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@l_26ml: #بعدستي #حمص #كرومات #لايك__explore___ #اكتوبر #عباراتكم_الفخمه📿📌 #تصاميمي #ستوريات #اعاده_نشر🔁

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ل͜͡ـيال🌸
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Thursday 16 October 2025 20:04:52 GMT
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aaa90t14
شہروقہ 1412 :
الحمدلله
2025-10-17 12:30:26
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user9131884093594
أميرة محمد :
🥰🥰🥰
2025-10-17 06:29:03
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user4556846631687
سارة البغدادي :
مشكله الضماد. من يخلصون منه يرموة
2025-10-17 07:40:49
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The image you sent is a conceptual diagram that contrasts how people perceive AI with what AI actually is. Perceptual AI The left side of the diagram depicts a singular, monolithic AI entity labeled simply “AI.” Arrows flow from a big box labeled “Data” into this AI, and another arrow flows out of the AI labeled “Value.” This simple depiction suggests that AI takes in data and produces value without any underlying processes. Actual AI The right side of the diagram depicts a more nuanced understanding of AI. Here, data goes through a multi-step process to create value. There are three main stages: Data Engineering: This involves selecting, cleaning, and preparing the data for use in AI models. Modeling: This involves building and training a machine learning model on the data. Operationalization: This involves deploying the model, monitoring its performance, and retraining it as needed. The data engineering stage includes several steps such as data selection, sourcing, and synthesis. Then data is transformed through processes including cleaning, normalization, and scaling. The modeling stage includes building the model, selecting features, training it on data, and evaluating its performance. The final stage of operationalization involves registering the model, deploying it, monitoring its performance, and retraining it as needed. The bottom of the right side also shows that there are legal, ethical, transparency, security, and historical bias constraints that need to be considered throughout the AI development process. In essence, this diagram highlights the misconception that AI is a simple black box. In reality, AI development is a complex process that involves many steps to create real-world value.
The image you sent is a conceptual diagram that contrasts how people perceive AI with what AI actually is. Perceptual AI The left side of the diagram depicts a singular, monolithic AI entity labeled simply “AI.” Arrows flow from a big box labeled “Data” into this AI, and another arrow flows out of the AI labeled “Value.” This simple depiction suggests that AI takes in data and produces value without any underlying processes. Actual AI The right side of the diagram depicts a more nuanced understanding of AI. Here, data goes through a multi-step process to create value. There are three main stages: Data Engineering: This involves selecting, cleaning, and preparing the data for use in AI models. Modeling: This involves building and training a machine learning model on the data. Operationalization: This involves deploying the model, monitoring its performance, and retraining it as needed. The data engineering stage includes several steps such as data selection, sourcing, and synthesis. Then data is transformed through processes including cleaning, normalization, and scaling. The modeling stage includes building the model, selecting features, training it on data, and evaluating its performance. The final stage of operationalization involves registering the model, deploying it, monitoring its performance, and retraining it as needed. The bottom of the right side also shows that there are legal, ethical, transparency, security, and historical bias constraints that need to be considered throughout the AI development process. In essence, this diagram highlights the misconception that AI is a simple black box. In reality, AI development is a complex process that involves many steps to create real-world value.


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