Artificial intelligence technology is changing with each passing day. While researchers continue to break through technological boundaries, they also continue to identify emerging security risks. This glossary will be updated regularly to incorporate the latest terms and concepts to help you keep up with the wave of AI development.
AI Agent (AI Agent)
AI agent refers to a tool that uses artificial intelligence technology to perform a series of complex tasks on behalf of humans, and its ability far exceeds that of basic chatbots. For example, it can handle reimbursement, book restaurants or air tickets, write and maintain codes, etc. However, there are still many uncertainties in this emerging field: the definition of “AI agent” may vary significantly in different contexts, and the infrastructure that supports its function is still under construction. Its core philosophy isAutonomous system——Complete multi-step tasks by calling multiple AI models.
Chain of Thought (Chain of Thought)
When humans answer simple questions (such as “Who is taller than a giraffe and a cat?"), ”) There is no need to think deeply, but complex questions (such as “There are 40 heads and 120 feet of chickens and cows on the farm, find the quantity”) often require the assistance of pen and paper to derive the answer through intermediate steps.
In the field of AI，Thinking chain reasoningRefers to the big language model to disassemble the problem into multiple intermediate steps to improve the accuracy of the results. Although it takes longer, the accuracy rate of the answer is significantly improved in logic or programming tasks. This type of ”reasoning model" is based on the traditional big language model and optimizes step-by-step thinking ability through intensive learning.
Deep Learning (Deep Learning)
Deep learning is a branch of machine learning. Its algorithms use a multi-layer artificial neural network (ANN) structure, which can capture complex associations between data, and its performance far exceeds simple systems such as linear models or decision trees. Its design is inspired by the neural network of the human brain.
Core features：
- Automatic feature extraction: There is no need to manually define data characteristics, and the algorithm can identify key patterns by itself.
- Self-optimization: Adjust the output through repeated trial and error to gradually improve performance.
- High cost and data dependence: Million-level data volume training is required, which takes a long time and has high development costs.
Fine Tuning (Fine Tuning)
Refers to optimizing its performance for specific tasks by entering new data in specific fields on the basis of existing AI models. For example, many start-up companies use the large language model (LLM) as the starting point, combining their own expertise to fine-tune the model, and develop commercial products in vertical fields.
Large Language Model (Large Language Model, LLM)
AI assistants such as DeepSeek, ChatGPT, Claude, Gemini, etc. are all based on the big language model. When users talk to AI, they are actually interacting with LLM-the model directly processes the request, or assists in the response with the help of tools such as Internet search and code interpreters.
Technical principle：
- LLM is a deep neural network containing billions of parameters (weights). It analyzes massive amounts of books, articles, and dialogue data to construct a “multi-dimensional mapping” of language.
- When generating a reply, the model predicts the most likely vocabulary sequence based on the input, and iterates word by word until it is completed.
Neural Network (Neural Network)
Neural networks are the algorithmic foundation of deep learning and the core of the explosive growth of generative AI. Its multi-layer structure mimics the connections of neurons in the human brain. Although the concept originated in the 1940s, its potential was not truly released until the popularization of GPUs (graphics processors). GPUs are good at processing multi-layer network training, promoting AI breakthroughs in speech recognition, autonomous driving, drug research and development, etc.
Weights (Weights)
Weight is the core parameter of AI training, which determines the degree of influence of different input characteristics on the output of the model. In the early stage of training, the weights are randomly assigned; with optimization, the model gradually adjusts the weights to make the output closer to the target value.
example: In the house price prediction model, the weights of characteristics such as the number of bedrooms and whether there is a garage reflect the intensity of the influence of these factors on house prices in historical data.
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