This post gives some vocabularies abot the topic of "DeepSeek: Pioneering AI's Future"

Try using it and discuss the topic!

Topic site: https://zh.purasbar.com/post.php?t=31282

Beginner Level Vocabulary List:

1. Basic Concepts

Artificial Intelligence (AI): The simulation of human intelligence in machines. 

Chatbot: A computer program designed to simulate conversation with human users. 

Search Engine: A system designed to search for information on the internet. 

Language Model: A type of AI that understands and generates human language. 

Data: Facts and statistics collected together for analysis.

Information: Knowledge or details about something.

Database: A structured set of data stored electronically.

Algorithm: A set of rules or instructions for solving a problem or performing a task. 

2. User Interaction

User-Friendly: Easy to use and understand. 

Response: A reply or reaction to a query. 

Interface: The point of interaction between a user and a system. 

Interaction: The process of communicating or engaging with a system. 

Input: Data or information entered into a system. 

Output: The result or response generated by a system. 

Chat Widget: A small application embedded in a website for chatting. 

Session: A period of interaction between a user and a system. 

3. Technology and Tools

Technology: The application of scientific knowledge for practical purposes. 

Device: A tool or machine used for a specific purpose. 

Application: A computer program designed for a particular purpose. 

Software: A set of instructions or programs that tell a computer what to do. 

Hardware: The physical components of a computer or device. 

Cybersecurity: The practice of protecting systems from malicious attacks. 

Server: A computer that stores and manages data for other computers. 

Internet Provider: A company that provides internet access to users.

Intermediate Level Vocabulary List:

1. Technical Features

Multilingual Support: The ability of a system to support multiple languages. 

Contextual Understanding: The ability to understand the context of a conversation or query. 

Natural Language Processing (NLP): The ability of a computer to understand human language. 

Machine Learning: A subset of AI that involves training algorithms to learn from data. 

Deep Learning: A subset of machine learning that uses neural networks with multiple layers. 

Neural Network: A computational model inspired by the structure of the human brain. 

Training Data: The data used to train a machine learning model. 

Model Architecture: The structure and design of a machine learning model. 

2. Data and Information

Data Collection: The process of gathering data from various sources.

Data Analysis: The process of examining data to identify patterns and insights.

Data Storage: The process of keeping data in a storage system for future use.

Data Security: Measures taken to protect data from unauthorized access or theft.

Data Privacy: The practice of protecting personal data from unauthorized use.

Data Integrity: The accuracy and consistency of data throughout its lifecycle.

Data Visualization: The presentation of data in a graphical or visual format.

Data Mining: The process of discovering patterns and correlations in large datasets.

3. Performance Metrics

Accuracy: The degree of correctness or precision. 

Response Time: The time it takes for a system to respond to a query. 

Throughput: The amount of data processed by a system in a given time. 

Latency: The delay before a transfer of data begins following an instruction for its transfer. 

Scalability: The ability of a system to handle increased load. 

Efficiency: The ability to accomplish a task with minimal resources. 

Reliability: The ability of a system to perform its required functions under stated conditions. 

Robustness: The ability of a system to handle errors and unexpected inputs. 

4. Ethical Considerations

Bias: A prejudice in favor of or against one thing, person, or group. 

Fairness: The quality of being just, impartial, and free from bias. 

Transparency: The quality of being open and easy to understand. 

Accountability: The obligation to account for one's actions. 

Privacy: The state of being free from unauthorized intrusion. 

Security: The state of being protected against potential harm or threats. 

Ethics: The branch of knowledge that deals with moral principles. 

Regulation: The act of controlling or supervising something. 

Advanced Level Vocabulary List:

1. Technical Architecture

Neural Architecture Search (NAS): An automated process of searching for the optimal neural network architecture. 

Sparse Attention Mechanism: A type of attention mechanism that only focuses on a subset of input elements. 

Model Compression: The process of reducing the size of a machine learning model to improve efficiency. 

Quantization: The process of converting a continuous range of values into a finite range of discrete values. 

Knowledge Distillation: A technique where a smaller model is trained to mimic the behavior of a larger model. 

Parameter Efficiency: The ability of a model to achieve high performance with a limited number of parameters. 

Modular Design: A design approach where a system is divided into separate functional units. 

Pipeline Parallelism: A technique where different parts of a model are processed in parallel. 

2. Advanced Applications

Predictive Analytics: The use of data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes. 

Sentiment Analysis: The process of determining the sentiment or emotion behind a piece of text. 

Image Recognition: The ability of a system to identify objects, scenes, and people in images. 

Speech Recognition: The ability of a system to convert spoken language into text. 

Natural Language Generation (NLG): The process of generating natural language text from data. 

Reinforcement Learning: A type of machine learning where an agent learns to behave in an environment by performing actions and seeing the results. 

Unsupervised Learning: A type of machine learning where a model learns from data without explicit instructions. 

Semi-Supervised Learning: A type of machine learning that uses both labeled and unlabeled data. 

3. Ethical and Regulatory Considerations

Algorithmic Bias: Systematic and unfair discrimination caused by the use of algorithms. 

Fairness in AI: The practice of ensuring that AI systems do not discriminate against any group. 

Transparency in AI: The practice of making AI systems understandable and open to scrutiny. 

Accountability in AI: The practice of holding AI systems and their creators responsible for their actions. 

Data Privacy Regulations: Laws and regulations that protect personal data. 

Cybersecurity Threats: Potential dangers to the security of computer systems and networks. 

Ethical AI Frameworks: Guidelines and principles for the ethical development and use of AI. 

Regulatory Compliance: The practice of adhering to laws, regulations, and policies. 

I think the words of the topic this week is more difficult than the last week, especially Advanced Level Vocabulary