Introduction: Demystifying AI

“Is artificial intelligence a form of mysticism?” “How can AI do everything?” “Are large models just ‘guessing’ answers?” These are some of the most common questions I hear.

The truth is: AI is not mysticism, but a perfect combination of mathematics, engineering, and data. This article will break down the essence of AI from the most authoritative technical perspective, using simple language, and outline the cutting-edge technological trends for 2025-2026.

What is AI? Authoritative Technical Analysis

Large Language Models: Super “Word Chain” Players

If we compare models like GPT, Kimi, and DeepSeek to something, they are more like “word chain” experts who have read every book in the world.

Core Principles (Transformer Architecture):

• Attention Mechanism: Just as humans focus on keywords while reading, the model calculates the relevance of each word to others. For example, when you say “apples are delicious,” the model knows that “apples” refers to the fruit, not the tech company.
• Autoregressive Prediction: The model generates text word by word, referencing all previous content each time it generates a new word. This is like a phrase chain—given “thousands of troops,” the model predicts the next character is likely “horse.”
• Parameters and Knowledge: The “knowledge” of large models is stored in hundreds of billions to trillions of parameters (weights). The training process involves continuously adjusting these parameters to make the model’s predictions increasingly accurate.

Deep Learning: Layered Filters for Feature Extraction

In simple terms: deep learning is like a series of progressively layered filters.

For image recognition:

• First Layer: Recognizes edges and color blocks
• Second Layer: Recognizes simple shapes (circles, corners)
• Third Layer: Recognizes components (eyes, wheels)
• Fourth Layer: Recognizes wholes (faces, cars)

Neural Network Forward and Backward Propagation:

1. Forward Propagation: Data passes through the input layer and hidden layers, resulting in an output.
2. Calculate Loss: Compare the output with the actual answer to determine the difference.
3. Backward Propagation: The error is sent back from the output layer to adjust parameters in each layer.
4. Iterative Optimization: This process is repeated millions of times until the model “learns.”

Scaling Law: Size Equals Power

Scaling Law is a core principle of contemporary AI development:

Model Performance ∝ Parameter Quantity × Data Quantity × Computational Power

In simple terms: A larger model + more data + stronger computing power = smarter AI.

However, this is not merely a case of “bigger is better.” Research in 2025 indicates that when a model reaches a certain critical scale, emergent capabilities arise—where the model suddenly exhibits new abilities not explicitly taught during training, such as logical reasoning and multi-step calculations.

Cutting-Edge AI Technologies for 2025-2026

According to the Beijing Academy of Artificial Intelligence (a top AI research institution in China), the following are the most noteworthy technological directions:

Trend 1: AI4S (AI for Science) — Revolutionizing Scientific Discovery

Core Breakthrough: AI is changing the way scientific discoveries are made.

• Biomedicine: Following AlphaFold’s prediction of protein structures, AI applications in drug discovery and gene editing are surging.
• Weather Prediction: AI weather models have improved prediction accuracy by over 30%, reducing computation time from days to minutes.
• Materials Science: The discovery cycle for new materials has been shortened from 10 years to 1-2 years, moving from “trial and error” to “AI prediction + validation.”

In simple terms: Scientists used to search for needles in a haystack; now AI tells them where the needles might be.

Trend 2: Embodied AI — Giving AI a Body

2025 is dubbed the “Year of Embodied AI” in the industry.

• Humanoid Robots: Tesla’s Optimus, Figure AI, and other humanoid robots are entering factory training.
• Brain-Body Coordination: Large models handle “brain” functions (high-level decision-making), while specialized models manage “body” functions (motor control).
• Industrial Applications: Warehousing, precision manufacturing, and operations in hazardous environments are leading the way in large-scale applications.

In simple terms: Previously, AI only had a “brain” for conversation; now it has a “body” to perform tasks.

Trend 3: Native Multimodal Large Models — True “Sensation” AI

Technological Evolution: From “stitched multimodal” to “native multimodal.”

In simple terms: Stitched is like a “translation team” (translating separately then summarizing), while native is like a “polymath” (thinking in multiple languages directly).

Trend 4: Reinforcement Learning + Large Models — Teaching AI to “Think”

Technological Breakthrough: Deep integration of RL (Reinforcement Learning) and LLMs.

• Chain-of-Thought: Models think step-by-step like humans instead of giving direct answers.
• Self-Play Learning: Models improve reasoning abilities by playing against themselves or engaging in dialogue.
• DeepSeek-R1 Breakthrough: A domestic open-source model that matches international top levels in mathematical reasoning and code generation, with significantly reduced training costs.

In simple terms: Previously, AI was about “rote memorization”; now it has learned to “generalize.”

Trend 5: World Models — AI’s “Imagination”

Core Concept: World models enable AI to possess causal reasoning abilities.

• Predicting the Future: Given the current state, predicting “what will happen if this is done.”
• Causal Understanding: Not just correlation, but true causal logic.
• Application Scenarios: Autonomous driving decisions, robot path planning, game AI.

In simple terms: World models are AI’s “inner theater”—simulating the consequences in its mind before taking action.

Trend 6: Synthetic Data — Breaking the Data Bottleneck

Industry Pain Point: High-quality training data is running out.

Solution:

• AI-Generated Data: Use Model A to generate data, validate it with Model B, and then train Model C with this data.
• Privacy Protection: Synthetic data does not involve real user information, ensuring compliance.
• Long-Tail Coverage: Generate rare scenario data (e.g., extreme weather driving scenarios).

In simple terms: Real data is like “wild fish,” nearly caught out; synthetic data is like “farm-raised fish,” providing sustainable supply.

Trend 7: Edge Inference — AI Becomes “Lightweight”

Technical Directions:

• Model Distillation: “Condensing” knowledge from large models into smaller ones.
• Quantization Compression: Using fewer bits to represent parameters (e.g., reducing from 32-bit to 4-bit).
• Dedicated Chips: The proliferation of mobile NPUs and edge AI chips.

In simple terms: Previously, AI was the domain of “cloud computing centers”; now your phone can run large models too.

Trend 8: Agentic AI — From “Chatting” to “Doing Work”

Evolution of Product Forms:

1. Chatbot: Simple Q&A
2. Copilot: Assists humans in completing tasks
3. Agent: Plans autonomously, calls tools, executes multi-step tasks
4. Agentic AI: Multi-agent collaboration to achieve complex goals

Representative Products:

• Alibaba’s “Zhi Xiaobao”: AI life assistant
• Ant Group’s “Ma Xiaocai”: AI financial assistant
• Doubao: A domestic AI application with over 70 million monthly active users.

In simple terms: Previously, AI was a “consultant”; now it is a “secretary”—not only giving advice but also helping to book flights, write code, and create spreadsheets.

Trend 9: Super App — The “Super Entrance” of the AI Era

Industry Expectation: Who will be the “WeChat” of the AI era?

Characteristics:

• High-frequency usage (daily usage > 10 times)
• Long dwell time (daily > 30 minutes)
• Multi-functional integration (chat, search, services, entertainment combined)

Current Status: A true AI Super App has yet to emerge, but products like Doubao, ChatGPT, and Claude are competing for this position.

Trend 10: AI Safety — Balancing Development and Governance

Core Challenges:

• Alignment Issues: Ensuring AI goals align with human values.
• Jailbreak Attacks: Preventing AI from being induced to generate harmful content.
• Deepfakes: The risk of AI generating false audio and video.

Governance Progress:

• The Academy is leading the formulation of the “Beijing AI Safety International Consensus.”
• International standards for large model safety are being developed by the United Nations.
• AI regulatory bills are being introduced in various countries.

The Rise of Domestic AI: The DeepSeek Phenomenon

In early 2025, the DeepSeek model, DeepSeek-R1, caused a global sensation:

Technological Breakthroughs:

• Mathematical reasoning capabilities on par with OpenAI’s o1.
• Code generation capabilities reaching industry-leading levels.
• Fully open-source, with training costs only 1/10 of similar models.

Industry Significance:

• Proves the “low-cost + high-efficiency” development path for AI.
• Breaks the myth that “AI competition is only a game for large companies.”
• Provides a “Chinese solution” for global AI development.

Conclusion: The Essence and Future of AI

What AI is Not:

• ❌ Not mysticism or magic.
• ❌ Not a simple database query.
• ❌ Not a truly conscious “intelligent life.”

What AI Is:

• ✅ The ultimate application of statistical laws.
• ✅ A super engine for pattern recognition.
• ✅ A compression and reorganization of human knowledge.
• ✅ A new infrastructure for productivity.

Future Outlook:

Conclusion: Understanding the principles of AI is essential for effectively harnessing its potential. AI is not mysticism; it is an extension of human wisdom, a revolution in tools, and a leap in productivity. In this AI-driven era, understanding, using, and collaborating with AI will become a necessary skill for everyone.