What is AI? - Don’t Overthink It

Many people associate “artificial intelligence” with robots from movies like “Terminator” or super-intelligent minds with emotions.
In reality, AI isn’t that mysterious.
Simply put, AI is a very smart computer program. It fundamentally operates like the calculators and office software we use daily—input data, perform calculations, and produce results.

The difference lies in:

• Regular software: Human programmers write all the rules.
• AI software: Humans write a “learning framework” and let machines find patterns from data themselves.

Think of it like teaching a child to recognize characters:

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

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 explain, “An apple is the fruit of the Rosaceae family, rich in pectin and dietary fiber,” because the alien wouldn’t understand!
Instead, you show it a bunch of apple pictures and say, “This is an apple.” After seeing enough, the alien concludes, “Oh, the round, red things with a stem are apples.”

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 sentence is a negative review.

The computer identifies the patterns for judgment. When it encounters new emails, images, or sentences, it can make decisions on its own.

Three Major Types of Machine Learning

Neural Networks: Mathematical Models Mimicking the Human Brain

From Brain to Computer

The human brain has 86 billion neurons connected through 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 (like pixel values of an image).
2. Hidden Layer: Multiple layers of “neurons” perform calculations and transformations.
3. Output Layer: Provides the final result (e.g., “This is a cat with 95% probability”).

Implementing “Thinking” with Mathematics

Each “artificial neuron” is actually 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 is Parameter Adjustment

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

Training Process:

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

This is akin to a student:

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

Deep Learning: The “Evolution” of Neural Networks

Why is it Called “Deep”?

Traditional neural networks have 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: Recognizing edges and lines.
• Layers 3-5: Recognizing shapes and textures.
• Layers 6-10: Recognizing eyes, ears, and noses.
• Deeper layers: Recognizing entire faces and objects.

This is like observing a tree:

• The first layer sees pixel points.
• 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.

The brilliance of CNN lies in using “convolutional kernels” 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 gradually combines 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.
The uniqueness of RNNs is their “memory”. When processing current data, they reference previous information.

Current State = f(Current Input, Previous State)
This is how RNNs can write poetry, compose music, and predict stock prices.

Transformer - The Foundation of Large Models

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

Core innovation: Attention Mechanism
Previously, RNNs processed words one by one, which was slow. Transformers can look at entire sentences 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 computation: Unlike RNNs that must process sequentially, Transformers can handle all words at once.
2. Long-distance dependencies: They can capture semantically relevant words that are far apart in a sentence.

This is the core technological foundation behind large language models like ChatGPT.

Large Language Models: The “Explosion” of AI

What are Large Language Models?

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

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

Why are Large Models “Smart”?

Traditional AI is “specialized”:

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

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

• They have read nearly every book and article 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 believe AI truly “understands” language. The truth 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 layers of the neural network.
3. Output a probability distribution: “true” 40%, “very” 35%, “not bad” 25%…
4. Select the word with the highest probability and continue predicting the next word.

It doesn’t “think”; it merely finds the most probable response through highly complex probability calculations.
However, due to the vast training data and large model size, this “probability prediction” often appears as genuine understanding and thought.

Cutting-Edge AI Technologies 2025-2026

Multimodal AI: Understanding, Listening, 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, Gemini can simultaneously process:

• Text
• Images
• Audio
• Video

You can show it an image and ask, “What plant is this? Is it poisonous? 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 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”:

• 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 create the target image.
4. Latent Diffusion
5. Operate not in pixel space but in compressed “latent space,” making it more efficient.

Small Models and Edge AI

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

• Model Distillation: Teach a small model using a large model, retaining 90% of its capabilities while reducing 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 internet.
• 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 not only generates videos but seems to understand physical laws:

• Objects don’t 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

The “Hallucination” Problem of AI

Large models sometimes fabricate facts:

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

Reasons:

• The training data itself contains 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.

Countermeasures:

• RAG (Retrieval-Augmented Generation): Allow AI to check information before answering.
• Multi-model validation: Cross-verify with multiple AIs.
• Human review: Key information still requires human confirmation.

Data Bias

AI learns from data, and if the data is biased, the AI will be too.
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 biases against certain ethnic groups.

This requires ongoing human supervision and correction.

Conclusion: The Essence and Future of AI

One-Sentence Summary

AI = Big Data + Big Computing Power + Big Models = Super Pattern Recognizer

It is not magic, nor is it metaphysics; it is the culmination of mathematics and engineering.