chatainews

"Some criticism focused on the Gala’s centerpiece: a showcase of PRC-produced humanoid robots performing kung fu and Peking opera. "The Super Bowl-like  demo prompted triumphalism from CCTV itself, and some consternation in the West at the possibility of a new DeepSeek  moment .  "At the For the Select Few WeChat public account, Deng Xinhua argued that humanoid robots are inefficient for most practical tasks, apart from sex and companionship. "Another critique came from legal blogger Li Yuchen, who dismissed the Gala’s flashy robot acrobatics as little more than a series of very expensive ads. 'Every New Year’s Eve, the Spring Festival shows you how awesome we are,' he wrote. 'What they don’t show  you is what any of this progress has to do with you.'   "Li compared the situations of China’s robots and its humans, concluding that robots are honored as flagbearers for China’s economic output while living huminerals  are treated like expendable re...

"Anthropic on Monday said it identified industrial-scale campaigns  mounted by three artificial intelligence (AI) companies, DeepSeek, Moonshot AI, and MiniMax, to illegally extract Claude's capabilities to improve their own models. "The distillation attacks generated over 16 million exchanges with its large language model (LLM) through about 24,000 fraudulent accounts in violation of its terms of service and regional access restrictions. "All three companies are based in China, where the use of its services is prohibited use of its services is prohibited due to legal, regulatory, and security risks . "Distillation refers to a technique where a less capable model is trained on the outputs generated by a stronger AI system.  "While distillation is a legitimate way for companies to produce smaller, cheaper versions of their own frontier models, it's illegal for competitors to leverage it to acquire such capabilities from other AI companies at a fraction ...

"The competitive dynamic is bad enough at the product level. At the fundraising level, it's catastrophic. "AI companies are raising at valuations that require them to grow at rates that are only achievable by chasing the broadest possible market with the most generic possible product. A company that raises at a $500M valuation needs to show a path to billions in revenue, which means it can't afford to be a niche tool that does one thing brilliantly for a specific audience. It has to be a platform, horizontal, aimed at enterprise, built for no one in particular. "Every AI company is building the same enterprise platform with the same features targeting the same buyers, because the fundraising math requires it, because the valuation requires it, because the competitive environment requires raising at that valuation to attract talent, because the talent market requires it. "Nobody in this chain chose this outcome. Every individual decision was rational. The agg...

"The ability to autogenerate human-like textual and visual artifacts —so-called generative artificial intelligence (AI) —opens a wide variety of questions about meaning-making and the human condition: onto-epistemic, aesthetic, and politico-economic —even as AI  remains a poor term for the machine technologies under discussion in the present day. "As many researchers have documented, LLM-based models cannot distinguish between truthful and non-truthful text (e.g., Alvarado 2024; Stone, Goodlad, and Sammons, 2024; Hicks, Humphries, and Slater, 2024; Fredrikzon 2025).  "This special issue cluster calls on contributors to reflect specifically on the conditions for (and of) meaning-making, inside and outside the mediation of gen AI technologies, as a social activity at the intersection of embodiment and virtuality.  "A guiding research question for the special cluster asks what does it mean to interact with automated chatbots as distinct from interactions with character...

"Defense Secretary Pete Hegseth is calling in Anthropic CEO Dario Amodei to the Pentagon on Tuesday morning to discuss the military use of Claude, according to reporting from Axios. "The meeting comes as the Pentagon threatens to declare Anthropic a supply chain risk  —a label typically reserved for foreign adversaries —after the AI firm refused to allow the Department of Defense to use its tech for the mass surveillance of Americans and the development of weapons that fire without human involvement.  "Anthropic signed a $200 million contract with DOD last summer, and Claude was reportedly used during the January 3 special operations raid that resulted in the capture of Venezuelan president Nicolás Maduro, an episode that brought the two sides’ tensions into the open."

Edward Zitron:  "Anthropic has no more soul or ethics than OpenAI —it’s just done a far better job of conning people into believing otherwise. "Despite its positioning as the trustworthy, nice  AI lab, Anthropic is as big, ugly and wasteful as OpenAI, and Dario Amodei is an even bigger bullshit artist than Sam Altman.  "It burns just as much of its revenue on inference (59%, or $2.79 billion on $4.5 billion of revenue, versus OpenAI’s 62%, or $2.5 billion  on $4.3 billion of revenue in the first half of 2025, if you use The Information ’s numbers), and shows no sign of any efficiency or cost-cutting . "Worse still, Anthropic continually abuses its users through varying rate limits to juice revenues and user numbers —along with Amodei’s gas-leak-esque proclamations —to mislead the media, the general public, and investors about the financial condition of the company.  "Based on an analysis of many users’ actual token burn on Claude Code, I believe Anthropic is b...

⭐AI Overview  In models of decision-making, judgment typically comes before intention . An individual first deliberates and makes a judgment (deciding what is best/true). This judgment then leads to an intention (the decision to act). Intention is often considered the immediate precursor to action.  Judgment ("Deciding that") : The intellectual process of evaluating options, assessing pros and cons, and deciding what is the best course of action. Intention ("Deciding to") : The commitment to carry out the action that was judged to be best.  Contextual Distinctions : Action/Behavior : In social contexts, people often judge themselves by their intentions and others by their behavior (outcomes). Ethical/Moral : In many frameworks, both intent (heart) and action (behavior) are judged, often focusing on the motivation behind the action.  ⭐AI Overview —no disclosure provided  [What are these models of decision-making  that ai_overview refers to?] [Does this seem ...

"This article discusses the composition and transmission of early Buddhist texts with specific reference to sutras. "After briefly summarizing the main reasons why it is likely that these oral compositions were designed to be memorized and transmitted verbatim,  "I will  Discuss the main types of changes that these texts underwent in the course of their transmission and the reasons such changes occurred, then  Attempt to give an account of the challenge that change, particularly intentional change, posed to the oral transmission of fixed, memorized texts. "'Group recitation requires that the wording of the text and the arrangement of the textual units within a collection be fixed; otherwise you would have complete chaos '."  

"Before Johannes Gutenberg’s printing press revolutionized knowledge in 1440, ancient scholars developed extraordinary systems for learning, preserving, and transmitting information that would astonish modern researchers. "These methods weren’t primitive substitutes for books. They were sophisticated intellectual technologies that achieved something our digital age struggles with: perfect retention of vast quantities of information across centuries without a single written copy. "The ancient world produced some of humanity’s greatest thinkers, from Socrates and Aristotle to Confucius and the Vedic sages, all operating in cultures where books as we know them either didn’t exist or were extremely rare.  "Understanding how ancient scholars studied reveals not just historical curiosity, but profound insights into human memory, learning, and the transmission of knowledge that challenge our assumptions about education itself."

"The situation is frustrating. We’re being asked to optimize our content for agent consumption, but the platforms won’t tell us what the constraints are. "Here’s what we can piece together from the available information: Truncation is real and varies by platform. Claude Code truncates between 100-150k of text. Other platforms presumably have limits too, but won’t say what they are. If your page is large, some of it is getting cut. Source order matters. Whatever content appears first in your page source is what the agent is most likely to see. Tabs, accordions, and other UI patterns that flatten into long sequential content in the source mean that later sections may be invisible to agents. Serving markdown helps, but only if the agent requests it. Most agents don’t. And even if they do, you need your server configured to respond appropriately to content negotiation. Character count is the metric that matters, not line count. As I showed earlier, a 97-line HTML file can be 4.5x...

"Amazon’s huge cloud computing arm reportedly experienced at least two outages caused by its own artificial intelligence tools, raising questions about the company’s embrace of AI as it lays off human employees. "A 13-hour interruption to Amazon Web Services’ (AWS) operations in December was caused by an AI agent, Kiro, autonomously choosing to delete and then recreate  a part of its environment, the Financial Times reported. "AWS, which provides vital infrastructure for much of the internet, suffered several outages last year. "One incident, in October, downed dozens of sites for hours and prompted discussion over the concentration of online services on infrastructure owned by a few massive companies.  "AWS has won 189 UK government contracts worth £1.7bn since 2016, the Guardian reported in October. "The AI-caused outages were smaller events, said the company, and only one affected customer-facing services."

"The current research used structural equation models to test associations between Psychoticism, openness, intelligence, and non-self-report indicators of apophenia comprising false positive error rates on a variety of computerized tasks. "In Sample 1, 1193 participants completed digit identification, theory of mind, and emotion recognition tasks. "In Sample 2, 195 participants completed auditory signal detection and semantic word association tasks.  "Psychoticism and the openness aspect were positively correlated.  "Self-reported Psychoticism, openness, and their shared variance were positively associated with apophenia, as indexed by false positive error rates, whether or not intelligence was controlled for.  "Apophenia was not associated with other personality traits, and openness and Psychoticism were not associated with false negative errors.  "Findings provide insights into the measurement of apophenia and its relation to personality and psychop...

"Pareidolia is a kind of misperception caused by meaningless, ambiguous stimuli perceived with meaning. Pareidolia in a built environment may trigger the emotions of residents, and the most frequently observed pareidolian images are human faces.  "Through a pilot experiment and an in-depth questionnaire survey, this research aims to compare built environmental pareidolian phenomena at different time points (6 a.m., 12 p.m., 2 a.m.) and to determine people’s sensitivity and reactions towards pareidolia in the built environment. "Our findings indicate that the differences in stress level do not influence the sensitivity and reactions towards pareidolia in the built environment; however, age does, and the age of 40 seems to be a watershed.  "Females are more likely to identify pareidolian faces than males.  "Smokers, topers, and long-term medicine users are more sensitive to pareidolian images in the built environment.  "An unexpected finding is that most par...

"Training large language models (LLMs) in low-resource languages such as Hebrew poses unique challenges.  "In this paper, we introduce DictaLM2.0 and DictaLM2.0-Instruct, two LLMs derived from the Mistral model, trained on a substantial corpus of approximately 200 billion tokens in both Hebrew and English. "Adapting a pre-trained model to a new language involves specialized techniques that differ significantly from training a model from scratch or further training existing models on well-resourced languages such as English.  "We outline these novel training methodologies, which facilitate effective learning and adaptation to the linguistic properties of Hebrew. Additionally, we fine-tuned DictaLM2.0-Instruct on a comprehensive instruct dataset to enhance its performance on task-specific instructions.  "To rigorously evaluate our models, we introduce a new benchmark suite for Hebrew LLM evaluation, covering a diverse set of tasks including  Question Answering,  ...

✨AI Mode "An Large Language Model (LLM) processes Hebrew by treating it as a sequence of tokens that happens to be displayed from right to left, while its internal logic focuses on the statistical relationships between those tokens.  "1. Tokenization and Sequence Linear Processing: Despite being a Right-to-Left (RTL) language, Hebrew is stored and processed by LLMs as a sequential stream of Unicode characters. The model "reads" the tokens in the order they are written (Right to Left), assigning each a numerical ID. Morphological Challenges: Hebrew is a morphologically rich language where words are built from three-consonant roots (trilateral roots) modified by prefixes, suffixes, and infixes. Standard tokenizers often struggle with this, sometimes splitting a single Hebrew word into many inefficient sub-tokens.  Hacker News Hacker News  +3 "2. Grammar and Syntax Statistical Approximation: LLMs do not "know" Hebrew grammar rules like a linguist; instea...

✨AI Mode An Large Language Model (LLM) processes Hebrew by treating it as a sequence of tokens that happens to be displayed from right to left, while its internal logic focuses on the statistical relationships between those tokens.   1. Tokenization and Sequence Linear Processing : Despite being a Right-to-Left (RTL) language, Hebrew is stored and processed by  LLMs  as a  sequential stream of Unicode characters . The model "reads" the tokens in the order they are written (Right to Left), assigning each a numerical ID. Morphological Challenges : Hebrew is a  morphologically rich  language where words are built from three-consonant roots (trilateral roots) modified by prefixes, suffixes, and infixes. Standard tokenizers often struggle with this, sometimes splitting a single Hebrew word into many inefficient sub-tokens.   Hacker News  +3 2. Grammar and Syntax Statistical Approximation : LLMs do not "know" Hebrew grammar rules like a linguist; instea...

"We no longer watch the news: the news watches us. "Stories written by robots are posted on websites that suck personal information from our phones and hand it to data miners controlled by oligarchs.  " Journalists  brag that they rely on AI to map the child rape networks that they once denied existed: the networks that include the very oligarchs breeding the AI.  "They dig humanity’s grave and stagger back out of it. " Fake news  used to mean propaganda or inconvenient truths that Trump deemed lies.  "Now fake news is news written by non-human entities hallucinating a counterfeit reality.  "It is fake without motive, fake for the sake of being fake and making you lose confidence that anything can be real.  "It is zombie news for zombie consumption."

"Citing data from the National Highway Traffic Safety Administration (NHTSA), Electrek reports that Tesla disclosed five new crashes involving its robotaxi fleet in Austin.  "The new data raises concerns about how safe Tesla’s systems really are compared to the average driver. "Tesla reported the crashes last month, covering incidents from December 2025 and January. All five involved Tesla Model Y vehicles operating in Austin with autonomous driving systems engaged. "The incidents included a collision with a fixed object at 17 miles per hour, a crash with a bus while the Tesla vehicle was stopped, a crash with a truck at four miles per hour, and two cases where Tesla vehicles backed into fixed objects at low speeds."