#natural-language-processing

#natural-language-processing

Edsger Dijkstra argued that the inherent ambiguities and slow evolution of natural languages were conclusive reasons to abandon any real idea of programming in human languages. He was right.

The bag-of-words model is a text representation technique that converts unstructured text into numerical vectors by tracking which words appear across a corpus. Rather than preserving grammar or word order, it simply represents each document as a 'bag' of its words, recording how often each one appears.

While humans have assembled a lot of weather data, flash floods are too short-lived and localized to be measured comprehensively, the way the temperature or even river flows are monitored over time. That data gap means that deep learning models, which are increasingly capable of forecasting the weather, aren't able to predict flash floods.

In the decades since natural language processing (NLP) first emerged as a research field, artificial intelligence has evolved from a linguistic curiosity into a catalyst reshaping how humans think, work, and create. Few people are as qualified to trace that journey, or to imagine what comes next, as Rada Mihalcea, Professor of Computer Science and Engineering and Director of the Michigan AI Lab at the University of Michigan.

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.

In the near future, AI agents will handle most computer-based tasks for us, such as browsing websites, filling out forms, booking tickets, and more.

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.