Understanding AI: From Basics to Advanced Models

What is AI? - Don’t Overthink It

Many people envision robots from movies like “Terminator” or super-intelligent brains when they hear “artificial intelligence.”

In reality, AI isn’t that mysterious.

In simple terms, AI is a very smart computer program. It fundamentally operates like the calculators and office software we use daily—input data, perform calculations, and output results.

The difference lies in:

Regular software: Human programmers write all the rules explicitly.
AI software: Humans write a “learning framework” and allow machines to find patterns from data.

Think of it like teaching a child to recognize characters:

Traditional programming: You tell the computer, “Three horizontal lines represent ’three’, and two horizontal with one vertical represent ‘工’.”
AI programming: You show the computer thousands of images of ’three’ and ‘工’, allowing it to summarize the rules on its own.

Core essence: AI = Mathematics + Data + Computing Power

Machine Learning: Teaching Computers to Generalize

What is Machine Learning?

Imagine teaching an alien to recognize an apple.

You wouldn’t say, “An apple is the fruit of the Rosaceae family, rich in pectin and dietary fiber”—the alien wouldn’t understand!

Instead, you would show it a bunch of apple photos and say, “This is an apple.” After seeing enough, the alien would conclude, “Oh, the round red thing with a stem is an apple.”

Machine learning works on this principle.

Scientists provide computers with numerous examples:

This is spam, this is regular mail.
This is a cat, this is a dog.
This sentence is a positive review, this one is negative.

The computer finds patterns on its own and can make judgments on new emails, images, or sentences.

Three Main Types of Machine Learning

Type	Simple Explanation	Everyday Example
Supervised Learning	Learning with standard answers	Students doing exercises and checking answers
Unsupervised Learning	Finding patterns without standard answers	Separating mixed red and green beans
Reinforcement Learning	Learning through trial and error, rewarded for correct actions	Training a dog to shake hands with treats

Neural Networks: Mathematical Models Mimicking the Brain

From Brain to Computer

The human brain has 86 billion neurons connected by synapses, forming a complex network. When you see a cat, visual signals travel from your eyes, processed through layers of neurons, leading to the conclusion, “This is a cat.”

Neural networks mimic this structure.

A typical neural network consists of three layers:

Input Layer: Receives raw data (like pixel values of an image).
Hidden Layer: Multiple layers of “neurons” perform calculations and transformations.
Output Layer: Provides the final result (e.g., “This is a cat, probability 95%.”)

Implementing “Thinking” with Mathematics

Each “artificial neuron” is essentially a mathematical formula:

Output = Activation Function(Input1 × Weight1 + Input2 × Weight2 + ... + Inputn × Weightn + Bias)

Weights: Determine how important each input is.
Bias: Adjusts the difficulty of activation.
Activation Function: Decides whether to “activate” the neuron.

Training is Parameter Adjustment

When a neural network is first created, all weights and biases are random—at this point, it knows nothing.

Training Process:

Feed a training sample (like a cat image).
The neural network makes a prediction (“This is a dog, probability 80%”).
Compare with the correct answer and calculate the error (it was wrong!).
Use the “backpropagation algorithm” to adjust all weights and biases.
Repeat thousands of times until the error is sufficiently small.

This is similar to a student:

First exam: guesses, scores 30.
Checks answers, learns from mistakes.
Adjusts study methods.
Second exam: scores 40.
…
By the 100th exam: scores 95.

Deep Learning: The “Evolution” of Neural Networks

Why is it Called “Deep”?

Traditional neural networks have only 2-3 hidden layers.

Deep learning networks can have dozens or even hundreds of layers!

The more layers, the more complex features they can learn:

Layers 1-2: Recognize edges and lines.
Layers 3-5: Recognize shapes and textures.
Layers 6-10: Recognize eyes, ears, and noses.
Deeper layers: Recognize entire faces and objects.

This is like viewing a tree:

The first layer sees pixels.
The middle layers see leaves and branches.
The top layer recognizes, “This is a pine tree.”

Convolutional Neural Networks (CNN) - Image Recognition Powerhouse

Processing images presents a unique challenge: a 1000×1000 photo has 1 million pixels!

If every neuron connects to all pixels, the parameters become too numerous to train effectively.

CNN’s cleverness lies in using “convolutional kernels” to scan images.

Imagine a 3×3 small window sliding over the image, calculating at each position. This window is the “convolutional kernel” that detects specific features (like edges and corners).

Through multiple convolutional layers, the network can progressively combine simple features into complex ones, ultimately recognizing objects.

Recurrent Neural Networks (RNN) - Handling Sequential Data

Images are static, but language, music, and stock prices are sequential data—they have an order.

RNNs are unique because they have “memory.” When processing current data, they reference previous information.

Current State = f(Current Input, Previous State)

This is why RNNs can write poetry, compose music, and predict stock prices.

Transformer - The Foundation of Large Models

In 2017, Google published a paper titled “Attention Is All You Need,” introducing the Transformer architecture.

Core innovation: Attention Mechanism

Previously, RNNs had to process one word at a time, which was slow. Transformers can look at entire sentences simultaneously and automatically determine which words are most closely related.

For example, in the sentence:

“The kitten is chasing its tail because it finds it very fun.”

The model will automatically identify that “it” relates most closely to “kitten” and that “fun” describes the action.

Two major advantages of Transformers:

Fast parallel computation: Unlike RNNs that must process sequentially, Transformers can handle all words at once.
Long-distance dependencies: They can capture words that are far apart but semantically related in a sentence.

This is the core technology behind large language models like ChatGPT.

Large Language Models: The “Explosion” of AI

What are Large Language Models?

Simply put, they are extremely large neural networks.

Models like GPT-4 have:

Parameter scale: Hundreds of billions of parameters (comparable to the number of synapses in the brain).
Training data: Massive amounts of text from the internet (books, webpages, papers, code, etc.).
Training costs: Tens of millions of dollars, consuming enormous computing power.

Why are Large Models “Smart”?

Traditional AI is “specialized”:

Translation models only translate.
Chess programs only play chess.
Facial recognition systems only recognize faces.

Large models are “generalists” because they learn from the knowledge of all humanity:

They have read nearly all books and articles across various fields.
They have learned various writing styles.
They understand complex logical reasoning.
They master multiple programming languages.

How do Large Models “Speak”?

Many people think AI truly “understands” language. The truth is:

Large models perform “next word prediction.”

When you input “Today’s weather,” the model will:

Convert the sentence into a mathematical vector.
Pass it through the neural network layer by layer.
Output a probability distribution: “true” 40%, “very” 35%, “not bad” 25%…
Choose the word with the highest probability and continue predicting the next word.

It does not “think”; it merely finds the most likely conversational continuation through extremely complex probability calculations.

However, due to the vast amount of training data and the model’s size, this “probability prediction” often appears as genuine understanding and thought.

Cutting-Edge AI Technologies 2025-2026

Multimodal AI: Seeing, Hearing, and Understanding

Early AI was “unimodal”:

Speech recognition only listens.
Image recognition only sees.
Language models only read.

The current trend is multimodal integration:

Models like GPT-4V, Claude 3, and Gemini can simultaneously process:

Text
Images
Audio
Video

You can show it an image and ask, “What plant is this? Is it toxic? How do I care for it?” It can understand the image, identify the plant, consult knowledge, and provide suggestions.

AI Agents

Large models + tool usage = agents.

Today’s AI can not only converse but also:

Search the web for the latest information.
Write and execute code.
Operate Excel and databases.
Call APIs to complete various tasks.

Core breakthrough: Function Calling

AI has learned, “If needed, I can call external tools.” For example:

User: Check the flight prices from Beijing to Shanghai tomorrow.

AI: I need to call the flight query API → Call → Get results → Reply to the user.

Generative AI: Creating Rather Than Recognizing

Traditional AI is “recognition-based”: determining if something is a cat or spam.

Generative AI is “creation-based”:

Creating images based on descriptions (Midjourney, Stable Diffusion, DALL-E).
Composing and arranging music (Suno, Udio).
Generating videos (Sora, Keling, Runway).
Writing code (Copilot, Cursor).

Generation Principle (using image generation as an example):

Diffusion Model
During training: gradually add noise to images until they become pure noise, then learn how to “denoise” and restore.
During generation: start from pure noise, progressively denoise, and ultimately generate the target image.
Latent Diffusion
Operate not in pixel space but in compressed “latent space” for higher efficiency.

Small Models and Edge AI

While large models are great, they are expensive, slow, and require internet connectivity.

New trend: making AI smaller, faster, and running on devices.

Model Distillation: Teach small models using large models, retaining 90% of capability while shrinking size by 100 times.
Quantization: Compress 32-bit floating-point numbers to 4 bits, making models smaller and faster.
Dedicated Chips: NPUs in phones and computers specifically accelerate AI computations.

This means:

Your phone can run an AI assistant locally without internet.
Smart home devices have their own “brains.”
AI assistants respond in milliseconds instead of seconds.

World Models: AI Understanding the Physical World

OpenAI’s Sora can not only generate videos but also seems to understand physical laws:

Objects do not disappear out of nowhere.
Light reflects and refracts.
Gravity affects object movement.

The goal of world models is to enable AI to have an intuitive “common sense” understanding of the world, similar to humans.

This could lead to true artificial general intelligence (AGI).

Limitations and Misunderstandings of AI

What AI Cannot Do

Misunderstanding	Truth
AI has self-awareness ❌	It is just mathematical computation, with no subjective experience.
AI truly “understands” content ❌	It only performs pattern matching and probability prediction.
AI does not make mistakes ❌	It can confidently produce nonsensical outputs (hallucinations).
AI is omnipotent ❌	It only works effectively in areas covered by training data.
AI will replace all jobs ❌	It more often changes job functions and creates new positions.

The “Hallucination” Problem of AI

Large models sometimes fabricate facts:

Citing non-existent papers.
Inventing biographies.
Providing incorrect code.

Reasons:

The training data itself may contain errors.
The model is trained to “answer questions” rather than “admit when it doesn’t know.”
Probability predictions may yield “seemingly reasonable but actually incorrect” answers.

Responses:

RAG (Retrieval-Augmented Generation): Let AI check information before answering.
Multi-model validation: Cross-verify with multiple AIs.
Human review: Critical information still needs human confirmation.

Data Bias

AI learns from data, and if the data is biased, the AI will be biased too.

For example:

Recruitment AI may “learn” to discriminate against women due to more male programmers in training data.
Judicial risk assessment AI may have systemic biases against certain ethnic groups.

This requires ongoing human supervision and correction.