#natural-language-processing

#natural-language-processing

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.

Technologies like Model Context Protocol (MCP) are reshaping both how we build AI-powered applications and what we can do with them. MCP makes it easier to add AI features to existing code and services.

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.