GoKawiil
Tech News
In a new essay, writer Cory Doctorow argues that people project intention and meaning onto AI systems like Anthropic's Claude much as religious believers see purpose in a sunset. He suggests this instinct to read agency into statistical text generators is a kind of collective delusion rather than evidence of genuine understanding inside the machine.
The United Nations unveiled the UN System Data Commons, a new platform built on Google's open-source Data Commons technology that lets people query UN statistics using plain-language questions and supports the Model Context Protocol so AI systems can pull data directly. It replaces the older UNData portal, which relied on manual browsing rather than conversational search. The announcement came alongside a UNICEF study showing leading chatbots answered development-data questions correctly only about 21% of the time.
IEEE Computer Society highlights that most AI products rely on large language models for reasoning and generation, but these models depend heavily on retrieving relevant background information before producing answers. When that retrieved data is outdated, weak, or irrelevant, the resulting output suffers regardless of the model's sophistication.
A study published in Cell introduces a specialized AI toolkit for longevity research, including large language models trained specifically on ageing-biology data, 17 benchmark tasks to evaluate performance on ageing-related questions, and an interface linking these models with AI research assistants. When tested against major commercial models from companies like OpenAI and DeepSeek, the purpose-built ageing models outperformed the larger general-purpose systems on most benchmark tasks.
Climate scientist Zeke Hausfather analyzed his own usage of Anthropic's Claude Code assistant and calculated that a typical day of work consumed between 1.2 and 5.9 kilowatt-hours of electricity, comparable to running two refrigerators nonstop for a day. He found that 96 percent of the roughly 3.2 billion tokens processed over eight weeks came from the agent repeatedly re-reading its own accumulated memory at each step, rather than generating visible output.
At the 18th BRICS Summit in New Delhi, President Xi Jinping said China would spearhead artificial-intelligence cooperation among the bloc's developing economies, including creating an open-source AI community, sharing large language model development, and running training seminars. He also proposed a digital ecosystem cloud platform, an engineer cultivation alliance, and a youth exchange program focused on scientific innovation.
AI companies are pouring billions into massive data centers and power plants to support 'agents'—AI systems that autonomously execute multi-step tasks rather than simply answering questions. Unlike a single chatbot query, an agent can generate hundreds of self-prompts to complete complex jobs, such as building an entire website over several hours. OpenAI recently touted a swarm of over 10,000 agents exchanging 2.7 million messages to tackle a longstanding math problem, though mathematicians have disputed the significance of that result.
Amid a public spat over an OpenAI math-proof controversy and an Anthropic researcher's resignation over safety concerns, AI ethicist Timnit Gebru weighed in publicly, arguing that apocalyptic warnings about AI 'killing humanity' are overstated and self-serving. Gebru, known for her contentious 2020 exit from Google over a suppressed bias research paper, is releasing a book next year detailing her experiences and views on the industry's ideological drift.
US federal agencies issued a joint advisory on Sept. 8 alleging that Chinese AI companies including Alibaba, DeepSeek, MiniMax, Moonshot AI, StepFun and Z.AI have run large-scale operations to pull outputs from US models like Claude, GPT, Gemini and Grok since late 2024. The advisory says these firms extracted billions of tokens through millions of queries, using techniques such as chain-of-thought extraction and automated evasion to dodge blocking measures, in order to train their own competing systems.
An Oxford University researcher who studies AI's effect on decision-making argues that generative AI tools should be treated as intellectual sparring partners rather than sources of ready-made answers. The piece cites data showing 94% of UK undergraduates use AI for assessed coursework and that over half of 2025 scientific papers show signs of LLM-generated text, up from roughly one in ten in 2023. The author contends fears about AI eroding critical thinking are misplaced, and that the real risk lies in passive use rather than active engagement with the technology.
Google DeepMind's Demis Hassabis proposed training a large language model only on scientific knowledge available before 1911 to see if it could independently derive general relativity, calling it a meaningful test for artificial general intelligence. Several research teams, including one led by Ido Kaminer at Technion, have since built such 'vintage' AI models restricted to historical data, but early results have exposed shortcomings in current systems rather than demonstrating breakthrough reasoning.
Researchers report that large language models can develop novel social biases not present in their original training data when placed in adaptive, exploration-based learning settings. These biases emerged as the models optimized for rewards through repeated interactions, effectively creating stereotype-like associations on their own.