Deep Learning（一）

Chapter 1 Introduction

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In the early days of artificial intelligence, the field rapidly tackled and solved problems that are intellectually difficult for human beings but relatively straight-forward for computers-problems that can be described by a list of formal, math-ematical rules. The true challenge to artificial intelligence proved to be solving the tasks that are easy for people to perform but hard for people to describe formally-problems that we solve intuitively, that feel automatic, like recognizing spoken words or faces in images.

  在早期阶段，人工智能被用于快速处理和解决对人类智力而言非常困难，但对计算机而言相对简单的问题，这些问题都是比较直接、易于描述的电脑问题或是数学规则。人工智能真正的挑战在于解决那些对人类而言能够轻易完成，但又难于用直观的形式描述执行过程的问题，例如自动感知，识别出正在讲话的词语或图像中的一个面孔。
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The performance of these simple machine learning algorithms depends heavily on the representation of the data they are given. For example, when logistic regression is used to recommend cesarean delivery, the AI system does not examine the patient directly. Instead, the doctor tells the system several pieces of relevant information, such as the presence or absence of a uterine scar. Each piece of information included in the representation of the patient is known as a feature. Logistic regression learns how each of these features of the patient correlates with various outcomes. However, it cannot influence the way that the features are defined in any way. If logistic regression was given an MRI scan of the patient, rather than the doctor’s formalized report, it would not be able to make useful predictions. Individual pixels in an MRI scan have negligible correlation with any complications that might occur during delivery.
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  这些简单机器学习算法的性能，很大程度上由他们能给出的数据的表示（representation）所决定。例如，当逻辑回归被用来推荐剖腹产手术时，AI并不直接检查病人。相反，医生会告诉系统数个相关的信息，例如有无子宫疤痕。病人表示（representation）的每个信息被称之为特征。逻辑回归学习怎样将病人的每一个特征与各种结果相关连。然而，它不能以任何方式影响特征定义的方式。如果逻辑回归被指定使用病人的核磁共振成像（MRI）扫描，而不是医生的正式报告，它就无法做出有用的预测。核磁共振成像扫描的单个像素与分娩过程中的任何并发症，几乎没有相关性。