Groundbreaking research reveals that AI language models wastefully activate their entire networks for social reasoning tasks while actually using only a tiny specialized subset of parameters, creating massive inefficiency compared to human brains and pointing toward revolutionary energy-saving AI designs.
New research testing 24 advanced AI language models reveals they systematically fail to distinguish between belief and knowledge, with accuracy plummeting from over 90% to as low as 14.4% when processing first-person false beliefs, exposing critical flaws in their reasoning capabilities.
MIT researchers unveil SEAL, a groundbreaking technique that enables AI models to permanently learn new information by creating their own study sheets and updating internal weights, boosting accuracy by 15% on question-answering and over 50% on skill-learning tasks.
AMD CEO Lisa Su projects 35% annual revenue growth over the next three to five years, targeting double-digit market share in Nvidia's AI chip dominance with 80% annual data center growth and a major OpenAI partnership launching in 2026.
Chinese AI firm Moonshot AI releases Kimi K2 Thinking, an open-source reasoning model that outperforms GPT-5 and Claude Sonnet 4.5, achieving record 44.9% on Humanity's Last Exam benchmark and marking a potential turning point as Chinese models now rival leading US systems.
Google Research unveils Nested Learning, a revolutionary AI paradigm that solves catastrophic forgetting by treating models as interconnected, multi-level systems where components update at different frequencies, enabling machines to learn new tasks without losing previous knowledge.
Generalist AI releases GEN-θ, breakthrough robot foundation models trained on 270,000 hours of real-world manipulation data, discovering a critical 7B parameter intelligence threshold where robots dramatically improve at complex tasks and need only thousands of training steps to master new skills.
Google Cloud launches revolutionary Ironwood TPUs delivering 10X performance boost over previous generation chips, while AI company Anthropic prepares to harness up to one million TPUs as customers report dramatic improvements in speed and cost efficiency.
NVIDIA releases open-source NeMo Automodel library that delivers breakthrough 280 TFLOPs/sec GPU performance for AI training, enabling developers to train massive mixture-of-experts models in PyTorch with simplified infrastructure while achieving near-linear scaling across thousands of GPUs.
Google unleashes Ironwood, its seventh-generation TPU chip that delivers four times faster AI processing than previous models and connects up to 9,216 chips in massive pods, with Anthropic planning to deploy 1 million units for Claude as the tech giant battles Nvidia's GPU dominance.