#natural-language-processing

#natural-language-processing

Perplexity is an answer engine designed to give accurate, sourced responses by searching the internet in real time, focusing on research and fact-finding.

Harness AI extends its capabilities to allow users to create pipelines using plain language, adhering to corporate standards, and analyzing pipeline logs for error correlation.

"Solvea is an AI-powered customer service chatbot that transforms support with instant, omnichannel, and multilingual availability, specializing in human-level accuracy and agency-specific compliance."

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.

Our team comprises three early career academics with expertise in Natural Language Processing and Computer Science, designed to teach non-coders data analysis using Python.

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.

Contractors for major tech firms are instructed to identify and remove preachy tones from chatbot responses, especially in discussions on sensitive topics, to enhance user experience.

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.

AI text analysis tools are essential for effectively processing vast amounts of data. They enhance decision-making and streamline communication across various sectors.

Think you know which AI assistant was the first to use natural language processing for everyday tasks? Or which researcher coined the term "deep learning," revolutionizing AI?

CocoIndex is an ultra performant real-time data transformation framework for AI, enabling seamless indexing of changed files within minutes.

"If you ever write anything, you'll probably need it"

End-to-end testing is vital for software function, but traditional tools suffer from steep learning curves and high maintenance costs. AI-powered tools alleviate these issues.

The shift to unstructured interactions with AI chatbots from traditional clicks and impressions creates complexities in accurately determining user intent.

The agreement results between different pairs of human annotators and the LLM annotator indicate that misalignment occurs primarily in interpreting user intent categories.

Darwin achieves markedly higher F1 scores in both NER and RE tasks than the LLaMA 7b and LLaMA2 7b models, indicating better performance in materials science.