Negative prompting tells a generative model which elements, styles, or defects should be avoided in an output. It is especially common in image generation, where a user may exclude text artifacts, particular colors, duplicate objects, or unwanted visual traits. The system uses that signal alongside the main prompt during generation, although different models interpret negative prompts in different ways. Long exclusion lists can conflict with positive instructions or introduce unexpected associations. Negative prompting is a steering technique rather than a safety guarantee: it cannot reliably block every prohibited result or correct limitations in training data. Effective use requires concise terms, model-specific testing, seed comparisons, and other content controls where consequences matter.
SoftBank has secured a $40 billion bridge loan to deepen its investment in OpenAI and accelerate its broader AI strategy.
AI-driven bots now generate more internet traffic than humans, according to a new report, highlighting the growing impact of automated systems online.
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.
Meta introduced four new in-house MTIA chips designed for AI training and inference as the company accelerates data center expansion. The chips aim to improve performance and reduce reliance on external hardware suppliers.
Google has rolled out Canvas in AI Mode to all U.S. users in English, enabling people to create documents, dashboards, and interactive tools directly within Search. The feature provides a dynamic workspace for planning projects and building simple applications.
Andrej Karpathy argues AI coding agents now fundamentally change programming workflows, enabling long-running autonomous tasks and redefining software engineering.
MSCI introduces AI connectors enabling access to its data via MSCI ONE, ChatGPT, and Claude, debuting IndexAI Insights to deliver conversational index analytics powered by large language models.
Google is rolling out Gemini 3.1 Pro across consumer, developer, and enterprise products, touting major gains in reasoning performance and benchmark results.
India’s AI Impact Summit in New Delhi delivered more than $250 billion in investment commitments, spanning data centers, AI infrastructure, and global expansion plans.
Fei-Fei Li’s World Labs secured a $200 million investment from Autodesk as part of a broader $1 billion round, deepening collaboration on AI systems that generate and reason about 3D environments.