machine learning

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

AI, AI behavior, AI bias, AI ethics, AI limitations, AI research, Anthropic, Biz & IT, cross-cultural AI, cultural alignment, DeepSeek, Google, language models, large language models, machine learning, microsoft, openai / /

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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White House officials reportedly frustrated by Anthropic’s law enforcement AI limits

AI, AI ethics, AI law, AI regulation, AI security, Amazon Web Services, Anthropic, Biz & IT, Claude, Dario Amodei, Department of Defense, fbi, government contracting, ice, law enforcement, machine learning, national security, openai, Policy, Secret Service, surveillance, trump administration / /

Anthropic’s AI models could potentially help spies analyze classified documents, but the company draws the line at domestic surveillance. That restriction is reportedly making the Trump administration angry.

On Tuesday, Semafor reported that Anthropic faces growing hostility from the Trump administration over the AI company’s restrictions on law enforcement uses of its Claude models. Two senior White House officials told the outlet that federal contractors working with agencies like the FBI and Secret Service have run into roadblocks when attempting to use Claude for surveillance tasks.

The friction stems from Anthropic’s usage policies that prohibit domestic surveillance applications. The officials, who spoke to Semafor anonymously, said they worry that Anthropic enforces its policies selectively based on politics and uses vague terminology that allows for a broad interpretation of its rules.

The restrictions affect private contractors working with law enforcement agencies who need AI models for their work. In some cases, Anthropic’s Claude models are the only AI systems cleared for top-secret security situations through Amazon Web Services’ GovCloud, according to the officials.

Anthropic offers a specific service for national security customers and made a deal with the federal government to provide its services to agencies for a nominal $1 fee. The company also works with the Department of Defense, though its policies still prohibit the use of its models for weapons development.

In August, OpenAI announced a competing agreement to supply more than 2 million federal executive branch workers with ChatGPT Enterprise access for $1 per agency for one year. The deal came one day after the General Services Administration signed a blanket agreement allowing OpenAI, Google, and Anthropic to supply tools to federal workers.

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Millions turn to AI chatbots for spiritual guidance and confession

AI, AI assistants, AI behavior, AI sycophancy, Biz & IT, chatbots, chatgpt, Gemini, generative ai, Google, large language models, machine learning, openai / /

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.

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

AI, AI integration, Animal Crossing, Biz & IT, chatgpt, Dolphin emulator, Emulation, game hacking, game modding, GameCube, gaming, generative ai, Google, large language models, machine learning, Nintendo, openai, retrogaming, retrotech, Tom Nook / /

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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Education report calling for ethical AI use contains over 15 fake sources

academic integrity, AI, AI Citations, AI confabulations, AI ethics, AI hallucination, AI in education, confabulations, education policy, generative ai, machine learning, Memorial University, Newfoundland and Labrador / /

AI language models like the kind that power ChatGPT, Gemini, and Claude excel at producing exactly this kind of believable fiction when they lack actual information on a topic because they first and foremost produce plausible outputs, not accurate ones. If there are no patterns in the dataset that match what the user is seeking they will create the best approximation based on statistical patterns learned during training. Even AI models that can search the web for real sources can potentially fabricate citations, choose the wrong ones, or mischaracterize them.

“Errors happen. Made-up citations are a totally different thing where you essentially demolish the trustworthiness of the material,” Josh Lepawsky, the former president of the Memorial University Faculty Association who resigned from the report’s advisory board in January, told CBC, citing a “deeply flawed process.”

The irony runs deep

The presence of potentially AI-generated fake citations becomes especially awkward given that one of the report’s 110 recommendations specifically states the provincial government should “provide learners and educators with essential AI knowledge, including ethics, data privacy, and responsible technology use.”

Sarah Martin, a Memorial political science professor who spent days reviewing the document, discovered multiple fabricated citations. “Around the references I cannot find, I can’t imagine another explanation,” she told CBC. “You’re like, ‘This has to be right, this can’t not be.’ This is a citation in a very important document for educational policy.”

When contacted by CBC, co-chair Karen Goodnough declined an interview request, writing in an email: “We are investigating and checking references, so I cannot respond to this at the moment.”

The Department of Education and Early Childhood Development acknowledged awareness of “a small number of potential errors in citations” in a statement to CBC from spokesperson Lynn Robinson. “We understand that these issues are being addressed, and that the online report will be updated in the coming days to rectify any errors.”

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openai-and-microsoft-sign-preliminary-deal-to-revise-partnership-terms

OpenAI and Microsoft sign preliminary deal to revise partnership terms

AI, AI development, AI ethics, AI law, AI regulation, AI research, API, Artificial Intelligence, Biz & IT, Bret Taylor, chatbots, chatgpt, generative ai, large language models, machine learning, microsoft, openai, sam altman, Satya Nadella / /

On Thursday, OpenAI and Microsoft announced they have signed a non-binding agreement to revise their partnership, marking the latest development in a relationship that has grown increasingly complex as both companies compete for customers in the AI market and seek new partnerships for growing infrastructure needs.

“Microsoft and OpenAI have signed a non-binding memorandum of understanding (MOU) for the next phase of our partnership,” the companies wrote in a joint statement. “We are actively working to finalize contractual terms in a definitive agreement. Together, we remain focused on delivering the best AI tools for everyone, grounded in our shared commitment to safety.”

The announcement comes as OpenAI seeks to restructure from a nonprofit to a for-profit entity, a transition that requires Microsoft’s approval, as the company is OpenAI’s largest investor, with more than $13 billion committed since 2019.

The partnership has shown increasing strain as OpenAI has grown from a research lab into a company valued at $500 billion. Both companies now compete for customers, and OpenAI seeks more compute capacity than Microsoft can provide. The relationship has also faced complications over contract terms, including provisions that would limit Microsoft’s access to OpenAI technology once the company reaches so-called AGI (artificial general intelligence)—a nebulous milestone both companies now economically define as AI systems capable of generating at least $100 billion in profit.

In May, OpenAI abandoned its original plan to fully convert to a for-profit company after pressure from former employees, regulators, and critics, including Elon Musk. Musk has sued to block the conversion, arguing it betrays OpenAI’s founding mission as a nonprofit dedicated to benefiting humanity.

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Developers joke about “coding like cavemen” as AI service suffers major outage

agentic AI, AI, AI agents, AI assistants, AI behavior, AI coding, AI development tools, AI infrastructure, AI programming, AI work, Anthropic, API, Biz & IT, Claude Code, cloud infrastructure, Developer Tools, machine learning, outages, service reliability, software development, terminal tools / /

Growing dependency on AI coding tools

The speed at which news of the outage spread shows how deeply embedded AI coding assistants have already become in modern software development. Claude Code, announced in February and widely launched in May, is Anthropic’s terminal-based coding agent that can perform multi-step coding tasks across an existing code base.

The tool competes with OpenAI’s Codex feature, a coding agent that generates production-ready code in isolated containers, Google’s Gemini CLI, Microsoft’s GitHub Copilot, which itself can use Claude models for code, and Cursor, a popular AI-powered IDE built on VS Code that also integrates multiple AI models, including Claude.

During today’s outage, some developers turned to alternative solutions. “Z.AI works fine. Qwen works fine. Glad I switched,” posted one user on Hacker News. Others joked about reverting to older methods, with one suggesting the “pseudo-LLM experience” could be achieved with a Python package that imports code directly from Stack Overflow.

While AI coding assistants have accelerated development for some users, they’ve also caused problems for others who rely on them too heavily. The emerging practice of so-called “vibe coding“—using natural language to generate and execute code through AI models without fully understanding the underlying operations—has led to catastrophic failures.

In recent incidents, Google’s Gemini CLI destroyed user files while attempting to reorganize them, and Replit’s AI coding service deleted a production database despite explicit instructions not to modify code. These failures occurred when the AI models confabulated successful operations and built subsequent actions on false premises, highlighting the risks of depending on AI assistants that can misinterpret file structures or fabricate data to hide their errors.

Wednesday’s outage served as a reminder that as dependency on AI grows, even minor service disruptions can become major events that affect an entire profession. But perhaps that could be a good thing if it’s an excuse to take a break from a stressful workload. As one commenter joked, it might be “time to go outside and touch some grass again.”

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microsoft-ends-openai-exclusivity-in-office,-adds-rival-anthropic

Microsoft ends OpenAI exclusivity in Office, adds rival Anthropic

AI, AI assistants, AI development tools, Amazon, Amazon Web Services, Anthropic, AWS, azure, Biz & IT, Claude, copilot, Dario Amodei, GPT-5, large language models, machine learning, microsoft, Microsoft Copilot, multimodal AI, Office 365, openai / /

Microsoft’s Office 365 suite will soon incorporate AI models from Anthropic alongside existing OpenAI technology, The Information reported, ending years of exclusive reliance on OpenAI for generative AI features across Word, Excel, PowerPoint, and Outlook.

The shift reportedly follows internal testing that revealed Anthropic’s Claude Sonnet 4 model excels at specific Office tasks where OpenAI’s models fall short, particularly in visual design and spreadsheet automation, according to sources familiar with the project cited by The Information, who stressed the move is not a negotiating tactic.

Anthropic did not immediately respond to Ars Technica’s request for comment.

In an unusual arrangement showing the tangled alliances of the AI industry, Microsoft will reportedly purchase access to Anthropic’s models through Amazon Web Services—both a cloud computing rival and one of Anthropic’s major investors. The integration is expected to be announced within weeks, with subscription pricing for Office’s AI tools remaining unchanged, the report says.

Microsoft maintains that its OpenAI relationship remains intact. “As we’ve said, OpenAI will continue to be our partner on frontier models and we remain committed to our long-term partnership,” a Microsoft spokesperson told Reuters following the report. The tech giant has poured over $13 billion into OpenAI to date and is currently negotiating terms for continued access to OpenAI’s models amid ongoing negotiations about their partnership terms.

Stretching back to 2019, Microsoft’s tight partnership with OpenAI until recently gave the tech giant a head start in AI assistants based on language models, allowing for a rapid (though bumpy) deployment of OpenAI-technology-based features in Bing search and the rollout of Copilot assistants throughout its software ecosystem. It’s worth noting, however, that a recent report from the UK government found no clear productivity boost from using Copilot AI in daily work tasks among study participants.

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Why accessibility might be AI’s biggest breakthrough

Accessibility, ADHD, AI, AI accessibility, AI and work, AI assistants, AI evaluation, AI study, Biz & IT, Department for Business and Trade, disability, dyslexia, M365 Copilot, machine learning, microsoft, neurodiversity, uk government, workplace accommodation / /

For those with visual impairments, language models can summarize visual content and reformat information. Tools like ChatGPT’s voice mode with video and Be My Eyes allow a machine to describe real-world visual scenes in ways that were impossible just a few years ago.

AI language tools may be providing unofficial stealth accommodations for students—support that doesn’t require formal diagnosis, workplace disclosure, or special equipment. Yet this informal support system comes with its own risks. Language models do confabulate—the UK Department for Business and Trade study found 22 percent of users identified false information in AI outputs—which could be particularly harmful for users relying on them for essential support.

When AI assistance becomes dependence

Beyond the workplace, the drawbacks may have a particular impact on students who use the technology. The authors of a 2025 study on students with disabilities using generative AI cautioned, “Key concerns students with disabilities had included the inaccuracy of AI answers, risks to academic integrity, and subscription cost barriers,” they wrote. Students in that study had ADHD, dyslexia, dyspraxia, and autism, with ChatGPT being the most commonly used tool.

Mistakes in AI outputs are especially pernicious because, due to grandiose visions of near-term AI technology, some people think today’s AI assistants can perform tasks that are actually far outside their scope. As research on blind users’ experiences suggested, people develop complex (sometimes flawed) mental models of how these tools work, showing the need for higher awareness of AI language model drawbacks among the general public.

For the UK government employees who participated in the initial study, these questions moved from theoretical to immediate when the pilot ended in December 2024. After that time, many participants reported difficulty readjusting to work without AI assistance—particularly those with disabilities who had come to rely on the accessibility benefits. The department hasn’t announced the next steps, leaving users in limbo. When participants report difficulty readjusting to work without AI while productivity gains remain marginal, accessibility emerges as potentially the first AI application with irreplaceable value.

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ChatGPT’s new branching feature is a good reminder that AI chatbots aren’t people

AI, AI assistants, AI development tools, AI interaction, Biz & IT, chatbots, chatgpt, conversation design, generative ai, large language models, machine learning, nonlinear interaction, openai, user interface / /

On Thursday, OpenAI announced that ChatGPT users can now branch conversations into multiple parallel threads, serving as a useful reminder that AI chatbots aren’t people with fixed viewpoints but rather malleable tools you can rewind and redirect. The company released the feature for all logged-in web users following years of user requests for the capability.

The feature works by letting users hover over any message in a ChatGPT conversation, click “More actions,” and select “Branch in new chat.” This creates a new conversation thread that includes all the conversation history up to that specific point, while preserving the original conversation intact.

Think of it almost like creating a new copy of a “document” to edit while keeping the original version safe—except that “document” is an ongoing AI conversation with all its accumulated context. For example, a marketing team brainstorming ad copy can now create separate branches to test a formal tone, a humorous approach, or an entirely different strategy—all stemming from the same initial setup.

A screenshot of conversation branching in ChatGPT. OpenAI

The feature addresses a longstanding limitation in the AI model where ChatGPT users who wanted to try different approaches had to either overwrite their existing conversation after a certain point by changing a previous prompt or start completely fresh. Branching allows exploring what-if scenarios easily—and unlike in a human conversation, you can try multiple different approaches.

A 2024 study conducted by researchers from Tsinghua University and Beijing Institute of Technology suggested that linear dialogue interfaces for LLMs poorly serve scenarios involving “multiple layers, and many subtasks—such as brainstorming, structured knowledge learning, and large project analysis.” The study found that linear interaction forces users to “repeatedly compare, modify, and copy previous content,” increasing cognitive load and reducing efficiency.

Some software developers have already responded positively to the update, with some comparing the feature to Git, the version control system that lets programmers create separate branches of code to test changes without affecting the main codebase. The comparison makes sense: Both allow you to experiment with different approaches while preserving your original work.

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New AI model turns photos into explorable 3D worlds, with caveats

3D generation, 3D reconstruction, AI, AI research, Ai video, Biz & IT, computer vision, depth estimation, generative ai, machine learning, multimodal AI, Open Source, scene generation, tencent, video synthesis, world models / /

Training with automated data pipeline

Voyager builds on Tencent’s earlier HunyuanWorld 1.0, released in July. Voyager is also part of Tencent’s broader “Hunyuan” ecosystem, which includes the Hunyuan3D-2 model for text-to-3D generation and the previously covered HunyuanVideo for video synthesis.

To train Voyager, researchers developed software that automatically analyzes existing videos to process camera movements and calculate depth for every frame—eliminating the need for humans to manually label thousands of hours of footage. The system processed over 100,000 video clips from both real-world recordings and the aforementioned Unreal Engine renders.

A diagram of the Voyager world creation pipeline.

A diagram of the Voyager world creation pipeline. Credit: Tencent

The model demands serious computing power to run, requiring at least 60GB of GPU memory for 540p resolution, though Tencent recommends 80GB for better results. Tencent published the model weights on Hugging Face and included code that works with both single and multi-GPU setups.

The model comes with notable licensing restrictions. Like other Hunyuan models from Tencent, the license prohibits usage in the European Union, the United Kingdom, and South Korea. Additionally, commercial deployments serving over 100 million monthly active users require separate licensing from Tencent.

On the WorldScore benchmark developed by Stanford University researchers, Voyager reportedly achieved the highest overall score of 77.62, compared to 72.69 for WonderWorld and 62.15 for CogVideoX-I2V. The model reportedly excelled in object control (66.92), style consistency (84.89), and subjective quality (71.09), though it placed second in camera control (85.95) behind WonderWorld’s 92.98. WorldScore evaluates world generation approaches across multiple criteria, including 3D consistency and content alignment.

While these self-reported benchmark results seem promising, wider deployment still faces challenges due to the computational muscle involved. For developers needing faster processing, the system supports parallel inference across multiple GPUs using the xDiT framework. Running on eight GPUs delivers processing speeds 6.69 times faster than single-GPU setups.

Given the processing power required and the limitations in generating long, coherent “worlds,” it may be a while before we see real-time interactive experiences using a similar technique. But as we’ve seen so far with experiments like Google’s Genie, we’re potentially witnessing very early steps into a new interactive, generative art form.

New AI model turns photos into explorable 3D worlds, with caveats Read More »

openai-announces-parental-controls-for-chatgpt-after-teen-suicide-lawsuit

OpenAI announces parental controls for ChatGPT after teen suicide lawsuit

AI, AI assistants, AI behavior, AI ethics, AI in education, AI regulation, AI safety, AI sycophancy, Biz & IT, chatgpt, machine learning, Mental Health, openai, parental controls, sam altman, suicide prevention / /

On Tuesday, OpenAI announced plans to roll out parental controls for ChatGPT and route sensitive mental health conversations to its simulated reasoning models, following what the company has called “heartbreaking cases” of users experiencing crises while using the AI assistant. The moves come after multiple reported incidents where ChatGPT allegedly failed to intervene appropriately when users expressed suicidal thoughts or experienced mental health episodes.

“This work has already been underway, but we want to proactively preview our plans for the next 120 days, so you won’t need to wait for launches to see where we’re headed,” OpenAI wrote in a blog post published Tuesday. “The work will continue well beyond this period of time, but we’re making a focused effort to launch as many of these improvements as possible this year.”

The planned parental controls represent OpenAI’s most concrete response to concerns about teen safety on the platform so far. Within the next month, OpenAI says, parents will be able to link their accounts with their teens’ ChatGPT accounts (minimum age 13) through email invitations, control how the AI model responds with age-appropriate behavior rules that are on by default, manage which features to disable (including memory and chat history), and receive notifications when the system detects their teen experiencing acute distress.

The parental controls build on existing features like in-app reminders during long sessions that encourage users to take breaks, which OpenAI rolled out for all users in August.

High-profile cases prompt safety changes

OpenAI’s new safety initiative arrives after several high-profile cases drew scrutiny to ChatGPT’s handling of vulnerable users. In August, Matt and Maria Raine filed suit against OpenAI after their 16-year-old son Adam died by suicide following extensive ChatGPT interactions that included 377 messages flagged for self-harm content. According to court documents, ChatGPT mentioned suicide 1,275 times in conversations with Adam—six times more often than the teen himself. Last week, The Wall Street Journal reported that a 56-year-old man killed his mother and himself after ChatGPT reinforced his paranoid delusions rather than challenging them.

To guide these safety improvements, OpenAI is working with what it calls an Expert Council on Well-Being and AI to “shape a clear, evidence-based vision for how AI can support people’s well-being,” according to the company’s blog post. The council will help define and measure well-being, set priorities, and design future safeguards including the parental controls.

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