From bd1bc8691c87b6cd137ac04213b9122d859a2dba Mon Sep 17 00:00:00 2001
From: Crowdin Bot <support+bot@crowdin.com>
Date: Wed, 27 Nov 2024 00:47:07 +0000
Subject: [PATCH] New Crowdin translations by GitHub Action

---
 i18n/zh-Hant/ai-chat.md | 20 ++++++++++----------
 1 file changed, 10 insertions(+), 10 deletions(-)

diff --git a/i18n/zh-Hant/ai-chat.md b/i18n/zh-Hant/ai-chat.md
index 1aceb8c6..fafb0d1c 100644
--- a/i18n/zh-Hant/ai-chat.md
+++ b/i18n/zh-Hant/ai-chat.md
@@ -2,7 +2,7 @@
 meta_title: 推薦的 AI 聊天：私密的 ChatGPT 替代方案 - Privacy Guides
 title: AI 聊天
 icon: material/assistant
-description: Unlike OpenAI's ChatGPT and its Big Tech competitors, these AI tools run locally so your data never leaves your desktop device.
+description: 與 OpenAI 的 ChatGPT 和與其競爭的科技巨擘產品不同，這些 AI 工具可在本地執行，因此您的資料從未離開您的電腦。
 cover: ai-chatbots.webp
 ---
 
@@ -10,23 +10,23 @@ cover: ai-chatbots.webp
 
 - [:material-server-network: 服務提供商](basics/common-threats.md#privacy-from-service-providers){ .pg-teal }
 - [:material-account-cash: 監控資本主義](basics/common-threats.md#surveillance-as-a-business-model){ .pg-brown }
-- [:material-close-outline: Censorship](basics/common-threats.md#avoiding-censorship){ .pg-blue-gray }
+- [:material-close-outline: 審查](basics/common-threats.md#avoiding-censorship){ .pg-blue-gray }
 
-Since the release of ChatGPT in 2022, interactions with Large Language Models (LLMs) have become increasingly common. LLMs can help us write better, understand unfamiliar subjects, or answer a wide range of questions. They can statistically predict the next word based on a vast amount of data scraped from the web.
+自 2022 年 ChatGPT 發表以來，人們與 大型語言模型(LLM) 的互動變得越來越普遍。 大型語言模型 可以幫助我們寫出更好的文章；瞭解不熟悉的主題；或是回答各式各樣的問題。 They can statistically predict the next word based on a vast amount of data scraped from the web.
 
-## Privacy Concerns About LLMs
+## 大型語言模型的隱私權疑慮
 
-Data used to train AI models, however, includes a massive amount of publicly available data scraped from the web, which can include sensitive information like names and addresses. Cloud-based AI software often [collects your inputs](https://openai.com/policies/row-privacy-policy), meaning your chats are not private from them. This practice also introduces a risk of data breaches. Furthermore, there is a real possibility that an LLM will leak your private chat information in future conversations with other users.
+訓練 AI 模型需要用到資料；然而，用於訓練 AI 模型的資料包括從網路上獲取的大量公開資料，其中可能包括姓名和地址等敏感資訊。 基於雲端的 AI 軟體通常會 [收集您輸入的資料](https://openai.com/policies/row-privacy-policy)，這表示您的聊天內容對他們而言並非私密的。 這種做法也會帶來資料外洩的風險。 此外，大型語言模型 真的有可能在未來與其他使用者的對話中洩露您的私人聊天資訊。
 
-If you are concerned about these practices, you can either refuse to use AI, or use [truly open-source models](https://proton.me/blog/how-to-build-privacy-first-ai) which publicly release and allow you to inspect their training datasets. One such model is [OLMoE](https://allenai.org/blog/olmoe-an-open-small-and-state-of-the-art-mixture-of-experts-model-c258432d0514) made by [Ai2](https://allenai.org/open-data).
+如果您擔心這些做法，您可以拒絕使用 AI，或是使用 [真正開放原始碼的模型](https://proton.me/blog/how-to-build-privacy-first-ai)，這些模型會公開釋出，並允許您檢查其訓練資料集。 由 [Ai2](https://allenai.org/open-data) 所製作的 [OLMoE](https://allenai.org/blog/olmoe-an-open-small-and-state-of-the-art-mixture-of-experts-model-c258432d0514)，就是這樣的一個模型。
 
-Alternatively, you can run AI models locally so that your data never leaves your device and is therefore never shared with third parties. As such, local models are a more private and secure alternative to cloud-based solutions and allow you to share sensitive information to the AI model without worry.
+另外，您也可以在本地執行 AI 模型，這樣您的資料就不會離開您的裝置，因此也不會與第三方共用。 因此，相較於雲端解決方案；本機模型是更私密、更安全的替代方案，讓您可以放心地將敏感資訊分享給 AI 模型。
 
-## AI Models
+## AI 模型
 
-### Hardware for Local AI Models
+### 本地 AI 模型的硬體
 
-Local models are also fairly accessible. It's possible to run smaller models at lower speeds on as little as 8GB of RAM. Using more powerful hardware such as a dedicated GPU with sufficient VRAM or a modern system with fast LPDDR5X memory offers the best experience.
+本地模型也相當容易運行。 只要 8GB 記憶體，就能以較低的速度運行較小的模型。 使用更強大的硬體，例如具有足夠 VRAM 的專用 GPU 或具有快速 LPDDR5X 記憶體的現代系統，可以提供最佳的體驗。
 
 LLMs can usually be differentiated by the number of parameters, which can vary between 1.3B to 405B for open-source models available for end users. For example, models below 6.7B parameters are only good for basic tasks like text summaries, while models between 7B and 13B are a great compromise between quality and speed. Models with advanced reasoning capabilities are generally around 70B.