翻译技术速递Why Does Training Data for AI and ML Matter?
为什么AI和ML的样本数据很重要？
Training data is perhaps one of the most integral pieces of machine learning and artificial intelligence. Without it, machine learning and artificial intelligence would be impossible. Models would not be able to learn, make predictions, or extract useful information without learning from training data. It’s safe to say that training data is the backbone of machine learning and artificial intelligence.
Similar to how humans learn from past experiences, artificial intelligence uses training data to learn and develop intelligence to make decisions. Because of that, a model is only as good as the quality of the training data. This means that training data of higher quality will yield a better-trained model and more accurate results, reducing the chances of a model with a high bias or high variance.
Through training data, artificial intelligence and machine learning have changed the world we live in and continue to do so. Tasks that would take humans hours or even years can now be solved within seconds. These capabilities have led to incredible advancements in healthcare, finance, retail, business, and many more other industries. Humans today rely on artificial intelligence without even knowing it. For example, email spam detection or predictive typing platforms are ways in which artificial intelligence and machine learning have shaped our day-to-day lives. This would not be possible without the presence of training data to enable these technologies.
样本数据可能是机器学习和人工智能中最重要的部分之一。没有它，机器学习和人工智能可能无法执行。如果不从样本数据中分析，模型将无法学习、做出预测或提取有用的信息。可以肯定地说，样本数据是机器学习和人工智能的支柱。
与人类如何从过去的经验中学习相似，人工智能使用样本数据来学习和发展智能以做出决策。因此，模型的好坏取决于样本数据的质量。这意味着更高质量的样本数据将产生更好的训练模型和更准确的结果，从而减少模型出现高偏差或高方差的可能性。
通过样本数据，人工智能和机器学习已经改变了我们生活的世界，并将持续下去。人类需要数小时甚至数年才能完成的任务现在可以在几秒钟内解决。这些能力在医疗保健、金融、零售、商业和其他许多行业带来了难以置信的进步。今天的人类甚至在不知情的情况下使用人工智能。例如，电子邮件垃圾邮件检测或预测性键入平台是人工智能和机器学习塑造我们日常生活的方式。如果没有样本数据来支持这些技术，这是不可能的。
以上中文文本为机器翻译，存在不同程度偏差和错误，请理解并参考英文原文阅读。
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