How Artificial Intelligence Acquires “Knowledge”

How Artificial Intelligence Acquires “Knowledge”

How does artificial intelligence acquire knowledge? This question may seem simple, but the answer is far more complex than it appears. The learning methods of AI are similar to those of humans in some surface-level ways, but fundamentally they follow two completely different paths. Humans accumulate experience through reading, observation, and practice, while AI finds patterns from massive examples through data and algorithms.

Today, no matter how intelligent AI may seem, its core capability is essentially one thing: discovering patterns from massive amounts of data and applying those patterns in new situations.

The formal name for this process is machine learning. It does not rely on rote memorization, but on parameter adjustment. You can imagine it as a huge mixing console with millions or even billions of knobs. Every time AI is shown training data, it turns these knobs slightly to make the output closer to the correct answer.

However, the specific path for AI to acquire knowledge is much more complex than this metaphor. It involves multiple progressive stages, including data collection and preparation, pre-training, fine-tuning and alignment, retrieval augmentation, and knowledge transfer.

1. The Raw Materials of Knowledge: Where Training Data Comes From

The first step for AI to acquire knowledge is to obtain massive textbooks, which are known as training data. Algorithms, computing power, and data are the three cornerstones supporting the development of large models. The massive amount of data used in the pre-training process is considered one of the core elements that determine model capability.

The training data for large language models comes from a wide range of sources, which can be roughly divided into two categories: general data and professional data.

Taking Meta’s LLaMA model as an example, its training data is extracted from multiple sources, including CommonCrawl, C4, GitHub, Wikipedia, books, ArXiv, and StackExchange. These different sources of data together form the raw materials of AI knowledge.

However, raw data often contains a large amount of noise and low-quality content. Therefore, data cleaning and preprocessing are indispensable steps. Only carefully screened and processed data can train high-quality AI models.

2. First-Level Learning: Pre-Training and Building a “World Model”

How can AI learn knowledge from data? The first step is pre-training.

You can imagine a large model as a super apprentice in the field of language. It gradually learns how to speak by reading the vast amount of text accumulated by humans. However, this apprentice’s learning style is completely different from that of humans. It has no emotions, no intuition, and is completely driven by mathematics and statistical laws.

The starting point of pre-training is like locking the apprentice inside a huge library composed of the internet, books, and articles. Its task is not to understand content in the human sense, but to complete countless fill-in-the-blank questions and continuation exercises.

For example, when it sees the sentence “The boiling point of water is 100__,” it will try to fill in the blank with “°C.” When encountering “The author of Dream of the Red Chamber is __,” it will fill in “Cao Xueqin.” Through trillions of such exercises, it gradually masters the rules of association between words.

Jakub Pachocki, the head of advanced model development at OpenAI, has explained that AI learning is divided into two stages. First is unsupervised pre-training, where the model absorbs massive amounts of data and constructs an unconscious, timeline-free world model to understand the basic framework of reality.

This world model is not a true understanding of the world, but a statistical network of associations. It knows that “cat” is often paired with “catching mice” and that “rain” is often associated with “carrying an umbrella,” but it does not know that “100°C” represents temperature in the human experiential sense, nor does it truly understand the literary value of Dream of the Red Chamber.

At the technical level, the core of pre-training is a neural network, which is a computing system designed to mimic the structure of the human brain. Taking cat recognition as an example, the neural network is like a stack of sieves placed one after another. The first layer detects the simplest features, such as edges and color changes. The middle layers piece together slightly more complex structures, such as circles, stripes, and corner contours. Deeper layers combine more advanced shapes, such as the triangles of cat ears and the ellipses of eyes. The final layer combines all clues to make a judgment.

This process is called forward propagation. If the result is incorrect, the backpropagation algorithm starts counting backward from the final layer, finding which knob was turned in the wrong direction and then correcting it layer by layer. By repeating this thousands or even billions of times, the network becomes increasingly accurate.

It is worth noting that the specific features detected by each layer are not deliberately designed by humans. They are explored by AI itself.

3. Second-Level Learning: From “Bookworm” to Practical Assistant

After pre-training, AI is like a bookworm who has read the entire library but does not know how to communicate with people. It knows that water can extinguish fire, but when faced with the question “What should I do if an oil pan catches fire?”, it may mistakenly answer “pour water.” Therefore, AI still needs to go through a post-training phase, mainly including fine-tuning and reinforcement learning from human feedback, also known as RLHF.

Fine-Tuning

Fine-tuning is equivalent to having a professional teacher teach this apprentice how to apply knowledge. Engineers show it a large number of question-and-answer examples. For example, when a user asks, “How should I deal with a fire in an oil pan?”, the correct answer is “Use a pot lid to isolate oxygen.” The model gradually learns to associate “oil pan fire” with the suffocation fire-extinguishing method by repeatedly comparing its incorrect answers with correct answers.

Fine-tuning usually involves adjusting parameters based on a pre-trained model using a small amount of annotated data, allowing the model to adapt to specific tasks.

RLHF: Reinforcement Learning from Human Feedback

RLHF is a further optimization method. In RLHF, human annotators score the output of the model as a reward signal. If the model provides dangerous content, it loses points. If it gives a correct and helpful answer, it gains points. After thousands of corrections, the model develops an error-avoidance mechanism similar to a conditioned reflex.

RLHF has become an important method for aligning large language models with human expectations of high-quality answers. It significantly improves the quality of interaction with AI assistants and makes their answers more useful and safer.

Pre-training solves the problem of general knowledge acquisition, fine-tuning focuses on vertical-domain optimization, reinforcement learning achieves strategy innovation, and RLHF improves model safety through human feedback. These four stages progress layer by layer and together form the complete evolutionary path of AI from knowledgeable to more useful and understandable.

4. The “Cheat Code” of Knowledge: Retrieval-Augmented Generation, RAG

Pre-training gives AI a vast amount of knowledge, but this knowledge is frozen in the model parameters. The model only knows what it learned during training and cannot directly obtain new information that appears after training is completed.

Retrieval-Augmented Generation, or RAG, solves this problem. RAG combines search with large language models by retrieving relevant information from external data sources and adding it as context to prompts before the model generates answers. It enables AI to generate accurate and contextual responses based on real-time or proprietary data, rather than relying only on model memory.

It can be understood this way: pre-training gives AI a brain, while RAG gives it an external library that can be accessed at any time. When users ask questions, AI does not rely solely on memory to answer. Instead, it first goes to the library to retrieve the latest and most relevant information and then generates answers based on those materials.

This enables AI knowledge to no longer be limited to the cut-off date of training data and allows the system to continuously maintain the timeliness of information.

5. The Inheritance of Knowledge: Knowledge Distillation and Transfer Learning

Another important way for AI to acquire knowledge is knowledge distillation. This allows a trained, highly accurate large model, known as the teacher model, to teach the knowledge it has learned to a smaller, lighter, and faster small model, known as the student model.

This process is like an experienced old professor condensing and refining a lifetime of knowledge for students. The students no longer need to read massive books from scratch, but can quickly master core competencies.

The core of knowledge distillation lies in soft labels. When making predictions, large models not only provide the final answer, but also output probability distributions, such as “positive 87%, negative 13%.” These subtle probabilities contain language rules, logical relationships, and knowledge associations learned by the large model.

The significance of knowledge distillation lies not only in model compression, but also in knowledge transfer. It allows AI capabilities to be inherited by next-generation models like biological genes.

The Geng Xin team from Southeast University proposed the concept of learning genes, a type of heritable information fragment that can encapsulate universal knowledge unrelated to specific tasks and transmit it to new models, thereby achieving more efficient and universal knowledge transfer.

Currently, every artificial intelligence model is born in a random initialization state. No matter how much knowledge the previous AI model has learned, its experience cannot naturally be inherited by the next generation of models.

The idea of learning genes is to allow new models to inherit some of the capabilities of previous models without having to start completely from scratch. This is similar to how human infants inherit learning ability, or meta-learning capability, even though they do not inherit specific memories from their parents.

6. Limitations and Future of AI Knowledge Acquisition

Although AI’s ability to acquire knowledge is astonishing, it has profound limitations.

First, AI Does Not Truly Understand

AI has only learned some kind of statistical mapping from inputs to outputs, such as from pixels to labels. A child truly establishes the concept of cat and can recognize it by its tail, by meowing, or even by seeing only half of its face. If lighting conditions and shooting angles change, AI may fail to recognize the object if it has not seen similar examples in the training data.

Second, AI Judgment Depends Entirely on Training Data

If there is bias in the data, AI will also produce bias. If most cats in the training data are orange cats, AI’s recognition rate for black cats may decrease. When incorrect answers frequently appear in the training data, the model may mistake them for reasonable options.

Third, AI Lacks Common Sense and Causal Understanding

All of its knowledge is related in a statistical sense, rather than being a true grasp of the laws governing the operation of the world.

Looking ahead, the way AI acquires knowledge is undergoing profound changes. Google DeepMind researchers predict that 2026 will be the year of continuous learning, where AI will have the ability to continuously absorb new knowledge without the need for retraining.

OpenAI executives have pointed out that AI inference models are demonstrating the potential for autonomous knowledge generation. This means they may not simply reproduce content from training data, but may discover new insights.

The way AI acquires knowledge is evolving from quantitative change to qualitative change. It moves from rote memorization of massive data, to learning how to apply knowledge through human feedback, to real-time lookup through RAG, and finally to the possibility of autonomous creation.

Understanding this process not only helps us use AI better, but also enables us to maintain rational and critical thinking when facing AI.

AI Knowledge Acquisition and Business Value

For global enterprises, AI knowledge acquisition is not only a technical topic, but also a practical business capability. A model’s knowledge quality determines whether it can provide accurate customer service, reliable product recommendations, effective document search, intelligent decision support, and scalable automation.

In e-commerce, manufacturing, finance, healthcare, logistics, education, and international trade, AI systems usually need three layers of knowledge: general knowledge from pre-training, industry knowledge from fine-tuning, and real-time enterprise knowledge from RAG systems.

Therefore, companies deploying AI should not only choose a model, but also build a complete AI knowledge infrastructure, including high-quality datasets, vector databases, storage systems, GPU computing resources, monitoring tools, and continuous data governance.

Related Forum FAQ