By Chen Kongyang, Associate Professor, School of Artificial Intelligence, Guangzhou University
Have you ever chatted with an AI assistant like DeepSeek or ChatGPT, asking it to draft an article, solve a complex problem, or even compose poetry? Have you marveled at how AI art tools generate stunning images from just a few words? Behind these impressive displays of “intelligence” lies the power of generative AI models, such as DeepSeek-R1. But how do these models achieve such remarkable capabilities? Today, we uncover their “intelligent genes.”
The Foundation of True Intelligence: Learning and Adaptation
Real intelligence isn’t just about executing predefined commands—it requires learning, adapting, reasoning, and solving novel problems. Large language models (LLMs) like DeepSeek achieve this through five key elements:
1. Massive Parameters & Complex Neural Architecture (The “Brain Structure”)
Models like DeepSeek-R1 contain billions (or even hundreds of billions) of parameters—akin to adjustable “dials” in the brain that determine how the model responds to input. Their self-attention mechanism allows the model to weigh the importance of different words in a sentence, enabling contextual understanding—a foundation for reasoning and sophisticated expression.
2. Pretraining on Vast, High-Quality Data (The “Knowledge Source”)
Before deployment, models undergo pretraining—DeepSeek-R1, for instance, was trained on 2 trillion tokens of text from encyclopedias, books, code repositories, and forums. By predicting the next word in a sentence, it learns language patterns, common sense, and conceptual relationships. This process enables not just memorization but generalization, allowing the model to tackle never-before-seen problems.
3. Powerful Generation & Contextual Understanding (The “Intelligent Performance”)
This is where AI’s “smartness” becomes tangible:
-
Given a prompt, it can generate coherent articles, functional code, or even poetry.
-
It retains context in long conversations, demonstrating deep comprehension and creativity.
-
Its outputs aren’t mere copies but novel combinations based on learned linguistic and knowledge structures.
4. Fine-Tuning & Alignment (Behavior Shaping)
A pretrained model isn’t always “reliable” or “aligned” with human intent. Fine-tuning, particularly through Reinforcement Learning from Human Feedback (RLHF), ensures the model:
-
Follows instructions accurately.
-
Adheres to ethical guidelines.
-
Reduces harmful or nonsensical outputs.
This transforms the model from a “powerful but erratic” tool into a “trustworthy assistant.”
5. Emergent Abilities (The Leap to Higher Intelligence)
A fascinating phenomenon occurs when models reach a critical scale: they spontaneously develop abilities absent in smaller models, such as:
-
Complex mathematical reasoning.
-
Debugging code.
-
Cross-task knowledge transfer.
These emergent capabilities—unprogrammed but arising from the model’s internal pattern recognition—epitomize how “quantity leads to qualitative leaps.”
A Milestone Toward the Future of Intelligence
Generative AI models like DeepSeek-R1 integrate these five elements—advanced architecture, massive pretraining, generation prowess, human-aligned fine-tuning, and emergent intelligence—to deliver the “smart” experiences we see today. While they still lack human-like consciousness and face challenges like “hallucinations” (fabricating false information), their abilities in language understanding, knowledge synthesis, and problem-solving are unprecedented.
These models represent a pivotal milestone in AI evolution—and a powerful tool for humanity to explore and expand the boundaries of intelligence itself.