Big Data refers to extremely large and complex datasets that are difficult to manage, process, and analyze using traditional data tools. It encompasses information collected from diverse sources such as social media, sensors, mobile devices, and online transactions, often generated in real time. In artificial intelligence, Big Data serves as the foundation for training machine learning models, improving accuracy, and uncovering insights that drive smarter decisions. The concept is typically defined by the “three Vs”: volume, velocity, and variety – representing the size, speed, and diversity of data. Modern AI systems rely on Big Data technologies and frameworks like Hadoop and Spark to store, process, and interpret this vast information efficiently.
Morgan Stanley’s E*Trade is in talks to lead retail distribution for SpaceX’s record IPO, potentially sidelining platforms like Robinhood and SoFi.
SpaceX is preparing a record-breaking IPO that could raise $75 billion, combining its space business with AI ambitions through xAI integration.
DDR5 memory prices are showing early signs of decline after Google’s TurboQuant algorithm reduced AI memory requirements, easing pressure on global DRAM supply.
A data leak at Anthropic exposed details of its upcoming Claude Mythos model, described as a major leap in AI capabilities, along with internal documents.
Google has expanded Search Live globally, enabling users to interact with AI using voice and camera through its Gemini-powered search experience.
A U.S. federal judge has blocked the Pentagon’s attempt to ban Anthropic’s AI tools, allowing continued use of its systems during an ongoing legal dispute.
AI-driven bots now generate more internet traffic than humans, according to a new report, highlighting the growing impact of automated systems online.
Google has introduced TurboQuant, a new compression algorithm that reduces memory usage in AI systems while maintaining accuracy, improving performance in large models and search.
President Donald Trump has appointed top tech executives, including leaders from Meta, Nvidia, and Oracle, to a council shaping U.S. AI policy and strategy.
New research shows that telling AI models they are “experts” can reduce accuracy in tasks like coding and math, while improving safety and alignment outcomes.