Overview of Multimodal AI Capabilities

Over the past year, the domain of LLMs has experienced a paradigm shift with the advent and progression of MultiModal Large Language Models (MM-LLMs) such as Google’s Gemini. These advanced models transcend the capabilities of conventional LLMs by integrating and processing a diverse array of data types, including text, images, audio, and video. A recent paper provides a comprehensive evaluation of these MM-LLMs, employing 18 Visual Language (VL) benchmarks. These benchmarks rigorously assess the models' abilities in tasks such as visual question answering, generating text from visual cues, and the intricate interplay between visual and textual elements. Notably, the model "VILA 13B + ShareGPT4V" emerges as a standout performer across these benchmarks. I have categorizes the multimodal models into three primary groups: 𝟏. 𝐆𝐞𝐧𝐞𝐫𝐚𝐥-𝐏𝐮𝐫𝐩𝐨𝐬𝐞 𝐌𝐮𝐥𝐭𝐢𝐦𝐨𝐝𝐚𝐥 𝐌𝐨𝐝𝐞𝐥𝐬: Versatile in nature, these models adeptly handle tasks involving both text and visual inputs. Notable examples include Flamingo, BLIP-2, MiniGPT-4, PandaGPT, and PaLI-X. 𝟐. 𝐒𝐩𝐞𝐜𝐢𝐚𝐥𝐢𝐳𝐞𝐝 𝐌𝐮𝐥𝐭𝐢𝐦𝐨𝐝𝐚𝐥 𝐌𝐨𝐝𝐞𝐥𝐬: Tailored for specific multimodal interactions, these models are focused on particular tasks or modalities. For instance, VideoChat exemplifies this category. 𝟑. 𝐒𝐜𝐚𝐥𝐚𝐛𝐥𝐞 𝐚𝐧𝐝 𝐄𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐭 𝐌𝐮𝐥𝐭𝐢𝐦𝐨𝐝𝐚𝐥 𝐌𝐨𝐝𝐞𝐥𝐬: Emphasizing resource efficiency and adaptability across various modalities, models like mPLUG-Owl and X-LLM belong to this category. Looking forward, the trajectory for MM-LLMs is marked by three promising trends: 𝟏. 𝐄𝐧𝐡𝐚𝐧𝐜𝐞𝐝 𝐌𝐨𝐝𝐚𝐥𝐢𝐭𝐲 𝐂𝐨𝐧𝐯𝐞𝐫𝐬𝐢𝐨𝐧: A shift towards more dynamic capabilities, moving from merely understanding to generating and inter-converting different modalities. 𝟐. 𝐃𝐢𝐯𝐞𝐫𝐬𝐞 𝐌𝐨𝐝𝐚𝐥 𝐂𝐚𝐩𝐚𝐛𝐢𝐥𝐢𝐭𝐢𝐞𝐬: An expansion in the range of modalities these models can process and interpret. 𝟑. 𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 𝐈𝐦𝐩𝐫𝐨𝐯𝐞𝐦𝐞𝐧𝐭: Focused on enhancing model performance through the use of high-quality training data and the adoption of more streamlined, efficient architectures. These developments indicate a future where MM-LLMs will become increasingly sophisticated and versatile, further blurring the lines between human-like understanding and artificial intelligence. Are you excited about the potential of MM-LLMs? Share your thoughts in the comments below! Paper: https://lnkd.in/e85U-N4F

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

Text is NOT the only data type we use in RAG pipelines! We are still in the infancy of Generative AI, and text is now the primary information that we feed to LLMs, but this is changing quickly! There is a lot more information contained in the different documents we use on a daily basis beyond just text data. For example, GPT-4, Gemini, and Claude are multimodal LLMs that can ingest images as well as text. The images are passed through a Vision Transformer, resulting in visual tokens. The visual tokens are then passed through a projection layer that specializes in aligning visual tokens with text tokens. The visual and text tokens are then provided to the LLM, which cannot make the difference between the different data modes. In the context of RAG, an LLM plays a role at indexing time, where it generates a vector representation of the data to index it in a vector database. It is also used at retrieval time, where it uses the retrieved documents to provide an answer to a user question. A multimodal LLM can generate embedding representations of images and text and answer questions using those same data types. If we want to answer questions that involve information in different data modes, using a multimodal LLM at indexing and retrieval time is the best option. If you want to build your RAG pipeline using API providers like OpenAI, you can use GPT-4 for question-answering using multimodal prompts. Even if it is available for text generation, it might not be available for embedding generation. Remains creating embedding for images, then? This can be achieved by prompting a multimodal LLM to describe in text the images we need to index. We can then index the images using the text descriptions and their vector representations. The complexity of generating a text description of an image is not the same as answering questions using a large context of different data types. With a small multimodal LLM, we might get satisfactory results in describing images but subpar results in answering questions. For example, it is pretty simple to build an image description pipeline with LlaVA models and Llama.cpp as LLM backbone. Those descriptions can be used for indexing as well as for answering questions that may involve those images. The LLM answering questions would use the text description of images instead of the images themselves. Today, that might be the simplest option for building a multimodal RAG pipeline. It might not be as performant, but the technology is improving very fast! -- 👉 Don't forget to subscribe to my ML newsletter https://lnkd.in/g4iKyRmS --

Google Unveils Gemini: A Multimodal AI Model with Human-like Performance Google Research has unveiled Gemini, a family of multimodal AI models that demonstrate human-level performance across diverse tasks. Boasting capabilities in image, audio, video, and text domains, Gemini represents a significant advancement in the field of artificial intelligence. Key Highlights: Human-Expert Performance: Gemini Ultra, the most advanced model, surpasses human experts on 57 subjects in the MMLU benchmark, achieving a score above 90%. Multimodal Reasoning: Gemini excels at tasks requiring both understanding and reasoning across different modalities. It can solve math problems from handwritten notes, analyze charts and generate tables, and even answer questions about video content. State-of-the-Art Benchmarks: Gemini sets new state-of-the-art results across 30 out of 32 benchmarks, including text, image, video, and speech understanding tasks. Democratizing Access: Available in various sizes, Gemini caters to different needs. Nano models are designed for on-device usage, Pro models are ideal for data centers, and the Ultra model tackles highly complex tasks. Responsible Development: Google emphasizes responsible deployment, addressing potential bias and harmful outputs through careful fine-tuning and instruction tuning. Applications: Education: Gemini's capabilities offer immense potential in education, providing personalized learning experiences and assisting students with complex concepts. Science & Research: Gemini can accelerate scientific discovery by analyzing vast data sets and generating insights across disciplines. Productivity & Creativity: Gemini can empower users through intelligent assistance in tasks like writing, coding, and problem-solving. Accessibility: Gemini's ability to process diverse modalities makes it a valuable tool for individuals with disabilities. Availability: As of today, Gemini Pro powers Bard, Google's AI-powered chatbot. On December 13th, developers can access Gemini Pro through APIs. Android users will have access to the Nano model on Pixel 8 Pro devices. Bard Advanced, powered by Gemini Ultra, will launch early next year. https://lnkd.in/gptk-K88 This groundbreaking technology marks a significant leap forward in AI, paving the way for a future where machines can collaborate with humans and solve problems in ways that were once unimaginable.

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