#natural language processing

#natural language processing

Unlike earlier search engines that primarily matched strings of text, AI search engines interpret the meaning behind the query, allowing for more accurate and relevant results. Examples of AI search engines include Google's recent AI integrations , Microsoft Bing's AI enhancements, and specialized platforms employing AI to tailor search results based on user behavior and preferences. These engines dynamically learn and improve their algorithms to respond more intelligently over time, a capability traditional search lacks.

AI spreadsheet generators are tools that utilize natural language prompts to facilitate tasks like generating formulas, building tables, and cleaning data without writing any code.

Perplexity aims to address friction points in online information interactions with Comet, a browser integrating natural language assistance for seamless research and task execution.

The Wall Street Journal reported in April 2025 on companies struggling to realize returns on AI. Just weeks later, it covered MIT's retraction of a technical paper about AI.

The MCP Server acts as a local bridge between GitHub Copilot in Agent Mode and a developer's Azure DevOps instance, making structured project data available.

Traditional intranet search engines struggle to keep up with the growing complexity of internal knowledge, with information scattered across tools like Google Drive, Notion, SharePoint, Confluence, Slack, and your project management platform.

These experiments with GPT-2 medium on OpenWebText validate the radius hypothesis from our theoretical framework, measuring activation distances in the last layer for next-token prediction.

MCPs are increasingly prevalent; they allow users to engage with apps through natural language, yet configurations remain cumbersome, impacting overall user experience.

The collaboration between VideoAmp and AWS GenAIIC resulted in a prototype chatbot that utilizes natural language processing to analyze media analytics data efficiently.

Glean has raised $150 million in Series F funding, propelling its valuation to $7.2 billion, a significant rise from $2.2 billion in early 2024.

Sequence labeling identifies and categorizes key text segments according to predefined labels, allowing for insightful analysis of feedback components in tutor responses.

The disconnect between tokenizer creation and model training allows certain inputs, termed 'glitch tokens,' to induce unwanted behavior in language models.