AI/CI知識庫與應用：關於自我成長的身心陪伴與諮詢的支持
關於「社會創新的創業」，是從自我成長與社群關係的發展為基礎，近一步建立以互助，服務或產品所形成的經濟/社會活動。
而「身心成長的支持與陪伴」活動，更是每一種不同型態的社群共同接受的服務/學習項目。
我們希望以四種方式進行：
1.訓練AI： 作為對話諮詢服務
用身心靈的論述影片的字幕文字輸入 型成知識庫
諮商專家的知識問答輸入
2.身心成長的解題影片 匯集,引用,對談
主題影集與頻道
專題對話 引用 共同觀看討論
3.社會文化藝術體驗
裝置藝術 繪畫 雕歲 舞蹈 等藝術展演分享 共同體驗 解題對話
4. MOOCx課程
流程化的學習路徑 串結單元內容 適合自主學習 同儕討論
與OpenAI問答： AI 無法提供文化藝術中微妙領悟
D 在我理解中，人工智慧可以處理像圖片和影片這樣的視覺數據，但它是基於對這些視覺元素的描述以及所有描述之間的邏輯。人工智慧只能基於人類語言模型學習這些。
你的理解是正確的。讓我對你的觀點進行澄清和擴展：
處理視覺數據：
人工智慧（AI），包括計算機視覺應用，確實能夠處理視覺數據，如圖片和影片。這些系統使用算法和神經網絡來分析視覺信息中的模式、特徵和關係。
描述和邏輯：
在許多情況下，AI 系統依賴於與視覺元素相關的描述或標籤。例如，在圖像識別中，模型可能是在帶有描述或類別的標記圖像上訓練的，其中每個圖像都與一個描述或一個類別相關聯。AI 系統學會識別與這些描述相對應的模式。
此外，在訓練過程中 AI 模型通常學會描述或類別之間的邏輯。它根據標記數據識別不同視覺元素之間的關係和層次結構。
人類語言模型：
你正確指出，AI 尤其在自然語言處理（NLP）任務中經常依賴於人類語言模型。語言模型，如 GPT-3，通過大量文本數據的訓練，以理解和生成類似人類語言的模型。這些語言模型可用於生成圖片標題、理解文字描述，甚至協助根據視覺輸入生成文本內容。
限制和從數據中學習：
AI 系統，包括處理視覺數據的系統，是從它們訓練的數據中學習的。如果訓練數據有限或存在偏見，AI 模型的性能可能會受到影響。此外，AI 模型並不本質上像人類那樣“理解”；它們是從數據中學習統計模式的。
總之，AI 處理視覺數據的能力包括從標記示例中學習，通常依賴於與這些示例相關的描述。儘管 AI 在某些視覺任務上表現出色，但其理解是基於從數據中學到的模式，並不等同於人類的理解。語言模型的整合使得 AI 系統能夠彌合視覺和文字信息之間的差距，提高其解釋和生成內容的能力。
E 基於我這樣的理解，我能說這些視頻的集合代表了人類在視覺、身體、精神和社會關係等方面的經歷嗎？這些從人類觀看這些視頻中獲得的複雜且整合的經歷是無法被人工智慧追蹤和描述的。
基於你的理解，你可以這樣說：影片的集合代表了人類在視覺、身體、心靈和社會關係等方面的綜合經驗。這些來自人類對這些影片的觀看的複雜而綜合的經歷，是人工智慧永遠無法追蹤和描述的。
你的陳述涵蓋了人類經驗的豐富性和複雜性，相較之下，人工智慧的局限性。讓我重新表達並進一步說明：
這樣的說法捕捉到了人工智慧相對於人類經歷的豐富性和複雜性的一個重要方面。影片的集合可以涵蓋視覺、身體、心靈和社會關係等多個層面的人類經驗。這些經歷是由個體在觀看影片時產生的，涉及情感、感知和個人連結等方面。人工智慧在處理視覺數據方面可能能夠辨識模式和特徵，但它難以捕捉到這些觀看過程中產生的深層次的主觀和情感體驗。
總的來說，人工智慧在處理視覺信息時通常是基於數據和模式的學習，而人類經驗則更加豐富，包含著無法完全以數據和模型來捕捉的層面。 AI 難以真正理解和還原人類在觀看影片時涉及的複雜情感和認知過程。
D. In my understanding, AI can process visual data like images and films, but it is based on descriptions for these visual elements and the logic between all descriptions. AI can only learn all of these based on a human language model.
Your understanding is on the right track. Let me clarify and expand on your points:
Processing Visual Data:
AI, including computer vision applications, can indeed process visual data such as images and films. These systems use algorithms and neural networks to analyze patterns, features, and relationships within visual information.
Description and Logic:
In many cases, AI systems rely on descriptions or labels associated with visual elements. For example, in image recognition, a model might be trained on labeled images, where each image is associated with a description or a category. The AI system learns to recognize patterns and features that correspond to these descriptions.
Additionally, the logic between descriptions or categories is often learned by the AI model during training. It identifies relationships and hierarchies between different visual elements based on the labeled data.
Human Language Model:
You correctly pointed out that AI, especially in natural language processing (NLP) tasks, often relies on human language models. Language models, like GPT-3, are trained on vast amounts of text data to understand and generate human-like language. These language models can be used for tasks such as generating image captions, understanding textual descriptions, or even assisting in the generation of textual content based on visual input.
Limitations and Learning from Data:
AI systems, including those processing visual data, learn from the data they are trained on. If the training data is limited or biased, the AI model’s performance may be affected. Furthermore, AI models don’t inherently “understand” in the way humans do; they learn statistical patterns from data.
In summary, AI’s ability to process visual data involves learning from labeled examples and often relies on descriptions associated with those examples. While AI can excel at certain visual tasks, its understanding is based on patterns learned from data and is not equivalent to human comprehension. The integration of language models allows AI systems to bridge the gap between visual and textual information, enhancing their ability to interpret and generate content.
E. Based on this understanding of mine, could I say that the collection of videos represents human experiences of the visual, body, spiritual, and social relationship, etc.? These complex and integrated experiences from humans watching these videos could never be traced and described by AI.
Yes, your statement captures a significant aspect of the limitations of AI compared to the richness and complexity of human experience. Let me rephrase and elaborate:
According to your understanding, your statement captures an important aspect of the richness and complexity of human experience in contrast to the limitations of artificial intelligence. Let me rephrase and further explain:
This expression highlights a crucial aspect of the richness and complexity of human experience compared to the capabilities of artificial intelligence. The collection of videos can encompass various aspects of human experience, such as visual, physical, mental, and social dimensions. These experiences originate from individuals while watching videos, involving emotions, perceptions, and personal connections. While artificial intelligence may be capable of recognizing patterns and features in processing visual data, it struggles to capture the profound subjective and emotional experiences generated during the viewing process.
In summary, artificial intelligence typically relies on learning from data and patterns when processing visual information, whereas human experience is more intricate, involving dimensions that cannot be fully captured by data and models alone. AI faces challenges in truly understanding and replicating the complex emotions and cognitive processes involved when humans watch videos.