Multimodal large models: broadening the way artificial intelligence understands the world

After gradually coming into contact with the changes brought about by artificial intelligence in productivity, people began to think about whether they could use it to analyze abstract things in the real world and break the barriers between different modes. Obviously, if you want to break the restrictions, you need to let artificial intelligence understand the content first. The emergence of multi-modal large models provides a solution to this problem.

First of all, we need to understand that the reason why humans have extremely excellent learning abilities is because we can observe and understand the same thing through multiple senses such as vision and hearing, and analyze it from different aspects. The content of the analysis is linked to the knowledge and experience we have accumulated in the past. However, even if there is no current relevant experience, humans can build up their understanding of this thing over and over again.

How to make artificial intelligence have the same learning ability as humans? There is no doubt that we need to broaden the channels through which artificial intelligence perceives the world. The current mainstream research direction is to first study chips that simulate human nerves and establish analytical capabilities. In terms of specific model development, the first step is to train the model to learn each modality and distinguish and understand it through marking; secondly, it is to carry out lightweight transformation of all its models and optimize the decoding method; thirdly, it is to establish different The correlation between modalities allows artificial intelligence to comprehensively understand the same content through dynamic marking of content.

This development process is essentially about splitting different contents and then conducting correlation training. By marking data, machines can understand human feelings about the same thing in different dimensions, thereby simulating real cognition. The development of multi-modal large models will undoubtedly promote artificial intelligence's in-depth understanding of the real world and enhance its logical capabilities, thereby developing more potential.

The development of large multi-modal models will undoubtedly further broaden the way humans perceive the world; allowing originally abstract things to be presented to us in a more easily understandable way.

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