停止用黑盒模型过度处理简单任务，改用透明模型
Stop overkilling simple tasks with black-box models and use transparent models instead
AI总结：
本文指出深度学习虽能自动提取特征并提升准确率，但不应过度用于简单任务，主张改用透明模型以替代黑盒模型。
摘要：
近年来，深度学习方法的运用带来了人工智能领域的若干重大突破。不同于传统机器学习模型，基于深度学习的方法能够从原始数据中自主提取特征。这使得可以绕过特征工程过程，而该过程通常被认为既容易出错又繁琐。此外，深度学习策略在准确率方面往往优于传统模型。
英文摘要：
In recent years, the employment of deep learning methods has led to several significant breakthroughs in artificial intelligence. Different from traditional machine learning models, deep learning-based approaches are able to extract features autonomously from raw data. This allows for bypassing the feature engineering process, which is generally considered to be both error-prone and tedious. Moreover, deep learning strategies often outperform traditional models in terms of accuracy.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
