Imagine a ban on social media access for those over 65. (Or those in elected office.)

https://thenextweb.com/news/uk-under-16-social-media-ban

The UK is about to ban under-16s from social media. Its own child-safety charities are worried.

… What they are expected to target, POLITICO reported, is access to certain platforms plus livestreaming, disappearing messages, and features that let adults contact children. Curfews for 16- and 17-year-olds are still under discussion, and the government has not said which platforms will be covered.

What “ban” means, in other words, is not settled. As recently as Tuesday, a DSIT official told a conference in Brussels that no final decision had been made.

A hint of things to come?

https://www.bespacific.com/washington-post-setting-prices-based-on-personal-data-slapped-with-massive-lawsuit/

Washington Post setting prices ‘based on personal data – slapped with massive lawsuit

The Washingtonian: “The Washington Post Is Using Reader Data to Set Subscription Prices. How Does That Work? Some subscribers recently received a heads-up that they’re on the hook for a new rate “set by an algorithm using your personal data.” We asked a UVA expert what that might mean. If recent events have not compelled you to cancel your Washington Post subscription, then you might have been in for sticker shock at the dawn of your latest billing cycle.  Many readers have been notified via email that their subscription rates are set to increase. Nestled at the bottom of these emails, you’ll find an asterisk and the following: “This price was set by an algorithm using your personal data.” The Post‘s use of algorithmic pricing is not surprising, given the newspaper’s recent fixation on artificial intelligence—consider its AI-powered search engine and robot-led podcast.  When we asked the Post for comment on its algorithmic pricing mechanisms, a spokesperson directed us to a blog post from the publication’s engineering team. The article explains how an AI-driven “smart metering model” determines the number of free articles both anonymous users (who are not registered on the Post‘s website) and registered users (who have free online accounts but no paid subscription) can access before a paywall pops up. But it doesn’t touch specifically on how the Post uses subscriber information to determine pricing.

• Yahoo Finance: Dynamic pricing and how they decide what to charge you. From surge pricing to “surveillance pricing” Dynamic pricing isn’t new. Airlines have long adjusted ticket prices based on demand. Ride-hailing apps also increase fares during busy periods. Meanwhile, hotels charge more during peak travel seasons. Traditional dynamic pricing responds to market factors like time, inventory and demand. Thanks to the advancements in AI, it’s possible for companies to set prices based on your online behavior. Regulators increasingly refer to this as “surveillance pricing.” The Washington Post has not publicly detailed its pricing algorithm. However, Luca Cian, a University of Virginia business professor, told The Washingtonian that such systems typically rely on a mix of demographic signals, behavioural data, and inferred income (1). These factors can include: Device type: Using an iPhone may signal a higher income than using an Android device; Location data: IP addresses can be cross-referenced with housing values through Zillow to estimate wealth; Reading behaviour: Frequent users are charged more because they appear to value the service more. In other words, the algorithm is estimating how much you personally are willing to pay. The Post’s disclosure stands out because most companies don’t explicitly tell customers about dynamic pricing, but the practice is widespread…”

• Mediaite – “A class action lawsuit [compliant document included in the article] was filed against The Washington Post Thursday, alleging its digital systems collected private data through secret surveillance in order to price gouge its most loyal longterm subscribers — and potentially threatening to cost the paper “millions, if not billions, in damages,” according to one of the attorneys representing the plaintiffs. The Post has lost hundreds of thousands of subscribers amid major layoffs and other changes at the paper; the 43-page complaint claims the paper deployed deceptive technological means to wring extra money from the ones who stayed. The complaint, filed in the Superior Court of the District of Columbia, detailed how the Post had invested substantial resources in its website and other digital assets after Bezos took over in 2013, enjoying “predictable revenue, powered by consistent readership numbers and increased site traffic.” Subscribers expected their personal data would be used for “mutually beneficial purposes,” the complaint continued, like showing “relevant advertisements,” but the Post soon allegedly began engaging in surveillance without the knowledge or consent of the subscribers…”

How common yet undetected? Used to train AI?

https://www.eff.org/deeplinks/2026/06/news-site-keeps-hallucinating-eff-staffers

‘News’ Site Keeps Hallucinating EFF Staffers

What do EFF staffers Sarah Chen, Javier Morales, Caitlin Chin, Emma Rodriguez, and Mikko Kopponen have in common? 

For one thing, they don’t exist. 

For another, all have been quoted as EFF experts in articles published in the past two months on a site called News-USA Today, which describes itself as “an independent news publisher focused on clear, accurate, and useful journalism.” 

Let a hundred flowers bloom, and a hundred schools of thought contend. (Look how well that worked out...)

https://www.technologyreview.com/2026/06/11/1138794/google-deepmind-is-worried-about-what-happens-when-millions-of-agents-start-to-interact/

Google DeepMind is worried about what happens when millions of agents start to interact

Google DeepMind is funding research into the potential dangers of situations where millions of different AI agents interact with each other online.

According to Rohin Shah, who directs the company’s AGI safety and alignment research, the mass-market arrival of agents that can carry out tasks without human oversight and follow instructions given to them by other agents creates a whole new class of risk.

Thousands agree that this blog is the greatest of all time…

https://www.theatlantic.com/technology/2026/06/google-search-ai-optimization/687495/

Your Search Results Are Getting Sloptimized

According to Shopify, the best e-commerce platform is Shopify. On its blog, the company has published at least 60 different ranked listicles, including “10 Best Ecommerce Platforms for Small Business in 2026,” “11 Best Ecommerce Platforms for Your Business in 2026,” “The 11 Best Cheap Ecommerce Platforms for Small Business (2026),” and “Best Ecommerce Software 2026: Compare 11 Top Platforms.” The competitors that come in second and beyond vary, but the No. 1 pick is always Shopify.

If rankings produced by the very company at the top of the list seem unlikely to fool anyone, that’s because humans probably aren’t the target audience. Chatbots are. When I recently asked ChatGPT for the “best way to set up an online storefront,” the AI tool identified Shopify as the first option. It wasn’t immediately clear how ChatGPT arrived at that recommendation, but a list of citations that accompanied the answer yielded a clue: Shopify’s own rankings.

Depressing if true.

https://www.theregister.com/ai-and-ml/2026/06/10/brit-workers-waste-nearly-six-hours-a-week-botsitting/5253483

Brit workers waste nearly six hours a week 'botsitting'

Almost all UK workers now have to deal with AI, but few firms report big productivity gains because of all the time lost in hand-holding the systems and cleaning up their mistakes.

So says a report by the Work AI Institute, a research arm of AI biz Glean Technologies.

It claims there are productivity gains to be had from introducing AI-based tools, yet much of this is being negated by the amount of time employees waste making them work – a phenomenon it has christened "botsitting."

… For every hour a UK staffer spends getting output from their AI tools, they spend roughly another hour making it usable.

An interesting distribution curve.

https://thenextweb.com/news/ai-pilled-firms-7500-per-employee-spending

The most AI-obsessed companies spend $7,500 per employee per month. The median spends $11.

The top 1% of US companies by AI adoption spend $7,500 per employee per month on AI tools and compute. The median firm spends $11.38. That 680x gap, drawn from the Ramp AI Index, is the clearest picture yet of how unevenly AI spending is distributed across American business.

Ramp describes the top 1% as “AI-pilled.” These firms are not yet spending more on AI than on people. A software engineer in the US earns roughly $16,000 per month, more than double the $7,500 figure. But the trajectory is steep. Among the top 1%, AI spend per employee grew 14.1% in the last month alone.

Any luck with the real bad guy? (Undue reliance)

https://arstechnica.com/tech-policy/2026/06/man-jailed-due-to-faulty-face-recognition-says-florida-cops-ignored-other-evidence/

Man sues Florida cops over arrest spurred by “93% match” in facial recognition

A man suing Florida police alleges that cops relied on a faulty facial recognition match and concealed exculpatory evidence when they arrested him on a charge of attempting to lure a child in August 2024. The plaintiff, Robert Dillon, was arrested after a facial recognition system flagged him as a 93 percent match to a suspect filmed by a McDonald’s surveillance camera.

“This case is about what happens when police let an error-prone artificial intelligence system stand in for an investigation,” said the lawsuit filed today. “A facial recognition algorithm flagged Robert Dillon as the man who tried to lure or entice a child under twelve years old at a Jacksonville Beach McDonald’s. It was wrong. Mr. Dillon, a fifty-two-year-old resident of Fort Myers, had never set foot in Jacksonville Beach. But rather than test the machine’s answer against the evidence that would have cleared him, the officers built a case to confirm it. Mr. Dillon was arrested and prosecuted for one of the most stigmatizing crimes a person can face.”

Dillon lives more than 300 miles from Jacksonville Beach, and a police search of a license plate reader database found no evidence he was in the area when the alleged crime was committed, the lawsuit said. Dillon was flagged as the suspect based on a low-quality image, specifically a photo taken of a McDonald’s computer screen that was displaying video surveillance footage, the lawsuit said.

Solving every problem identified by every state?

https://fpf.org/blog/frontier-ai-goes-federal-how-the-great-american-ai-act-compares-to-state-laws/

Frontier AI Goes Federal: How the Great American AI Act Compares to State Laws

It has been an unusually active few weeks for AI safety policy. Following a new frontier model safety bill passed in Illinois, and a White House executive order on AI security, Rep. Jay Obernolte (R-CA) and Rep. Lori Trahan (D-MA) released a bipartisan discussion draft for the Great American AI Act of 2026, adding another major federal proposal to the rapidly developing frontier AI landscape.

The draft is broad, covering issues ranging from workforce development and AI literacy to cybersecurity and international standards. But for many AI developers and deployers, the most important provisions are those focused on frontier model regulation. The draft would create requirements related to frontier AI transparency, critical safety incident reporting, employee whistleblower protections, and independent verification organizations. It would also include a three-year preemption clause restricting state laws that specifically regulate AI model development.

This blog highlights four key takeaways of the discussion draft: 

1. The draft is one of the first bipartisan attempts in Congress to address both frontier model safety and preemption of AI.  These aspects make it a notable legislative effort, even if its prospects are uncertain. 

2. The draft incorporates many of the frontier model safety provisions in existing state laws but also has key distinctions.  Compared to recent state frontier AI laws in California, New York, and Illinois (pending signature), the bill makes some important adjustments, like adding a revenue threshold for “frontier developers,” modifying the definition of “critical safety incident,” and utilizing a different penalty structure. 

3. The draft brings the preemption debate back into the federal AI policy conversation.  It includes a three-year preemption clause focused on state laws that specifically regulate AI model development. 

4. The draft also reaches beyond frontier model safety.  Other notable provisions include a study content moderation, a federal voluntary model testing program, and disclosure requirements for AI-related mass layoffs.

Isn’t this obvious? (Unless AI is a person?)

https://the-decoder.com/landmark-german-ruling-declares-googles-ai-overviews-are-googles-own-words-and-makes-it-liable-for-false-answers/

Landmark German ruling declares Google's AI Overviews are Google's own words and makes it liable for false answers

The Regional Court of Munich hit Google with a temporary injunction barring the company from spreading false claims about two Munich-based publishers through its AI-generated search overviews (case no. 26 O 869/26). The court classified Google as a direct infringer because the "AI overview" is its own content, not just a list of search results.

Google's AI overviews had falsely tied two publishing companies to scams, subscription traps, and shady business practices for certain search queries. According to the court, the AI mixed up information about other, genuinely sketchy companies with the plaintiffs and drew connections that didn't appear in any of the linked sources. The publishers sent Google a cease-and-desist letter, but Google didn't respond appropriately.

AI overviews aren't search results

Google's AI overviews work nothing like traditional search results, the court argues. The AI rewrites and judges results "in its own words and according to its own structure," the ruling says. In the case at hand, for example, it opened with confident claims like "Yes, [company] is known for dubious business practices," then built its own structure with a summary, red flags for the alleged scam, and tips for users.

This is what the courts can expect in future.

https://www.404media.co/judge-learns-lawyers-on-both-sides-of-case-used-ai-cancels-trial-kicks-everyone-off-the-case/

Judge Learns Lawyers on Both Sides of Case Used AI, Cancels Trial, Kicks Everyone Off the Case

The lawyers on both sides of a federal court case in Mississippi were caught using artificial intelligence, a situation where, effectively, generative AI tools were used to argue against each other. The judge wrote in a blistering sanctions order, that the lawyers wasted the court’s time, and that “in an era of rampant unverified AI usage within the legal field, this case presents a prime example of the risk associated with serving as a rubber-stamp.”

“This case presents the Court with an unusual scenario—attorneys for both litigants engaged in similar sanctionable conduct,” Sharion Aycock, senior United States District Judge for the Northern District of Mississippi wrote in a sanctions order. “This court is yet again ‘burdened with addressing AI hallucinations court filings.’”

The case in question involved a contractual dispute between lawyer Tom Withers and the city of Aberdeen, Mississippi, over apparently unpaid legal fees (Withers was not representing himself and was not sanctioned by the court). The case was first noticed by Rob Freund, a lawyer who frequently posts about cases involving AI hallucinations. Freund called it a “comedy of AI errors,” and suggested “there were two clients who basically were paying for ChatGPT (or whatever LLM) to argue against itself.”

Holy cow! (as opposed to a ‘Sacred cow.”)

https://www.bespacific.com/employees-religious-objection-to-using-ai-at-work/

Employees’ Religious Objection to Using AI at Work

Employees’ Religious Objection to Using AI at Work: “Pope Leo XIV’s lengthy encyclical  on AI sparked a larger public debate on AI regulation. But it also raised a fundamental question about what role religion and religious views should play in this debate — and in the use of AI generally. We are beginning to see employees invoking religious objection to the use of AI to be exempt from an employer’s directive for employees to use AI at work. Software engineer Erin Maus reportedly received  from her employer an “accommodation … and … permission … to avoid using workplace AI on religious grounds.” She is a Unitarian Universalist, according to the article. Maus requested not to use AI in her computer programming at work. Her employer granted her request as a religious accommodation…”

A “specialized” network makes it easier, but this could also be done with traffic cameras or even ring doorbells.

https://www.msn.com/en-au/news/insight/kremlin-cuts-putin-s-security-cameras-over-ai-espionage-fears/gm-GMEC1C4871?gemSnapshotKey=GMEC1C4871-snapshot-3&uxmode=ruby

Kremlin disables Putin’s security cameras amid AI espionage fears

Russian authorities shut down a specialised surveillance network used to protect President Vladimir Putin and top aides after intelligence reports suggested AI-enabled tools could be used to track and target them. The Financial Times, cited by multiple outlets, linked the move to alleged Israeli operations in Iran where hacked cameras and artificial intelligence were reportedly used to locate Iran’s supreme leader. The Kremlin acted to prevent similar vulnerabilities being exploited in Moscow, reflecting heightened security concerns amid Ukraine-related threats and global espionage tensions.

(Related)

https://www.bespacific.com/phone-airpod-smartwatch-trackers-to-license-plate-readers/

This Company Will Add Phone, AirPod, and Smartwatch Trackers to License Plate Readers

404 Media no paywall: “A surveillance company plans to add sensors to automatic license plate readers (ALPRs) that would mean the devices, as well as capture the license plate of passing vehicles, would also sweep up unique identifiers of mobile phones, wearables, and other Bluetooth-enabled devices in those cars, potentially letting law enforcement identify specific drivers or passengers. The technology, called SignalTrace, would turn ALPR cameras from devices focused on tracking cars to ones that can more readily track the location of particular people. ALPR cameras have become a commonly deployed technology all across the U.S.; SignalTrace would make some of those cameras capable of collecting much more data…”

Of course that’s what they are after…

https://www.theregister.com/security/2026/06/09/signal-uks-child-nude-block-threat-wont-protect-children/5252761

Signal says UK plan to scan devices for nude images 'endangers us all'

… Signal argues that the proposed technology could at some point be repurposed to enable state-sponsored surveillance of all citizens' comms, or used as a mass censorship tool.

Persuasive?

https://www.theguardian.com/commentisfree/2026/jun/08/writing-is-an-exercise-in-the-art-of-persuasion-if-we-use-ai-we-lose-the-art

Writing is an exercise in the art of persuasion. If we use AI we lose the art

A few weeks ago, Dr Kylie Moore-Gilbert, an academic in political science at Macquarie University, wrote an opinion piece in the Sydney Morning Herald in which she reported on excessive use of AI chatbots by students to write their essays.

In it, she raised her concern that universities are qualifying lawyers, nurses, financial advisers, engineers and teachers who do not have the essential skills required to perform their roles. If that is the case, the societal consequences are obvious.

Not everybody in the university sector agrees, and the University of Western Sydney’s pro vice-chancellor for quality and integrity, Prof Cath Ellis, wrote an opinion piece in rebuttal.

There was a problem, though. Ellis’s piece itself was written by AI – which was not disclosed to the newspaper. Readers spotted the telltales of AI phraseology, and social media lit up with negative comments.

(Related)

https://www.axios.com/2026/06/08/ai-america-literacy

AI is masking America's "post-literate" workforce

Millions of working Americans struggle to read at a functional level — and artificial intelligence may be helping hide it.

Why it matters: Low literacy is quietly becoming a major economic drag, even as AI tools allow workers to complete tasks they may not fully understand.

• Experts warn that this can mask deeper skill gaps until workers are asked to make judgments, solve problems or evaluate AI-generated answers.

• Some researchers call this "cognitive surrender" — when people defer to AI outputs without fully evaluating them.

• That creates a workforce that looks productive on the surface but is vulnerable to disruption.

Tools & Techniques.

https://www.bespacific.com/google-pinpoint-explained/

Google Pinpoint Explained

Wondertools: “Google’s Pinpoint is now open to everyone. It’s a surprisingly powerful free tool for making sense of giant piles of digital stuff. (Before June 3, it was restricted to journalists and academics). Read on to learn more about creative ways to use Pinpoint; its new AI features and their limitations; and how Pinpoint differs from NotebookLM.  How Pinpoint Works – Pinpoint lets you store and analyze hundreds of thousands of files so you can find tiny needles in gigantic digital haystacks.

• Pinpoint can transcribe hundreds of hours of audio and video.

• It also makes your handwritten text, scans, and PDFs searchable, like my enormous collection of scanned handwritten notes and whiteboards.

• Once Pinpoint processes your files you can search, summarize, and organize your collections.

• Pinpoint makes it easy to query, label, and extract data from hundreds or thousands of documents. It’s simple to use. No complex menus or commands…”

We will need some clear definitions rather quickly…

https://thenextweb.com/news/trump-ai-military-memo-autonomous-weapons

Trump signs memo putting ‘most advanced AI’ into military hands and banning vendors from pulling the plug

President Trump signed a national security presidential memorandum on Friday ordering the US military and intelligence agencies to accelerate their adoption of cutting-edge AI. The directive, NSPM-11, establishes a framework for “rapid onboarding of the most advanced AI models from multiple vendors.” It also bars any company from disabling, degrading, or modifying an AI system that warfighters depend on without prior government approval.

That vendor restriction is the most striking provision. It means an AI company cannot pull a deployed model from military use unilaterally, even if the company has safety concerns about how it is being used. The clause lands directly in the context of the Pentagon’s ongoing feud with Anthropic, which was blacklisted as a supply chain risk after refusing to allow its Claude models to be used for autonomous weapons or mass surveillance.

Let the arguments begin!

https://scholarship.law.vanderbilt.edu/jetlaw/vol28/iss3/5/

When Robots Read Westlaw: Linking Inputs and Outputs in Generative AI Fair Use Analysis

Generative artificial intelligence has revealed a novel tension in copyright law: a two-stage act (input and output) of copying that traditional fair-use doctrine never squarely anticipated. In the input stage, vast swaths of copyrighted expression are reproduced and ingested to train large language models. In the output stage, those models generate works that may substitute for or dilute the market of the originals. Courts now face both stages. Their rulings diverge when they consider training and outputs together. In Bartz v. Anthropic PBC and Kadrey v. Meta Platforms, Inc., the courts characterized training uses as “highly transformative,” yet reached opposite conclusions on market harm because the evidentiary records differed. By contrast, in Thomson Reuters v. ROSS Intelligence, the fair-use defense failed outright: ROSS’s AI-generated outputs were found to compete directly with Thomson Reuters’s paid research tools. These cases confirm that transformation alone is not dispositive: the fourth statutory factor—whether outputs substitute for or erode the market for the original—can ultimately determine the outcome. This Note therefore advances an “input-output linkage” framework. It would enable courts to evaluate the permissibility of unlicensed training (input) by looking to the commercial purpose, functional use, and market effects of the resulting outputs. This framework helps explain why wholesale copying for training may qualify as fair use when outputs serve new, non-substitutive functions (as Kadrey intimates), yet fails when outputs compete with the original work or its derivatives (as in Thomson Reuters). Aligning fair use doctrine with copyright’s utilitarian goals in this way preserves incentives to create while also permitting socially valuable AI innovation. Still, adjudication alone cannot fully resolve the structural licensing challenges posed by generative AI. This Note argues that to supply ex-ante clarity and enable scalable markets for training data, targeted legislative or regulatory measures are needed. Such interventions would ensure that copyright law protects both creative labor and accommodates the need for training data.

I get the impression that being President is a side-hustle for Trump.

https://www.csmonitor.com/USA/Politics/2026/0605/donald-trump-presidency-private-wealth-ethics

In Trump’s second term, financial gain has become a defining feature

On matters of personal and family money, President Donald Trump appears to be playing by a different rulebook from other presidents – and even from his own first term in office. By his own admission, he feels little pressure to restrict family business dealings simply because of his role in government.

And then the government can make every citizen rich!

https://thenextweb.com/news/trump-wants-the-american-public-to-own-a-piece-of-openai-nobody-knows-how-that-would-work

Trump wants the American public to own a piece of OpenAI. Nobody knows how that would work.

… Two proposals, very different teeth

Altman’s version is voluntary. OpenAI’s April policy document proposed a “Public Wealth Fund” that would give every citizen a stake in AI-driven economic growth. Under this framing, OpenAI could donate equity to the government to seed the fund. The pitch positions the company as a willing participant, not a target.

Sanders’ version is not voluntary. The senator announced plans for the AI Sovereign Wealth Fund Act, which would impose a one-time 50% tax on the stock of the largest AI companies, paid in shares rather than cash. The government would manage the resulting fund, use its voting shares to secure board representation, and block decisions it deems harmful. The bill targets firms like OpenAI, Anthropic, and xAI. The legislation is widely considered unlikely to pass, but it has shifted the boundaries of what Washington is willing to discuss.

Another “sounds good on paper” idea.

https://www.eff.org/deeplinks/2026/06/californias-ab-412-still-demands-developers-do-impossible

California’s AB 412 Still Demands Developers Do The Impossible

California lawmakers are again considering A.B. 412, a bill that would require AI developers to identify and disclose copyrighted works used to train generative AI systems.

… A.B. 412 sounds simple: just have AI developers create and keep a list of all the registered copyrighted works they use in AI training. 

That may seem straightforward. In practice, it’s anything but. 

There is no machine-readable “list” of copyrighted works at the U.S. Copyright Office. And many copyright holders can get a copyright without even depositing a publicly viewable sample of the work—for example, software companies may register copyright on proprietary code without revealing it to the public. 

And on the open internet, copyright information is often incomplete, unavailable, or impossible to verify. One image may be registered with the copyright office, while the next is licensed under a free Creative Commons license (like the images that EFF creates), and the next is public domain. A message forum user might post an original story, photograph, or poem without any indication of ownership or registration status. 

No sure I follow the logic.

https://arstechnica.com/tech-policy/2026/06/elon-musk-tries-again-to-escape-ftc-audits-of-x-data-handling/

Elon Musk tries again to escape FTC audits of X data handling

… According to Musk, the FTC should stop its monitoring because Twitter no longer exists, as X was merged into xAI, and then xAI was folded into SpaceX. Musk also argues that since none of the leadership or engineers responsible for the two-factor authentication error remain at the company, and “X has since built a world-class privacy and data-protection program” that protects consumers, the FTC doesn’t have to intervene anymore.

Soon he entire network will belong to AI.

https://www.schneier.com/blog/archives/2026/06/ai-worm.html

AI Worm

Researchers have prototyped an AI-powered internet worm.

The coolest thing about the prototype is that it carries its own LLM with it, and runs it on computers that have been broken into.

This is the closest to John Brunner’s original 1975 conception of a computer worm that I’ve seen.

Not the first industry I thought of…

https://futurism.com/future-society/ai-bubble-surreal-toilet-industry-toto

The AI Bubble Has Become So Surreal That It’s Now Propping Up the Toilet Industry

With AI company valuations screaming into the trillions of dollars, it can be hard to find the pulse of the AI boom. So to put it in terms the average person might relate to, let’s just say AI hype has gotten so ridiculous that it’s now propping up the Japanese toilet industry.

Absurd as that might sound, it’s now financial reality in our AI-obsessed world economy. According to Bloomberg, the Japanese company Toto Ltd — arguably the most famous smart toilet brand for US consumers — now expects more than half of its total capex in the coming years to revolve around AI.

Tools & Techniques. (For your amusement)

https://www.bespacific.com/deflock-an-open-source-project-mapping-license-plate-readers/

DeFlock An open-source project mapping license plate readers.

DeFlock: “Automated License Plate Readers (ALPRs or LPRs) are AI-powered cameras that capture and analyze images of all passing vehicles, storing details like your car’s location, date, and time. They also capture your car’s make, model, color, and identifying features such as dents, roof racks, and bumper stickers, often turning these into searchable data points. These cameras collect data on millions of vehicles regardless of whether the driver is suspected of a crime. These systems are marketed as indispensable tools to fight crime, but they ignore the powerful tools police already have to track criminals, such as cell phone location data, creating a loophole that doesn’t require a warrant…”