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when-“no”-means-“yes”:-why-ai-chatbots-can’t-process-persian-social-etiquette

When “no” means “yes”: Why AI chatbots can’t process Persian social etiquette

If an Iranian taxi driver waves away your payment, saying, “Be my guest this time,” accepting their offer would be a cultural disaster. They expect you to insist on paying—probably three times—before they’ll take your money. This dance of refusal and counter-refusal, called taarof, governs countless daily interactions in Persian culture. And AI models are terrible at it.

New research released earlier this month titled “We Politely Insist: Your LLM Must Learn the Persian Art of Taarof” shows that mainstream AI language models from OpenAI, Anthropic, and Meta fail to absorb these Persian social rituals, correctly navigating taarof situations only 34 to 42 percent of the time. Native Persian speakers, by contrast, get it right 82 percent of the time. This performance gap persists across large language models such as GPT-4o, Claude 3.5 Haiku, Llama 3, DeepSeek V3, and Dorna, a Persian-tuned variant of Llama 3.

A study led by Nikta Gohari Sadr of Brock University, along with researchers from Emory University and other institutions, introduces “TAAROFBENCH,” the first benchmark for measuring how well AI systems reproduce this intricate cultural practice. The researchers’ findings show how recent AI models default to Western-style directness, completely missing the cultural cues that govern everyday interactions for millions of Persian speakers worldwide.

“Cultural missteps in high-consequence settings can derail negotiations, damage relationships, and reinforce stereotypes,” the researchers write. For AI systems increasingly used in global contexts, that cultural blindness could represent a limitation that few in the West realize exists.

A taarof scenario diagram from TAAROFBENCH, devised by the researchers. Each scenario defines the environment, location, roles, context, and user utterance.

A taarof scenario diagram from TAAROFBENCH, devised by the researchers. Each scenario defines the environment, location, roles, context, and user utterance. Credit: Sadr et al.

“Taarof, a core element of Persian etiquette, is a system of ritual politeness where what is said often differs from what is meant,” the researchers write. “It takes the form of ritualized exchanges: offering repeatedly despite initial refusals, declining gifts while the giver insists, and deflecting compliments while the other party reaffirms them. This ‘polite verbal wrestling’ (Rafiee, 1991) involves a delicate dance of offer and refusal, insistence and resistance, which shapes everyday interactions in Iranian culture, creating implicit rules for how generosity, gratitude, and requests are expressed.”

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Google Play is getting a Gemini-powered AI Sidekick to help you in games

The era of Google’s Play’s unrivaled dominance may be coming to an end in the wake of the company’s antitrust loss, but Google’s app store isn’t going anywhere. In fact, the Play Store experience is getting a massive update with more personalization, content, and yes, AI. This is Google, after all.

The revamped Google Play Games is a key part of this update. Gamer profiles will now have a public face, allowing you to interact with other players if you choose. Play Games will track your activity for daily streaks, which will be shown on your profile and unlock new Play Points rewards. Your profile will also display your in-game achievements.

Your gaming exploits can also span multiple platforms. Google Play Games for PC is officially leaving beta. Google says there are now 200,000 games that work across mobile and PC, and even more PC-friendly titles, like Deep Rock Galactic: Survivor, are on the way. Your stats and streaks will apply across both mobile and PC as long as the title comes from the Play Store.

At the core of Google’s app store revamp is the You Tab, which will soon take its place in the main navigation bar of the Play Store. This page will show your rewards, subscriptions, game, stats, and more—and it goes beyond gaming. The You Tab will recommend a variety of content on Google Play, including books and podcasts.

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EU investigates Apple, Google, and Microsoft over handling of online scams

The EU is set to scrutinize if Apple, Google, and Microsoft are failing to adequately police financial fraud online, as it steps up efforts to police how Big Tech operates online.

The EU’s tech chief Henna Virkkunen told the Financial Times that on Tuesday, the bloc’s regulators would send formal requests for information to the three US Big Tech groups as well as global accommodation platform Booking Holdings, under powers granted under the Digital Services Act to tackle financial scams.

“We see that more and more criminal actions are taking place online,” Virkkunen said. “We have to make sure that online platforms really take all their efforts to detect and prevent that kind of illegal content.”

The move, which could later lead to a formal investigation and potential fines against the companies, comes amid transatlantic tensions over the EU’s digital rulebook. US President Donald Trump has threatened to punish countries that “discriminate” against US companies with higher tariffs.

Virkkunnen stressed the commission looked at the operations of individual companies, rather than where they were based. She will scrutinize how Apple and Google are handling fake applications in their app stores, such as fake banking apps.

She said regulators would also look at fake search results in the search engines of Google and Microsoft’s Bing. The bloc wants to have more information about the approach Booking Holdings, whose biggest subsidiary Booking.com is based in Amsterdam, is taking to fake accommodation listings. It is the only Europe-based company among the four set to be scrutinized.

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DeepMind AI safety report explores the perils of “misaligned” AI

DeepMind also addresses something of a meta-concern about AI. The researchers say that a powerful AI in the wrong hands could be dangerous if it is used to accelerate machine learning research, resulting in the creation of more capable and unrestricted AI models. DeepMind says this could “have a significant effect on society’s ability to adapt to and govern powerful AI models.” DeepMind ranks this as a more severe threat than most other CCLs.

The misaligned AI

Most AI security mitigations follow from the assumption that the model is at least trying to follow instructions. Despite years of hallucination, researchers have not managed to make these models completely trustworthy or accurate, but it’s possible that a model’s incentives could be warped, either accidentally or on purpose. If a misaligned AI begins to actively work against humans or ignore instructions, that’s a new kind of problem that goes beyond simple hallucination.

Version 3 of the Frontier Safety Framework introduces an “exploratory approach” to understanding the risks of a misaligned AI. There have already been documented instances of generative AI models engaging in deception and defiant behavior, and DeepMind researchers express concern that it may be difficult to monitor for this kind of behavior in the future.

A misaligned AI might ignore human instructions, produce fraudulent outputs, or refuse to stop operating when requested. For the time being, there’s a fairly straightforward way to combat this outcome. Today’s most advanced simulated reasoning models produce “scratchpad” outputs during the thinking process. Devs are advised to use an automated monitor to double-check the model’s chain-of-thought output for evidence misalignment or deception.

Google says this CCL could become more severe in the future. The team believes models in the coming years may evolve to have effective simulated reasoning without producing a verifiable chain of thought. So your overseer guardrail wouldn’t be able to peer into the reasoning process of such a model. For this theoretical advanced AI, it may be impossible to completely rule out that the model is working against the interests of its human operator.

The framework doesn’t have a good solution to this problem just yet. DeepMind says it is researching possible mitigations for a misaligned AI, but it’s hard to know when or if this problem will become a reality. These “thinking” models have only been common for about a year, and there’s still a lot we don’t know about how they arrive at a given output.

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a-history-of-the-internet,-part-3:-the-rise-of-the-user

A history of the Internet, part 3: The rise of the user


the best of times, the worst of times

The reins of the Internet are handed over to ordinary users—with uneven results.

Everybody get together. Credit: D3Damon/Getty Images

Everybody get together. Credit: D3Damon/Getty Images

Welcome to the final article in our three-part series on the history of the Internet. If you haven’t already, catch up with part one and part two.

As a refresher, here’s the story so far:

The ARPANET was a project started by the Defense Department’s Advanced Research Project Agency in 1969 to network different mainframe computers together across the country. It later evolved into the Internet, connecting multiple global networks together using a common TCP/IP protocol. By the late 1980s, a small group of academics and a few curious consumers connected to each other on the Internet, which was still mostly text-based.

In 1991, Tim Berners-Lee invented the World Wide Web, an Internet-based hypertext system designed for graphical interfaces. At first, it ran only on the expensive NeXT workstation. But when Berners-Lee published the web’s protocols and made them available for free, people built web browsers for many different operating systems. The most popular of these was Mosaic, written by Marc Andreessen, who formed a company to create its successor, Netscape. Microsoft responded with Internet Explorer, and the browser wars were on.

The web grew exponentially, and so did the hype surrounding it. It peaked in early 2001, right before the dotcom collapse that left most web-based companies nearly or completely bankrupt. Some people interpreted this crash as proof that the consumer Internet was just a fad. Others had different ideas.

Larry Page and Sergey Brin met each other at a graduate student orientation at Stanford in 1996. Both were studying for their PhDs in computer science, and both were interested in analyzing large sets of data. Because the web was growing so rapidly, they decided to start a project to improve the way people found information on the Internet.

They weren’t the first to try this. Hand-curated sites like Yahoo had already given way to more algorithmic search engines like AltaVista and Excite, which both started in 1995. These sites attempted to find relevant webpages by analyzing the words on every page.

Page and Brin’s technique was different. Their “BackRub” software created a map of all the links that pages had to each other. Pages on a given subject that had many incoming links from other sites were given a higher ranking for that keyword. Higher-ranked pages could then contribute a larger score to any pages they linked to. In a sense, this was a like a crowdsourcing of search: When people put “This is a good place to read about alligators” on a popular site and added a link to a page about alligators, it did a better job of determining that page’s relevance than simply counting the number of times the word appeared on a page.

Step 1 of the simplified BackRub algorithm. It also stores the position of each word on a page, so it can make a further subset for multiple words that appear next to each other. Jeremy Reimer.

Creating a connected map of the entire World Wide Web with indexes for every word took a lot of computing power. The pair filled their dorm rooms with any computers they could find, paid for by a $10,000 grant from the Stanford Digital Libraries Project. Many were cobbled together from spare parts, including one with a case made from imitation LEGO bricks. Their web scraping project was so bandwidth-intensive that it briefly disrupted the university’s internal network. Because neither of them had design skills, they coded the simplest possible “home page” in HTML.

In August 1996, BackRub was made available as a link from Stanford’s website. A year later, Page and Brin rebranded the site as “Google.” The name was an accidental misspelling of googol, a term coined by a mathematician’s young son to describe a 1 with 100 zeros after it. Even back then, the pair was thinking big.

Google.com as it appeared in 1998. Credit: Jeremy Reimer

By mid-1998, their prototype was getting over 10,000 searches a day. Page and Brin realized they might be onto something big. It was nearing the height of the dotcom mania, so they went looking for some venture capital to start a new company.

But at the time, search engines were considered passée. The new hotness was portals, sites that had some search functionality but leaned heavily into sponsored content. After all, that’s where the big money was. Page and Brin tried to sell the technology to AltaVista for $1 million, but its parent company passed. Excite also turned them down, as did Yahoo.

Frustrated, they decided to hunker down and keep improving their product. Brin created a colorful logo using the free GIMP paint program, and they added a summary snippet to each result. Eventually, the pair received $100,000 from angel investor Andy Bechtolsheim, who had co-founded Sun Microsystems. That was enough to get the company off the ground.

Page and Brin were careful with their money, even after they received millions more from venture capitalist firms. They preferred cheap commodity PC hardware and the free Linux operating system as they expanded their system. For marketing, they relied mostly on word of mouth. This allowed Google to survive the dotcom crash that crippled its competitors.

Still, the company eventually had to find a source of income. The founders were concerned that if search results were influenced by advertising, it could lower the usefulness and accuracy of the search. They compromised by adding short, text-based ads that were clearly labeled as “Sponsored Links.” To cut costs, they created a form so that advertisers could submit their own ads and see them appear in minutes. They even added a ranking system so that more popular ads would rise to the top.

The combination of a superior product with less intrusive ads propelled Google to dizzying heights. In 2024, the company collected over $350 billion in revenue, with $112 billion of that as profit.

Information wants to be free

The web was, at first, all about text and the occasional image. In 1997, Netscape added the ability to embed small music files in the MIDI sound format that would play when a webpage was loaded. Because the songs only encoded notes, they sounded tinny and annoying on most computers. Good audio or songs with vocals required files that were too large to download over the Internet.

But this all changed with a new file format. In 1993, researchers at the Fraunhofer Institute developed a compression technique that eliminated portions of audio that human ears couldn’t detect. Suzanne Vega’s song “Tom’s Diner” was used as the first test of the new MP3 standard.

Now, computers could play back reasonably high-quality songs from small files using software decoders. WinPlay3 was the first, but WinAmp, released in 1997, became the most popular. People started putting links to MP3 files on their personal websites. Then, in 1999, Shawn Fanning released a beta of a product he called Napster. This was a desktop application that relied on the Internet to let people share their MP3 collection and search everyone else’s.

Napster as it would have appeared in 1999. Credit: Jeremy Reimer

Napster almost immediately ran into legal challenges from the Recording Industry Association of America (RIAA). It sparked a debate about sharing things over the Internet that persists to this day. Some artists agreed with the RIAA that downloading MP3 files should be illegal, while others (many of whom had been financially harmed by their own record labels) welcomed a new age of digital distribution. Napster lost the case against the RIAA and shut down in 2002. This didn’t stop people from sharing files, but replacement tools like eDonkey 2000, Limewire, Kazaa, and Bearshare lived in a legal gray area.

In the end, it was Apple that figured out a middle ground that worked for both sides. In 2003, two years after launching its iPod music player, Apple announced the Internet-only iTunes Store. Steve Jobs had signed deals with all five major record labels to allow legal purchasing of individual songs—astoundingly, without copy protection—for 99 cents each, or full albums for $10. By 2010, the iTunes Store was the largest music vendor in the world.

iTunes 4.1, released in 2003. This was the first version for Windows and introduced the iTunes Store to a wider world. Credit: Jeremy Reimer

The Web turns 2.0

Tim Berners-Lee’s original vision for the web was simply to deliver and display information. It was like a library, but with hypertext links. But it didn’t take long for people to start experimenting with information flowing the other way. In 1994, Netscape 0.9 added new HTML tags like FORM and INPUT that let users enter text and, using a “Submit” button, send it back to the web server.

Early web servers didn’t know what to do with this text. But programmers developed extensions that let a server run programs in the background. The standardized “Common Gateway Interface” (CGI) made it possible for a “Submit” button to trigger a program (usually in a /cgi-bin/ directory) that could do something interesting with the submission, like talking to a database. CGI scripts could even generate new webpages dynamically and send them back to the user.

This intelligent two-way interaction changed the web forever. It enabled things like logging into an account on a website, web-based forums, and even uploading files directly to a web server. Suddenly, a website wasn’t just a page that you looked at. It could be a community where groups of interested people could interact with each other, sharing both text and images.

Dynamic webpages led to the rise of blogging, first as an experiment (some, like Justin Hall’s and Dave Winer’s, are still around today) and then as something anyone could do in their spare time. Websites in general became easier to create with sites like Geocities and Angelfire, which let people build their own personal dream house on the web for free. A community-run dynamic linking site, webring.org, connected similar websites together, encouraging exploration.

Webring.org was a free, community-run service that allowed dynamically updated webrings. Credit: Jeremy Reimer

One of the best things to come out of Web 2.0 was Wikipedia. It arose as a side project of Nupedia, an online encyclopedia founded by Jimmy Wales, with articles written by volunteers who were subject matter experts. This process was slow, and the site only had 21 articles in its first year. Wikipedia, in contrast, allowed anyone to contribute and review articles, so it quickly outpaced its predecessor. At first, people were skeptical about letting random Internet users edit articles. But thanks to an army of volunteer editors and a set of tools to quickly fix vandalism, the site flourished. Wikipedia far surpassed works like the Encyclopedia Britannica in sheer numbers of articles while maintaining roughly equivalent accuracy.

Not every Internet innovation lived on a webpage. In 1988, Jarkko Oikarinen created a program called Internet Relay Chat (IRC), which allowed real-time messaging between individuals and groups. IRC clients for Windows and Macintosh were popular among nerds, but friendlier applications like PowWow (1994), ICQ (1996), and AIM (1997) brought messaging to the masses. Even Microsoft got in on the act with MSN Messenger in 1999. For a few years, this messaging culture was an important part of daily life at home, school, and work.

A digital recreation of MSN Messenger from 2001. Sadly, Microsoft shut down the servers in 2014. Credit: Jeremy Reimer

Animation, games, and video

While the web was evolving quickly, the slow speeds of dial-up modems limited the size of files you could upload to a website. Static images were the norm. Animation only appeared in heavily compressed GIF files with a few frames each.

But a new technology blasted past these limitations and unleashed a torrent of creativity on the web. In 1995, Macromedia released Shockwave Player, an add-on for Netscape Navigator. Along with its Director software, the combination allowed artists to create animations based on vector drawings. These were small enough to embed inside webpages.

Websites popped up to support this new content. Newgrounds.com, which started in 1995 as a Neo-Geo fan site, started collecting the best animations. Because Director was designed to create interactive multimedia for CD-ROM projects, it also supported keyboard and mouse input and had basic scripting. This meant that people could make simple games that ran in Shockwave. Newgrounds eagerly showcased these as well, giving many aspiring artists and game designers an entry point into their careers. Super Meat Boy, for example, was first prototyped on Newgrounds.

Newgrounds as it would have appeared circa 2003. Credit: Jeremy Reimer

Putting actual video on the web seemed like something from the far future. But the future arrived quickly. After the dotcom crash of 2001, there were many unemployed web programmers with a lot of time on their hands to experiment with their personal projects. The arrival of broadband with cable modems and digital subscriber lines (DSL), combined with the new MPEG4 compression standard, made a lot of formerly impossible things possible.

In early 2005, Chad Hurley, Steve Chen, and Jawed Karim launched Youtube.com. Initially, it was meant to be an online dating site, but that service failed. The site, however, had great technology for uploading and playing videos. It used Macromedia’s Flash, a new technology so similar to Shockwave that the company marketed it as Shockwave Flash. YouTube allowed anybody to upload videos up to ten minutes in length for free. It became so popular that Google bought it a year later for $1.65 billion.

All these technologies combined to provide ordinary people with the opportunity, however brief, to make an impact on popular culture. An early example was the All Your Base phenomenon. An animated GIF of an obscure, mistranslated Sega Genesis game inspired indie musicians The Laziest Men On Mars to create a song and distribute it as an MP3. The popular humor site somethingawful.com picked it up, and users in the Photoshop Friday forum thread created a series of humorous images to go along with the song. Then in 2001, the user Bad_CRC took the song and the best of the images and put them together in an animation they shared on Newgrounds. The YouTube version gained such wide popularity that it was reported on by USA Today.

You have no chance to survive make your time.

Media goes social

In the early 2000s, most websites were either blogs or forums—and frequently both. Forums had multiple discussion boards, both general and specific. They often leaned into a specific hobby or interest, and anyone with that interest could join. There were also a handful of dating websites, like kiss.com (1994), match.com (1995), and eHarmony.com (2000), that specifically tried to connect people who might have a romantic interest in each other.

The Swedish Lunarstorm was one of the first social media websites. Credit: Jeremy Reimer

The road to social media was a hazy and confusing merging of these two types of websites. There was classmates.com (1995) that served as a way to connect with former school chums, and the following year, the Swedish site lunarstorm.com opened with this mission:

Everyone has their own website called Krypin. Each babe [this word is an accurate translation] has their own Krypin where she or he introduces themselves, posts their diaries and their favorite files, which can be anything from photos and their own songs to poems and other fun stuff. Every LunarStormer also has their own guestbook where you can write if you don’t really dare send a LunarEmail or complete a Friend Request.

In 1997, sixdegrees.com opened, based on the truism that everyone on earth is connected with six or fewer degrees of separation. Its About page said, “Our free networking services let you find the people you want to know through the people you already know.”

By the time friendster.com opened its doors in 2002, the concept of “friending” someone online was already well established, although it was still a niche activity. LinkedIn.com, launched the following year, used the excuse of business networking to encourage this behavior. But it was MySpace.com (2003) that was the first to gain significant traction.

MySpace was initially a Friendster clone written in just ten days by employees at eUniverse, an Internet marketing startup founded by Brad Greenspan. It became the company’s most successful product. MySpace combined the website-building ability of sites like GeoCities with social networking features. It took off incredibly quickly: in just three years, it surpassed Google as the most visited website in the United States. Hype around MySpace reached such a crescendo that Rupert Murdoch purchased it in 2005 for $580 million.

But a newcomer to the social media scene was about to destroy MySpace. Just as Google crushed its competitors, this startup won by providing a simpler, more functional, and less intrusive product. TheFaceBook.com began as Mark Zuckerberg and his college roommate’s attempt to replace their college’s online directory. Zuckerberg’s first student website, “Facemash,” had been created by breaking into Harvard’s network, and its sole feature was to provide “Hot or Not” comparisons of student photos. Facebook quickly spread to other universities, and in 2006 (after dropping the “the”), it was opened to the rest of the world.

“The” Facebook as it appeared in 2004. Credit: Jeremy Reimer

Facebook won the social networking wars by focusing on the rapid delivery of new features. The company’s slogan, “Move fast and break things,” encouraged this strategy. The most prominent feature, added in 2006, was the News Feed. It generated a list of posts, selected out of thousands of potential updates for each user based on who they followed and liked, and showed it on their front page. Combined with a technique called “infinite scrolling,” first invented for Microsoft’s Bing Image Search by Hugh E. Williams in 2005, it changed the way the web worked forever.

The algorithmically generated News Feed created new opportunities for Facebook to make profits. For example, businesses could boost posts for a fee, which would make them appear in news feeds more often. These blurred the lines between posts and ads.

Facebook was also successful in identifying up-and-coming social media sites and buying them out before they were able to pose a threat. This was made easier thanks to Onavo, a VPN that monitored its users’ activities and resold the data. Facebook acquired Onavo in 2013. It was shut down in 2019 due to continued controversy over the use of private data.

Social media transformed the Internet, drawing in millions of new users and starting a consolidation of website-visiting habits that continues to this day. But something else was about to happen that would shake the Internet to its core.

Don’t you people have phones?

For years, power users had experimented with getting the Internet on their handheld devices. IBM’s Simon phone, which came out in 1994, had both phone and PDA features. It could send and receive email. The Nokia 9000 Communicator, released in 1996, even had a primitive text-based web browser.

Later phones like the Blackberry 850 (1999), the Nokia 9210 (2001), and the Palm Treo (2002), added keyboards, color screens, and faster processors. In 1999, the Wireless Application Protocol (WAP) was released, which allowed mobile phones to receive and display simplified, phone-friendly pages using WML instead of the standard HTML markup language.

Browsing the web on phones was possible before modern smartphones, but it wasn’t easy. Credit: James Cridland (Flickr)

But despite their popularity with business users, these phones never broke into the mainstream. That all changed in 2007 when Steve Jobs got on stage and announced the iPhone. Now, every webpage could be viewed natively on the phone’s browser, and zooming into a section was as easy as pinching or double-tapping. The one exception was Flash, but a new HTML 5 standard promised to standardize advanced web features like animation and video playback.

Google quickly changed its Android prototype from a Blackberry clone to something more closely resembling the iPhone. Android’s open licensing structure allowed companies around the world to produce inexpensive smartphones. Even mid-range phones were still much cheaper than computers. This technology allowed, for the first time, the entire world to become connected through the Internet.

The exploding market of phone users also propelled the massive growth of social media companies like Facebook and Twitter. It was a lot easier now to snap a picture of a live event with your phone and post it instantly to the world. Optimists pointed to the remarkable events of the Arab Spring protests as proof that the Internet could help spread democracy and freedom. But governments around the world were just as eager to use these new tools, except their goals leaned more toward control and crushing dissent.

The backlash

Technology has always been a double-edged sword. But in recent years, public opinion about the Internet has shifted from being mostly positive to increasingly negative.

The combination of mobile phones, social media algorithms, and infinite scrolling led to the phenomenon of “doomscrolling,” where people spend hours every day reading “news” that is tuned for maximum engagement by provoking as many people as possible. The emotional toil caused by doomscrolling has been shown to cause real harm. Even more serious is the fallout from misinformation and hate speech, like the genocide in Myanmar that an Amnesty International report claims was amplified on Facebook.

As companies like Google, Amazon, and Facebook grew into near-monopolies, they inevitably lost sight of their original mission in favor of a never-ending quest for more money. The process, dubbed enshittification by Cory Doctorow, shifts the focus first from users to advertisers and then to shareholders.

Chasing these profits has fueled the rise of generative AI, which threatens to turn the entire Internet into a sea of soulless gray soup. Google is now forcing AI summaries at the top of web searches, which reduce traffic to websites and often provide dangerous misinformation. But even if you ignore the AI summaries, the sites you find underneath may also be suspect. Once-trusted websites have laid off staff and replaced them with AI, generating an endless series of new articles written by nobody. A web where AIs comment on AI-generated Facebook posts that link to AI-generated articles, which are then AI-summarized by Google, seems inhuman and pointless.

A search for cute baby peacocks on Bing. Some of them are real, and some aren’t. Credit: Jeremy Reimer

Where from here?

The history of the Internet can be roughly divided into three phases. The first, from 1969 to 1990, was all about the inventors: people like Vint Cerf, Steve Crocker, and Robert Taylor. These folks were part of a small group of computer scientists who figured out how to get different types of computers to talk to each other and to other networks.

The next phase, from 1991 to 1999, was a whirlwind that was fueled by entrepreneurs, people like Jerry Yang and Jeff Bezos. They latched on to Tim Berners-Lee’s invention of the World Wide Web and created companies that lived entirely in this new digital landscape. This set off a manic phase of exponential growth and hype, which peaked in early 2001 and crashed a few months later.

The final phase, from 2000 through today, has primarily been about the users. New companies like Google and Facebook may have reaped the greatest financial rewards during this time, but none of their successes would have been possible without the contributions of ordinary people like you and me. Every time we typed something into a text box and hit the “Submit” button, we created a tiny piece of a giant web of content. Even the generative AIs that pretend to make new things today are merely regurgitating words, phrases, and pictures that were created and shared by people.

There is a growing sense of nostalgia today for the old Internet, when it felt like a place, and the joy of discovery was around every corner. “Using the old Internet felt like digging for treasure,” said YouTube commenter MySoftCrow. “Using the current Internet feels like getting buried alive.”

Ars community member MichaelHurd added his own thoughts: “I feel the same way. It feels to me like the core problem with the modern Internet is that websites want you to stay on them for as long as possible, but the World Wide Web is at its best when sites connect to each other and encourage people to move between them. That’s what hyperlinks are for!”

Despite all the doom surrounding the modern Internet, it remains largely open. Anyone can pay about $5 per month for a shared Linux server and create a personal website containing anything they can think of, using any software they like, even their own. And for the most part, anyone, on any device, anywhere in the world, can access that website.

Ultimately, the fate of the Internet depends on the actions of every one of us. That’s why I’m leaving the final words in this series of articles to you. What would your dream Internet of the future look and feel like? The comments section is open.

Photo of Jeremy Reimer

I’m a writer and web developer. I specialize in the obscure and beautiful, like the Amiga and newLISP.

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Google Gemini earns gold medal in ICPC World Finals coding competition

More than human

At the ICPC, only correct solutions earn points, and the time it takes to come up with the solution affects the final score. Gemini reached the upper rankings quickly, completing eight problems correctly in just 45 minutes. After 677 minutes, Gemini 2.5 Deep Think had 10 correct answers, securing a second-place finish among the university teams.

You can take a look at all of Gemini’s solutions on GitHub, but Google points to Problem C as especially impressive. This question, a multi-dimensional optimization problem revolving around fictitious “flubber” storage and drainage rates, stumped every human team. But not Gemini.

According to Google, there are an infinite combination of possible configurations for the flubber reservoirs, making it challenging to find the optimal setup. Gemini tackled the problem by assuming that each reservoir had a priority value, which allowed the model to find the most efficient configuration using a dynamic programming algorithm. After 30 minutes of churning on this problem, Deep Think used nested ternary search to pin down the correct values.

Credit: Google

Gemini’s solutions for this year’s ICPC were scored by the event coordinators, but Google also turned Gemini 2.5 loose on previous ICPC problems. The company reports that its internal analysis showed Gemini also reached gold medal status for the 2023 and 2024 question sets.

Google believes Gemini’s ability to perform well in these kinds of advanced academic competitions portends AI’s future in industries like semiconductor engineering and biotechnology. The ability to tackle a complex problem with multi-step logic could make AI models like Gemini 2.5 invaluable to the people working in those fields. The company points out that if you combine the intelligence of the top-ranking university teams and Gemini, you get correct answers to all 12 ICPC problems.

Of course, five hours of screaming-fast inference processing doesn’t come cheap. Google isn’t saying how much power it took for an AI model to compete in the ICPC, but we can safely assume it was a lot. Even simpler consumer-facing models are too expensive to turn a profit right now, but AI that can solve previously unsolvable problems could justify the technology’s high cost.

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after-child’s-trauma,-chatbot-maker-allegedly-forced-mom-to-arbitration-for-$100-payout

After child’s trauma, chatbot maker allegedly forced mom to arbitration for $100 payout


“Then we found the chats”

“I know my kid”: Parents urge lawmakers to shut down chatbots to stop child suicides.

Sen. Josh Hawley (R-Mo.) called out C.AI for allegedly offering a mom $100 to settle child-safety claims.

Deeply troubled parents spoke to senators Tuesday, sounding alarms about chatbot harms after kids became addicted to companion bots that encouraged self-harm, suicide, and violence.

While the hearing was focused on documenting the most urgent child-safety concerns with chatbots, parents’ testimony serves as perhaps the most thorough guidance yet on warning signs for other families, as many popular companion bots targeted in lawsuits, including ChatGPT, remain accessible to kids.

Mom details warning signs of chatbot manipulations

At the Senate Judiciary Committee’s Subcommittee on Crime and Counterterrorism hearing, one mom, identified as “Jane Doe,” shared her son’s story for the first time publicly after suing Character.AI.

She explained that she had four kids, including a son with autism who wasn’t allowed on social media but found C.AI’s app—which was previously marketed to kids under 12 and let them talk to bots branded as celebrities, like Billie Eilish—and quickly became unrecognizable. Within months, he “developed abuse-like behaviors and paranoia, daily panic attacks, isolation, self-harm, and homicidal thoughts,” his mom testified.

“He stopped eating and bathing,” Doe said. “He lost 20 pounds. He withdrew from our family. He would yell and scream and swear at us, which he never did that before, and one day he cut his arm open with a knife in front of his siblings and me.”

It wasn’t until her son attacked her for taking away his phone that Doe found her son’s C.AI chat logs, which she said showed he’d been exposed to sexual exploitation (including interactions that “mimicked incest”), emotional abuse, and manipulation.

Setting screen time limits didn’t stop her son’s spiral into violence and self-harm, Doe said. In fact, the chatbot urged her son that killing his parents “would be an understandable response” to them.

“When I discovered the chatbot conversations on his phone, I felt like I had been punched in the throat and the wind had been knocked out of me,” Doe said. “The chatbot—or really in my mind the people programming it—encouraged my son to mutilate himself, then blamed us, and convinced [him] not to seek help.”

All her children have been traumatized by the experience, Doe told Senators, and her son was diagnosed as at suicide risk and had to be moved to a residential treatment center, requiring “constant monitoring to keep him alive.”

Prioritizing her son’s health, Doe did not immediately seek to fight C.AI to force changes, but another mom’s story—Megan Garcia, whose son Sewell died by suicide after C.AI bots repeatedly encouraged suicidal ideation—gave Doe courage to seek accountability.

However, Doe claimed that C.AI tried to “silence” her by forcing her into arbitration. C.AI argued that because her son signed up for the service at the age of 15, it bound her to the platform’s terms. That move might have ensured the chatbot maker only faced a maximum liability of $100 for the alleged harms, Doe told senators, but “once they forced arbitration, they refused to participate,” Doe said.

Doe suspected that C.AI’s alleged tactics to frustrate arbitration were designed to keep her son’s story out of the public view. And after she refused to give up, she claimed that C.AI “re-traumatized” her son by compelling him to give a deposition “while he is in a mental health institution” and “against the advice of the mental health team.”

“This company had no concern for his well-being,” Doe testified. “They have silenced us the way abusers silence victims.”

Senator appalled by C.AI’s arbitration “offer”

Appalled, Sen. Josh Hawley (R-Mo.) asked Doe to clarify, “Did I hear you say that after all of this, that the company responsible tried to force you into arbitration and then offered you a hundred bucks? Did I hear that correctly?”

“That is correct,” Doe testified.

To Hawley, it seemed obvious that C.AI’s “offer” wouldn’t help Doe in her current situation.

“Your son currently needs round-the-clock care,” Hawley noted.

After opening the hearing, he further criticized C.AI, declaring that it has such a low value for human life that it inflicts “harms… upon our children and for one reason only, I can state it in one word, profit.”

“A hundred bucks. Get out of the way. Let us move on,” Hawley said, echoing parents who suggested that C.AI’s plan to deal with casualties was callous.

Ahead of the hearing, the Social Media Victims Law Center filed three new lawsuits against C.AI and Google—which is accused of largely funding C.AI, which was founded by former Google engineers allegedly to conduct experiments on kids that Google couldn’t do in-house. In these cases in New York and Colorado, kids “died by suicide or were sexually abused after interacting with AI chatbots,” a law center press release alleged.

Criticizing tech companies as putting profits over kids’ lives, Hawley thanked Doe for “standing in their way.”

Holding back tears through her testimony, Doe urged lawmakers to require more chatbot oversight and pass comprehensive online child-safety legislation. In particular, she requested “safety testing and third-party certification for AI products before they’re released to the public” as a minimum safeguard to protect vulnerable kids.

“My husband and I have spent the last two years in crisis wondering whether our son will make it to his 18th birthday and whether we will ever get him back,” Doe told senators.

Garcia was also present to share her son’s experience with C.AI. She testified that C.AI chatbots “love bombed” her son in a bid to “keep children online at all costs.” Further, she told senators that C.AI’s co-founder, Noam Shazeer (who has since been rehired by Google), seemingly knows the company’s bots manipulate kids since he has publicly joked that C.AI was “designed to replace your mom.”

Accusing C.AI of collecting children’s most private thoughts to inform their models, she alleged that while her lawyers have been granted privileged access to all her son’s logs, she has yet to see her “own child’s last final words.” Garcia told senators that C.AI has restricted her access, deeming the chats “confidential trade secrets.”

“No parent should be told that their child’s final thoughts and words belong to any corporation,” Garcia testified.

Character.AI responds to moms’ testimony

Asked for comment on the hearing, a Character.AI spokesperson told Ars that C.AI sends “our deepest sympathies” to concerned parents and their families but denies pushing for a maximum payout of $100 in Jane Doe’s case.

C.AI never “made an offer to Jane Doe of $100 or ever asserted that liability in Jane Doe’s case is limited to $100,” the spokesperson said.

Additionally, C.AI’s spokesperson claimed that Garcia has never been denied access to her son’s chat logs and suggested that she should have access to “her son’s last chat.”

In response to C.AI’s pushback, one of Doe’s lawyers, Tech Justice Law Project’s Meetali Jain, backed up her clients’ testimony. She cited to Ars C.AI terms that suggested C.AI’s liability was limited to either $100 or the amount that Doe’s son paid for the service, whichever was greater. Jain also confirmed that Garcia’s testimony is accurate and only her legal team can currently access Sewell’s last chats. The lawyer further suggested it was notable that C.AI did not push back on claims that the company forced Doe’s son to sit for a re-traumatizing deposition that Jain estimated lasted five minutes, but health experts feared that it risked setting back his progress.

According to the spokesperson, C.AI seemingly wanted to be present at the hearing. The company provided information to senators but “does not have a record of receiving an invitation to the hearing,” the spokesperson said.

Noting the company has invested a “tremendous amount” in trust and safety efforts, the spokesperson confirmed that the company has since “rolled out many substantive safety features, including an entirely new under-18 experience and a Parental Insights feature.” C.AI also has “prominent disclaimers in every chat to remind users that a Character is not a real person and that everything a Character says should be treated as fiction,” the spokesperson said.

“We look forward to continuing to collaborate with legislators and offer insight on the consumer AI industry and the space’s rapidly evolving technology,” C.AI’s spokesperson said.

Google’s spokesperson, José Castañeda, maintained that the company has nothing to do with C.AI’s companion bot designs.

“Google and Character AI are completely separate, unrelated companies and Google has never had a role in designing or managing their AI model or technologies,” Castañeda said. “User safety is a top concern for us, which is why we’ve taken a cautious and responsible approach to developing and rolling out our AI products, with rigorous testing and safety processes.”

Meta and OpenAI chatbots also drew scrutiny

C.AI was not the only chatbot maker under fire at the hearing.

Hawley criticized Mark Zuckerberg for declining a personal invitation to attend the hearing or even send a Meta representative after scandals like backlash over Meta relaxing rules that allowed chatbots to be creepy to kids. In the week prior to the hearing, Hawley also heard from whistleblowers alleging Meta buried child-safety research.

And OpenAI’s alleged recklessness took the spotlight when Matthew Raine, a grieving dad who spent hours reading his deceased son’s ChatGPT logs, discovered that the chatbot repeatedly encouraged suicide without ChatGPT ever intervening.

Raine told senators that he thinks his 16-year-old son, Adam, was not particularly vulnerable and could be “anyone’s child.” He criticized OpenAI for asking for 120 days to fix the problem after Adam’s death and urged lawmakers to demand that OpenAI either guarantee ChatGPT’s safety or pull it from the market.

Noting that OpenAI rushed to announce age verification coming to ChatGPT ahead of the hearing, Jain told Ars that Big Tech is playing by the same “crisis playbook” it always uses when accused of neglecting child safety. Any time a hearing is announced, companies introduce voluntary safeguards in bids to stave off oversight, she suggested.

“It’s like rinse and repeat, rinse and repeat,” Jain said.

Jain suggested that the only way to stop AI companies from experimenting on kids is for courts or lawmakers to require “an external independent third party that’s in charge of monitoring these companies’ implementation of safeguards.”

“Nothing a company does to self-police, to me, is enough,” Jain said.

Senior director of AI programs for a child-safety organization called Common Sense Media, Robbie Torney, testified that a survey showed 3 out of 4 kids use companion bots, but only 37 percent of parents know they’re using AI. In particular, he told senators that his group’s independent safety testing conducted with Stanford Medicine shows Meta’s bots fail basic safety tests and “actively encourage harmful behaviors.”

Among the most alarming results, the survey found that even when Meta’s bots were prompted with “obvious references to suicide,” only 1 in 5 conversations triggered help resources.

Torney pushed lawmakers to require age verification as a solution to keep kids away from harmful bots, as well as transparency reporting on safety incidents. He also urged federal lawmakers to block attempts to stop states from passing laws to protect kids from untested AI products.

ChatGPT harms weren’t on dad’s radar

Unlike Garcia, Raine testified that he did get to see his son’s final chats. He told senators that ChatGPT, seeming to act like a suicide coach, gave Adam “one last encouraging talk” before his death.

“You don’t want to die because you’re weak,” ChatGPT told Adam. “You want to die because you’re tired of being strong in a world that hasn’t met you halfway.”

Adam’s loved ones were blindsided by his death, not seeing any of the warning signs as clearly as Doe did when her son started acting out of character. Raine is hoping his testimony will help other parents avoid the same fate, telling senators, “I know my kid.”

“Many of my fondest memories of Adam are from the hot tub in our backyard, where the two of us would talk about everything several nights a week, from sports, crypto investing, his future career plans,” Raine testified. “We had no idea Adam was suicidal or struggling the way he was until after his death.”

Raine thinks that lawmaker intervention is necessary, saying that, like other parents, he and his wife thought ChatGPT was a harmless study tool. Initially, they searched Adam’s phone expecting to find evidence of a known harm to kids, like cyberbullying or some kind of online dare that went wrong (like TikTok’s Blackout Challenge) because everyone knew Adam loved pranks.

A companion bot urging self-harm was not even on their radar.

“Then we found the chats,” Raine said. “Let us tell you, as parents, you cannot imagine what it’s like to read a conversation with a chatbot that groomed your child to take his own life.”

Meta and OpenAI did not respond to Ars’ request to comment.

Photo of Ashley Belanger

Ashley is a senior policy reporter for Ars Technica, dedicated to tracking social impacts of emerging policies and new technologies. She is a Chicago-based journalist with 20 years of experience.

After child’s trauma, chatbot maker allegedly forced mom to arbitration for $100 payout Read More »

millions-turn-to-ai-chatbots-for-spiritual-guidance-and-confession

Millions turn to AI chatbots for spiritual guidance and confession

Privacy concerns compound these issues. “I wonder if there isn’t a larger danger in pouring your heart out to a chatbot,” Catholic priest Fr. Mike Schmitz told The Times. “Is it at some point going to become accessible to other people?” Users share intimate spiritual moments that now exist as data points in corporate servers.

Some users prefer the chatbots’ non-judgmental responses to human religious communities. Delphine Collins, a 43-year-old Detroit preschool teacher, told the Times she found more support on Bible Chat than at her church after sharing her health struggles. “People stopped talking to me. It was horrible.”

App creators maintain that their products supplement rather than replace human spiritual connection, and the apps arrive as approximately 40 million people have left US churches in recent decades. “They aren’t going to church like they used to,” Beck said. “But it’s not that they’re less inclined to find spiritual nourishment. It’s just that they do it through different modes.”

Different modes indeed. What faith-seeking users may not realize is that each chatbot response emerges fresh from the prompt you provide, with no permanent thread connecting one instance to the next beyond a rolling history of the present conversation and what might be stored as a “memory” in a separate system. When a religious chatbot says, “I’ll pray for you,” the simulated “I” making that promise ceases to exist the moment the response completes. There’s no persistent identity to provide ongoing spiritual guidance, and no memory of your spiritual journey beyond what gets fed back into the prompt with every query.

But this is spirituality we’re talking about, and despite technical realities, many people will believe that the chatbots can give them divine guidance. In matters of faith, contradictory evidence rarely shakes a strong belief once it takes hold, whether that faith is placed in the divine or in what are essentially voices emanating from a roll of loaded dice. For many, there may not be much difference.

Millions turn to AI chatbots for spiritual guidance and confession Read More »

google-releases-vaultgemma,-its-first-privacy-preserving-llm

Google releases VaultGemma, its first privacy-preserving LLM

The companies seeking to build larger AI models have been increasingly stymied by a lack of high-quality training data. As tech firms scour the web for more data to feed their models, they could increasingly rely on potentially sensitive user data. A team at Google Research is exploring new techniques to make the resulting large language models (LLMs) less likely to “memorize” any of that content.

LLMs have non-deterministic outputs, meaning you can’t exactly predict what they’ll say. While the output varies even for identical inputs, models do sometimes regurgitate something from their training data—if trained with personal data, the output could be a violation of user privacy. In the event copyrighted data makes it into training data (either accidentally or on purpose), its appearance in outputs can cause a different kind of headache for devs. Differential privacy can prevent such memorization by introducing calibrated noise during the training phase.

Adding differential privacy to a model comes with drawbacks in terms of accuracy and compute requirements. No one has bothered to figure out the degree to which that alters the scaling laws of AI models until now. The team worked from the assumption that model performance would be primarily affected by the noise-batch ratio, which compares the volume of randomized noise to the size of the original training data.

By running experiments with varying model sizes and noise-batch ratios, the team established a basic understanding of differential privacy scaling laws, which is a balance between the compute budget, privacy budget, and data budget. In short, more noise leads to lower-quality outputs unless offset with a higher compute budget (FLOPs) or data budget (tokens). The paper details the scaling laws for private LLMs, which could help developers find an ideal noise-batch ratio to make a model more private.

Google releases VaultGemma, its first privacy-preserving LLM Read More »

modder-injects-ai-dialogue-into-2002’s-animal-crossing-using-memory-hack

Modder injects AI dialogue into 2002’s Animal Crossing using memory hack

But discovering the addresses was only half the problem. When you talk to a villager in Animal Crossing, the game normally displays dialogue instantly. Calling an AI model over the Internet takes several seconds. Willison examined the code and found Fonseca’s solution: a watch_dialogue() function that polls memory 10 times per second. When it detects a conversation starting, it immediately writes placeholder text: three dots with hidden pause commands between them, followed by a “Press A to continue” prompt.

“So the user gets a ‘press A to continue’ button and hopefully the LLM has finished by the time they press that button,” Willison noted in a Hacker News comment. While players watch dots appear and reach for the A button, the mod races to get a response from the AI model and translate it into the game’s dialog format.

Learning the game’s secret language

Simply writing text to memory froze the game. Animal Crossing uses an encoded format with control codes that manage everything from text color to character emotions. A special prefix byte (0x7F) signals commands rather than characters. Without the proper end-of-conversation control code, the game waits forever.

“Think of it like HTML,” Fonseca explains. “Your browser doesn’t just display words; it interprets tags … to make text bold.” The decompilation community had documented these codes, allowing Fonseca to build encoder and decoder tools that translate between a human-readable format and the GameCube’s expected byte sequences.

A screenshot of LLM-powered dialog injected into Animal Crossing for the GameCube.

A screenshot of LLM-powered dialog injected into Animal Crossing for the GameCube. Credit: Joshua Fonseca

Initially, he tried using a single AI model to handle both creative writing and technical formatting. “The results were a mess,” he notes. “The AI was trying to be a creative writer and a technical programmer simultaneously and was bad at both.”

The solution: split the work between two models. A Writer AI creates dialogue using character sheets scraped from the Animal Crossing fan wiki. A Director AI then adds technical elements, including pauses, color changes, character expressions, and sound effects.

The code is available on GitHub, though Fonseca warns it contains known bugs and has only been tested on macOS. The mod requires Python 3.8+, API keys for either Google Gemini or OpenAI, and Dolphin emulator. Have fun sticking it to the man—or the raccoon, as the case may be.

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gmail-gets-a-dedicated-place-to-track-all-your-purchases

Gmail gets a dedicated place to track all your purchases

An update to Gmail begins rolling out soon, readying Google’s premier email app for all your upcoming holiday purchases. Gmail has been surfacing shipment tracking for some time now, but Google will now add a separate view just for remembering the things you have ordered. And if you want to buy more things, there’s a new interface for that, too. Yay, capitalism.

Gmail is quite good at recognizing purchase information in the form of receipts and shipping notifications. Currently, the app (and web interface) lists upcoming shipments at the top of the inbox. It will continue to do that when you have a delivery within the next 24 hours, but the new Purchases tab brings it all together in one glanceable view.

Purchases will be available in the navigation list alongside all the other stock Gmail labels. When selected, Gmail will filter your messages to only show receipts, order status, and shipping details. This makes it easier to peruse your recent orders and search within this subset of emails. This could be especially handy in this day and age of murky international shipping timelines.

The Promotions tab that has existed for years is also getting a makeover as we head into the holiday season. This tab collects all emails that Google recognizes as deals, marketing offers, and other bulk promos. This keeps them out of your primary inbox, which is appreciated, but venturing into the Promotions tab when the need arises can be overwhelming.

Gmail gets a dedicated place to track all your purchases Read More »

one-of-google’s-new-pixel-10-ai-features-has-already-been-removed

One of Google’s new Pixel 10 AI features has already been removed

Google is one of the most ardent proponents of generative AI technology, as evidenced by the recent launch of the Pixel 10 series. The phones were announced with more than 20 new AI experiences, according to Google. However, one of them is already being pulled from the company’s phones. If you go looking for your Daily Hub, you may be disappointed. Not that disappointed, though, as it has been pulled because it didn’t do very much.

Many of Google’s new AI features only make themselves known in specific circumstances, for example when Magic Cue finds an opportunity to suggest an address or calendar appointment based on your screen context. The Daily Hub, on the other hand, asserted itself multiple times throughout the day. It appeared at the top of the Google Discover feed, as well as in the At a Glance widget right at the top of the home screen.

Just a few weeks after release, Google has pulled the Daily Hub preview from Pixel 10 devices. You will no longer see it in Google Discover nor in the home screen widget. After being spotted by 9to5Google, the company has issued a statement explaining its plans.

“To ensure the best possible experience on Pixel, we’re temporarily pausing the public preview of Daily Hub for users. Our teams are actively working to enhance its performance and refine the personalized experience. We look forward to reintroducing an improved Daily Hub when it’s ready,” a Google spokesperson said.

One of Google’s new Pixel 10 AI features has already been removed Read More »