Vanshika Bansal’s Post

Is your organization leveraging vector databases? These systems have emerged as powerful tools, offering a more intuitive and efficient way to manage multi-modal data. By helping organizations uncover patterns and similarities in large data sets that extend beyond just text and numbers, vector databases are increasingly essential. Thanks to advancements in large language models (LLMs) and generative AI, vector databases have become invaluable assets. They transform complex data—such as audio, video, and images—into vector representations that can be stored and analyzed to drive more valuable connections and findings across data sets. #DataEngineering #GenerativeAI #Data #LLM

🚀 Excited to share my latest blog on accelerating Retrieval-Augmented Generation (RAG) with cutting-edge tools! 🚀 In this post, I dive into how I supercharged the vector search and graph-based knowledge extraction engine from my post yesterday using Qwen 2.5, GPU-accelerated FAISS, and parallel graph processing with IGraph. This enhanced system can handle massive datasets with millions of vectors, providing real-time results, meaningful text-based responses, and powerful graph-based insights. 🔑 Key Highlights: - Leverage Qwen 2.5 to transform vector retrieval into meaningful, human-readable responses. - Achieve lightning-fast vector searches with GPU-accelerated FAISS. - Scale graph-based operations using parallel processing for real-time applications. - Use cases ranging from recommendation engines to conversational AI and social network analysis. Check out the full post here for a deep dive into the technical details! 💻 #AI #MachineLearning #RAG #DataScience #Qwen #FAISS #GraphProcessing #LLM

🚀🧠 Exciting News in AI: Mixtral 8x22B vs 8x7B vs Mistral 7B - Which Model Tops the Chart? 🤖🔍 The AI community is abuzz as Mistral AI unleashes its latest innovation, the Mixtral 8x22B. This new entrant not only surpasses its predecessors—the 8x7B and Mistral 7B—in terms of parameter size but sets a new standard in the capabilities of Large Language Models (LLMs). 📊 Here’s What You Need to Know: Comprehensive Comparison: We break down the performance, capabilities, and technological advances of the Mixtral 8x22B compared to the 8x7B and Mistral 7B. Advanced Features: Discover the unique features that differentiate the 8x22B and how it's pushing the boundaries of what AI can achieve. Industry Impact: Learn about the potential applications of these models across different sectors and how they can drive innovation in your projects. Whether you're in tech development, data science, or just keen on the latest AI trends, understanding these models’ strengths can significantly influence your strategic decisions. 👉 Check out our latest article for a detailed analysis and find out which model is best suited for your needs! See here - https://lnkd.in/g_P5CQPr #MistralAI #LLM #AIModels #TechInnovation #DataScience #ArtificialIntelligence #TechnologyNews #MachineLearning #AIResearch

Which one’s better? Math or language-based AI? Hmmmmmm.... 🤔 At Adderbee we believe that basic language is the foundation of all effective AI interaction and in order to make technology available to everyone, we are building a semantic cognitive architecture that uses basic language instead of relying on the rigidity of math. This allows our Peer-to-Peer Personal AI to be used by anyone, not just techies. Make sure you visit our website to learn more, and sign up for our waitlist to keep up-to-date: https://lnkd.in/gjutvnUf #AI #AIinnovation #peertopeer

🚀 Exciting Insights into the Future of AI! 🤖 Andrej Karpathy stands out as a captivating YouTube content creator specializing in AI education. Through his engaging videos, he delves into the intricacies of AI, particularly illuminating topics like transformer architecture and the inner workings of Large Language Models (LLMs). His content has been instrumental in broadening my understanding of these concepts, making learning both accessible and fascinating. In a recent discussion, Andre, a key figure in the AI landscape, shared valuable insights into the future of Artificial General Intelligence (AGI) and Large Language Models (LLMs). Here are some key takeaways: 🔍 Industry Trends: The conversation highlighted the dominance of OpenAI and the evolving ecosystem of AI players. What opportunities do you see for new entrants in the AI space? 💡 Technical Challenges: Andre discussed the importance of scale, data efficiency, and algorithmic improvements in AI development. What do you think are the most pressing challenges in AI research and development? 🛠️ Innovation in Computer Architecture: The need for innovation in computer architecture to improve the efficiency of AI systems was emphasized. How do you envision the future of computer architecture in the context of AI? I would recommend everyone to watch this conversation and share your thoughts on the future of AI! Let's explore the possibilities together. #AI #FutureTech #AGI #LLMs #OpenAI

The Art of #Chunking: Boosting AI Performance in #RAG Architectures. Smart people are lazy. They find the most efficient ways to solve complex problems, minimizing effort while maximizing results. In Generative AI applications, this efficiency is achieved through chunking. Just like breaking a book into chapters makes it easier to read, chunking divides significant texts into smaller, manageable parts, making them easier to process and understand. Before exploring the mechanics of chunking, it’s essential to understand the broader framework in which this technique operates: Retrieval-Augmented Generation or RAG. https://lnkd.in/ghUg5X8Q

The future of multimodal large language models is here - Uni-MoE is revolutionizing AI development! Uni-MoE is a unified multimodal large language model with a MoE architecture that efficiently handles multiple modalities and experts, while reducing computational costs. With its sparse Mixture of Expert layers, the Uni-MoE framework boosts training and inference efficiency, while its progressive training strategy enhances generalization and multi-expert collaboration. How can we apply Uni-MoE in AI scenarios to develop powerful and efficient models? — Hi, 👋🏼 my name is Doug, I love AI, and I post content to keep you up to date with the latest AI news. Follow and ♻️ repost to share the information! #unimoeframework #multimodallanguagemodels #aiinnovation

The use cases for #generativeAI keep growing rapidly. So does the pressure on software engineers and #data scientists, who must learn emerging #AI technologies with few or no best practices established. Retrieval augmented generation (RAG) is a great example. It’s a powerful approach for enhancing the large language models (#LLMs) that power many #GenAI systems. But just keeping up with the latest RAG strategies is a full-time job given how quickly the RAG space is moving. To help you cut through the noise, WillowTree’s Data & AI Research Team (DART) has compiled this guide on advanced RAG techniques. We’ve deployed many of these techniques when building and testing clients’ generative AI applications, including compliant conversational AI assistants for highly regulated industries like #healthcare and #financialservices. Trust these techniques to optimize your RAG system for: 🚀 Faster, more contextually relevant searches 🚀 Concise, accurate response generation 🚀 Efficient, optimal semantic search 🚀 Greater overall system safety and security Get the guide on advanced RAG 👉 https://lnkd.in/eepnKAhB

Exploring Model Efficiency with Quantization and LoRA for Large Language Models Excited to share some insights into two powerful techniques that are revolutionizing how we fine-tune and optimize Large Language Models (LLMs) for better efficiency and performance! 🚀 Quantization: This technique reduces the computational complexity of large models by converting high-precision weights (e.g., 32-bit) into lower precision (e.g., 8-bit) without significant loss in accuracy. This results in faster inference speeds and lower memory usage, making it perfect for deploying models in resource-constrained environments. Low-Rank Adaptation (LoRA): LoRA enables efficient fine-tuning of large models by introducing low-rank matrix updates instead of retraining the entire model. This drastically reduces the number of trainable parameters, making fine-tuning faster and more memory-efficient while maintaining high performance. By utilizing these methods, we can make large models more accessible and scalable for various real-world applications. Excited to explore these techniques further in upcoming projects! #AI #MachineLearning #LLMs #Quantization #LoRA #ModelOptimization #DataScience #AIInnovation #EfficientAI

Ready for the next big leap in making AI truly accessible? We are excited to release #CliMB, a no-code AI-enabled partner for clinical predictive modelling! With CliMB, you can build predictive models using natural language. CliMB supports data exploration, engineering, model building, and interpretation—enabling clinician scientists to utilise cutting-edge tools in the fields of data-centric AI, AutoML, and interpretable ML. This proof of concept is a huge step towards breaking barriers and #empowering clinician scientists to build predictive models using cutting-edge tools! You can read our paper here: https://lnkd.in/eC2M5WMw   You can download CliMB here: https://lnkd.in/eeWWU_uV We recently discussed CliMB extensively with clinical researchers during a #RevolutionizingHealthcare session. You can watch the full episode on YouTube to hear their insights and watch our demonstrations of the tool: https://lnkd.in/ekGefg3d