Generate beautiful color pages with one click + AI training revealed: make your content instantly high quality!
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Have you ever struggled to create beautiful presentations? Are you curious about the training process of AI? Today, let's explore how to use AI to generate beautiful colorful pages with one click, and reveal the training process of ChatGPT!
🌟 Skyworks AI coloring pages: a revolution in content creation
🤔 The origin of the matter: a coworker asked me how AI is actually trained. I then wrote a note about the ChatGPT training steps. But plain text is too boring, what to do? Luckily, with the color page function of Tiangong AI, my notes instantly came to life! 🎈
💡 Coloring page feature highlights.
- One-click generation: just paste text or upload a file
- Smart Matching:Automatically search for images matching the theme
- Aesthetically pleasing: professional design to make your content stand out
👀 Effect Show: Here are the notes of "Uncovering the Birth of Artificial Intelligence: How ChatGPT is Practiced" and the color page effect generated by Tinker AI! Doesn't it feel like the presentation is much more vivid? 👏
🖼️ Example of a coloring page section.
Look! The original boring AI training process has instantly become lively and interesting!
🖼️ Featured Image Gallery.
Each diagram is precisely matched to the content, does it not feel like knowledge has suddenly become visualized?
🌐 Example of a coloring page: Want to see more? Click here: color page generation example!/cp-detail/1857256177048281088?from=share Guaranteed to blow your mind! 👀
🔥 Tip. Although the color pages can not be exported, but its design concept is definitely worth PPT production to learn from!
While we're on the subject of AI, let's dive into how ChatGPT is made!
ChatGPT is undoubtedly a shining star in today's wave of artificial intelligence. So, how is this powerful language model trained? Let's take a deeper look into the training process of ChatGPT!
🔍 The Four Phases of ChatGPT Training
ChatGPT is trained in four main phases: pre-training, supervised fine-tuning, reward modeling, and reinforcement learning. Each phase is crucial and cannot be accomplished without the other!
(i) Pre-training phase
Pre-training is the basis of ChatGPT training and occupies more than 95% of the model training time! In this phase, a large amount of corpus is used to train the model with basic language comprehension and generation capabilities.
- source of corpus: The corpus used for pre-training includes a large amount of text crawled from the web, as well as relatively high-quality corpus from Github, Wikipedia, and so on. For example, news articles, blogs, forum posts, etc., all provide rich material for the pre-training of ChatGPT.
- subjunctive unit training: Before training, the text content is broken down into sub-word units for training. This has the advantage of dealing with unknown words, reducing the size of the vocabulary, and capturing root and affix information. For example, the word "unhappiness" can be broken down into three sub-word units "un-" "happy" "-ness " into three sub-word units.
- Predicting the next word: The pre-training process essentially allows the model to guess the next word. Through a large amount of textual data, the model learns the statistical laws and patterns of the language and is able to predict the words that may follow based on the previous words. For example, given the phrase "It's a nice day today", the model can be trained to predict the word "nice".
(ii) Monitoring fine-tuning phase
To address the problem of the base model not understanding human output instructions, ChatGPT entered a supervised fine-tuning phase. In this phase, the model was provided with a large number of examples to understand the relationship between "prompt" and "response" and the meaning of the instructions. In this way, the model moves from simply predicting the next word to being able to play the role of an assistant, generating appropriate responses based on the user's instructions.
(iii) Incentive modeling phase
Reward modeling is a stage in the service of reinforcement learning. In this phase, the model generates some content that is then rated by a human outsourcer. These ratings are used to train the model to be able to predict the human ratings a response might receive.
(iv) Intensive learning phase
In the reinforcement learning phase, the model is constantly iterated and optimized to generate higher scoring responses. This process essentially makes the model more compatible with human preferences and generates better quality responses.
🎉 Summary
The training process of ChatGPT is a complex and lengthy process that requires a large amount of corpus, computational resources and human input. Through continuous optimization in the four stages of pre-training, supervised fine-tuning, reward modeling and reinforcement learning, ChatGPT gradually becomes a powerful language model that can provide users with high-quality language interaction services.
In the future, as technology continues to advance, we can expect ChatGPT and other language ��� models to become even more intelligent and powerful, bringing more convenience and innovation to our life and work!
📣Warm reminder: Want to learn more about the mysteries of AI? Follow me to take you to explore the infinite possibilities of AI! 🌌
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#AI Revealed #ChatGPT Training Process #AI Coloring Page Generation