Semantic Scholar is a free, AI-powered research tool developed by the Allen Institute for AI, designed to help researchers efficiently discover and understand scientific literature. Launched in 2015, it indexes over 200 million academic papers across all fields of science, utilizing advanced natural language processing and machine learning techniques to extract meaningful insights and identify key connections within the literature. Key Features and Functionality: - AI-Enhanced Search and Summarization: Employs machine learning to extract key points and generate concise summaries of papers, enabling faster comprehension. - Semantic Reader: An augmented reading tool that provides in-line citation cards with TLDR summaries, skimming highlights, and contextual definitions to improve the paper reading experience. - Comprehensive Academic Corpus: Indexes over 200 million papers from multiple disciplines, including computer science, biomedicine, and more, sourced from publishers and web crawls. - Personalized Research Feeds and Alerts: An adaptive recommender system that learns user preferences to suggest relevant new papers and allows users to save and organize research libraries. - Rich Citation and Metadata Analysis: Provides detailed citation graphs, author profiles, and paper impact metrics to help users evaluate research significance. Primary Value and User Solutions: Semantic Scholar addresses the challenge of information overload in scientific research by offering AI-driven tools that streamline the discovery and comprehension of academic papers. By providing concise summaries, personalized recommendations, and enhanced reading experiences, it enables researchers to stay current with developments in their field, efficiently conduct literature reviews, and identify influential works, thereby accelerating scientific breakthroughs.