What is AI? - Don’t Overthink It

Many people envision robots from movies like “Terminator” or super-intelligent brains when they hear the term “artificial intelligence”.
In reality, AI is not that mysterious.
Simply put, AI is a very smart computer program. It is fundamentally similar to 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 let the machine find patterns from the data itself.

This is akin to teaching a child to recognize words:

• Traditional programming: You tell the computer, “Three horizontal lines represent ’three’, and two horizontal lines with one vertical line represent ‘工’.”
• AI programming: You show the computer thousands of images labeled ’three’ and ‘工’, allowing it to summarize the patterns itself.

The 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!
You would show it a bunch of apple pictures 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 operates on this principle.
Scientists provide computers with numerous examples:

• This is spam, this is a normal email.
• This is a cat, this is a dog.
• This sentence is a positive review, this one is negative.

The computer finds the patterns for judgment through these examples. When it encounters new emails, images, or sentences, it can make its own judgments.

Three Types of Machine Learning

Neural Networks: Mathematical Models Mimicking the Human Brain

From Human Brain to Computers

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 your brain to conclude, “This is a cat.”

Neural networks mimic this structure.
A typical neural network consists of three layers:

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

Implementing “Thinking” with Mathematics

Each “artificial neuron” is essentially a mathematical formula:

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

• Weights: Determine the importance of each input.
• Bias: Adjusts the difficulty of activation.
• Activation Function: Decides whether to “activate” this neuron.

Training Means Adjusting Parameters

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

Training Process:

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

This is like a student:

• First exam: Guessed randomly, scored 30.
• Checked answers and learned where they went wrong.
• Adjusted study methods.
• Second exam: Scored 40.
• …
• 100th exam: Scored 95.

Deep Learning: The “Evolved Version” 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 looking at a tree:

• The first layer only sees pixel points.
• 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.

The brilliance of CNNs lies in using a “convolutional kernel” to scan images.
Imagine a 3×3 small window sliding over the image, calculating at each position. This small window is the “convolutional kernel,” capable of detecting 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 an entire sentence simultaneously, automatically determining which words are most closely related.

For instance, in the sentence:

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

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

Two Major Advantages of Transformers:

1. Fast Parallel Computing: Unlike RNNs, which must process in sequence, Transformers can handle all words simultaneously.
2. Long-Distance Dependencies: They can capture semantically related words that are far apart 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?

In simple terms, they are extremely large neural networks.
Models like GPT-4 have:

• Parameter Scale: Hundreds of billions of parameters (equivalent 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 immense computing power.

Why are Large Models “Smart”?

Traditional AI systems are “specialists”:

• Translation models only translate.
• Chess programs only play chess.
• Facial recognition only recognizes faces.

Large models are “generalists” because they learn from all human knowledge:

• 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 reality is:
Large models perform “next word prediction”.
When you input “Today’s weather,” the model will:

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

It does not “think”; it merely finds the most likely way to respond through extremely complex probability calculations.
However, due to sufficient training data and a large model, this “probability prediction” appears to demonstrate genuine understanding and thought.

Cutting-Edge AI Technologies in 2025-2026

Multimodal AI: Understanding, Hearing, and Comprehending

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 = intelligent 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 ticket prices from Beijing to Shanghai for tomorrow.
AI: I need to call the flight query API → call → get results → reply to the user.

Generative AI: Creating Instead of Recognizing

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

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

Generation Principle (using image generation as an example):

1. Diffusion Model
2. During training: Gradually add noise to an image until it becomes pure noise, then learn how to “denoise” and restore it.
3. During generation: Start from pure noise, progressively denoise, and ultimately generate the target image.
4. Latent Diffusion
5. Operate in compressed “latent space” rather than pixel space for greater efficiency.

Small Models and Edge AI

While large models are impressive, they are expensive, slow, and require internet connectivity.
The new trend is to make AI smaller, faster, and run on devices.

• Model Distillation: Teach a small model using a large model, retaining 90% of its capabilities while reducing its size by 100 times.
• Quantization: Compress 32-bit floating-point numbers to 4 bits, making the model 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 needing an internet connection.
• Smart home devices can have their own “brains”.
• AI assistants can respond in milliseconds rather than seconds.

World Models: AI Understanding the Physical World

OpenAI’s Sora can generate videos, but more importantly, it seems to understand physical laws:

• Objects do not disappear out of thin air.
• Light reflects and refracts.
• Gravity affects the movement of objects.

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?

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 select “seemingly reasonable but actually incorrect” answers.

Countermeasures:

• RAG (Retrieval-Augmented Generation): Allow AI to check information before answering.
• Multi-Model Validation: Cross-verify with multiple AIs.
• Human Review: Critical information still requires human confirmation.

Data Bias

AI learns from data; if the data is biased, AI will be biased as well.
For example:

• Recruitment AI may learn to discriminate against women due to a higher number of male programmers in the training data.
• Judicial risk assessment AI may have systemic bias against certain ethnic groups.

This requires continuous human oversight and correction.

Conclusion: The Essence and Future of AI