Venugopal Adep’s Post

Getting AI into production has us once again revisiting another older (relatively speaking) data tool: graph databases. Specifically, some companies are evaluating adding an extra step beyond just traditional RAG. Databases and developers I’ve spoken with have started to see early signs of a two-stage process that wraps standard RAG around graph traversal to improve the accuracy and relevancy of search results. Rather than just retrieve basic information from a vector database, RAG is used as a first step to retrieve nodes of a knowledge graph. That provides a wider, and potentially richer, starting point for traversal. It adds a layer of complexity to the process, but for some companies that have stricter requirements around accuracy (say, medical environments) it stands to potentially be another tool in the future AI data stack. Graphs, too, offer another benefit: explainability. Because relationships on a graph are well-defined, retrieving nodes takes away some of the opaqueness of the relationships in a vector retrieval. Here’s how Emil Eifrem at Neo4j explained it: “Vector spaces are completely opaque—it’s a bunch of numbers, and human beings can’t parse that. Graph spaces meanwhile are completely explicit. I show an apple and a tennis ball, and vector similarity search will say they’re similar but not tell us why. It’s some dimensions in some kind of latent space out there. In graph space, though, an apple and an orange we see are related explicitly because they’re both fruit.” Still, there’s a question of whether machine learning powered search techniques end up “skipping” these steps in the future—and if vector embeddings are all that’s needed. Here’s what Bob van Luijt from Weaviate told me: The vector datatype is everywhere now. So it doesn't matter what type of database you have: time series, document DB, or graph; you can attach embeddings to everything. What embeddings do is find stuff, so in the context of a graph database, that's finding nodes in the graph. Where it becomes interesting is when a database is AI-native. E.g., Weaviate stores graph connections but is by no means comparable with a purpose-built graph DB. However, in machine learning, we have graph representation learning. How is that expressed? Vector embeddings.” How we get to that point of a direct knowledge traversal clearly has a lot of pathways. But it does seem like from all this experimentation that we could see a lot more activity around graph databases in the coming months. #ai #bigdata #graphdatabases https://lnkd.in/gX9ciTU5

🔍 Chad Meley, CMO at Kinetica, shares his thoughts on how generative AI is transforming structured enterprise data analysis. This marks a pivotal shift in the AI landscape, and we at Kinetica are at the forefront of this revolution. 🌐 From Natural Language to SQL: Imagine querying databases in plain language and getting the answers you need! Generative AI now makes this possible by converting natural language queries into structured SQL. This innovation democratizes data access, making it easier for everyone within an organization to interact with databases and derive insights. 🔎 Vector Similarity Search Unlocks New Insights: Structured data like orders, inventory, and web traffic is now more accessible than ever. Generative AI's ability to convert this data into high-dimensional vectors paves the way for discovering trends and correlations previously hidden in vast datasets. It's not just about more data – it's about smarter ways to analyze it. 💡 The Future of Business Analytics: These advancements in AI are not just technical feats; they represent a fundamental shift in how businesses approach data analysis. The integration of natural language processing and vector similarity search by generative AI is set to redefine the analytics landscape, offering unprecedented insights and decision-making capabilities. 🤖 Overcoming Challenges and Looking Ahead: While this technology is transformative, it's not without its challenges. We are actively addressing issues like syntax inaccuracies and AI-generated "hallucinations" to refine and perfect these tools. The future of business analytics is about making data more intuitive, accessible, and actionable. 🌟 Join the Conversation: How do you see generative AI revolutionizing your data analysis? Share your thoughts with us on LinkedIn, Facebook, and Twitter. We're excited to be part of this journey and even more thrilled to see where it leads us all. Read more here: https://lnkd.in/eCeaVGAH

🚀 𝗚𝗿𝗮𝗽𝗵𝗥𝗔𝗚: 𝗧𝗵𝗲 𝗡𝗲𝘅𝘁 𝗘𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻 𝗶𝗻 𝗪𝗲𝗯 𝗦𝗲𝗮𝗿𝗰𝗵 𝗮𝗻𝗱 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 🌐 In the ever-evolving world of AI, a new concept is making waves: GraphRAG. Presented as a compelling innovation in the landscape of retrieval-augmented generation (RAG) models, GraphRAG could potentially redefine how we think about contextual data retrieval. 🌟 For those immersed in the nuances of AI, it represents a step forward in utilizing Knowledge Graphs within RAG frameworks to significantly enhance the quality and relevance of retrieved results. 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗚𝗿𝗮𝗽𝗵𝗥𝗔𝗚? 🧩 It introduces a more refined retrieval process by leveraging Knowledge Graphs. It involves three primary steps: 1. Vector Search Initiation: The process begins with a standard vector search to identify an initial set of nodes. This is a well-understood step in RAG models, where vector embeddings help pinpoint relevant data points. 2. Graph Traversal for Context: Unlike traditional methods, GraphRAG then traverses the knowledge graph around these nodes, enriching the initial results with additional context. This step allows the model to leverage the structured relationships encoded in the graph, resulting in a more comprehensive understanding of the query. 3. Ranking & Passing to LLM (Optional): Finally, the system can rank these enriched results and pass the top-n documents to the LLMs for generating a response. This step is particularly useful in narrowing down to the most relevant information. 𝗪𝗵𝘆 𝗚𝗿𝗮𝗽𝗵𝗥𝗔𝗚 𝗠𝗮𝘁𝘁𝗲𝗿𝘀 🌐 GraphRAG stands out for several reasons: 𝗘𝗻𝗵𝗮𝗻𝗰𝗲𝗱 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗲 𝗤𝘂𝗮𝗹𝗶𝘁𝘆: By incorporating the relational structure of knowledge graphs, GraphRAG provides responses that are not just contextually accurate but also richer in information, addressing the need for high-quality data retrieval. 𝗦𝗶𝗺𝗽𝗹𝗶𝗳𝗶𝗲𝗱 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 (𝗪𝗵𝗲𝗻 𝗮 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗚𝗿𝗮𝗽𝗵 𝗘𝘅𝗶𝘀𝘁𝘀): For organizations that already maintain a knowledge graph, integrating GraphRAG can be straightforward. The most challenging part—creating and maintaining a robust knowledge graph—is already done. The rest is about harnessing that graph effectively to improve search outcomes. Moreover, GraphRAG offers a highly intuitive way to visualize relationships between data points, moving beyond the abstraction of vector representations. This visual clarity can be invaluable in diagnosing issues and optimizing data structures—transforming what could be opaque processes into understandable, actionable insights. 𝗔𝗰𝗸𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗺𝗲𝗻𝘁 🎥 Special thanks to Emil Eifrem for his insightful discussion on this topic in his presentation on the AI Engineer YouTube channel. For a deeper understanding, check this out: - https://lnkd.in/da4DFyHS #GraphRAG #KnowledgeGraphs #AI #MachineLearning #DataScience #WebSearchInnovation #LLM #FutureOfSearch #ArtificialIntelligence #AIResearch

GPT-4o: Pioneering Multimodal Data Analysis for the Modern Era In the dynamic realm of data analysis, professionals are constantly seeking innovative solutions to unlock deeper insights and drive strategic decision-making. Enter GPT-4o ("o" for "omni"), a groundbreaking advancement in artificial intelligence (AI) developed by OpenAI, poised to redefine the landscape of data analysis with its multimodal capabilities. GPT-4o's integration of text, audio, and image inputs and outputs opens up a wide array of possibilities for data analysts. Let's explore how GPT-4o is reshaping the future of data analysis: - Multimodal Mastery: GPT-4o transcends the limitations of traditional AI models by seamlessly integrating text, audio, and image inputs and outputs. Analysts can now leverage a unified platform to process diverse data formats, enabling a more comprehensive understanding of complex datasets. - Real-time Responsiveness: With response times as swift as 232 milliseconds, GPT-4o facilitates real-time interactions, revolutionizing scenarios such as live data analysis sessions and dynamic decision-making processes. Its rapid audio processing capabilities ensure timely insights delivery, empowering analysts to stay ahead of the curve. - Visionary Insights: GPT-4o's advanced vision understanding capabilities enable analysts to extract valuable insights from visual data sources, ranging from charts and graphs to photographs and videos. By leveraging sophisticated image recognition algorithms, analysts can derive deeper understanding and make more informed decisions. - Conversational Collaboration: GPT-4o supports natural language interactions, allowing analysts to engage with the model in a conversational manner. This fosters seamless communication and collaboration, breaking down barriers to entry and making data analysis more accessible to users of all skill levels. - Streamlined Workflow: GPT-4o's end-to-end training across text, audio, and vision modalities simplifies the analytical workflow, eliminating the need for multiple models and pipelines. Analysts can now focus on extracting insights rather than managing complex infrastructure, enhancing efficiency and productivity. - The Future of Data Analysis: As data continues to proliferate in volume and complexity, the need for advanced analytical tools becomes increasingly paramount. GPT-4o stands at the forefront of this technological evolution, offering data analysts a powerful ally in their quest for deeper insights and greater business impact. GPT-4o heralds a new era of data analysis, where multimodal capabilities pave the way for deeper understanding, faster insights delivery, and more effective decision-making. By embracing GPT-4o, analysts can unlock new possibilities and drive innovation in their organizations, propelling them towards success in an increasingly data-driven world.

Hi Everyone, I recently attended the 'Unstructured Data, Vector DB and LLMs Evaluation Meetup' at Microsoft NYC hosted by AICamp in collaboration with Microsoft on 20th June, 2024. It was a wonderful evening with insightful discussions on AI, GenAI, LLMs, and machine learning, and a great opportunity to network with fellow speakers and developers. Here’s a brief overview of the topics discussed: 1) Unstructured Data and Vector Databases  Speaker: Tim Spann 🥑 (Zilliz) Tim discussed unstructured data and vector databases, highlighting their advantages over traditional databases and when to use them. He explained the capabilities and architecture of the popular vector database 'Milvus' with brevity, entertaining the audience with his delightful persona. Learnings: The session introduced 'Milvus,' a crucial component of one of my current projects in the GenAI space. Milvus is an extremely powerful vector database adept at handling use cases like hybrid search efficiently and storing unstructured data in various forms. 2) Unstructured Data Processing   Speaker: George Portillo (ChainFuse) George talked about refining high-stakes unstructured data into meaningful data using AI and LLMs to detect churn and filter high-quality leads. He discussed mapping user messages to their journey and exploring new data discoveries. Learnings: George, an ex-Google employee and the technical founder of ChainFuse, aims to help product, success, and community teams by automating and presenting user feedback data. I was inspired by this innovative idea, learning how AI can generate actionable insights from large feedback data. I wish George and his team lots of success. 3) Decoding LLMs: Evaluations is All You Need  Speaker: Jayeeta Putatunda (Fitch Ratings) Jayeeta emphasized the need for evaluation methodologies for LLMs that adapt to changing contexts. She briefly discussed the journey of evaluation metrics from traditional NLP models to modern LLM-based models, presenting state-of-the-art methodologies for validating LLM performance. Her expertise could be easily reflected in her talk. Jayeeta recently became a NY State Ambassador for Women in AI USA Learnings: I realized the importance of evaluating LLMs for future improvements and AI model reliability. There are many challenges in this domain, and we need strategies with human-in-the-loop systems to create a responsible AI ecosystem. I hope you enjoyed the post. Please do attend AICamp events if you want to learn about new and interesting work in the AI domain. Please follow me to read more about the latest breakthroughs and research in the world of technology and AI. #AICamp #ChainFuse #FitchRatings #DataScience #AI #LLM #Zillis #Milvus #Microsoft #VectorDatabase #Developers #EthicalAI #GenAI #NYC #StevensInstituteOfTechnolgy #Research #NLP #UnstructuredData #ChurnAnalysis #EvaluationMetrics

GenSQL: The Future of Data Analysis Powered by AI Researchers at MIT have unveiled a groundbreaking AI tool called GenSQL that is poised to revolutionize the field of data analysis. GenSQL is a generative AI system that seamlessly integrates with tabular datasets, allowing users to perform complex statistical analyses with just a few keystrokes. Unlike traditional SQL, GenSQL incorporates powerful probabilistic models that can account for uncertainty and adjust decision-making based on new data. This integration enables sophisticated queries, such as evaluating the likelihood of specific outcomes based on complex data relationships. One of the key advantages of GenSQL is its ability to handle sensitive data, such as in the healthcare industry, by generating and analyzing synthetic data that mirrors real data while ensuring privacy. Additionally, GenSQL has been shown to outperform current AI-based data analysis methods in terms of speed and accuracy. The researchers envision a future where GenSQL will enable natural language queries, allowing users to have a conversational interaction with the system and extract valuable insights from databases, much like ChatGPT. This vision has the potential to democratize access to complex data analysis, empowering non-experts to uncover hidden patterns and make informed decisions. As we move towards a data-driven world, tools like GenSQL will be at the forefront of transforming how we interact with and derive insights from our data. Keep an eye on this revolutionary AI-powered technology as it continues to shape the future of data analysis. https://lnkd.in/dRjQC52w #SQL #Data #DataAnalysis #AI

Unique high-quality, market-leading products and services are often born from proprietary data insights. Want a market-edge? Dive into your data (and you don't need data analytics credentials). Analyzing data for insights used to be only in the realm of data science, but CDI's conversational AI makes querying data more like having a conversation with a colleague. Conversational Data Intelligence (CDI) is what we're calling the synthesis of data analysis with conversational AI. Over the past few months, Presence has been creating custom CDI systems for our partners so they can query proprietary data sets to derive valuable insights. The possibilities resulting from more accessible data analysis seem endless. - Imagine being able to query large, diverse data sets to get unique information about your business, customers, or industry statistics. - Imagine what you might discover that could lead to novel business applications. A few basic applications might be: - Querying internal data to gather insights for improving workflows, processes, and overall efficiency and accuracy. - Querying customer and product data for insights leading to product improvements or unique value props. - Querying proprietary data for insights that lead to completely NEW business products or services. If you could analyze your data with simple questions, what would you ask? #businessinsights #AI #dataanalysis

How to Use AI to Summarize an Excel Sheet: A Game-Changer for Data Analysis 📊🤖 In today’s fast-paced world, efficiency is key—especially when it comes to data analysis. Excel sheets can quickly become overwhelming, with rows and columns of data stretching endlessly. But what if you could quickly extract the key insights without manually sifting through every detail? Enter AI, your new best friend for summarizing Excel sheets! 🚀 Why Use AI to Summarize Excel Sheets? AI tools can analyze large datasets in a fraction of the time it would take a human. By using AI, you can: Save time ⏰: AI automates the data summarization process, allowing you to focus on interpreting the results. Reduce errors 📉: Automated summaries minimize the risk of human error in data interpretation. Uncover hidden patterns 🔍: AI can highlight trends and insights that may not be immediately apparent. How to Use AI for Summarizing Excel Sheets There are several ways you can use AI to summarize an Excel sheet. Below are some popular methods and tools: 1. Excel’s Built-In AI Capabilities 🧠 Excel itself has some powerful AI-driven features: Ideas (formerly Insights): This tool provides automated insights into your data. Just click on the “Ideas” button in Excel, and it will analyze your dataset, offering summaries and key takeaways. Power Query: Use Power Query for data transformation and summarization. Although not strictly AI, it allows for advanced data manipulation with a few clicks. 2. Using Power BI for Advanced Summarization 📈 Power BI, an extension of Excel, integrates AI capabilities for more complex data analysis. By importing your Excel sheet into Power BI: You can create custom visualizations that highlight trends and summaries. Use AI visuals like "Key Influencers" to understand which factors drive certain outcomes in your data. 3. Leveraging GPT-based Tools 🧑💻 AI models like GPT-4 can be used to summarize Excel data by processing the data and generating a natural language summary. You can: Let’s say you have an Excel sheet with sales data for different regions and products. You want to know the key insights without diving into the minutiae. Using Excel’s Ideas Tool: Click on "Ideas," and Excel might tell you something like, "Sales increased by 20% in the North region compared to the previous quarter." Using GPT-Based Tool: Upload your data to a GPT-powered tool, and it might generate a summary like, "The North region saw a significant sales increase, especially in Q3, while the South region experienced a decline in product A sales." #PowerBI #DataAnalytics #BusinessIntelligence #DataScience #DataVisualization #BigData #DataDriven #DataAnalysis #AI #MachineLearning #PredictiveAnalytics #DataInsights #MicrosoftPowerBI #TechInnovation #DigitalTransformation #DataStrategy #BI #DataTools #DataSolutions #FutureOfData #DataTechnology #BusinessAnalytics #DataIntegration #AdvancedAnalytics