A UK engineering researcher spearheads an NSF-funded initiative, collaborating with Michigan State University, to develop cutting-edge machine learning models capable of simulating the progression of heart fibrosis, advancing computational modeling of heart disease.
In a coding marathon showdown at the AtCoder World Tour Finals, Polish programmer Przemysław Dębiak triumphs over OpenAI's AI model, outscoring it by 9.5% with an innovative heuristic-driven approach that showcases human creativity's edge over AI's raw speed.
In a groundbreaking achievement, the AI system Gemini, powered by Deep Think, conquers the prestigious International Mathematical Olympiad, earning a gold medal with a perfect score on five out of six complex math problems through innovative parallel thinking and reinforcement learning techniques.
Groundbreaking AI models from OpenAI and Google DeepMind achieve gold medal-level performance at the prestigious International Mathematical Olympiad, solving complex problems and providing natural language proofs, marking a milestone in general-purpose AI for math and reasoning.
Meta unveils a groundbreaking AI framework, MTTT, that enables models to learn how to learn at test time, removing the need for human-designed self-supervised tasks and outperforming linear attention on long sequences while maintaining linear complexity.
OpenAI researchers Jason Wei and Hyung Won Chung depart for Meta's superintelligence lab, amid an AI talent war where Meta offers up to $300 million over four years to top AI talent.
Ex-Google AI pioneers unveil a software-savvy agent trained to comprehend the software building process, inching closer to superintelligent AI by enabling more capable software assistants.
Meta intensifies the race for artificial general intelligence by poaching two prominent researchers, Jason Wei and Hyung Won Chung, from OpenAI, offering lucrative compensation to bolster its AI capabilities under CEO Mark Zuckerberg's leadership.
Researchers unveil a groundbreaking 'self-driving lab' technique that accelerates materials discovery tenfold by continuously varying and monitoring chemical mixtures in real-time, enabling smarter and faster machine learning predictions while dramatically expediting research and slashing costs.
A groundbreaking self-driving lab accelerates materials discovery through real-time data collection, gathering 10 times more data than previous techniques by continuously monitoring chemical reactions, enabling machine learning algorithms to make faster, more accurate predictions while reducing waste.