Training data is the foundational dataset used to teach an artificial intelligence or machine learning model how to perform specific tasks. It contains examples of inputs and their expected outputs, allowing the model to learn patterns, relationships, and decision-making rules. High-quality, diverse training data is critical for building accurate and unbiased AI systems. In supervised learning, data is labeled to guide the model, while in unsupervised learning, the model identifies patterns on its own. The quantity and quality of training data directly impact model performance, making data preparation and curation key steps in any AI development process.
Anthropic has reportedly signed a 20-year, $9.1 billion data center agreement with Riot Platforms for 191 MW of AI computing capacity at the bitcoin miner’s Rockdale campus in Texas.
Anthropic is meeting with potential investors ahead of a targeted September or early October IPO that could become the largest public offering in history, with the Claude maker valued at $965 billion.
Meta has released Muse Glimmer, a 30-billion-parameter open-weight model built for local AI agents, coding, tool use, and multimodal tasks on a single consumer GPU or high-end Mac.
Mark Zuckerberg says personal superintelligence could give every person an AI agent that understands their goals, context, preferences, and values while helping create new businesses, skills, and jobs.
Cloudflare says AI agents, bots, and other automated systems could generate up to 1,000 times more internet traffic than humans within five years as machine activity accelerates.
OpenAI is replacing GPT-5.5 Instant with GPT-5.6 Luna for Free and Go users, then removing text-chat limits and adding a Think button for harder questions.
Demis Hassabis is becoming Alphabet chief scientist as Koray Kavukcuoglu takes operational control of DeepMind and four Google veterans launch Discovery Loop.
Meta has launched Muse Code, a terminal coding agent powered by Muse Spark 1.2, targeting long-running software projects and established rivals Codex and Claude Code.
AI startups captured 53% of global venture funding in July, their lowest share since December, even as total investment reached $65 billion in a record month for mega-rounds.
More than 1,200 employees from leading AI companies are asking the U.S. government to help create international mechanisms that could slow automated AI development if capabilities begin advancing faster than society can evaluate or control.