本次 2025 亞洲 AI 與計算機科學菁英賽，其題目範圍覆蓋多個 AI 與計算機科學領域，涉及基礎知識以及高階的技術分析和難題解讀，具體如下：

基礎知識

 

1. 計算機基礎概念：二進制、十進制、十六進制等數制的轉換及應用，如將十六進制數轉換為二進制和十進制。理解計算機系統的組成，包括硬體（CPU、內存、硬盤等）和軟件（操作系統、應用軟件等）的功能和相互關係，像分析操作系統在計算機運行中的核心作用。

2. 程式設計語言基礎：熟練掌握一種主流程序設計語言（如 Python、Java、C++ 等）的基本語法，包括變量、數據類型、控制結構（循環、分支）、函數定義與調用等。例如，使用 Python 語言編寫一個函數，用於計算斐波那契數列的前 n 項。理解面向對象程式設計的概念，如類、對象、繼承、多態等，並能用 Java 語言創建一個簡單的類繼承體系。

3. 數據結構與演算法基礎：理解線性表、堆棧、隊列、樹、圖等基本數據結構的概念、特點和操作方法，如使用 Python 實現一個簡單的二叉搜索樹的插入和查找操作。掌握基本演算法的思想和實現，如排序演算法（冒泡排序、快速排序）、查找演算法（順序查找、二分查找），並能分析演算法的時間複雜度和空間複雜度，例如分析快速排序在不同情況下的時間複雜度。
    

AI 技術基礎：

1. 機器學習基礎：理解機器學習的基本概念和分類，如監督學習、無監督學習、強化學習的區別。掌握常見的機器學習演算法，如線性回歸、邏輯回歸、決策樹、支持向量機等，能使用 Python 的 Scikit - learn 庫實現簡單的線性回歸模型，並用於預測房屋價格。理解模型評估指標，如精確率、召回率、F1 值、均方誤差等，能根據模型評估指標選擇合適的機器學習模型。

2. 深度學習基礎：瞭解深度學習的基本概念，如神經網絡的結構（輸入層、隱藏層、輸出層）、啟動函數（Sigmoid、ReLU 等）。掌握簡單的深度學習模型，如多層感知機（MLP）、卷積神經網絡（CNN）、循環神經網絡（RNN）及其變體（LSTM、GRU）等，能使用 Keras 或 TensorFlow 框架搭建一個簡單的 CNN 模型用於圖像分類。理解深度學習模型的訓練過程，包括損失函數、優化器（梯度下降、Adam 等）的選擇和使用。
    

應用開發：

1. Web 應用開發：掌握 Web 開發的基本技術，如 HTML、CSS、JavaScript 的使用，能創建一個簡單的靜態網頁。瞭解 Web 框架（如 Django、Flask for Python，Spring Boot for Java）的基本原理和使用，能使用 Django 框架搭建一個包含用戶註冊和登錄功能的 Web 應用。理解數據庫（如 MySQL、MongoDB）在 Web 應用中的作用，能使用 SQL 語句進行數據庫的增刪改查操作。

2. 移動應用開發：瞭解移動應用開發的基本流程和工具，如使用 Android Studio 進行 Android 應用開發，或使用 Xcode 進行 iOS 應用開發。掌握移動應用介面設計的基本原則和方法，能使用 XML 或 SwiftUI 創建一個簡單的移動應用介面。理解移動應用與服務器端的通信方式，如 HTTP 請求、RESTful API 的使用。
    

技術分析與解決問題：

1. 基於技術原理的分析：依據演算法的時間和空間複雜度理論，分析不同演算法在不同場景下的性能表現，如比較在大數據量下，快速排序和冒泡排序的效率差異。從機器學習模型的原理角度，探討模型的泛化能力和過擬合問題，如分析決策樹模型容易過擬合的原因及解決方法。

2. 技術與應用的關聯：根據 AI 技術的特點推斷其可能的應用場景，如根據自然語言處理技術中語義理解的能力，分析其在智能客服、機器翻譯等領域的應用。由應用場景反推所需的技術支持，如從自動駕駛的需求，推斷需要的感知技術（雷達、攝像頭）和 AI 演算法（目標檢測、路徑規劃）。

3. 跨知識模組的綜合分析：結合 Web 開發和 AI 技術知識，分析智能 Web 應用中的技術實現，如在一個智能推薦系統的 Web 應用中，分析如何將機器學習的推薦演算法與 Web 前端和後端技術相結合。綜合程式設計和數據庫知識，解決實際的數據管理問題，如設計一個圖書管理系統，包括書籍資訊的存儲、查詢和借閱管理。
    

前沿技術與行業熱點：

1. 人工智慧前沿：分析生成式對抗網絡（GAN）、Transformer 架構等前沿 AI 技術的原理和應用，如闡述 GAN 在圖像生成中的工作原理及應用成果。探討 AI 在醫療、金融、教育等領域的創新應用，如 AI 在醫療影像診斷中的輔助作用及面臨的挑戰。

2. 區塊鏈與計算機安全：瞭解區塊鏈的基本原理和應用場景，如分析區塊鏈在金融交易、身份驗證等方面的應用。研究計算機安全的相關技術，如加密演算法、網絡攻擊與防禦等，探討如何保障區塊鏈系統的安全。

3. 大數據技術：介紹大數據的特點（Volume、Velocity、Variety、Value）及處理技術，如 Hadoop、Spark 等框架的原理和使用。討論大數據分析在商業決策、智慧城市等領域的應用，如通過大數據分析提高城市交通管理效率。
    

解決實際相關問題：

1. 面對生活中的技術問題：針對智能家居中的控制問題，如如何使用手機 APP 控制家電設備，運用計算機網絡和移動應用開發知識進行解決，並提出改進建議。解決日常生活中的資訊管理問題，如使用 Excel 或 Python 編程對個人收支數據進行整理和分析。

2. 針對行業中的技術問題：在軟件開發行業中，考慮項目進度、成本、質量等因素，優化軟件開發流程，如使用敏捷開發方法提高軟件項目開發效率。分析互聯網行業中數據隱私和安全問題，並提出相應的技術解決方案，如採用加密技術和訪問控制機制保護用戶數據安全。

 

參賽者要扎實掌握基本 AI 與計算機科學知識，熟練運用相關技術和方法解題。大賽設置挑戰題，激發學生創新思維，突破自身，探索 AI 與計算機科學新領域，力求在有限的考試資源下，全面、高效地檢驗學生的 AI 與計算機科學素養和能力，避免不必要的繁瑣和重複內容，做到精准、有效地考核。
 

The questions in the 2025 Asian Elite Competition in AI and Computer Science cover multiple fields in AI and computer science, involving basic knowledge as well as advanced technical analysis and interpretation of difficult problems. The details are as follows:

Basic Knowledge
 

• Computer Fundamental Concepts：Understand the conversion and application of number systems such as binary, decimal, and hexadecimal. For example, convert a hexadecimal number to binary and decimal. Comprehend the composition of a computer system, including the functions and interrelationships of hardware (such as CPU, memory, and hard disk) and software (such as operating systems and application software). For instance, analyze the core role of an operating system in computer operation.

• Programming Language Basics：Master the basic syntax of a mainstream programming language (such as Python, Java, C++, etc.), including variables, data types, control structures (loops, branches), function definition and call. For example, write a Python function to calculate the first n terms of the Fibonacci sequence. Understand the concepts of object - oriented programming, such as classes, objects, inheritance, and polymorphism, and be able to create a simple class inheritance system using Java.

• Data Structure and Algorithm Basics：Understand the concepts, characteristics, and operation methods of basic data structures such as linear lists, stacks, queues, trees, and graphs. For example, implement the insertion and search operations of a simple binary search tree using Python. Grasp the ideas and implementations of basic algorithms, such as sorting algorithms (bubble sort, quick sort) and search algorithms (sequential search, binary search), and be able to analyze the time and space complexity of algorithms. For example, analyze the time complexity of quick sort under different circumstances.
   

AI Technology Basics

• Machine Learning Basics：Understand the basic concepts and classifications of machine learning, such as the differences between supervised learning, unsupervised learning, and reinforcement learning. Master common machine - learning algorithms, such as linear regression, logistic regression, decision trees, and support vector machines. Be able to implement a simple linear regression model using Python's Scikit - learn library for predicting house prices. Comprehend model evaluation metrics, such as precision, recall, F1 - score, and mean squared error, and be able to select appropriate machine - learning models based on these metrics.

• Deep Learning Basics：Learn the basic concepts of deep learning, such as the structure of neural networks (input layer, hidden layer, output layer) and activation functions (Sigmoid, ReLU, etc.). Master simple deep - learning models, such as multi - layer perceptrons (MLP), convolutional neural networks (CNN), recurrent neural networks (RNN) and their variants (LSTM, GRU). Be able to build a simple CNN model for image classification using Keras or TensorFlow frameworks. Understand the training process of deep - learning models, including the selection and use of loss functions and optimizers (gradient descent, Adam, etc.).
   

Application Development

• Web Application Development：Master the basic technologies of web development, such as HTML, CSS, and JavaScript, and be able to create a simple static web page. Understand the basic principles and usage of web frameworks (such as Django, Flask for Python, Spring Boot for Java). Be able to build a web application with user registration and login functions using the Django framework. Comprehend the role of databases (such as MySQL, MongoDB) in web applications and be able to perform database operations (insertion, deletion, modification, and query) using SQL statements.

• Mobile Application Development：Understand the basic process and tools of mobile application development, such as using Android Studio for Android application development or Xcode for iOS application development. Master the basic principles and methods of mobile application interface design and be able to create a simple mobile application interface using XML or SwiftUI. Comprehend the communication methods between mobile applications and servers, such as HTTP requests and the use of RESTful APIs.

• Technical Analysis and Problem - Solving

• Analysis Based on Technical Principles：Analyze the performance of different algorithms in different scenarios based on the theory of time and space complexity. For example, compare the efficiency of quick sort and bubble sort when dealing with large - scale data. Discuss the generalization ability and over - fitting problems of machine - learning models from the perspective of model principles. For example, analyze the reasons why decision tree models are prone to over - fitting and the corresponding solutions.

• Association between Technology and Application：Deduce possible application scenarios based on the characteristics of AI technologies. For example, analyze the applications of semantic understanding in natural language processing in fields such as intelligent customer service and machine translation. Infer the required technical support from application scenarios. For example, from the requirements of autonomous driving, deduce the needed perception technologies (radar, cameras) and AI algorithms (target detection, path planning).

• Comprehensive Cross - Module Analysis：Combine web development and AI technology knowledge to analyze the technical implementation of intelligent web applications. For example, in an intelligent recommendation system web application, analyze how to integrate machine - learning recommendation algorithms with web front - end and back - end technologies. Combine programming and database knowledge to solve practical data management problems. For example, design a library management system, including the storage, query, and borrowing management of book information.
   

Cutting - Edge Technologies and Industry Hotspots

• Cutting - Edge AI Technologies：Analyze the principles and applications of cutting - edge AI technologies such as generative adversarial networks (GAN) and Transformer architectures. For example, explain the working principle and application achievements of GAN in image generation. Discuss the innovative applications of AI in fields such as healthcare, finance, and education. For example, analyze the auxiliary role and challenges of AI in medical image diagnosis.

• Blockchain and Computer Security：Understand the basic principles and application scenarios of blockchain, such as its applications in financial transactions and identity verification. Study relevant computer security technologies, such as encryption algorithms, network attacks, and defenses. Discuss how to ensure the security of blockchain systems.

• Big Data Technologies：Introduce the characteristics (Volume, Velocity, Variety, Value) of big data and related processing technologies, such as the principles and usage of Hadoop and Spark frameworks. Discuss the applications of big data analysis in business decision - making and smart cities. For example, analyze how big data analysis can improve urban traffic management efficiency.
   

Solving Practical Problems

• Addressing Daily Life Technical Problems：Solve control problems in smart homes, such as using a mobile app to control home appliances, by applying computer network and mobile application development knowledge, and propose improvement suggestions. Solve daily information management problems, such as organizing and analyzing personal income and expenditure data using Excel or Python programming.

• Solving Industry - Related Technical Problems：Optimize the software development process in the software development industry by considering factors such as project progress, cost, and quality. For example, use agile development methods to improve software project development efficiency. Analyze data privacy and security problems in the Internet industry and propose corresponding technical solutions. For example, use encryption technologies and access control mechanisms to protect user data security.

 

Contestants are required to have a solid grasp of basic AI and computer science knowledge and be proficient in using relevant technologies and methods to solve problems. The competition sets up challenging questions to stimulate students' innovative thinking, encourage them to break through themselves, and explore new areas in AI and computer science. The aim is to comprehensively and efficiently test students' AI and computer science literacy and abilities with limited examination resources, avoiding unnecessary complexity and repetition to achieve accurate and effective assessment

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