World Conference on Qualitative Research’s Post

#WCQR2025 | Demo Sessions (Kraków) 👩💼 The rise of #GenerativeAI in qualitative research software has ignited both enthusiasm and debate. But did you know the journey of integrating #AI and #MachineLearning into qualitative tools started years ago? Back in 2007, #QDAMiner 3.0 introduced the first AI-powered feature, the Query-by-Example search tool. By 2011, #QDAMiner 4.0 added groundbreaking capabilities like Cluster Extraction and Code Similarity searches, revolutionizing how researchers handle larger datasets with faster, more consistent coding. Curious about how #AI can enhance your research? Join this interactive demo session at WCQR2025 to explore cutting-edge AI tools in QDA Miner and their practical applications in qualitative research. 🌐 Learn more: https://lnkd.in/e_u4Ei6v 📅 Secure your spot: wcqr.ludomedia.org #CAQDAS #QualitativeResearch #ResearchTools #HybridEvent #DemoSession #WCQR

Day 3 of the 5-Day Gen AI Intensive was all about Generative AI Agents! 🤖💡 Today, I explored how to build sophisticated AI agents and connect them to real-world systems. From understanding their core components to hands-on learning, it’s been an exciting journey! Key Learnings Today: 1.) Building AI agents: gaining insights into the development process and essential components of agentic systems. 2.) Real-World Integration: connected LLMs to external systems, enabling AI to interact meaningfully with the real world. 3.) Function Calling: learned to give SQL tools to a chatbot for database interactions. 4.) LangGraph Agent: Designed a café ordering system using LangGraph, blending creativity with practicality. Resources Explored: >> Readings: “Generative AI Agents” whitepaper for theoretical grounding. >> Code Labs: Hands-on practice through Kaggle Labs on database communication and agentic system building. >> Discussions: Engaged with the vibrant community on Discord to clarify concepts and share experiences. What’s the most exciting use case for AI agents you’ve encountered recently? Let’s discuss it! 🚀 #GenerativeAI #AIAgents #MachineLearning #LangGraph #DatabaseIntegration #AIinAction #TechLearning #DataScience #AICommunity #Kaggle

Are you a history buff?! Imagine creating a query system that can answer all questions about key historical events like the World Wars. Today, advanced AI technologies make this possible and we will show you how... 🚀 👇 We are excited to share a comprehensive guide on building Retrieval-Augmented Generation (RAG) applications using DSpy and Llama, featured on Superteams.ai. In this in-depth tutorial, you will discover: 1. RAG Architecture Fundamentals: Explore how combining retrieval and generation boosts accuracy and efficiency in AI applications. 2. Setting Up DSPy, Llama 3 and Indexify: Follow clear, step-by-step instructions for seamless installation and configuration. 3. Developing and Deploying RAG Applications: Gain practical insights on constructing a RAG app from scratch, illustrated with real-world use cases. For AI professionals and enthusiasts aiming to advance their solutions, this guide is a must-read. Dive into the full article here: https://lnkd.in/gnr2nx8H #AI #MachineLearning #RAG #DSpy #Llama3 #TechInnovation #AIDevelopment

Join us for an exclusive session on advanced techniques in Retrieval Augmented Generation (RAG) for developers, data scientists, and AI enthusiasts on July 18, 2024, at 2.30 PM IST. Enhance your RAG systems and optimize performance in production environments with crucial insights. Register now to unlock the latest patterns and techniques for boosting your RAG performance. Don’t miss this opportunity to delve deep into building robust RAG-based systems. Register here: https://lnkd.in/gZcy7yuv Key Details: 📅 Date & Time: July 18, 2024, at 2.30 PM IST 🌐 Virtual Event Highlights: - Building your retrieval pipeline - Integrating data with generative AI - Deploying RAG systems - Introduction to Elastic & Cohere #RAG #AI #DataScience #VirtualEvent #AdvancedTechniques

Anthropic's Silent Upgrade: Claude 3.5 Haiku Now Accessible to All Anthropic has quietly made its fastest AI model, Claude 3.5 Haiku, accessible to all users on web and mobile platforms. Anthropic, known for its advanced AI models, has taken a significant step forward with the wider release of Claude 3.5 Haiku. Initially available only through API, this powerful model is now accessible to a broader audience. Key Points: 📌Enhanced Performance: Haiku 3.5 outperforms its predecessor, 3 Opus, on key benchmarks, especially in coding and data processing tasks. 📌Expanded Context Window: With a 200K context window, Haiku can process and generate more complex text. 📌Seamless Integration: The model integrates with Artifacts, providing a real-time content workspace. 📌Tiered Access: Free users can access Haiku with limited usage, while Pro subscribers enjoy expanded access and priority. 🚀This silent upgrade from Anthropic underscores the rapid pace of AI development. Haiku 3.5's enhanced capabilities and wider accessibility have the potential to revolutionize various industries, from software development to content creation. Why This is a Game Changer: By democratizing access to cutting-edge AI, Anthropic is empowering individuals and businesses to leverage the power of AI for innovation and problem-solving. #anthropic #ai #claude #haiku #machinelearning #innovation #technology #future

🌟 Day 7 of 100 Days, 100 Open Source Libraries! 🚀 ✨ phidata Agents in Action - A Hands-On Guide Phidata is a framework for building multi-modal agents that empower AI to collaborate, reason, and solve problems effectively. Here's what you can do with Phidata: 💡 What You Can Do with Phidata: 🔗 Build multi-modal agents equipped with memory, tools, and advanced reasoning capabilities. 🤝 Create agent teams that work seamlessly to solve complex challenges. 🗨️ Chat with agents via a sleek Agent UI for intuitive interactions. 📊 Monitor, evaluate, and optimize agent performance in real-time. 📖 Explore the tutorial: 👉 https://lnkd.in/dr29q-h7 📂 Discover the full repository of open-source AI experiments: 👉 https://lnkd.in/dxudcvXe 🌟 If this inspires you, star ⭐ the repo and share it with your network! Together, let’s shape the future of open-source AI. 🚀 ====================== Experts predict 2025 will be the defining year for Gen AI implementation. Want to be ahead of the curve? Join Build Fast with AI’s Gen AI Launch Pad 2025 - your accelerated path to mastering AI tools and building revolutionary applications. Register now: https://lnkd.in/dgCEJ9Ed #Phidata #MultiModalAI #OpenSource #AIFramework #GenerativeAI #AgenticSystems #AICommunity #100DaysOfAI #BuildFastWithAI

This guide dives deep into Retrieval-Augmented Generation (RAG) and Agentic RAG Systems, covering their core concepts, workflows, and practical implementations. It explores: • Definitions and differences between RAG, AI Agents, and Agentic RAG. • Typical workflows and key components of these systems. • Hands-on code examples for building both RAG and Agentic RAG solutions. The article, authored by Pankaj and published by Analytics Vidhya, offers a mix of theory and practice to enhance your understanding of these AI frameworks. Highly recommended for AI enthusiasts and developers! Kudos to Dipanjan S. for creating this amazing pdf. #RAG #AgenticRAG #AIAgents #AIFrameworks #AnalyticsVidhya #ArtificialIntelligence #MachineLearning #GenerativeAI #LLMs #DataScience #AI

The Dagster Abstractions makes it easy to build production-grade AI applications. The RAG process or AI-enabled search is a use case that every company can utilize. In this tutorial, I build a RAG from scratch in Dagster that replicates our Ask-ai functionality on our website and Slack. The link is in the original post!

🚀 Excited to share my latest project! 🎉 --PDF Question Answering Application-- I’ve built a Streamlit application using the power of Generative AI from Gemini. This tool leverages RAG (Retrieval-Augmented Generation) to allow users to upload a PDF and get real-time answers to their queries based on the document’s content. Whether it’s research papers, reports, or any lengthy document, this application simplifies extracting relevant information quickly and efficiently! Do checkout my GitHub repository for the full project - https://lnkd.in/gVf2qZy3 #GenerativeAI #Streamlit #RAG #AI #TechInnovation #PDFAnalysis #Automation #GeminiAI #DataScience

🚀 Object Detection with YOLOv10 and Custom Dataset 🎯 I'm excited to share my latest project where I implemented object detection using YOLOv10 on a custom dataset! To label the dataset, I used CVAT, a highly efficient tool for image annotation that made the process seamless. If you're interested in exploring object detection or working with custom datasets, you can check out the project here: 🔗 GitHub Repository: https://lnkd.in/d6zSwddG Feel free to clone the repository, experiment with it, or share your feedback. Let's innovate and create together! 💡 #MachineLearning #YOLOv10 #ObjectDetection #ComputerVision #AI #CVAT #CustomDataset #OpenSource