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.
Opera today introduced Neon, an AI-powered browser that can run tasks and navigate pages autonomously — aiming to shift web browsing from passive to “agentic.”
AI search startup Perplexity has reportedly raised $200 million at a $20 billion valuation, just two months after a $100 million round – signaling its rapid rise as a major rival to Google.