Importance of Multimodal AI

New blog post: Multimodality and Large Multimodal Models (LMMs) Link: https://lnkd.in/gJAsQjMc Being able to work with data of different modalities -- e.g. text, images, videos, audio, etc. --  is essential for AI to operate in the real world. Many use cases are impossible without multimodality, especially those in industries that deal with multimodal data such as healthcare, robotics, e-commerce, retail, gaming, etc. Not only that, data from new modalities can help boost model performance. Shouldn’t a model that can learn from both text and images perform better than a model that can learn from only text or only image? OpenAI noted in their GPT-4V system card that “incorporating additional modalities (such as image inputs) into LLMs is viewed by some as a key frontier in AI research and development.” This post covers multimodal systems, including LMMs (Large Multimodal Models). It consists of 3 parts. * Part 1 covers the context for multimodality, including use cases, different data modalities, and types of multimodal tasks. * Part 2 discusses how to train a multimodal system, using the examples of CLIP, which lays the foundation for many LMMs, and Flamingo, whose impressive performance gave rise to LMMs. * Part 3 discusses some active research areas for LMMs, including generating multimodal outputs and adapters for more efficient multimodal training. Even though we’re still in the early days of multimodal systems, there’s already so much work in the space. At the end of the post, I also compiled a list of models and resources for those who are interested in learning more about multimodal. As always, feedback is appreciated! #llm #lmm #multimodal #genai #largemultimodalmodel

Your AI Will See You Now: Unveiling the Visual Capabilities of Large Language Models The frontier of AI is expanding with major advancements in vision capabilities across Large Language Models (LLMs) such as OpenAI’s ChatGPT, Google’s Gemini, and Anthropic’s Claude. These developments are transforming how AI interacts with the world, combining the power of language with the nuance of vision. Key Highlights: • #ChatGPTVision: OpenAI’s GPT-4V introduces image processing, expanding AI’s utility from textual to visual understanding. • #GeminiAI: Google’s Gemini leverages multimodal integration, enhancing conversational abilities with visual data. • #ClaudeAI: Anthropic’s Claude incorporates advanced visual processing to deliver context-rich interactions. Why It Matters: Integrating visual capabilities allows #AI to perform more complex tasks, revolutionizing interactions across various sectors: • #Robots and Automation: Robots will utilize the vision part of multimodality to navigate and interact more effectively in environments from manufacturing floors to household settings. • #Security and Identification: At airports, AI-enhanced systems can scan your face as an ID, matching your image against government databases for enhanced security and streamlined processing. • #Healthcare Applications: In healthcare, visual AI can analyze medical imagery more accurately, aiding in early diagnosis and tailored treatment plans. These advancements signify a monumental leap towards more intuitive, secure, and efficient AI applications, making everyday tasks easier and safer. Engage with Us: As we continue to push AI boundaries, your insights and contributions are invaluable. Join us in shaping the future of multimodal AI. #AIRevolution #VisualAI #TechInnovation #FutureOfAI #DrGPT 🔗 Connect with me for more insights and updates on the latest trends in AI and healthcare. 🔄 Feel free to share this post and help spread the word about the transformative power of visual AI!

📢 One patient, many contexts, yet our AIs are still refreshing outdated prompts We envision context-switching AI will adapt to infinitely many medical contexts, new medical specialties, healthcare roles, diseases, and populations https://lnkd.in/e7ck8BhQ ♾ Prompting and fine-tuning are great early examples of AI context-switching, but we need to go beyond those. Why⁉️ ♾ Disease incidence rates vary geographically; however, fine-tuned or prompted models largely ignore this context. The choice of diagnostics and treatments depends on local, regional, social, and other contexts largely irrelevant elsewhere. Fine-tuning and prompting alone can't solve this at scale ♾ Clinical specialties differ vastly in terminology, workflows, and guidelines. Oncology needs molecular profiling and tumor staging, while emergency medicine prioritizes rapid triage. Can AI models adapt to infinitely many contexts, dynamically and without pre-specification? Context-switching in multimodal models: AI must integrate medical images, genomic data, electronic health records, and real-time sensor inputs. Context-switching models decide which data sources are relevant on the fly to enable precise clinical insights Context-switching in generative models: Clinical reports, diagnostic summaries, and personalized treatment plans vary dramatically between specialties. Generative AI models must dynamically adapt outputs to produce specialized outputs for each clinical scenario Context-switching in AI agents: Modular AI systems flexibly reorganize their reasoning pathways based on real-time clinical scenarios. The same AI might reason differently during acute trauma care versus chronic disease management, improving accuracy and patient safety Many thanks to @_michellemli Ben Y. Reis @AdamRodmanMD Tianxi Cai Noa Dagan @RanBalicer Joseph Loscalzo @zakkohane @marinkazitnik Harvard Medical School Department of Biomedical Informatics Harvard Medical School Harvard Data Science Initiative Broad Institute of MIT and Harvard Boston Children's Hospital Clalit Innovation Kempner Institute at Harvard University

🧠 Part 3 of My Gemini AI Series: Real-World Impact In this third installment of my ongoing series on Google’s Gemini AI, I shift focus from architecture and strategy to real-world results. 💡 This article highlights how leading organizations are applying Gemini’s multimodal capabilities—connecting text, images, audio, and time-series data—to drive measurable transformation across industries: 🏥 Healthcare: Reduced diagnostic time by 75% by integrating medical images, patient notes, and vitals using Gemini Pro on Vertex AI. 🛍️ Retail: Achieved 80%+ higher conversions with Gemini Flash through real-time personalization using customer reviews, visual trends, and behavioral signals. 💰 Finance: Saved $10M+ annually with real-time fraud detection by analyzing call audio and transaction patterns simultaneously. 📊 These use cases are not just proof of concept—they’re proof of value. 🧭 Whether you're a CTO, a product leader, or an AI enthusiast, these case studies demonstrate how to start small, scale fast, and build responsibly. 📌 Up Next – Part 4: A technical deep dive into Gemini’s architecture, model layers, and deployment patterns. Follow #GeminiImpact to stay updated. Let’s shape the future of AI—responsibly and intelligently. — Dr. Veera B. Dasari Chief Architect & CEO | Lotus Cloud Google Cloud Champion | AI Strategist | Multimodal AI Evangelist #GeminiAI #VertexAI #GoogleCloud #HealthcareAI #RetailAI #FintechAI #LotusCloud #AILeadership #DigitalTransformation #AIinAction #ResponsibleAI

Multimodality of AI for Education: Towards Artificial General Intelligence published at arxiv.org from Cornell University This paper presents a comprehensive examination of how multimodal artificial intelligence (AI) approaches are paving the way towards the realization of Artificial General Intelligence (AGI) in educational contexts. It scrutinizes the evolution and integration of AI in educational systems, emphasizing the crucial role of multimodality, which encompasses auditory, visual, kinesthetic, and linguistic modes of learning. This research delves deeply into the key facets of AGI, including cognitive frameworks, advanced knowledge representation, adaptive learning mechanisms, strategic planning, sophisticated language processing, and the integration of diverse multimodal data sources. It critically assesses AGI's transformative potential in reshaping educational paradigms, focusing on enhancing teaching and learning effectiveness, filling gaps in existing methodologies, and addressing ethical considerations and responsible usage of AGI in educational settings. The paper also discusses the implications of multimodal AI's role in education, offering insights into future directions and challenges in AGI development. This exploration aims to provide a nuanced understanding of the intersection between AI, multimodality, and education, setting a foundation for future research and development in AGI.   5️⃣ key takeaways from the study: #Multimodal AI in Education: The paper discusses the integration of multimodal artificial intelligence (AI) in educational contexts, highlighting its potential to achieve Artificial General Intelligence (AGI). #CognitiveFrameworks: It emphasizes the importance of cognitive frameworks, knowledge representation, and adaptive learning mechanisms in developing AGI for education. #StrategicPlanning: The study explores strategic planning and sophisticated language processing as crucial elements of AGI that can enhance teaching and learning effectiveness. #Ethical Considerations: Ethical, explainable, and responsible usage of AGI in educational settings is critically assessed, addressing the transformative potential and challenges. #Future Directions: The paper offers insights into future directions for AGI development, including the implications of multimodal AI’s role in education and the challenges ahead.

Explainable AI is essential for precision medicine—but here's what many are missing My latest blog post unpacks a fascinating Nature Cancer paper from showing multimodal AI outperforming traditional clinical tools by up to 34% in predicting outcomes. What surprised me most? Elevated C-reactive protein—typically a concerning marker—actually indicates LOWER risk when combined with high platelet counts. Some physicians may do this in their heads but they simply cannot do this same analysis across thousands of variables systematically.  With the right multimodal data and AI systems, we can create a fundamental shift in how we develop therapies and treat patients. Here's the twist: many argue we need randomized trials before implementing these AI tools. But that’s the wrong framework entirely. Google Maps doesn't drive your car—it gives you better navigation. Similarly, clinical AI doesn't treat patients—it reveals biological patterns that already exist. The real question: Can we afford to ignore these multimodal patterns and connections in precision medicine? Or should we use AI as a tool to uncover them and help inform our decision making? Read my full analysis here: https://lnkd.in/gGA4KTip -- I'd love to hear from others working at this intersection: How is your organization approaching multimodal data integration in precision medicine? #PrecisionMedicine #HealthCareAI #CancerCare

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)

Multimodal AI: Transformative enterprise insights - SiliconANGLE In the rapidly changing field of multimodal AI, systems can process multiple data inputs to provide insight or make predictions by training with and using video, audio, speech, images and text. These inputs offer a way to gain the benefits of generative artificial intelligence without the complexity associated with building large language models. “In looking at the use of this particular technology set, we may be able to solve problems that enterprises have without engaging larger generative AI systems or building LLMs, which are going to be very significant and complex to build and also very expensive to train,” said David Linthicum, principal analyst for theCUBE Research. “In some cases, multimodal AI will be just fine for the purposes that you need to use it for as it’s embedded in a business application.” The AI Insights and Innovation series from theCUBE, SiliconANGLE Media’s livestreaming studio, is the go-to podcast for the latest news, trends and insights in artificial intelligence, including generative AI. In this segment, Linthicum provides an overview of multimodal AI and how it offers businesses a potentially attractive set of options versus the cost and complexity required to train LLMs.

When ChatGPT first appeared, it embodied Douglas Adams's quote: “Any significantly advanced technology is indistinguishable from magic.” But that was just the beginning. From deepfake images of the Pope wearing Balenciaga to AI-generated songs rivaling pop stars, the potential of generative AI has only started to unfold. Now, we are seeing the benefits of the convergence: Multimodal AI. 💡 What is Multimodal AI? It’s the next leap in AI, enabling systems to process and synthesize multiple data types—text, images, audio, and video—all within a single framework. This unified approach delivers insights that were previously locked away in data silos. In industries like healthcare, manufacturing, and retail, the impact is profound: Healthcare: Advanced diagnostics combining imaging, lab results, and patient history to personalize treatment. Manufacturing: Predictive maintenance systems preventing equipment failures before they happen. Retail: Omnichannel insights that blend online and in-store data for a seamless customer experience. But it’s not all smooth sailing. Adopting multimodal AI requires tackling data integration, ensuring privacy, and managing computational costs. Yet, the rewards for businesses ready to innovate are massive: improved decision-making, deeper automation, and better customer experiences. That's the topic of this week's deep dive article on the AIE. Let me know what you think.

💡 The relevance, trustworthiness and quality of AI and #GenAI applications is increasingly dependent on the quality of enterprise private data and documents for grounding 💡 Without including #unstructureddata and #semistructureddata management into data fabric processes, the generativeAI experience in the enterprise will continue to have major hallucination problems. 💡 Institutional knowledge and intellectual property are locked into #multimodal documents. The vast majority of official communication documents are multimodal. These multimodal documents are internal (presentations, policies, audits, research etc.) and external (contracts, PR, messaging, etc.). They have a mix of text, images, numbers, tabular content and document structures with sections, headers and other artifacts. 💡 The #moderndatastack needs to evolve to support the multimodal-focused data fabric data and compute structures and unify the structure and unstructured metadata of organizations. 💡 Vendors offering #intelligentdocumentprocessing, #graphtechnologies (#knowledgegraphs and #graphdatabases) for #GraphRAG and #LLMfinetuning, #enterpriseretrieval, and services surrounding these technologies, will be best positioned for this new wave of data and metadata management needs. Read more on how technology vendors can react to this new wave of demand in the new Emerging Tech: Data Fabrics With Multimodal Data Focus for Generative AI-Enabled Applications (https://lnkd.in/eXzFcQ2S) note from Gartner. Sharat Menon, Ehtisham Zaidi and Ramke Ramakrishnan thank you for all the support and guidance in publishing this research!