


Meta is an open source multi-sensory artificial intelligence model that integrates six types of data including text, audio, and vision.
Meta Inc. has released ImageBind, a new open source artificial intelligence model that integrates multiple data streams, including text, audio, visual data, temperature and motion readings, and more. The model is currently just a research project and has no direct consumer or practical applications yet, but it demonstrates the possibilities for future generative AI systems that can create immersive, multi-sensory experiences. At the same time, the model also shows Meta's open attitude in the field of artificial intelligence research, while its competitors such as OpenAI and Google are becoming increasingly closed.
#The core concept of the research is to integrate multiple types of data into a multidimensional index (or in artificial intelligence terminology, "embedding space"). The concept may be a little abstract, but it’s the basis of the recent boom in generative artificial intelligence. For example, AI image generators such as DALL-E, Stable Diffusion, and Midjourney rely on systems that tie text and images together during the training phase. They look for patterns in visual data while connecting this information to the description of the image. This is why these systems are able to generate images based on user text input. The same goes for many AI tools that can generate video or audio in the same way.
Meta says its model ImageBind is the first to integrate six types of data into a single embedding space. The six types of data include: visual (including images and videos); thermal (infrared images); text; audio; depth information; and, the most interesting of all, motion readings produced by an inertial measurement unit (IMU). (IMUs are found in phones and smartwatches and are used to perform a variety of tasks, from switching a phone from landscape to portrait to distinguishing between different types of movement.)
Future AI systems will be able to perform tasks as current Just like systems for text input, cross-reference this data. For example, imagine a future virtual reality device that is capable of generating not only audio and visual input, but also motion of your environment and physical platform. You can ask it to simulate a long sea journey, and it not only puts you on a ship with the sound of waves in the background, but also makes you feel the decks rocking under your feet and the sea breeze blowing.
Meta noted in a blog post that future models could also add other sensory input streams, including "tactile, speech, odor, and brain fMRI signals." The company also claims that this research "brings machines closer to the human ability to learn from many different forms of information simultaneously, comprehensively, and directly."
Of course, a lot of this is based on predictions, And it's likely that the direct applications of this research will be very limited. Last year, for example, the company Meta demonstrated an AI model capable of generating short, blurry videos based on text descriptions. Research like ImageBind shows how future versions of the system can incorporate other data streams, such as generating audio that matches the video output.
For industry observers, this research is also interesting, because IT House noticed that Meta Company has open sourced the underlying model, which is a practice that has attracted increasing attention in the field of artificial intelligence.
The above is the detailed content of Meta is an open source multi-sensory artificial intelligence model that integrates six types of data including text, audio, and vision.. For more information, please follow other related articles on the PHP Chinese website!

Hot AI Tools

Undresser.AI Undress
AI-powered app for creating realistic nude photos

AI Clothes Remover
Online AI tool for removing clothes from photos.

Undress AI Tool
Undress images for free

Clothoff.io
AI clothes remover

AI Hentai Generator
Generate AI Hentai for free.

Hot Article

Hot Tools

Notepad++7.3.1
Easy-to-use and free code editor

SublimeText3 Chinese version
Chinese version, very easy to use

Zend Studio 13.0.1
Powerful PHP integrated development environment

Dreamweaver CS6
Visual web development tools

SublimeText3 Mac version
God-level code editing software (SublimeText3)

Hot Topics



This site reported on June 27 that Jianying is a video editing software developed by FaceMeng Technology, a subsidiary of ByteDance. It relies on the Douyin platform and basically produces short video content for users of the platform. It is compatible with iOS, Android, and Windows. , MacOS and other operating systems. Jianying officially announced the upgrade of its membership system and launched a new SVIP, which includes a variety of AI black technologies, such as intelligent translation, intelligent highlighting, intelligent packaging, digital human synthesis, etc. In terms of price, the monthly fee for clipping SVIP is 79 yuan, the annual fee is 599 yuan (note on this site: equivalent to 49.9 yuan per month), the continuous monthly subscription is 59 yuan per month, and the continuous annual subscription is 499 yuan per year (equivalent to 41.6 yuan per month) . In addition, the cut official also stated that in order to improve the user experience, those who have subscribed to the original VIP

Improve developer productivity, efficiency, and accuracy by incorporating retrieval-enhanced generation and semantic memory into AI coding assistants. Translated from EnhancingAICodingAssistantswithContextUsingRAGandSEM-RAG, author JanakiramMSV. While basic AI programming assistants are naturally helpful, they often fail to provide the most relevant and correct code suggestions because they rely on a general understanding of the software language and the most common patterns of writing software. The code generated by these coding assistants is suitable for solving the problems they are responsible for solving, but often does not conform to the coding standards, conventions and styles of the individual teams. This often results in suggestions that need to be modified or refined in order for the code to be accepted into the application

Large Language Models (LLMs) are trained on huge text databases, where they acquire large amounts of real-world knowledge. This knowledge is embedded into their parameters and can then be used when needed. The knowledge of these models is "reified" at the end of training. At the end of pre-training, the model actually stops learning. Align or fine-tune the model to learn how to leverage this knowledge and respond more naturally to user questions. But sometimes model knowledge is not enough, and although the model can access external content through RAG, it is considered beneficial to adapt the model to new domains through fine-tuning. This fine-tuning is performed using input from human annotators or other LLM creations, where the model encounters additional real-world knowledge and integrates it

To learn more about AIGC, please visit: 51CTOAI.x Community https://www.51cto.com/aigc/Translator|Jingyan Reviewer|Chonglou is different from the traditional question bank that can be seen everywhere on the Internet. These questions It requires thinking outside the box. Large Language Models (LLMs) are increasingly important in the fields of data science, generative artificial intelligence (GenAI), and artificial intelligence. These complex algorithms enhance human skills and drive efficiency and innovation in many industries, becoming the key for companies to remain competitive. LLM has a wide range of applications. It can be used in fields such as natural language processing, text generation, speech recognition and recommendation systems. By learning from large amounts of data, LLM is able to generate text

Machine learning is an important branch of artificial intelligence that gives computers the ability to learn from data and improve their capabilities without being explicitly programmed. Machine learning has a wide range of applications in various fields, from image recognition and natural language processing to recommendation systems and fraud detection, and it is changing the way we live. There are many different methods and theories in the field of machine learning, among which the five most influential methods are called the "Five Schools of Machine Learning". The five major schools are the symbolic school, the connectionist school, the evolutionary school, the Bayesian school and the analogy school. 1. Symbolism, also known as symbolism, emphasizes the use of symbols for logical reasoning and expression of knowledge. This school of thought believes that learning is a process of reverse deduction, through existing

Editor |ScienceAI Question Answering (QA) data set plays a vital role in promoting natural language processing (NLP) research. High-quality QA data sets can not only be used to fine-tune models, but also effectively evaluate the capabilities of large language models (LLM), especially the ability to understand and reason about scientific knowledge. Although there are currently many scientific QA data sets covering medicine, chemistry, biology and other fields, these data sets still have some shortcomings. First, the data form is relatively simple, most of which are multiple-choice questions. They are easy to evaluate, but limit the model's answer selection range and cannot fully test the model's ability to answer scientific questions. In contrast, open-ended Q&A

Editor | KX In the field of drug research and development, accurately and effectively predicting the binding affinity of proteins and ligands is crucial for drug screening and optimization. However, current studies do not take into account the important role of molecular surface information in protein-ligand interactions. Based on this, researchers from Xiamen University proposed a novel multi-modal feature extraction (MFE) framework, which for the first time combines information on protein surface, 3D structure and sequence, and uses a cross-attention mechanism to compare different modalities. feature alignment. Experimental results demonstrate that this method achieves state-of-the-art performance in predicting protein-ligand binding affinities. Furthermore, ablation studies demonstrate the effectiveness and necessity of protein surface information and multimodal feature alignment within this framework. Related research begins with "S

According to news from this site on August 1, SK Hynix released a blog post today (August 1), announcing that it will attend the Global Semiconductor Memory Summit FMS2024 to be held in Santa Clara, California, USA from August 6 to 8, showcasing many new technologies. generation product. Introduction to the Future Memory and Storage Summit (FutureMemoryandStorage), formerly the Flash Memory Summit (FlashMemorySummit) mainly for NAND suppliers, in the context of increasing attention to artificial intelligence technology, this year was renamed the Future Memory and Storage Summit (FutureMemoryandStorage) to invite DRAM and storage vendors and many more players. New product SK hynix launched last year
