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> AI can not only talk, but also see images, hear sounds, and understand videos — master multimodal tools and techniques. For a long time, AI was single-sensory. * Text AI can only process text, like having ChatGPT write articles. * Image AI can only process images, like having Midjourney draw pictures. * Audio AI can only process sound, like having Whisper transcribe speech. But the real world is multimodal — we see illustrations when reading books, have visuals and sound when watching movies, and observe expressions and tone when communicating with others. Multimodal AI is AI that can simultaneously understand and generate multiple types of content. It's like giving AI eyes, ears, and a mouth, allowing it to interact with the world in a more human-like way. > A practical example: You give AI a photo of a shopping receipt, and it can recognize the text on it (OCR), understand what items were purchased, help you organize them into a table, and even read it aloud to you. This is a typical multimodal task. * * * ## What is Multimodal AI Let's clarify some basic concepts first. ### Single Modality vs Multimodal Modality refers to the form in which information is expressed. Text, images, audio, and video are all different modalities. | Type | Description | Typical Products | | --- | --- | --- | | Single Modality AI | Can only process one modality | Early GPT-3 (text only), Stable Diffusion (images only) | | Multimodal AI | Can process multiple modalities simultaneously | GPT-4o, Claude 3, Gemini | The core breakthrough of multimodal AI is: it can convert information from different modalities into the same language for understanding. For example, when seeing an image of a "cat", it can convert the image into a vector (a set of numbers); when seeing the word "cat", it can also convert it into a vector. These two vectors are close in mathematical space because they represent the same concept. ### Core Challenges of Multimodal AI Multimodal sounds simple, but it's actually very difficult to implement. * The first challenge is "alignment" — how to make the "cat" in an image and the "cat" in text appear as the same thing to the model? * The second challenge is "fusion" — when seeing an image and text at the same time, how to combine their information? * The third challenge is "generation" — how to generate images from text descriptions, or generate text descriptions from images? * Fortunately, these problems are being gradually solved. After 2024, almost all mainstream large models are multimodal. * * * ## Image Understanding (Vision) Image understanding means enabling AI to "see" images — describing content, recognizing text, analyzing charts, and discovering details. All mainstream large models now support image input. There are usually two ways to use it: * One is to directly upload an image file, such as clicking the image icon to upload in the ChatGPT web version. * The other is to embed the image in the API request using Base64 encoding, suitable for program calls. | Model | Image Input Method | Supported Image Formats | | --- | --- | --- | | GPT | URL, Base64, Direct Upload | JPG, PNG, WEBP, GIF | | Claude | Base64, Direct Upload | JPG, PNG, WEBP, GIF | | Gemini | Direct Upload, Google Drive | JPG, PNG, WEBP, GIF | Common vision tasks include: * Image Description — What's in this image? * OCR — Extract text from images. * Chart Analysis — What is this bar chart saying? * Detail Discovery — Help me check if there are any issues with this design. ### Practical: Using Python to Call GPT-4o Vision API Let's look at a complete example — using the API to analyze an image. ## Example # ============================================ # File: tutorial_vision_demo.py # Function: Use GPT-4o Vision API to analyze images # ============================================ import base64 import requests import os # Configure API key (please replace with your real key) # Get it from: https://platform.openai.com/api-keys OPENAI_API_KEY ="sk-your-api-key-here" OPENAI_API_URL ="https://api.openai.com/v1/chat/completions" def encode_image_to_base64(image_path: str) ->str: """ Encode local image file to Base64 string This is the required transmission format for the API """ with open(image_path,"rb")as image_file: # Read binary file content and encode with Base64 base64_data =base64.b64encode(image_file.read()).decode("utf-8