Huawei breaks years of chip silence with roadmap through 2028 as China bans Nvidia purchases—a coordinated tech offensive timed for Trump-Xi talks. The clustering strategy and memory breakthrough claims signal parallel infrastructure.
Upscale AI's $100M bet: open standards can outperform proprietary networking stacks that dominate AI infrastructure. Serial founders from Palo Alto Networks claim their SONiC-based fabric can match Big Tech's performance while breaking vendor lock-in.
Three infrastructure bugs hit Claude simultaneously, affecting up to 16% of requests by August's end. Anthropic's unprecedented technical transparency reveals how AI reliability depends as much on routing logic as model training.
Read about AI research breakthroughs, development insights, and practical applications. Learn how companies build AI apps, integrate Claude technology, and solve real problems with artificial intelligence. Get updates on the latest trends reshaping how we work with AI tools and understand their impact on business and technology.
MIT researchers monitored students' brains while they wrote essays with ChatGPT. The AI users showed weaker neural activity and couldn't quote their own work. When they switched back to writing alone, their brains stayed weakened.
Anthropic says multiple AI agents working together beat single models by 90%. The catch? They use 15x more computing power. This trade-off between performance and cost might reshape how we build AI systems for complex tasks.
AI models typically learn by memorizing patterns, then researchers bolt on reasoning as an afterthought. A new method called Reinforcement Pre-Training flips this approach—teaching models to think during basic training instead.
Meta just paid $15 billion for a 49% stake in Scale AI after its own models flopped. CEO Alexandr Wang gets control while leading Meta's new "superintelligence" team. The deal reveals how desperate big tech has become to acquire AI talent at any cost.
AI's "thinking" models hit a wall at certain complexity levels and actually reduce their reasoning effort when problems get harder. Apple researchers found these models can't follow explicit algorithms reliably, revealing gaps in logical execution that more compute can't fix.
Researchers found that AI models learn math better when punished for wrong answers than rewarded for correct ones. This challenges how we think about teaching machines and could change AI training across many fields.
Investigating the complex relationship between humans and AI chatbots. Can genuine affection develop with machines?
UC Berkeley researchers discovered AI models can teach themselves complex reasoning by monitoring their own confidence levels—no human feedback or answer keys required. The implications reach far beyond the lab.
Why do AI systems perform worse when they think harder about visual tasks? New research shows GUI agents achieve better accuracy by skipping reasoning steps that help language models excel.
A surprising new study reveals that when AI models stop to think, they actually get worse at following basic instructions.
Researchers have found a way to make AI solve complex problems using just one-fifth of its usual computing power. The method could help bring advanced AI capabilities to ordinary computers - reducing both cost and energy use.
When asked how they reach decisions, advanced AI systems write elaborate explanations—but new research reveals these justifications are often fabricated, raising critical questions about current safety measures.
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