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Deep Learning for Text Data on Mobile Devices

机译:在移动设备上对文本数据进行深度学习

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With the rise of Artificial Intelligence (AI), it is becoming a significant phenomenon in our lives. As with many other powerful tools, AI brings many advantages but many risks as well. Predictions and automation can significantly help in our everyday lives. However, sending our data to servers for processing can severely hurt our privacy. In this paper, we describe experiments designed to find out whether we can enjoy the benefits of AI in the privacy of our mobile devices. We focus on text data since such data are easy to store in large quantities for mining by third parties. We measure the performance of deep learning methods in terms of accuracy (when compared to fully-fledged server models) and speed (number of text documents processed in a second). We conclude our paper with findings that with few relatively small modifications, mobile devices can process hundreds to thousands of documents while leveraging deep learning models.
机译:随着人工智能(AI)的兴起,它已成为我们生活中的重要现象。与许多其他强大工具一样,人工智能带来了很多优势,但同时也带来了许多风险。预测和自动化可以极大地帮助我们的日常生活。但是,将我们的数据发送到服务器进行处理会严重损害我们的隐私。在本文中,我们描述了旨在发现我们是否可以在移动设备的隐私中享受AI好处的实验。我们专注于文本数据,因为此类数据易于大量存储以供第三方挖掘。我们根据准确性(与成熟的服务器模型相比)和速度(每秒处理的文本文档数)来衡量深度学习方法的性能。我们的结论是,本文的结论是,只需进行相对较小的修改,移动设备就可以利用深度学习模型来处理成百上千的文档。

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