AI at Meta’s Post

#𝐂𝐡𝐚𝐦𝐞𝐥𝐞𝐨𝐧: Mixed-Modal Early-Fusion Foundation Models by the Chameleon Team presents a new family of models called Chameleon. These models are designed to handle both images and text in any sequence through an early-fusion, token-based approach. Key highlights include: - 𝐒𝐭𝐚𝐛𝐥𝐞 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐚𝐧𝐝 𝐀𝐥𝐢𝐠𝐧𝐦𝐞𝐧𝐭: The paper outlines a stable training method and an alignment strategy for these models. - 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐚𝐥 𝐏𝐚𝐫𝐚𝐦𝐞𝐭𝐞𝐫𝐢𝐳𝐚𝐭𝐢𝐨𝐧: Tailored specifically for early-fusion, token-based, mixed-modal tasks. - Versatile Capabilities: Chameleon excels in tasks such as visual question answering, image captioning, text and image generation, and long-form mixed-modal generation. - 𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞: It achieves state-of-the-art results in image captioning, surpasses Llama-2 in text tasks, and competes well with Mixtral 8x7B and Gemini-Pro. It also demonstrates strong image generation capabilities. - 𝐂𝐨𝐦𝐩𝐚𝐫𝐚𝐭𝐢𝐯𝐞 𝐀𝐝𝐯𝐚𝐧𝐭𝐚𝐠𝐞: Chameleon matches or exceeds the performance of larger models like Gemini Pro and GPT-4V in human evaluations of long-form mixed-modal generation. Overall, Chameleon represents a significant advancement in unified modeling for multimodal documents.

Multimodal foundation models have shown significant promise, with several attempts proposed since 2020 - https://lnkd.in/eEzTmTER. I look forward to reading this new development. AI at Meta

This should be an interesting read when multimodal LLM becomes more popular after GPT-4o.

Big News in AI! Meet Chameleon: The Mixed-Modal Early-Fusion Foundation Model Here are some insights on Chameleon, a groundbreaking model that seamlessly integrates text and images. Here’s why it’s a game-changer: 1- Unified Processing Chameleon uses an early-fusion approach to merge visual and textual data from the start. This creates a deeper, context-aware understanding, resulting in more coherent and insightful outputs. 2- Versatile Applications - Image Captioning: Automatically generate detailed descriptions of images. - Visual Question Answering: Answer questions about image content accurately. - Text Generation: Create rich narratives and descriptions from visual prompts. - Mixed-Modal Content Creation: Blend text and images effortlessly for marketing, educational materials, and creative projects. 3- Top-Notch Performance Chameleon competes with the best, like GPT-4V and Gemini Pro, while being more efficient. It offers high performance without needing extensive computational resources. 4- Simplified Integration Chameleon’s unified approach simplifies development, making it easier to create applications that require seamless integration of visual and textual data. From content creation to educational tools and accessibility improvements, Chameleon is set to impact many industries positively. Embrace the future of AI with Chameleon! Curious to learn more? Check out the full paper (https://lnkd.in/e8a63pdC). #AI #MachineLearning #Innovation #TechNews #ArtificialIntelligence #ContentCreation #Education #Accessibility #Research #ChameleonModel

🎉 Exciting News! 🎉 I am thrilled to announce that our paper is finally out! This was a culmination of countless hours of hard work, dedication, and collaboration. Arxiv link: https://lnkd.in/geigEcAr We present Chameleon, a family of early-fusion token-based mixed-modal models capable of understanding and generating images and text in any arbitrary sequence. We outline a stable training approach from inception, an alignment recipe, and an architectural parameterization tailored for the early-fusion, token-based, mixed-modal setting. The models are evaluated on a comprehensive range of tasks, including visual question answering, image captioning, text generation, image generation, and long-form mixed modal generation. Chameleon demonstrates broad and general capabilities, including state-of-the-art performance in image captioning tasks, outperforms Llama-2 in text-only tasks while being competitive with models such as Mixtral 8x7B and Gemini-Pro, and performs non-trivial image generation, all in a single model. It also matches or exceeds the performance of much larger models, including Gemini Pro and GPT-4V, according to human judgments on a new long-form mixed-modal generation evaluation, where either the prompt or outputs contain mixed sequences of both images and text. Chameleon marks a significant step forward in a unified modeling of full multimodal documents.

It was incredibly exciting to help safety and privacy efforts for the latest AI model Meta has just released! It’s an honor to be included in the acknowledgments section of this paper :) We present Chameleon, a family of early-fusion token-based mixed-modal models capable of understanding and generating images and text in any arbitrary sequence. We outline a stable training approach from inception, an alignment recipe, and an architectural parameterization tailored for the early-fusion, token-based, mixed-modal setting. The models are evaluated on a comprehensive range of tasks, including visual question answering, image captioning, text generation, image generation, and long-form mixed modal generation. Chameleon demonstrates broad and general capabilities, including state-of-the-art performance in image captioning tasks, outperforms Llama-2 in text-only tasks while being competitive with models such as Mixtral 8x7B and Gemini-Pro, and performs non-trivial image generation, all in a single model. It also matches or exceeds the performance of much larger models, including Gemini Pro and GPT-4V, according to human judgments on a new long-form mixed-modal generation evaluation, where either the prompt or outputs contain mixed sequences of both images and text. Chameleon marks a significant step forward in a unified modeling of full multimodal documents.

Please review the conversation with Dall-E. 🙂 Question: ML is a data-driven approach to modeling. When producing videos, we want to embed real physics (first principles) into the models that support the video creation.  How to integrate first principle modeling with data-driven modeling (ML) in video creation? I am pretty amazed by the depth of the answer by DALL-E.

Exciting new research has been published exploring the Challenges of Image Generation Models in Generating Multi-Component Images. The study delves into the complexity of prompts and how they affect the quality of images generated by generative models. Click the link below to read the full paper and discover the proposed metric, Components Inclusion Score (CIS), used to evaluate models' ability to generate multiple components accurately. The findings uncover critical limitations and shed light on the challenge of creating high-quality, context-aware images. Don't miss out on this fascinating research! Read the full paper here: https://bit.ly/49QYqsu

I'm pleased to share an arXiv pre-print written by Viktoriya Morozova from my group titled, "High-Order, Implicit Time Integration of Discrete, Chaotic Dynamical Systems." For wall-resolved turbulent flow simulations of practical geometries, the stability limit of explicit time integrators is often overly restrictive compared to the scales of interest, leading to the use of implicit time integrators. This paper investigates the question of whether or not high-order time-marching is advantageous for stiff chaotic systems, and the short answer is surprisingly "no". The increase in time-step size afforded by higher-order SDIRK schemes is insufficient to offset the additional computational cost, which includes both additional stages and linear solves. The computational efficiency of 2nd-order schemes wins out for the test problems considered, with the single-step, multi-stage (E)SDIRK22 scheme edging out multi-step, single-stage BDF2 scheme. https://lnkd.in/eiw8R9TF

Exciting new blog post alert! Learn how to quantify uncertainty in generative models and detect incorrect responses with high probability. Check out the latest post "Experts Don't Cheat: Learning What You Don't Know By Predicting Pairs" on arXiv:2402.08733v1. Discover a general strategy for teaching a model to both approximate $p\(Y\|X\)$ and estimate the remaining gaps between $\{\widehat\{p\}\}\_\{\theta\}\(Y\|X\)$ and $p\(Y\|X\). See the full post at: https://bit.ly/4bxvHd7. \#machinelearning \#datascience \#uncertaintyquantification