總結挑戰 · Summary Challenge

Which statement about Machine Learning (ML) is correct?關於機器學習（ML），下列哪項敘述正確？

ML is a subset of AI that focuses on the development of algorithms and statistical models that enable computers to learn and make decisions based on data.ML 是 AI 的一個子集，專注於開發能使電腦根據資料學習並做出決策的演算法與統計模型。ML is a broad term encompassing various technologies aimed at creating machines capable of performing tasks that typically require human-like intelligence.ML 是一個廣泛的術語，涵蓋各種旨在打造具備類似人類智慧能力的機器的技術。ML is synonymous to AI.ML 與 AI 同義。ML techniques cannot be used to train AI systems.ML 技術無法用來訓練 AI 系統。

What is the main difference between generative and predictive AI?生成式 AI 與預測式 AI 之間的主要差異是什麼？

Predictive AI analyzes past and current data to forecast future events or behaviors, while generative AI creates new content or data by learning from existing data sets.預測式 AI 分析過去與目前的資料以預測未來的事件或行為，而生成式 AI 則透過從現有資料集學習來建立新的內容或資料。Generative AI leverages statistical models to analyze past data, while predictive AI uses machine learning techniques to mimic the characteristics of training data.生成式 AI 運用統計模型分析過去的資料，而預測式 AI 則使用機器學習技術模仿訓練資料的特性。Generative AI is based on VAE, while predictive AI is based on GANs.生成式 AI 以 VAE 為基礎，而預測式 AI 以 GAN 為基礎。Generative AI is exclusively used for network performance optimizations, while predictive AI is used for content creation like generating images or text.生成式 AI 專門用於網路效能最佳化，而預測式 AI 用於產生圖像或文字等內容建立。

What is backpropagation in the context of neural networks?在神經網路的情境中，反向傳播（backpropagation）是什麼？

A process where the weights and biases are recalculated to minimize the error between the actual and expected output.一種重新計算權重與偏差值的過程，以將實際輸出與預期輸出之間的誤差降到最低。A process where the network randomly reinitializes its weights and biases until the error is minimized.一種讓網路隨機重新初始化其權重與偏差值，直到誤差最小化為止的過程。A phase where the neural network freezes the weights and biases after each training round to prevent further adjustments.在每輪訓練後將權重與偏差值凍結，以防止進一步調整的階段。A starting phase of the training where the initial values of weights and biases are set based on the input data.訓練開始階段，根據輸入資料設定權重與偏差值的初始值。

What are the two characteristics of a neural network? (Choose two.)神經網路的兩個特性是什麼？（選擇兩項。）

It is composed of layers of interconnected nodes, where each connection is weighted.它由多層互相連接的節點組成，每個連接都有其權重。It operates solely on binary input values (0s and 1s) for all types of data processing tasks.在所有類型的資料處理工作中，它僅以二進位輸入值（0 與 1）運作。Each node in a neural network, except for those in the input layer, incorporates a bias term, which helps the network learn complex patterns.神經網路中除輸入層以外的每個節點都包含一個偏差項，有助於網路學習複雜的模式。Each node in a neural network functions independently without influencing or being influenced by other nodes.神經網路中的每個節點都獨立運作，不會影響也不受其他節點影響。The output of a neural network does not depend on weights or biases but only on the input data provided.神經網路的輸出不取決於權重或偏差值，僅取決於所提供的輸入資料。

Which method preserves the parameters of a Large Language Model, such as GPT, while adding data that was not part of the original training data set?哪種方法能在保留大型語言模型（例如 GPT）參數的同時，加入未包含在原始訓練資料集中的資料？

training訓練fine-tuning微調RAGRAGGANGAN

Which statement is true for RAG?關於 RAG，下列哪項敘述正確？

It was designed as a cost-effective and risk-free method to provide topic-specific context to a query.它被設計為一種具成本效益且風險較低的方法，可為查詢提供特定主題的情境。It is computationally more expensive compared to fine-tuning.相較於微調，它的計算成本更高。It is computationally more expensive compared to training.相較於訓練，它的計算成本更高。It can potentially cause a phenomenon that is known as catastrophic forgetting.它有可能導致一種稱為「災難性遺忘」的現象。

Which statement is true regarding tokens in the context of splitting text into smaller pieces?在將文字拆分為較小片段的情境中，關於權杖（token），下列哪項敘述正確？

Tokens are always single characters.權杖一律是單一字元。Tokens are always whole sentences, ensuring that the context is fully preserved during text processing.權杖一律是完整的句子，確保在文字處理過程中完全保留情境。Tokens are only created by removing punctuation from text, simplifying the input for text analysis software.權杖僅透過移除文字中的標點符號來建立，以簡化文字分析軟體的輸入。Tokens can be characters, words, phrases, or even parts of words, depending on the tokenizer used.依所使用的權杖化工具（tokenizer）而定，權杖可以是字元、單詞、片語，甚至是單詞的一部分。

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