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#ChatGPT # You may have heard of the GPT-3 series models behind ChatGPT. This is a powerful language generative model developed by OpenAI. It can generate various text outputs according to the given text input, from articles to code, from poetry to dialogue.

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But do you know that the successor to GPT-3, GPT-4, is about to be released? Moreover, it is not just a language model, but a multimodal artificial intelligence model that can handle different types of inputs such as video, image, and sound, opening up new possibilities for artificial intelligence applications. What is GPT-4?

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GPT-4 is a generative pre trained transformer. GPT-3, the predecessor of GPT-4, the latest version of a series of deep learning based natural language processing models, caused a sensation when it was released in 2020, because it has 175 billion parameters, is the largest language model at that time, and shows amazing text generation ability.

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The scale and performance of GPT-4 have not been announced yet, but according to Andreas Braun, Microsoft's German Chief Technology Officer, revealed at the AI in Focus - Digital Kickoff event on March 10, 2023, GPT-4 will be launched next week and will support multimodality.

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Multimodal refers to the ability to process different types of data, such as text, images, sound, and video, and to transform, fuse, and reason between these data. For example, a multimodal artificial intelligence model can generate an image based on a piece of text, music based on an image, or a text description based on a video.

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Why is GPT-4 important in making artificial intelligence more flexible, creative, and intelligent? The multimodal capabilities of GPT-4 will bring revolutionary changes to artificial intelligence applications. Currently, most artificial intelligence applications are based on a single type of data, such as text, images, or sound, which limits the way and depth of communication and understanding between artificial intelligence and humans.

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If artificial intelligence can simultaneously process multiple types of data and transform and integrate them, it can better adapt to different scenarios and needs, and provide richer and more interesting experiences. For example, a multimodal artificial intelligence model can help us create more vivid and personalized content, such as blogs, videos, music, games, etc; It can also help us obtain more comprehensive and accurate information, such as search, translation, abstract, Q&A, etc; It can also help us improve efficiency and quality, such as writing, design, education, healthcare, etc.

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In short, multimodal artificial intelligence will open up a whole new world for us. How can GPT-4 be used? At present, GPT-4 has not been officially released, so we are not sure what specific functions and interfaces it has. However, we can speculate about the usage of GPT-4 based on the usage of GPT-3.

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GPT-3 provides services through OpenAI APIs, where users can obtain text responses by sending text requests, or complete specific tasks using predefined templates such as writing, summarization, classification, etc. GPT-4 may also provide similar APIs, but in addition to text, it also supports other types of data, such as images, sound, and video.

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Users can obtain multimodal responses by sending multimodal requests, or by using predefined templates to complete specific tasks such as generation, transformation, fusion, etc. For example, if we want to generate an image based on a piece of text, we can send these requests: {"task": "text to image", "input": "A blue sky with white clouds and a rainbow."}

Then, we may receive a response such as {"task": "text to image", "output": "[image data]"} where [image data] is the encoding of an image file, which we can decode and display.

If we want to generate a piece of music based on an image, we can send a request like this: {"task": "image to sound", "input": "[image data]"} Then, we may receive a response like this: {"task": "image to sound", "output": "[sound data]"}

Among them, [sound data] is the encoding of an audio file, which we can decode and play back. If we want to generate a text description based on a video, we can send the following request: {"task": "video to text", "input": "[video data]"}.

Then, we may receive a response like this: {"task": "video to text", "output": "A man is playing guitar and singing in front of a crowd."}

Among them, [video data] is the encoding of a video file that we can decode and watch. Of course, these are just some simple examples. GPT-4 may provide more complex and interesting multimodal tasks and functions, and we can only wait for its official release to personally experience and explore.

Summary: GPT-4 is an upcoming multimodal artificial intelligence model that can handle different types of inputs such as video, image, and sound, and can transform, fuse, and reason between them. GPT-4's multimodal capabilities will bring revolutionary changes to artificial intelligence applications and open up a whole new world for us.

GPT-4 may provide services through APIs and support multiple multimodal tasks and functions. We look forward to the official release and use of GPT-4