One of the core directions of artificial intelligence is multimodal AI, which refers to the technology that combines multiple data sources (such as images, text, audio, etc.) for comprehensive analysis and processing. Breakthroughs in multimodal AI include advances in generative algorithms, large models, and multimodal technologies that will drive changes in the quality and performance of AI applications.
The development of multimodal AI has achieved a series of important results. For example, the advent of the AI painting system DALL-E2 and StableDiffusion, as well as the emergence of the chatbot ChatGPT, are all manifestations of the gradual improvement of multimodal AI technology. Among them, the large model has made a major breakthrough in text processing, and the performance of the model is improved through the training of a large number of network texts. However, the development of multimodal AI is not only limited to text processing, but also involves more types of data such as images and **.
On November 29, 2023, Pikalabs, an American AI startup, launched the generative model Pika10。The model can generate and edit various styles of ** based on text input. This is an important breakthrough for the film and television industry and the field of creative production, which will greatly improve the efficiency and quality of production, and provide more inspiration and creative space for artists in the creative process.
In addition, Google has also launched its own native multimodal large model gemini1Version 0. The model integrates multimodal technology, which can integrate text and image information to more accurately judge users' interests and needs when giving recommended content. gemini1.The release of 0 marks a new stage in the development of multimodal large models. It is expected that the commercial application of AI in various industries will accelerate in the near future.
Gemini also performed very well in 32 academic benchmarks, surpassing the current state-of-the-art 30. These tests cover a wide range of domains, from natural images and audio to mathematical reasoning. This achievement shows the great potential of multimodal large models in academic research and practical applications.
At present, the competition between domestic and foreign technology giants for multimodal AI technology is becoming increasingly fierce, which will further promote the rapid development of multimodal large models and multimodal underlying technologies. The continuous progress of multimodal AI technology will promote the application of artificial intelligence in various industries to be more widely promoted and applied.
Multimodal AI has many advantages over unimodal models. The unimodal model mainly processes specific types of data, and the design is concise, which can better extract the features of specific data types. This specificity makes unimodal models excellent when working with relevant data. However, it can be difficult to meet the demands of complex tasks due to the inability to capture the interactions and associations between multiple types of data.
However, the multimodal model has the ability to process multiple data inputs, and the design is relatively complex, which may require the integration of the outputs of multiple subnetworks. This design enables the multimodal model to capture the interaction and correlation between different data sources, providing multi-dimensional information for the task. The multimodal capability enables the model to obtain a wider variety of real-world data, such as images, text, reports, handwritten materials, and video materials, thereby improving the performance of the model.
In addition, multimodal capabilities can also help the model identify richer scenarios in practical applications to meet the important needs of AI technology and achieve the sustainable development of general AI. The main application scenarios of multimodal recognition include in-vehicle systems, intelligent robots, and identity identification.
Through the comprehensive use of voice recognition, face recognition, expression analysis, lip movement status, eye tracking, gesture recognition, tactile monitoring and other technologies, multimodal recognition can accurately judge people's emotions and fatigue states, realize identity verification, and provide people with more accurate, active and personalized human-computer interaction.
In the development of multimodal large-scale models, visual generation technology plays an important role. Models capable of understanding and generating visual content can engage in deeper and more complex tasks, such as image annotation, visual storytelling, and complex design tasks. These tasks require models to be able to understand and generate visual content that is closer to the way humans perceive and better process and generate information.
In the multimodal AI application market, large technology companies are gradually transforming the traditional AI solution business model, increasing the research and development of AI large language models, and further exploring the field of multimodal large models. Tech giants such as Google, OpenAI, and Meta are studying the potential of multimodal large models in robotics applications. Some companies optimize large language models by fine-tuning bot training data, while others leverage transformer architectures to train multiple sensory data simultaneously. Some of these companies focus on solving the high-level decision-making problems of robots, and some study the large models that directly participate in the underlying motion planning problems of robots, thus producing a series of specific large models.
From the perspective of business model, AI models are mainly divided into two ways. One is to provide API interfaces to enterprise users, in the form of Model-as-a-Service, enterprises can call the corresponding multimodal AI model for processing according to their own needs. The other is to embed multimodal AI models into its own products and services to provide specific solutions. Both of these methods have huge market potential and can be applied in various fields, such as intelligent transportation, intelligent manufacturing, smart home, etc.
According to market research firm Tractica, the multimodal AI technology market is expected to reach $28.2 billion by 2025, with a growth rate of more than 28%. Coupled with the development of cloud computing and edge computing technologies, the application of multimodal AI will be more extensive.
At present, multimodal AI technology still faces some challenges. One of them is the acquisition and processing of multimodal data, where different types of data require different sensors and algorithms to process. In addition, the quality and accuracy of the data is also a challenge, especially since the data acquired in various environments and scenarios can be noisy and inaccurate.
Therefore, the development of multimodal AI technology in the future needs to further solve the problem of data acquisition and processing, and improve the performance and usability of the model. At the same time, it is also necessary to strengthen the research and innovation of multimodal AI technology, promote the expansion of multimodal AI application scenarios to a wider range of fields, and realize the comprehensive application of artificial intelligence in various industries.