Reframing, which lets you shift the perspective that a photo was taken from, and Extend, which can use generative AI to expand the content on the edges of a photo, Apple is pushing the boundaries of that definition. Also, the upgraded version of Clean Up lets you remove larger objects, using AI-generated capabilities to fill in the scene. Apple still views all of these features as coherent with the idea of enhancing the shot you took, versus remixing it to create something that might be more akin to a work of art than a photograph.

Apple could still learn from some of its Android rivals on a few AI and ML features. Some basic AI photo-editing features could align with Apple's view of preserving the integrity of the shot you took while enhancing it to focus on the elements most important to you (similar to Clean Up). For example, I recently took a 10x zoom of a mountain lion at the zoo and then uploaded it to ChatGPT and asked it to "remove the fence in the foreground," which was a major distraction. The result was terrific, and I could see parents wanting to do something similar when taking photos of their kids at a soccer or baseball game and having to shoot a photo through a fence. That's the kind of super-smart AI feature that doesn't have to make a big deal about being AI but can simply make things better. Apple's flagship AI feature of 2026, the new Siri AI, is important. But it's also just keeping pace with the industry. Where Apple can be a leader in AI is by doing more things like what it has done in photography: using AI to make great features and experiences that don't need to shout from the rooftops that they're AI.

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HARDWARE

Arm wants to make robots speak one language

Arm is looking to grow its foothold in AI's next frontier: physical AI. 

On Monday at Arm Everywhere China, the company's flagship conference, Arm announced several expansions to its physical AI ecosystem, with the goals of reducing fragmentation in the robotics industry and lowering the barriers to adoption to make the tech easier to scale. 

In a briefing with the press, Drew Henry, executive vice president of Arm's physical AI unit, said that while the current market sits at roughly $25 billion, "we view [it] as growing and becoming one of the largest [total addressable markets] in the history of computing as this market shifts over time." 

Arm announced two new initiatives to plant its flag in the ground on robotics:

• The company is expanding Arm Total Design, its ecosystem initiative aimed at simplifying chip development, into physical AI. The expansion encompasses more than 80 companies in AI hardware, software, sensors, and robotics, including firms like AWS, Hugging Face, and Unitree. The goal is to enable more streamlined development of physical AI and reduce complexity. 

• As part of this expansion, Arm unveiled the Robotics Capability Framework, inviting industry experts from across the field to help create a standardized vocabulary around robotics and define "levels of increasing sophistication for robotic systems." This is comparable to the self-driving industry's levels of automation that range from Level 0 to Level 5. 

Arm targeting the robotics industry isn't random. Henry said the company has already carved a place in the market and has shipped two billion units into physical AI use cases in the past 12 months, ranging from microcontrollers and sensors all the way up to compute platforms for autonomous vehicles and robotics. 

"We've been in this marketplace for a very long time, but this market is poised now as AI embeds itself into physical devices, to get some really exponential growth," said Henry. 

It's also not the first time the physical AI industry has called for broader coordination. Luma, a video AI and world model startup aimed at creating "multimodal AGI," unveiled the Open Physical AI Lab in June, a collaborative initiative to solve generalization by bringing together the best minds in robotics rather than siloing them in individual companies.

A lot of teams are betting on physical AI right now, with some speculating that the market could eventually overtake conventional AI and language models and may be the only path to the elusive concept of artificial general intelligence. With so much on the line, initiatives like this may be an attempt to prevent the dichotomy that currently exists between proprietary US-based AI models and open-source Chinese models from playing out in the physical AI realm. Especially when you consider that a large majority of physical AI hardware, particularly humanoid robots, is manufactured in China, the physical AI industry can't afford to be divided. Starting with something as simple as a shared vocabulary could be a first step to making the industry play nice. 

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GOVERNANCE

How agents supercharged the hacker playbook

Agents have turned AI from a tool into a digital coworker. Now, they're doing the same thing for hackers. 

On Tuesday, Google's Threat Intelligence Group released its third-quarter threat tracking report, revealing that AI-enabled cyberattacks have evolved from assistance to automation as agents become a growing part of the process. The report finds that "human-in-the-loop latency" has dramatically decreased, cutting the time it takes to carry out and defend against cyberattacks. 

According to the research, Google's threat team observed multiple instances of adversaries deploying multi-agent frameworks and autonomously carrying out parts of attacks, including scanning pipelines and harvesting credentials. In one instance, threat actors compromised a cloud, then planned, built and executed a mass-credential harvesting attack in just under six hours using agents. 

The attack marks a shift from "passive, endpoint-focused infostealers to offensive agentic harvesting," the report notes, as threat actors leverage autonomous AI to research vulnerabilities, scan infrastructure and perform exploits. 

"Like everyone else, we’re concerned about the vulnerability problem, but AI is being applied to several other areas, and it will be especially challenging as it is applied agentically, creating a scaled, faster adversary," John Hultquist, chief analyst of the Google Threat Intelligence Group, said in a statement. 

Agents aside, the report points to a number of concerning trends: 

• AI-coding tools and open-source software, while accelerating software development cycles, have also increased operational risks by widening the attack surface. 

• Adversaries are also targeting proprietary AI IP, including code, prompts, research and the models themselves. 

• AI is being used across the attack lifecycle, including targeting reconnaissance, social engineering, custom malware obfuscation and scaling information operation campaigns. 

• Bad actors are also stealing developer credentials, purchasing compromised AI accounts and breaking into cloud infrastructure to get around AI access costs.

Google's threat report cements into reality the thing that has the AI industry on edge in the wake of OpenAI's accidental breach of Hugging Face: autonomous, agent-driven cyberattacks are here. Though many fear what agents could do if they go rogue, Google's report paints a potentially more nerve-racking picture: bad actors are harnessing powerful AI tools to systematically do damage. This means that the approach to fighting these attacks has to be two-pronged. The obvious one is fighting fire with fire. Using AI agents to automatically detect and deflect cyberattacks is no longer novel, but a necessity. This, however, could be more effective when done in tandem with more creative means of defense, such as Cloudflare's recently announced tech that stalls cyberattacks by making attacks more expensive. What cyber defenders may need most is confidence that they have the tools and partners to defend against AI-enabled attacks, which is what CrowdStrike emphasized at its annual event last week.

LINKS

• Tesla Cybercab launches limited rides in Austin with 45 registered in Texas

• Ray Kurzweil joins startup for nanoparticle-based brain computer system

• Thinking Machines Lab in talks to raise $6 billion at $40 billion valuation

• Pentagon's Emil Michael contradicts Lutnick on Anthropic risk status

• Gimlet Labs raises $300 million dollars at a $3 billion valuation

• Mark Zuckerberg called Trump to oppose national AI regulator plans

• Humain-m3: a frontier Arabic language model, commissioned by HUMAIN

• Gemini 3.8 Flash: Google's best reasoning & coding model yet

• Google Workspaces: We’re bringing new voice capabilities

• Meta Muse Image: debuts at #4 on the Artificial Analysis Image Editing Leaderboard

• TEKsystems: AI Security Engineer

• Moring AI: AI Engineer - Forward Deployed (FDE)

• Flotive AI: Founding Software Engineer

• Freight Hero: AI Deployment Accelerator

GAMES

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