David Font’s Post

Building Visual AI Agents with Generative AI and NVIDIA NIM Traditional Video Analytics Limitations Fixed-function, limited models for detecting predefined objects Narrow perception and contextual understanding Introducing NVIDIA AI Blueprint for Video Search and Summarization Leverages generative AI, NVIDIA NIM microservices, and foundation models Enables broad perception and rich contextual understanding with fewer models Key Components Vision Language Models (VLMs) for visual understanding Large Language Models (LLMs) for natural language processing Graph-RAG techniques for long-form video understanding Building a Visual AI Agent Combines VLMs, LLMs, and datastores for scalable video understanding Performs tasks like summarization, Q&A, and event detection on live streams NVIDIA AI Blueprint Architecture Stream handler for managing interactions and synchronization NeMo Guardrails for filtering invalid user prompts VLM pipeline for generating embeddings and per-chunk responses VectorDB for storing intermediate responses CA-RAG module for aggregating responses and generating summaries Graph-RAG module for capturing complex relationships and storing knowledge graphs Video Ingestion and Retrieval Pipeline GPU-accelerated video ingestion for building comprehensive indexes VLM pipeline and CA-RAG for generating dense captions and metadata Knowledge graph construction for storing complex information Applications and Benefits Summarization, Q&A, and alerts on live streams and long videos Improved decision-making with richer insights from natural interactions Deployable in various industries, including factories, warehouses, retail, and more Get Started with NVIDIA AI Blueprint Apply for early access to the NVIDIA AI Blueprint for Video Search and Summarization Explore the possibilities of building visual AI agents with generative AI and NVIDIA NIM. ref - Build a Video Search and Summarization Agent with NVIDIA AI Blueprint **link in comment #generativeAI #nvidia #VLM #LLM

Transform Video Analysis with NVIDIA’s AI Blueprint! https://lnkd.in/g-vznzVQ Traditional video analytics can only go so far with their limited, predefined object detection. Enter NVIDIA’s AI Blueprint—a powerful framework combining Vision Language Models (VLMs) and Large Language Models (LLMs) to enable smarter, context-aware video understanding. This means industries like retail or warehousing can leverage it for real-time video summarization, detailed Q&A, and event alerts. Examples? Imagine live monitoring that provides instant feedback during quality checks or flags security events as they happen. Thanks, Sagar, for showcasing this cutting-edge solution!

Lifting the hood, let me clarify the connection between new tech and AI applications as we see it in VSL labs. This week, NVIDIA quietly released a groundbreaking new AI model that is set to change the game. Their **NVLM-D-72B** model has 72 billion parameters and delivers exceptional performance in both text and visual tasks. According to NVIDIA, this model outperforms leading and much bigger AI models like OpenAI's GPT-4 and Anthropic's Claude-3.5 on critical benchmarks. But why is this important for us at VSL Labs? The NVLM-D-72 B is designed to handle multimodal tasks, combining text and visual analysis with high accuracy. This makes it an ideal tool for real-time sign language translation systems, a core focus of our technology. Translating spoken language to sign language requires processing both the words and contextual cues from visual data, like gestures and facial expressions. NVIDIA's new model offers precisely the type of **multimodal capability** that can drastically improve the performance of our systems. Moreover, **NVIDIA's open-source approach** to this model democratizes access to cutting-edge AI, empowering smaller companies like ours to build on top of these robust foundations. Incorporating this advanced model into our systems can refine our real-time translation capabilities, ensuring more accurate, faster, and accessible communication for deaf and hard-of-hearing individuals. Studies like Chen et al. (2023) emphasize the importance of user-centered design and continuous feedback from real users to enhance AI systems. NVIDIA's open-source model allows us to iterate more quickly based on community feedback, integrate improvements in real time, and ensure that our solutions stay at the forefront of AI and accessibility. The future is now: With NVIDIA's breakthrough model, we're poised to make sign language translation more accurate and seamless than ever. Together, we're pushing the boundaries of AI-powered accessibility. #AI #NVIDIA #Accessibility #Innovation #DeepTech #VSLabs--- **References:** - Wang, J., Liu, Y., & Zhang, H. (2022). Real-Time Machine Translation: Challenges and Applications. *Journal of Artificial Intelligence Research*. - Chen, R., Park, J., & Smith, K. (2023). User-Centered Design in AI Applications: Enhancing Accessibility. *Human-Computer Interaction Review*.

The article discusses NVIDIA's NVEagle, a newly released vision language model that significantly improves how AI understands and processes both images and text. Here's a simplified breakdown: - 🚀 What is NVEagle? NVEagle is a type of AI model designed to understand both visual (images) and textual information together. It can "see" images, interpret them, and combine this understanding with text to make sense of complex scenarios. - 🧠 Challenges it Addresses Traditional models often struggle with high-resolution images or complex visual tasks, sometimes making mistakes or "hallucinations" where they generate inaccurate results. NVEagle tackles these issues by using advanced techniques to better align visual information with text, making it more reliable. - 🔑 Key Features - Multiple Variants: NVEagle comes in three versions, each suited for different tasks, including general use and conversational AI. - Improved Visual Perception: It uses a strategy where different parts of the model focus on different types of visual information, choosing the best method for each task. - Efficiency: Despite its complexity, NVEagle remains efficient and effective, outperforming other models in various benchmarks like OCR (reading text from images) and visual question answering. - 🌟 Why It Matters This model represents a significant advancement in how AI can process and understand the world around it by effectively combining what it "sees" with what it "reads." This makes NVEagle a powerful tool for tasks that require detailed visual understanding, such as document analysis or answering questions based on images. In essence, NVEagle pushes the boundaries of how AI can understand and interact with both visual and textual information, making it a valuable tool in fields that require complex visual analysis. https://lnkd.in/gScgkhHA

🎉 Big News in the world of AI! NVIDIA is shining a spotlight on Abacus.AI’s groundbreaking LLM, Smaug-72B! This is redefining what open-source AI can achieve. 🎯 Topping the charts, Smaug-72B has achieved an average score of 80 across all major language model evaluations, surpassing even proprietary models like GPT-3.5 and Mistral Medium. The era of open-source AI challenging Big Tech’s capabilities is here! 📈 Under the hood, Smaug-72B leverages cutting-edge techniques that enhance reasoning and math skills, as evidenced by its high GSM8K scores. 🌐 Smaug-72B’s release signifies a shift. No longer confined to secretive tech giants, open-source AI models like Smaug-72B empower a global community of innovators. Exciting times in the world of AI! Read more about Smaug-72B's capabilities in NVIDIA's latest blog post. #AI #OpenSource #AbacusAI #Smaug72B #NVIDIA #LLM #LanguageModels

🎉 Big News in the world of AI! NVIDIA is shining a spotlight on Abacus.AI’s groundbreaking LLM, Smaug-72B! This is redefining what open-source AI can achieve. 🎯 Topping the charts, Smaug-72B has achieved an average score of 80 across all major language model evaluations, surpassing even proprietary models like GPT-3.5 and Mistral Medium. The era of open-source AI challenging Big Tech’s capabilities is here! 📈 Under the hood, Smaug-72B leverages cutting-edge techniques that enhance reasoning and math skills, as evidenced by its high GSM8K scores. 🌐 Smaug-72B’s release signifies a shift. No longer confined to secretive tech giants, open-source AI models like Smaug-72B empower a global community of innovators. Exciting times in the world of AI! Read more about Smaug-72B's capabilities in NVIDIA's latest blog post. #AI #OpenSource #AbacusAI #Smaug72B #NVIDIA #LLM #LanguageModels https://lnkd.in/grKWsy8k

🚀This week we published a case study with NVIDIA: LILT powers mission-critical law enforcement applications in both the US and abroad. The use case demonstrates how Generative AI empowers government agencies to solve problems that they couldn't without AI. 1. Law enforcement agencies seize large volumes of digital evidence in multiple languages and modalities (speech/video/text). 2. They must convert and bulk-translate the evidence to English for analysis, usually with downstream analytics products. 3. They must select the most pertinent data and raise it to an evidentiary standard, usually using human-in-the-loop verification. === Traditional localization products aren't a fit for these types of workflows. This is a clear example of post-TMS / AI-powered workflows in action. Read the case study: https://bit.ly/48ax7Hz #ai #ArtificialIntelligence #EnterpriseAI #defensetech

Data governance for LLMs is crucial in the upcoming age, with numerous challenges to navigate across industries and enterprises worldwide! At SimplAI, we address these challenges through cloud-agnostic or on-premises deployments, implementing guardrails to protect models against various aspects like PII and gibberish text. Exciting Time Ahead !!!! SimplAI

🌐 Navigating Large Language Models (LLMs) in Data Governance Exploring the Impact of LLMs The multifaceted challenges posed by Large Language Models (LLMs). These advanced AI algorithms, exemplified by GPT-3, have revolutionized natural language processing with their ability to synthesize vast datasets, driving widespread adoption across industries. However, their integration presents unique risks, particularly in data governance. This needs to address issues like privacy, bias, and ethical usage within robust governance frameworks. Mitigating LLM Risks As organizations integrate LLMs, managing associated risks becomes critical. we need to advocate for: - Ethical guidelines for LLM use - Bias detection and mitigation strategies - Transparency in decision-making influenced by LLM outputs - Continuous monitoring of LLM performance Integrating LLMs into Data Governance Adapting data governance to include LLMs requires: - Collaborative approaches across teams - Training for data stewards and analysts - Clear accountability for LLM-influenced decisions - Adaptive governance frameworks for evolving LLM challenges Key Challenges and Solutions Privacy concerns, bias in data synthesis, and intellectual property protection are pivotal. Effective data governance programs are essential for managing these risks, ensuring compliance, and safeguarding sensitive information. Enhancing Decision-Making with Generative AI Generative AI, like ChatGPT, empowers data-driven decisions, though it demands meticulous governance to navigate complexities and ensure ethical data usage. Best Practices for LLMs Patience, fact-checking, dedicated teams, and validation are crucial for maximizing LLM effectiveness while upholding accuracy and reliability standards. Conclusion: Embracing AI in Data Governance The above Insights underscore the synergy between LLMs and data governance. Proactively managing risks enables organizations to leverage LLMs effectively, driving innovation while preserving ethical standards in the AI era. Understanding and addressing these challenges are pivotal in navigating the evolving landscape of AI and data governance. #AI #DataGovernance #LLMs #EthicsInAI #Innovation Sandeep DinodiyaSimplAIUtkarsh Mangal Santhosh Kumar K.