What is Generative AI?生成模型是什麼？

Generative AI enables users to quickly generate new content based on a variety of inputs. Inputs and outputs to these models can include text, images, sounds, animation, 3D models, or other types of data.
生成人工智能基於多元輸入而使利用者能快速產生新的內容。這些生成模型輸入與輸出的內容可包括文章、圖像、聲音、動漫畫、3D模型或其他類型的資料。

How Does Generative AI Work?生成AI如何運作？

Generative AI models use neural networks to identify the patterns and structures within existing data to generate new and original content.
生成AI利用神經網路去辨識既存資料之模式與結構進而產生新的內容；簡言之，利用舊的資料去產生新的資料，但前提須判斷資料是新還是舊（是否係舊而既存的）。

One of the breakthroughs with generative AI models is the ability to leverage different learning approaches, including unsupervised or semi-supervised learning for training. This has given organizations the ability to more easily and quickly leverage a large amount of unlabeled data to create foundation models. As the name suggests, foundation models can be used as a base for AI systems that can perform multiple tasks. 
人工智能生成模型劃時代的成就之一即是提供不同的學習途徑並使之發揮平衡功效，包括為了訓練而無監督或半監督之學習途徑。此給予組織更輕易快速地平衡大量無標籤數據得以創造基礎模型的能力。顧名思義，基礎模型能被作為執行多功能任務之人工智能系統的基礎。

Examples of foundation models include GPT-3 and Stable Diffusion, which allow users to leverage the power of language. For example, popular applications like ChatGPT, which draws from GPT-3, allow users to generate an essay based on a short text request. On the other hand, Stable Diffusion allows users to generate photorealistic images given a text input.
基礎模型包括GPT 3及Stable Diffusion等例子，其允許利用人借助語言的力量。舉例來說，像受歡迎應用程式ChatGPT，其從GPT-汲取而出，基於利用者簡短訊息之需求，其允許利用產生短文。另一方面，利用人輸入短信需求與Stable Diffusion，其將產生近乎真實的圖像。

How to Evaluate Generative AI Models?如何提升生成AI？

The three key requirements of a successful generative AI model are:  成功之人工智能生成模型有三個關鍵因素；品質、多元、速度。

1. Quality: Especially for applications that interact directly with users, having high-quality generation outputs is key. For example, in speech generation, poor speech quality is difficult to understand. Similarly, in image generation, the desired outputs should be visually indistinguishable from natural images.
   品質：特別是應用在直接與利用者互動的模型，有高品質的生成輸入是關鍵。舉例來說，語言生成模型，爛的語言品質使人無法理解。同樣地，於圖像生成模型，心目中的輸出圖像應與真實圖像在視覺上無可區別才是。
2. Diversity: A good generative model captures the minority modes in its data distribution without sacrificing generation quality. This helps reduce undesired biases in the learned models.
   異變：一個好的生成模型當係無犧牲生成品質，於資料散布中擷取關鍵的類型。其再以學習的模型減低不要的偏差。
3. Speed: Many interactive applications require fast generation, such as real-time image editing to allow use in content creation workflows.
   速度：