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Shrinking models and an on-device AI future

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In this week’s Sunday Reboot, Apple’s AI model-shrinking talk could have massive benefits, with a chance of also being a billion-dollar deal if it plays its cards right.

Sunday Reboot is a weekly column covering some of the lighter stories within the Apple reality distortion field from the past seven days. All to get the next week underway with a good first step.

Small AI models, big pricing

Back on July 9, a startup in the AI world gained a lot of attention. PrismML used the mythical power of mathematics and science to somehow cut down the size of large language models (LLMs) by a considerable amount.

The process resulted in models like the 54GB Qwen 3.6 being compressed down to an astoundingly tiny 4GB. That’s a 27 billion-parameter model being smushed down to a size that would fit on some promotional USB thumb drives from back in the day.

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While there was some suspicion that Apple was looking at the startup, it was confirmed by PrismML itself on July 14. In an interview, PrismML CEO Babak Hassibi said that Apple and other companies were evaluating the work of its technology.

Hassibi’s public confirmation was an interesting one, especially since Apple has a tendency to NDA everything it does with other companies. While it is unknown if Apple did the same with PrismML, Hassibi seems confident enough in the tech to ignore the possible ire from a potentially massive client.

I say “client” because this sort of important thing in the current AI-centric market warrants acquisition talks. It’s something that, if one company buys PrismML and makes the tech exclusive to it, the buyer then has a serious advantage over the rest of the industry.

An acquisition is entirely a possibility for Apple, based on the July 15 report about the reign of John Ternus is believed that Apple is showing signs of changing its acquisition tactics, from spending hundreds of millions on a single company to spending billions.

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It’s an extremely small change. What’s an extra decimal place between friends?

This sort of advantage could well be valued at the billion-dollar level to Apple, if it does acquire the startup. That said, it would also require the startup to be willing to be purchased, too.

Hassibi’s interview doesn’t really seem like someone willing to sell up. It feels more like he’s trying to get more attention from other AI firms to create a bidding war, if the company does want to be sold.

The alternative is that he’s trying to secure as many lucrative licensing agreements as possible from AI companies in general. All while remaining independent.

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Either route seems like a win for PrismML.

On-device advantage

For Apple, if the technology is sound, it has big benefits, specifically for its aim of increasing the amount of on-device processing.

Apple’s current problem is that iPhones don’t have massive amounts of memory, nor do most entry-level consumer devices. While some AI-related tasks can be performed with on-device processing, it can get offloaded to a cloud server.

The remote processing can perform much bigger tasks and workloads than a smartphone. Partly because of the higher amount of processing capability, but mostly because the server can have massive amounts of memory to handle the sizable models in the first place.

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Smartphone screen showing Siri AI text conversation about Apple subscription pricing, with a dark blurred background, white chat bubbles, and part of a colorful circular graphic at the bottom

Siri on the iPhone is better under iOS 27 and macOS 27 Golden Gate.

Apple has managed to use a process of distillation and training to recreate some of Google Gemini’s functionality in a smaller iPhone-friendly model. But something like PrismML could be a force multiplier.

Not only could the big models potentially work entirely on an iPhone in the first place, but these distilled models could get even smaller. That frees up more memory for processing the tasks that use these distilled models.

Add in the continuing improvements in AI processing that are coming down the line, and a future iPhone could be an absolute beast.

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Better AI on more hardware

There’s also the possibility of Apple doing something completely unexpected: expanding Apple Intelligence’s reach.

During WWDC, Apple said that the most powerful on-device models would be available only on specific iPhone and Mac models. That includes the iPhone Air and iPhone 17 Pro with 12GB of memory, the M4 iPad also with 12GB of memory, and an M3 Mac with the same memory or better.

Colorful abstract star logo beside text listing compatible Apple devices: iPhone Air, iPhone 17 Pro, iPad with M4 and later, and Mac with M3 and later, each requiring 12GB memory

Apple says the most powerful on-device AI models will not be on all devices – image credit: Apple

While processing is a factor, memory is certainly going to be another. It’s not hard to imagine Apple using the PrismML tech to cut the size of that “most powerful on-device model” to fit into the smaller memory allowances of earlier models.

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Extrapolating that further, maybe Apple could expand the availability of Apple Intelligence to older, lower-specification devices.

Sure, there are some tradeoffs, such as much slower processing of tasks. Owners of older hardware would probably be fine with that.

PrismML is a prime opportunity for Apple to take a massive leap when it comes to AI.

If it wants to dominate the field when it comes to on-device processing, it has a chance to take a very major step toward that goal.

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Last week’s Sunday Reboot talked about the iPhone 17 Pro Max going into a time capsule for 250 years, and pondered if it will emerge in one piece.

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Protesters confront Microsoft CSO over carbon goals and AI, disrupting climate event

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Melanie Nakagawa, Microsoft chief sustainability officer, left, speaking with GeekWire reporter Lisa Stiffler at a fireside chat at Seattle City Hall on July 17. (PNW Climate Week / Fer Sagastume Photo)

Microsoft Chief Sustainability Officer Melanie Nakagawa faced a barrage of pointed questions from the audience Friday during a session at the annual Pacific Northwest Climate Week in Seattle.

Protesters challenged Nakagawa through most of the 30-minute session held in a conference room at Seattle’s City Hall, calling out the company’s use of fossil fuel energy sources to power its AI data centers and challenging Microsoft’s commitment to climate goals set years ago.

As a reporter covering sustainability issues for GeekWire, I moderated the session. Many of the issues raised by the crowd were on my list of questions for Nakagawa. The disruptions also included chants from protesters seated among attendees, at times going beyond climate issues to condemn Microsoft’s technology deals with Israel.

Security guards ultimately ushered some protesters out of the space, while others remained. Interruptions from the audience continued for all but the final 10 minutes of the session.

The event capped off Pacific Northwest Climate Week, which included conversations around the city and region about climate change solutions, policies and innovations.

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Microsoft has for many years been viewed as an environmental corporate leader, setting an ambitious goal in 2020 to become carbon negative within a decade. It created an internal carbon tax — one of the corporate world’s largest — that charges individual Microsoft divisions for emissions from sources like air travel to fund climate-friendly initiatives. The company is credited with helping create and sustain the carbon dioxide removal sector, among other roles.

But the rapid expansion of AI data centers and their huge energy demands are undercutting Microsoft’s standing. The company recently released its annual sustainability report, disclosing that its carbon footprint grew 25% last year, moving it further from its 2030 target.

Microsoft CSO Melanie Nakagawa, left, and GeekWire reporter Lisa Stiffler before a fireside chat was derailed by protesters. (PNW Climate Week / Fer Sagastume Photo)

One protester’s question was about a deal announced earlier this year in which Microsoft is partnering with Chevron to build a 2.7 gigawatt natural gas facility to power a data center campus in Texas. I asked Nakagawa how the company defends the agreement, and she pointed to the 4.7 gigawatts of renewable energy that Microsoft has supported in the state. I followed up by asking about the Redmond, Wash.-based company’s commitment to carbon dioxide removal (CDR) projects given recent reports about a pause on new deals.

Nakagawa was unable to answer before the crowd drowned her out with a call-and-response chant: “Microsoft, you can’t hide. We can see your dirty side.”

Another protester criticized the escalating pursuit of AI. “You’re selling us a product that we don’t even need, and we never should ask for,” he said. “No one wants AI. You’re destroying the climate with AI.”

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I brought up legislation proposed earlier this year in Washington to mandate clean energy use and bring transparency to data center impacts in the state. Microsoft opposed and helped defeat the bill, though the company says it wants to work with lawmakers to pass rules next year. I asked what needed to change in the legislation for Microsoft to support it.

Nakagawa didn’t provide specifics, but noted that this year, for the first time, the company shared facility-level information in its annual report on electricity and water use for data centers worldwide.

“People want to know more about the data, and we believe you can have an honest and candid conversation with transparency and access to that information and data,” she said.

Given the obvious public concerns, I asked Nakagawa, “Do you really honestly believe that by 2030, the company can hit that carbon-negative goal?”

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Nakagawa pointed to wide-ranging initiatives that are starting to help curb specific emissions, including investments to make Xbox devices lower carbon and financial support for the recent opening of a production plant in Moses Lake, Wash., for sustainable aviation fuel company Twelve.

“There are a couple areas where we’re seeing a lot of promising progress,” she said. “Look, this is going to be a hard target. We’ve not been at all shying away from the fact that this is a difficult goal.”

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Apple Music is the latest streaming service to get hit by a price rise

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Apple has quietly increased the price of Apple Music in the US, with individual subscriptions now costing $11.99 per month instead of $10.99.

The change also affects Family and Student plans. In addition, several Apple One bundles are impacted. The price rise comes almost four years after Apple last increased Apple Music subscription fees in October 2022.

The biggest jump is for the Apple Music Family plan, which now costs $19.99 per month, up from $16.99. The Student plan has also increased by $1, bringing the monthly fee to $6.99.

Apple has also adjusted pricing for two of its Apple One bundles. While the Individual plan remains unchanged, the Family tier now costs $27.95 per month, up by $2. Meanwhile, the Premier plan has increased to $39.95 per month. Apple has also confirmed similar price increases in Brazil.

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In a statement to Music Business Worldwide, Apple said the latest changes are “the result of rising licensing costs,” suggesting that higher payments to rights holders and music labels are behind the increase.

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The price hike means Apple Music now sits closer to many of its biggest streaming rivals. However, the service continues to distinguish itself with features such as Lossless Audio, Hi-Res Lossless, Spatial Audio with Dolby Atmos, live radio stations and a catalogue of more than 100 million songs.

For existing subscribers, the increase is relatively modest on the Individual plan, but families will notice a more significant jump. Moreover, an extra $3 each month adds up to $36 more per year. This makes it one of the largest increases Apple has introduced for the service.

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Spotify, YouTube Music and other services have all introduced price increases in recent years. As a result, premium music subscriptions are becoming steadily more expensive across the board.

If you’re already subscribed, the updated pricing should appear on your next billing cycle. New customers signing up from today will pay the new rates immediately. Meanwhile, anyone considering Apple’s wider ecosystem may want to compare whether an Apple One bundle now offers better overall value than paying for Apple Music on its own.

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Echolocation For Drones | Hackaday

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Bats are remarkable creatures, able to fly at night or inside the confines of caves without light to guide their way. A team of researchers at Worcester Polytechnic Institute (WPI) has determined how to use low power ultrasonic sensors to guide drones in obscured environments.

While radar, lidar, and GPS are all great for navigation and sensing, they can run into issues when light is obscured or can take too much power to be practical for the limited battery life of a drone. The researchers found that a dual sonar array could be used to implement a much lower power sensing system for a drone that performs well in environments that would stymie a computer vision system.

A shield placed behind the array cuts down on the sound of the propellers that would otherwise drown out the signal, and further signal analysis via a neural net separates the echoes of objects in front of the drone from the background. The prototype could navigate in various simulated environments like forests, smoke, and snow. It looks like it even got a chance to go for a flight in the actual woods. All the code and hardware designs are Open Source, so have at it!

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We’ve covered mosquito-inspired drone sensors before, and if you want to get into echolocation yourself, apparently humans can learn to do it too.

via Entertainment Engineering.

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Trump’s latest AI czar has already resigned

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Chris Fall, the director of the Center for AI Standards and Innovation (CAISI), has resigned, the agency confirmed to multiple news outlets.

He was appointed just three months ago after the last appointee, Collin Burns, left in less than a week, The Washington Post reported at the time. Burns was reportedly “pushed out” of the job in April because he previously worked for Anthropic and the Trump administration had been battling with the company, sources told the Post.

No reason was given for Fall’s departure. Prior to leading CAISI, Fall was the director of the Department of Energy’s Office of Science during the first Trump administration and had been the acting director of the DOE’s Advanced Research Projects Agency-Energy. He worked in the DOE’s Office of Naval Research (ONR) prior to that.

Before Burns and Fall, the agency was led by venture capitalist David Sacks, whose title at the time was White House AI and crypto czar. Sacks stepped down in March.

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CAISI, which operates under the National Institute of Standards and Technology, is the primary organization for developing technical standards and testing methods for AI models as well as assessing cybersecurity risks. Yet it was not the agency at the center of the most recent model-risk brouhaha.

That occurred in June when the U.S. Commerce Department invoked an obscure export control directive that effectively forced Anthropic to pull its Mythos and Fable models from the market. The ban was lifted by the end of the month, when Secretary of Commerce Howard Lutnick said he was satisfied with Anthropic’s safety plans.

Earlier this month, the White House also signed an executive order for a new AI safety oversight program called “Gold Eagle” that creates a clearinghouse for cybersecurity vulnerability coordination. A host of federal organizations were named as part of the program, including the Commerce Department and Department of Homeland Security. But, as CNBC pointed out, CAISI was not among the federal organizations mentioned.

Meanwhile, after Anthropic’s models were freed from the ban, Google DeepMind CEO Demis Hassabis began calling for the creation of an independent, industry-run standards body to regulate frontier AI modeled after FINRA — the same sort of mission that CAISI was formed to tackle.

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Fall’s resignation also follows this weekend’s handwringing over Chinese AI lab Moonshot’s new version of its open model Kimi, which performed competitively against flagship frontier models. The administration was weighing efforts to somehow ban Chinese open models, Axios reported. This sparked immediate debate and outrage over the weekend, including from Sacks, who argued that regulations shouldn’t be used as a protectionism strategy for U.S. proprietary AI labs.

While CAISI has released a few reports on the capabilities of Chinese open-weight models Z.ai’s GLM-5.2 and DeepSeek V4 Pro, it hasn’t talked much about its processes for testing. (Open weight means these models can be publicly downloaded and run locally, but its training code and datasets are not available). Since July 9, TechCrunch has sent multiple inquiries to both the DoC and NIST about how its LLM evaluations work and has not received a response.

When you purchase through links in our articles, we may earn a small commission. This doesn’t affect our editorial independence.

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NYT Connections hints and answers for Tuesday, July 21 (game #1136)

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Looking for a different day?

A new NYT Connections puzzle appears at midnight each day for your time zone – which means that some people are always playing ‘today’s game’ while others are playing ‘yesterday’s’. If you’re looking for Monday’s puzzle instead then click here: NYT Connections hints and answers for Monday, July 20 (game #1135).

Good morning! Let’s play Connections, the NYT’s clever word game that challenges you to group answers in various categories. It can be tough, so read on if you need Connections hints.

What should you do once you’ve finished? Why, play some more word games of course. I’ve also got daily Strands hints and answers and Quordle hints and answers articles if you need help for those too, while Marc’s Wordle today page covers the original viral word game.

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Google Maps Killed The Restaurant Star

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We all know that Google and other big players pick and choose what information people see, but we sometimes overlook it outside of the search and social media space. [Lauren Leek] decided to take a look at how Google Maps picks winners and losers in the restaurant scene in London.

Building a machine learning model to determine a new restaurant recommendation (as one does), [Leek] uncovered interesting, and perhaps concerning, elements of how Google Maps ranks restaurants. Broken down by relevance, proximity, and prominence, many new restaurants face the issue of not drawing traffic without reviews and vice-versa causing a vicious cycle. Relevance and proximity are fairly straightforward, but what goes into “prominence?”

[Leek] found that “it is not just what people think of a place – it is how often people interact with it, talk about it, and already recognise it.” This leads to chains and high foot traffic areas awash in reviews while more out-of-the-way places find it more difficult to draw traffic. Some of this is expected and would be happening even when word of mouth was the primary way to find out where to eat, but as with many things, the algorithm amplifies this, along with the undisclosed paid placement of restaurants in Maps results.

While still in its infancy, [Leek] built a public dashboard where people can sort restaurants in the city. The machine learning algorithm is designed to identify places that are hidden gems that punch above their Google Maps weight and may make you look like the trendy one (if you live in London).

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Zooming out further, [Leek] found larger clusters that revealed restaurant “diversity, in other words, is not just about taste. It is about where families settled, which high streets remained affordable long enough for a second generation to open businesses, and which parts of the city experienced displacement before culinary ecosystems could mature.”

If you want to step outside the algorithm mayhem, how about a good old-fashioned Web Ring? We’ve also addressed what’s an AI versus an algorithm, and Cory Doctorow advised us on how to reverse course on the current wave of enshittification.

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Adobe Firefly AI video editor review

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Why you can trust TechRadar


We spend hours testing every product or service we review, so you can be sure you’re buying the best. Find out more about how we test.

Adobe is a popular brand when it comes to all things creative, including AI content generation and editing. With its newly improved Firefly tool, Adobe has taken AI content generation a step further with realistic, real-time outputs.

In addition to being highly accurate, Firefly is also one of the most affordable tools on the market today. But is it the best AI video editor for your needs? Read on to find out. In this article, we’ve put Firefly to the test, examining its features, pricing, ease of use, and overall value for money.

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Adobe Firefly: Plans and pricing

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Google Cloud outage shows it’s still hard to understand hyperscalers’ real resilience regimes

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off prem

Single datacenter and just three services taken down by ‘upstream’ power problem, while the rest of a zone and region kept humming

Google Cloud last week experienced an outage that analysts say demonstrates that not all promises of cloudy resilience are created equal.

Google’s incident report explained that three services – the VMware Engine (GCVE), NetApp Volumes, and Bare Metal Solutions (BMS) – experienced a 15-hour outage due to a cooling failure in its europe-west4-a zone.

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The report includes the following detail: “The datacenter serving europe-west4-a for GCVE, BMS, and NetApp has experienced a power failure, which subsequently caused a cooling failure.”

The important detail there is that Google uses a discrete datacenter for those three services.

Another notable element of the incident report is the admission that “An electrical fault occurred on the utility grid upstream of the datacenter, disrupting the electrical distribution gear and cooling equipment.”

Google hasn’t explained how an upstream failure caused that disruption but did say it “proactively turned down workloads in order to protect customer data from any risks posed by running infrastructure in a high temperature environment.”

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Whenever your correspondent talks to hyperscalers or datacenter operators about how they ensure resilience, they tell me about their use of multiple redundant pieces of energy infrastructure, plus on-site generation capabilities that can keep a datacenter powered for days if necessary.

We’ve asked Google if it had generators or other energy sources at this site, and if so, why it nonetheless had to turn down workloads. We’ve not received a response at the time of writing. Google told us its incident analysis “is currently ongoing” and promised to follow up once it is available.

Hidden dependencies

We also asked Google if it advertises the fact that some of its services are tied to a single datacenter, a matter of interest because like other hyperscalers it divides its cloud into “regions” that typically comprise multiple “zones” spread across a city or other locale. Like its hyperscale peers, Google recommends placing workloads across different zones and regions to ensure resilience. Yet this incident shows some services can be tied to a single datacenter in a zone – and that those single datacenters can experience problems while the rest of the zone keeps working.

Analysts told The Register the outage shows organizations need to dig into clouds’ promises of resilience.

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“The real issue is transparency: customers are generally told to use multiple zones and regions for resilience but are rarely given visibility into whether a particular managed service has a single-datacenter dependency within a zone,” said Biswajeet Mahapatra, principal analyst at Forrester. “As a result, many organizations assume the cloud abstraction provides more facility-level redundancy than may actually exist for specialized services.”

“The underlying architecture is not necessarily unusual,” he added. “AWS, Azure, and Google all operate services that rely on dedicated hardware, storage platforms, or tightly coupled infrastructure that may not be distributed across multiple facilities in the same way as core compute and storage services.”

Gartner Director Analyst Adrian Wong reminded The Register of the 2023 outage at Google Cloud’s europe-west9-a region, the cause of which was a water leak that Google said “originated in a non-Google portion of the facility.”

Google uses a tool called “Spanner” to replicate data across zones, but in the flooded zone Google’s Spanner configuration didn’t work once one building became unavailable.

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“It is very hard to figure out how an individual region is architected,” Wong said. “Our customers are often surprised by that,” he added.

The incident report for last week’s outage includes an apology.

“We know how much you rely on Google Cloud, and we regret the impact on your productivity,” the document states, before promising a final incident report will detail “preventative actions.”

But as this incident shows, knowing how Google plans to avoid future incidents of this sort won’t arm customers with the knowledge to understand if those mitigations will address hidden design issues that can reduce resilience. ®

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New Ryzen 7 7700X3D drops to $279, just days after launch

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Newegg is knocking $50 off AMD’s Ryzen 7 7700X3D with promo code PKC337, bringing it down from its $329 to $279. At that price, it becomes a stronger value proposition against the Ryzen 7 7800X3D while gaming performance stays nearly identical. Our full review is coming this week.

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Google Photos adds a quick toggle between AI and classic search

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Google Photos is giving users an easier way to leave its Gemini-powered search when a simple keyword would do. A new toggle now sits at the top of the results page, letting users switch between Ask Photos and classic search without digging through settings.

The change appears to address feedback from people who opted into Ask Photos but wanted a quicker way to return to classic search when Gemini was slower or less useful for a straightforward query.

Why did Google add the toggle?

Ask Photos is built for complicated requests. It can understand natural-language questions and search for details inside images, including prompts such as “What’s my license plate?” Classic search is quicker when you only need to find a person, place, object, date, or keyword.

Reddit users complained about irrelevant results, missing keyword matches, and the disappearance of an obvious route back to classic search. Some found that the most reliable workaround was disabling Ask Photos through the Gemini settings entirely.

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Google had already acknowledged the problems. It paused the rollout in June 2025 over latency, quality, and user-experience concerns before bringing Ask Photos back with faster handling for simple searches.

What the new toggle actually does

Users who have enabled Ask Photos will see classic search on the left and Ask Photos on the right. Google still chooses which system it believes suits the query, but either view is only a tap away. Classic search can remain selected across several basic searches. A more complicated question may switch the app back to Ask Photos, where it stays until changed manually.

People who have not opted in can continue using regular search. Google may still show them the toggle, but selecting the AI side opens the onboarding process first. Gemini still gets a prominent place in Google Photos. At least users no longer have to hunt through settings whenever classic search would work better.

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