Understanding Multimodal Processing in AI

We’re making AI assistants smarter, but we’re missing a layer............................ You know how today’s chatbots can write poems, crack jokes, or even debug code? But ask them to help you fix your bike, plan a trip, or solve a problem that’s not just text-deep, and they fumble. They’re like brilliant minds floating in a void—no eyes, no hands, no memory of how the real world connects. Here’s what keeps me up at night: What if we gave these AI brains a nervous system? That’s where MULTIMODAL KNOWLEDGE GRAPHS come in. Think of them as a digital twin of reality—not just words, but images, sounds, maps, sensor data, even emotions—all wired together like neurons. When you plug this into an LLM, magic happens: 1) A farmer in Kenya asks, “Why are my crops dying?” The chatbot doesn’t just spit out generic advice. It cross-references soil data from satellite images, local weather patterns, and crowdsourced photos of similar blights—then shows her a video of the exact fix used by a farmer in Brazil last monsoon. 2) A kid learning math says, “I don’t get fractals.” Instead of a textbook definition, the AI grabs a 3D model of a mountain range, overlays it with a fern leaf from a biology database, and says: “Here’s what nature’s been doing for millions of years.” 3) You’re arguing with a friend about “Was Rome’s architecture inspired by Greece?” The chatbot doesn’t just debate—it pulls up side-by-side blueprints of the Parthenon and the Pantheon, layers in a historian’s podcast clip, and ends with a meme. Now you get it. This isn’t about making chatbots “better.” It’s about grounding AI in the messy, beautiful chaos of reality. Less “Here’s an answer,” more “Let me show you how the dots connect.” I don’t want AI that mimics humans. I want AI that collaborates with them—that can glance at a blueprint, hear a sigh of frustration, or scan a forest fire’s heat map and say: “Let’s solve this together.” The future of AI isn’t bigger models. It’s richer senses. What do you think? Are we ready to stop chasing ‘smarter’ and start building ‘aware’? #MultimodalKnowledgeGraphs #Ontologies #KnowledgeGraphs #ArtificialIntelligence #MachineLearning #Technology #AIWithSenses #

Happy Friday, this week in #learnwithmz lets discuss 𝐌𝐮𝐥𝐭𝐢𝐦𝐨𝐝𝐚𝐥 𝐌𝐨𝐝𝐞𝐥𝐬 [My Prediction] Imagine a future where for example, for dinner, you simply tell a system what you’d like to eat. You can visually and verbally confirm the items and ingredients, and the system customizes the order based on your past preferences and interactions. Best part? It places the order 𝐫𝐞𝐥𝐢𝐚𝐛𝐥𝐲 without you searching/clicking on a single screen. It sounds wild, but it’s not far off! Multimodal models (MM-LLMs) are changing the way we interact with technology by integrating multiple types of data, such as text, images, and audio, into a single model. These models are not only enhancing our understanding of complex data but also opening up new possibilities for innovation. 𝐍𝐨𝐭𝐚𝐛𝐥𝐞 𝐌𝐨𝐝𝐞𝐥𝐬 - Microsoft's OmniParser: (https://lnkd.in/gRNsYHDk) OmniParser uses two sequential models: 1. Object Detection: A fine-tuned YOLOv8 model detects interactable regions on a UI screen. This allows the Set of Marks approach, where a multimodal LLM like GPT-4V is fed a screenshot with bounding boxes marking these regions, rather than just the screenshot alone. 2. Image Captioning: A fine-tuned BLIP-2/Florence-2 model generates descriptions for each detected region. This way, GPT-4V receives not just the screenshot with marked regions, but also captions that explain the function of each region. This combination enhances the model's understanding and interaction capabilities. - Apple's Ferret-UI: (https://lnkd.in/gg5UDE_P) A cutting-edge model that enhances user interfaces by processing multimodal inputs efficiently. It utilizes two pre-trained large language models, Gemma-2B and LLaMa-8B, to enable the model to comprehend and analyze screenshots of user interfaces and classify widgets. - OpenAI GPT-4o: (https://lnkd.in/grhpWDB6) OpenAI's GPT-4o is optimized for performance and supports text and image processing. It accepts as input any combination of text, audio, image, and video and generates any combination of text, audio, and image outputs. 𝐅𝐮𝐫𝐭𝐡𝐞𝐫 𝐑𝐞𝐚𝐝𝐢𝐧𝐠 - MM-LLMs: Recent Advances in MultiModal Large Language Models: A comprehensive survey on the latest developments in multimodal models: https://lnkd.in/g39QDuaG - Awesome-Multimodal-Large-Language-Models: A curated list of resources and projects on GitHub: https://lnkd.in/gHkh6EmD What use cases could you imagine with these Multimodal Models? Share your thoughts in the comments! Follow + hit 🔔 to stay updated. #MachineLearning #AI #DataScience #TechTrends #ML #Multimodal #MMLM P.S. 1st image is generated via Bing Copilot Microsoft Designer 2nd Image is Apple's Ferret-UI (Source: https://lnkd.in/gTJ5sNd2)

How AI is Bridging the Gap Between Vision and Language with Multimodal? Imagine an AI that can understand text and analyze images and videos! Multimodal: These advanced models are breaking new ground by integrating vision and language capabilities. Merging Text & Vision: They transform both textual and visual data into a unified representation, allowing them to connect the dots between what they see and what they read. Specialized Encoders: Separate encoders handle text and visuals, extracting key features before combining them for deeper processing. Focused Attention: The model learns to focus on specific parts of the input (text or image) based on the context, leading to a richer understanding. So, how can we leverage this exciting technology? The applications are vast: Image Captioning 2.0: MM-GPTs can generate detailed and insightful captions that go beyond basic descriptions, capturing the essence of an image. Visual Q&A Master: Imagine asking a question about an image, and MM-GPTs can analyze the content and provide the answer! Smarter Search: MM-GPTs can revolutionize image search by allowing users to find images based on textual descriptions. Immersive AR/VR Experiences: MM-GPTs can dynamically generate narratives and descriptions within AR/VR environments, making them more interactive and engaging. Creative Text Generation: Imagine MM-GPTs composing poems or writing scripts inspired by images, blurring the lines between human creativity and machine generation. Enhanced Accessibility: MM-GPTs can generate detailed audio descriptions of images, making the digital world more inclusive for visually impaired users. The future of AI is undeniably multimodal, and MM-GPTs are at the forefront of this exciting new era. #AI #MachineLearning #NaturalLanguageProcessing #ComputerVision #MultimodalLearning #Innovation #FutureofTechnology