Popularization of important AI concepts and analysis of popular technologies

The AI ​​era is here. If ordinary people don’t know much about the relevant concepts, they may have to live in a world competing with AI in the future. To give a simple example, AI will directly absorb excellent human poetry and give an output of at least 80 to 90 points, which directly raises the lower limit of such text output. The same is true for other industries. Fortunately, humans still have the ability to perceive the world (the embodied intelligence of the future will also be in this direction), which AI does not currently have. If humans want to control their own destiny, they must master the technical application and principles of AI, so that AI can serve people instead of being backfired by AI. ?! This is a bit far-fetched. OK, let’s first understand the basic concepts related to AI!

1. Popularization of basic concepts related to AI

Artificial intelligence (AI) covers a variety of technologies and applications. The following are the core concepts and technologies in the current AI field.

1. Large Language Model (LLM)

A large language model (LLM) is a natural language processing (NLP) model based on deep learning, such as GPT-4, DeepSeek Chat, Claude, Gemini, and Mistral.

• Core technologies : Transformer architecture (self-attention mechanism), large-scale pre-training data, and RLHF (human feedback reinforcement learning).
• Applications : text generation, code completion, conversational AI, document analysis.

2. Transformer

• Introduction : Proposed by Google in 2017 to replace RNN/LSTM.
• Features : Parallel computing and efficient processing of long texts.
• Key mechanisms : Self-Attention, Feedforward Neural Network (FFN).

3. Fine-tuning

• Full fine-tuning : adjust all parameters, suitable for large-scale computing environments.
• LoRA (Low Rank Adaptation) : Only adjust some parameters to improve fine-tuning efficiency.
• SFT (Supervised Fine-tuning) : Training specific task capabilities based on labeled data.
• RLHF (Reinforcement Learning with Human Feedback) : Make the model more in line with human preferences.

4. Model Optimization Technology

• Knowledge Distillation : Let the small model learn the knowledge of the large model to improve computing efficiency.
• Quantization : Reduce the model calculation accuracy (such as FP16 → INT4) to reduce the inference cost.
• Tensor Parallelism & Pipeline Parallelism : Optimize distributed training of large models.

5. Open source ecosystem

• Open source LLM (such as LLaMA, Qwen, and DeepSeek)  is suitable for enterprises to develop their own AI.
• LlamaIndex / LangChain : Build AI agents, tool calls, and RAG applications.
• Hugging Face : The largest open source community in the field of AI, providing pre-trained models.

2. Analysis of popular AI technologies

1. RAG (Retrieval-Augmented Generation)

(1) Concept

RAG allows LLM to retrieve external knowledge first and then generate answers, which reduces hallucinations and improves answer accuracy.

(2) Key technologies

• Vector Search : Use embedding models (such as OpenAI Embeddings, DeepSeek Embeddings) to convert text into vectors and find the most relevant documents in databases such as FAISS, Milvus, and Weaviate.
• Context Fusion :
• Directly stitch document content
• Generate document summary
• Select the most relevant paragraph

(3) Typical applications

• Enterprise Knowledge Base Q&A
• Legal and medical document search
• Code search (like GitHub Copilot)

2. MCP (Model Context Protocol)

(1) Concept

MCP aims to address the limited memory capacity of LLM, allowing AI to "remember" history across multiple interactions and share context between different applications.

(2) Key technologies

• Persistent Context Storage :
• Structured databases (such as PostgreSQL, Redis) store JSON structured information.
• Vector databases such as FAISS store semantic information.
• Semantic Search :
• The most relevant historical context is found through vector search and dynamically injected into the prompt.
• Multi-Agent Coordination :
• AI can call APIs, databases, and code computing tools to perform complex tasks.

(3) Typical applications

• AI personal assistants (such as Notion AI that remembers your note-taking habits)
• Enterprise AI memory (let customer service AI remember user historical consultation records)
• Multi-agent collaboration (allowing multiple AIs to exchange information with each other)

3. Tool Use

(1) Concept

Let LLM call external APIs or plug-ins to complete tasks beyond the model's capabilities.

(2) Key technologies

• OpenAI Function Calling
• DeepSeek Agent
• LangChain Tool Execution

(3) Typical applications

• AI query database
• AI-controlled robots
• AI runs Python code

4. MoE (Mixture of Experts)

(1) Concept

MoE reduces the amount of computation and improves reasoning efficiency by allowing LLM to call different “expert” sub-models.

(2) Key technologies

• Only some expert networks are activated to reduce computing resource consumption.
• DeepSeek-V2 and Mistral use the MoE mechanism to improve the inference speed.

(3) Typical applications

• AI voice assistant (reducing inference latency)
• Machine translation (using different experts to optimize translation quality)

Conclusion

Through the combination of these technologies, AI will perform more intelligently and efficiently in different scenarios. If you are more interested in a certain concept or technical direction, you can study it further! ?