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✨ Goodbye AI Terminology Anxiety! 😎 Beginner's Guide: Understand AI Jargon in One Post【LLM/GPT/Transformer/Distillation...】! ✨#

Hello everyone! 🙋♂️

I believe many of you, like me, feel both excited and a bit confused in the wave of AI. Various discussions about AI are heating up in forums, but the professional terms that pop up from time to time always leave you feeling lost, right? ☁️

Don't worry!

This 【AI Beginner's Guide】 is here to save you! 🎉 Before we dive into terms like “Distillation” and “Token,” let's first understand a few more basic yet super important AI concepts!

I will still explain in the simplest and most straightforward way, ensuring that even beginners can understand instantly! Inspired by this post on the Linux.do forum^[1], thanks to the insights from the experts! 🙏

Let's dive in! 🚀

0. 🗣️ Large Language Model (LLM) — The "Super Brain" of AI#

🤔 Simple Explanation:

A “Large Language Model”, as the name suggests, is a “very large” “language model”!

You can think of it as the “super brain” of the AI world, filled with vast amounts of knowledge, especially about language and text!

It can understand human language and can also write articles, code, and chat with you like a pro! 🤯

📚 Professional Definition:

A large language model is a type of language model composed of artificial neural networks with a large number of parameters, trained on vast amounts of unlabelled text using self-supervised or semi-supervised learning.

🔑 Key Terms Explained:

• • “Large”: Huge number of parameters! The more parameters, the more complex the model, allowing it to learn more and become more capable! Just like the more neurons in the human brain, the smarter it is!

• • “Language Model”: A model specifically designed to handle language. Its task is to predict the next word, then string them together to generate sentences and articles.

🌟 Common LLM Representatives: GPT series, DeepSeek series, LLaMA series, PaLM series... all are renowned “super brains”!

1. 🤖 GPT (Generative Pre-trained Transformer) — “Generative Pre-trained Transformer”#

🤔 Simple Explanation:

GPT is actually a type of large language model, fully known as “Generative Pre-trained Transformer”. The name sounds impressive, but it’s simple when broken down!

• • “Generative”: Indicates that GPT is good at “creating” text, such as articles, code, and conversations, like a “content generator”!

• • “Pre-trained”: Indicates that GPT has already read a vast amount of books (data) before “debuting,” learning a lot of language knowledge, like a “top student” with a solid foundation!

• • “Transformer”: Indicates that GPT's core technology is the “Transformer model”, a powerful neural network that helps GPT understand language better! Like a “secret technique” of a martial arts master!

📚 Professional Definition:

GPT is a generative model based on the Transformer architecture. It is first pre-trained on vast amounts of text to learn language patterns, then automatically predicts and generates coherent text based on the preceding context.

🔑 Key Terms Explained:

• • “Transformer Architecture”: The “soul” of GPT, a powerful neural network structure, which will be explained in detail later!

🌟 GPT's “Family Members”: GPT-3, GPT-3.5, GPT-4, GPT-4o ... all are different “models” of the GPT family, each stronger than the last!

2. 🧠 Transformer — AI's “Attention Mechanism”#

🤔 Simple Explanation:

The Transformer model is one of the most important innovations in the AI field in recent years!

It equips AI with an “attention mechanism,” allowing AI to “focus” on the key points of sentences, better understanding meanings and writing more fluent articles! 🤩

📚 Professional Definition:

The Transformer model is a deep learning model that uses an attention mechanism. It can focus on information from all positions in a sequence while processing text or other sequential data, capturing long-distance dependencies, thus significantly improving processing efficiency.

🔑 Key Terms Explained:

• • “Attention Mechanism”: The core of the Transformer! Simply put, it allows the model to “look at” other words in the sentence while examining one word, and calculate their relationships to better understand the context. Just like how we relate context while reading!

🚀 The “Revolutionary” Significance of Transformers:

• • Significant Efficiency Improvement: Transformers are faster and more user-friendly!

• • Foundation of LLMs: Currently, most powerful large language models (like GPT, DeepSeek) are built on the Transformer architecture! It can be said that Transformers have enabled the current AI boom!

3. 🎨 Diffusion Model — The Magic of “Turning Noise into Images”#

🤔 Simple Explanation:

The Diffusion Model is quite magical; it excels at “creating images from scratch”!

The principle is somewhat like “magic,” starting from random “noise” (like TV static), gradually “denoising” it, slowly “restoring” a clear image! Just like “turning decay into magic”! 🪄

📚 Professional Definition:

The diffusion model is a type of generative model that starts from random noise and gradually restores the target data (mainly used for image generation) through a multi-step “denoising” process.

🔑 Key Terms Explained:

• • “Denoising”: The core step of the diffusion model! Like an “eraser,” it gradually removes noise from the image, making it clearer and transforming it into what we want!

🌟 Representative Works of Diffusion Models: Stable Diffusion, DALL-E 2, Midjourney ... all are “stars” in the image generation field, capable of creating various stunning images!

4. 📝 Prompt — Your “Instructions” to Communicate with AI#

🤔 Simple Explanation:

A “Prompt” is the “instruction” or “question” you give to AI!

Whatever you want AI to help you with, just use a “prompt” to tell it! A prompt can be a sentence, a question, or even a detailed description; the key is to clearly express your intention so that AI understands what you want! Just like giving commands to a “magical sprite”! ✨

📚 Professional Definition:

In the AI field, a prompt typically refers to the input text or instruction provided to guide an AI model (especially large language models) to generate specific outputs.

🔑 “Prompt Engineering”:

• • “Prompt Engineering” is a new “art” and “science”! It studies how to write better and more effective “prompts” to maximize AI's potential, enabling AI to perform tasks better! A good prompt can make AI “perform exceptionally”! 💪

5. 🍶 Distillation — “Master” Teaching the “Apprentice”#

🤔 Simple Explanation:

Imagine a super skilled “master” (large model) who is knowledgeable and can do everything, but works relatively slowly 🐌. Meanwhile, there’s a “junior apprentice” (small model) who isn’t as capable but learns quickly and works efficiently 🏃♂️.

“Distillation” is about having the “master” teach the “junior apprentice”, allowing the “junior apprentice” to grow rapidly and become both fast and efficient! 💨

📚 Professional Definition:

Transferring the knowledge trained from a larger complex model (teacher model) to a smaller model (student model).

🌰 Real-Life Example:

• • Sometimes, Deepseek seems to “pretend” to be GPT, claiming to be GPT 😂. This might be because Deepseek has GPT generate some high-quality data, then uses that data to train itself, rapidly enhancing its capabilities! Saving time and effort, efficiency UpUp! 🚀

6. 🪙 Token — The “Small Parts” in AI's Eyes#

🤔 Simple Explanation:

If you want AI to understand speech, it doesn’t just “swallow” it whole! AI will first “break down” your speech into individual “small parts,” like dismantling building blocks 🧱. These “small parts” are Tokens!

AI reads articles, understands instructions, and calculates costs all based on Tokens! So, Tokens can be said to be the smallest unit through which AI understands the world.

📚 Professional Definition:

The smallest semantic unit in text.

🖼️ Understand at a Glance:

[Insert image: User-provided token image]

💰 Billing Model:

When you see “how much for a million Tokens,” you’ll know it’s based on how many “parts” you’ve “fed” to AI! Isn’t that clear all of a sudden? 😉

7. 🌈 Multimodal — AI's “Versatile Skills”#

🤔 Simple Explanation:

Previous AI might have been a “bookworm,” only capable of handling text 🤓. Now, “multimodal AI” is like a “jack of all trades,” mastering “various skills”! Not only can it read text, but it can also see images and hear sounds! 🤩

📚 Professional Definition:

A model being multimodal means it can simultaneously process different types of data, such as text + images + audio.

🌟 Representative Player:

• • GPT-4o is a “multimodal” powerhouse! Show it a scenic photo, and it can immediately recognize “blue skies, white clouds, and beaches,” and it can even “compose poetry” with you! 😎

⚠️ Small Bug Reminder:

• • Current multimodal models are not “perfect”! Some models may still struggle with understanding complex images. For example, Deepseek R1 may be better at recognizing text in images (OCR), but its understanding of the “meaning” of images still has room for improvement. This is also one reason why AI sometimes “stumbles” during human verification! 😉

8. 😫 Overfitting — The Troubles of a “Narrow-Specialized Student”#

🤔 Simple Explanation:

“Overfitting” is like a “narrow-specialized student” during school days 😫. The model performs exceptionally well on “training questions” (training data), scoring full marks 💯! However, when it comes to “exams” (new data/test set), it “reveals its flaws” and can’t do anything 🤯!

“Overfitting” indicates that the model has merely “memorized” the answers without truly learning to “generalize”! Such AI can only solve old problems and gets “lost” in new situations.

📚 Professional Definition:

A model performs well on training data but poorly on unseen data (test set).

🧠 Avoiding “Overfitting” is crucial! We want AI to solve various problems in real life, not just the questions in the “question bank”!

💪 Human Advantages:

• • One of the areas where humans excel over AI is “flexibility and adaptability”! In complex environments, humans can react quickly, while AI still needs to improve!

9. 🪅 Reinforcement Learning — The “Reward and Punishment” Teaching Method#

🤔 Simple Explanation:

“Reinforcement Learning” is like a “teacher who rewards and punishes” 👨🏫. When AI learns, it constantly tries various “problem-solving methods” (actions).

• • Did it right 👍 (Good performance): Reward AI with “rewards” (positive feedback), letting it know “you did well, keep it up next time!”

• • Did it wrong 👎 (Poor performance): Punish AI with “penalties” (negative feedback), letting it know “not good, improve next time!”

Through “trial and error” + “rewards/penalties,” AI can quickly learn the correct “problem-solving posture”!

📚 Professional Explanation:

The core of reinforcement learning can be understood as “trial and error.” Simply put, it means that during the training process of the large model, if it performs well, it gets rewarded; if it performs poorly, it gets punished.

🤫 Deepseek R1's “Secret Weapon”?

• • “Reinforcement Learning” is a “high-frequency term” in Deepseek R1's technical documentation! This shows that this “reward and punishment” learning method is crucial for enhancing AI capabilities!

10. 🏋️♀️ Pre-training — AI's “Massive Reading”#

🤔 Simple Explanation:

“Pre-training” is like “on-the-job training” for AI before it officially “starts working” 🏋️♀️. During “pre-training,” AI needs to “read extensively” various “books” (unlabeled data), allowing it to “gain a broad understanding” of various knowledge, becoming a “jack of all trades”!

📚 Professional Definition:

Training a model on a large-scale dataset before specific tasks, enabling it to learn general features.

🧱 Laying a Good “Foundation”:

• • “Pre-training” is like “laying the foundation” for building a house or “standing firm” while practicing martial arts! A solid “foundation” and strong “basic skills” are essential for better handling complex tasks!

❓ Why use “unlabeled data”?

• • Because manually labeling data is too labor-intensive 😫! Moreover, manual selection may be “biased.” It’s better to let AI “read extensively” on its own, making it more comprehensive and objective!

11. 🎯 Post-training — AI's “Special Skill Enhancement”#

🤔 Simple Explanation:

“Post-training” is about giving AI “special skill enhancement” 🎯 based on “pre-training.” It’s like a “jack of all trades” “deepening” its expertise in a specific field to become a “specialist”!

During the “post-training” phase, we will “feed” AI more “specialized books” (labeled data for specific tasks), allowing it to “master” knowledge and skills in specific areas!

📚 Professional Definition:

Further training on top of a pre-trained model to improve performance on specific tasks.

💻 For Example:

• • If you want to train an “expert in coding” AI, you would “feed” it a large amount of code data to make it “proficient” in programming!

🦾 Artificially Synthesized Data to Help:

• • “Post-training” often uses high-quality artificially synthesized data to ensure efficiency and effectiveness in training!

12. 📉 Training Loss — AI's “Exam Score”#

🤔 Simple Explanation:

“Training Loss” is like an “exam score” that measures AI's “learning effectiveness” 📉. The lower the “training loss,” the better AI has “learned,” and the more accurate its predictions are!

During AI's “learning” process, we hope the “training loss” continues to decrease, just like exam scores getting higher, which is pleasing to see! 😄

📚 Professional Explanation:

Training loss is an indicator used to measure the difference between predicted and actual results during the training process.

👁️ The “Monitor” of the Training Process:

• • “Training Loss” can monitor in real-time AI's “learning progress,” checking if it is studying diligently!

13. 🛠️ Fine-tuning — AI's “Refinement”#

🤔 Simple Explanation:

“Fine-tuning” is like “refining” a “well-prepared large model” 🛠️ to make it more “tailored to needs” and more “understanding of you”!

📚 Professional Definition:

Continuing to train a pre-trained model on a smaller labeled dataset to adapt it to specific tasks.

👗 The Feeling of “Tailor-Made”:

• • “Fine-tuning” is like buying clothes and then having a tailor “alter” them to make them more fitting and beautiful!

💪 Small Datasets, Big Impact:

• • The data used for “fine-tuning” is much less than for “pre-training,” but the effects are significant! It can make AI more outstanding and more “professional” in specific fields!

🎉 Summary — AI Terminology Isn’t That Difficult!#

Phew~ After explaining so many AI terms in one breath, I hope this 【AI Beginner's Guide】 can help everyone eliminate terminology anxiety and deepen their understanding of AI! 🚀

In fact, AI isn’t that mysterious; many “high-sounding” terms become clear when explained in simple language! 😎 From now on, when you encounter AI terminology, you won’t have to be afraid anymore! 💪

Of course, I am still a “villager in the AI beginner village,” and my understanding may not be perfect. If there’s anything wrong above, feel free to leave comments for discussion! 🤝

Let's keep exploring the amazing world of AI! 🤖 See you in the next post! 👋

Reference Link#

[1] This post on the Linux.do forum: https://linux.do/t/topic/424597