AI pioneer Yann LeCun launches AMI Labs, raising funds at a staggering $3.5 billion valuation before officially opening its doors, with ambitions to build 'world models' that surpass today's large language models on the path to human-level AI.
AI systems reach a performance ceiling at the 95th percentile due to mathematical limitations, scoring just 37.5% on expert-level questions while human specialists achieve 90%, though human-AI collaboration delivers 40% higher quality results when experts can catch AI's frequent hallucinations.
Flapping Airplanes secures $180 million in seed funding to develop revolutionary AI systems inspired by human brain algorithms that require 1000 times less training data than current OpenAI and DeepMind models, potentially unlocking breakthrough applications in robotics and scientific discovery.
Google launches upgraded Gemini 3 Deep Think AI that achieves gold-medal performance on International Physics and Chemistry Olympiads and scores 84.6% on advanced reasoning tests, now available to Ultra subscribers and select researchers.
Google DeepMind's Gemini Deep Think AI achieves groundbreaking scientific breakthroughs by autonomously solving decade-old mathematical conjectures, generating research papers independently, and settling four problems from Bloom's Erdős database while demonstrating collaborative capabilities across mathematics, physics, and computer science disciplines.
MIT researchers unveil a breakthrough AI training technique called self-distillation fine-tuning that allows large language models to continuously learn new skills without forgetting old ones, potentially eliminating the need for companies to maintain multiple specialized AI models.
Harvard researchers discover AI tools paradoxically increase employee workloads at tech companies, as workers take on broader responsibilities and extend hours despite completing individual tasks faster, with non-technical staff now tackling coding while failing to recognize their escalating burden until initial excitement fades.
Multi-agent AI research explodes from 820 to 2,500 papers as companies discover that system architecture, not prompts, determines whether AI teams succeed or fail in real-world applications.
EPFL scientists breakthrough 'error recycling' method teaches AI to learn from its own mistakes, enabling unlimited-length video generation that maintains quality for minutes instead of the previous 30-second limit without requiring extra computing power.
Scientists declare AI-human intelligence comparisons fundamentally flawed, revealing that human intelligence operates collectively through social interaction and cultural transmission while AI systems merely process statistical patterns without true comprehension, embodied experience, or awareness of cultural context and values.