The twenty-first century brought tremendous technological advancement that we could not dream about a couple of decades earlier. Today, it can be found that people benefit from Google’s AI-controlled predictions, Ridesharing apps such as Uber and Lyft, as well as commercial flights with an AI autopilot that uses everyday music recommender systems to involve artificial intelligence, Google maps, and many more apps are powered with AI. However, there is still a misunderstanding between artificial intelligence, machine learning, and deep learning. One of Google’s popular queries reads: “is machine learning and artificial intelligence the same thing?” or “is artificial intelligence a subset of machine learning?”

二十一世纪带来了巨大的技术进步，这是几十年前我们无法想象的。 如今，人们可以从Google的AI控制的预测，Uber和Lyft之类的Ridesharing应用程序以及带有AI自动驾驶仪的商业飞行中受益，这些AI使用日常音乐推荐系统来参与人工智能，Google地图等应用程序都支持AI。 但是，人工智能，机器学习和深度学习之间仍然存在误解。 Google最受欢迎的查询之一是：“机器学习和人工智能是一回事吗？” 或“人工智能是机器学习的子集吗？”

AI, machine learning, and deep learning are interchangeable and easily confusing, so begin with a brief intro about them.

人工智能，机器学习和深度学习是可互换的，并且容易造成混淆，因此首先简要介绍一下它们。

Artificial Intelligence: Artificial intelligence, also called machine intelligence, can be understood by an intelligence, unlike the natural intelligence shown by humans and animals, which is demonstrated by machines. It looks at ways of designing intelligent devices and systems that can address problems creatively that are often treated as a human prerogative. Thus, AI means that a machine somehow imitates human behavior.

人工智能：人工智能(也称为机器智能)可以通过一种智力来理解，而人类和动物所表现出的自然智能则是通过机器来证明的。 它着眼于设计智能设备和系统的方法，这些设备和系统可以创造性地解决通常被视为人的特权的问题。 因此，AI意味着机器以某种方式模仿了人类的行为。

Machine Learning: Machine learning is an AI subset and consists of techniques that enable computers to recognize data and supply AI applications. Different algorithms (e.g., neural networks) contribute to problem resolution in ML.

机器学习：机器学习是AI的子集，包含使计算机能够识别数据并提供AI应用程序的技术。 不同的算法(例如神经网络)有助于ML解决问题。

Deep Learning: Deep learning, often called deep neural learning or deep neural network, is a subset of machine learning that uses neural networks to evaluate various factors with a similar framework to a human neural system. It has networks that can learn from unstructured or unlabeled data without supervision.

深度学习：深度学习(通常称为深度神经学习或深度神经网络)是机器学习的一个子集，它使用神经网络来评估各种因素，并具有与人类神经系统相似的框架。 它的网络可以在无监督的情况下从非结构化或未标记的数据中学习。

人工智能 (ARTIFICIAL INTELLIGENCE)

Let’s dive into the branches of Artificial Intelligence:

让我们深入研究人工智能的分支：

AI systems are categorized by their ability to replicate human characteristics, their technology applications, their applications in the real world, and mind theory, which will be further discussed below.

人工智能系统按其复制人类特征的能力，其技术应用，其在现实世界中的应用和思维理论进行分类，这些将在下面进一步讨论。

1. Artificial narrow intelligence (ANI), which has a narrow range of abilities.

   人工窄智能 (ANI)，具有窄范围的能力。

2. Artificial general intelligence (AGI), which is on par with human capabilities.

   人工智慧 (AGI)，与人类能力相当。

3. Artificial superintelligence (ASI), which is more capable than actual human intelligence.

   人工超智能 (ASI)，比实际的人类智能更强大。

窄/弱AI (Narrow/Weak AI)

Artificial narrow intelligence (ANI), also referred to as weak AI or narrow AI, is the only type of artificial intelligence we have successfully realized. Narrow AI is aim-oriented to perform different tasks — i.e., face recognition, speech recognition/voice assistants, automobile driving, and internet search — and is smart when carrying out a particular task.

人工狭窄人工智能(ANI)，也称为弱AI或狭窄AI，是我们成功实现的唯一人工智能类型。 Narrow AI具有针对性，可以执行不同的任务(例如，面部识别，语音识别/语音助手，汽车驾驶和互联网搜索)，并且在执行特定任务时非常聪明。

You may have learned of Deep Blue, the first machine in chess to beat a human. Not just any human — in 1996 Garry Kasparov. Deep Blue can produce about 200 million chess positions per second and analyze them. In the whole scenario, some did not readily call it AI in its entirety, while others thought that it was one of the first examples of weak AI.

您可能已经了解了深蓝，这是国际象棋中第一个击败人类的机器。 不只是人类-1996年，加里·卡斯帕罗夫(Garry Kasparov)。 深蓝每秒可以产生约2亿个国际象棋位置，并对其进行分析。 在整个场景中，有些人并没有整体上将其称为AI，而另一些人则认为这是弱AI的首例。

强大的AI /深度AI (Strong AI / Deep AI)

General Intelligence (AGI) is a concept of a computer that imitates human intelligence and behaviors, with its ability to learn and use its intelligence to solve any problem, which is also referred to as a Strong AI or deep AI. AGI can, in any given situation, think, understand, and behave in a manner that can be no different from that of a human being.

通用情报(AGI)是一种计算机的概念，该计算机模仿人类的情报和行为，具有学习和使用其情报来解决任何问题的能力，这也称为“强AI”或“深度AI”。 在任何给定的情况下，AGI都能以与人类相同的方式思考，理解和表现。

That is where robots can become human-like in the future. They decide themselves and learn without human intervention. They learn. They are not only able to solve intellectual problems but feelings.

这就是将来机器人可以变得像人一样的地方。 他们决定自己，无需人工干预就能学习。 他们学习了。 他们不仅能够解决智力问题，而且能够解决感情问题。

When one sees the human brain as the model of the creation of general intelligence, the immense challenge of achieving strong AI is not surprising. Scientists fail to reproduce the essential functions of sight and motion without a solid understanding of the human brain’s features.

当人们将人类的大脑视为创造通用智能的典范时，实现强大的AI的巨大挑战就不足为奇了。 如果不对人脑的功能有深入的了解，科学家将无法再现视觉和运动的基本功能。

超级智能 (Superintelligence)

Superintelligence is the conceptual AI that does not merely mimic or recognize human intelligence and behavior; ASI means that computers are self-conscious and outperform human ability and knowledge.

超级智能是概念性的AI，它不仅模仿或识别人类的智力和行为； ASI意味着计算机具有自我意识，并且胜过人类的能力和知识。

It is the material that everyone wants to learn about AI. Machines, long before humans. Articulate, articulate, imaginative, and outstanding professional competence. Its purpose is to either enhance or kill the lives of human beings.

这是每个人都想学习有关AI的材料。 机器早于人类。 表达，表达，想象力和杰出的专业能力。 其目的是改善或杀死人类的生命。

It may seem enticing to have such powerful tools at our fingertips, but there are many unknown implications for the idea itself. If super-intelligent and self-conscious beings were to be created, they would be able to have concepts like autonomy. It is mere speculation that will impact humanity, our future, and our way of life.

唾手可得的如此强大的工具似乎很诱人，但是对于这个想法本身有许多未知的含义。 如果要创造超智能和自我意识的生物，他们将能够拥有诸如自治的概念。 仅仅是推测会影响人类，我们的未来和我们的生活方式。

The word AI says nothing about solving these issues. Include rules-based or expert systems; there are many different techniques. In the 1980s, one group of methods became more common: machine learning.

AI一词并没有说明解决这些问题。 包括基于规则的系统或专家系统； 有很多不同的技术。 在1980年代，一组方法变得更加普遍：机器学习。

机器学习 (MACHINE LEARNING)

What exactly is Machine Learning?With new computer technologies, today’s machine learning is not like machine learning from the past. It was born from the identification of patterns and the idea that computers would learn without programming themselves for specific tasks; scientists interested in artificial intelligence wanted to see if computers could learn from data.

机器学习到底是什么？ 借助新的计算机技术，当今的机器学习已不再像过去的机器学习。 它源于模式的识别以及计算机无需为特定任务编写程序即可学习的思想。 对人工智能感兴趣的科学家想看看计算机是否可以从数据中学习。

The early researchers found that problems were many more difficult because the old techniques used for AI were not compatible with those problems. Hard-coded algorithms or fixed, rule-based systems didn’t work very well for things like image recognition or text-based extraction.

早期的研究人员发现问题要困难得多，因为用于AI的旧技术与这些问题不兼容。 硬编码算法或固定的基于规则的系统在诸如图像识别或基于文本的提取之类的系统中效果不佳。

“The solution proved not only to mimic human behavior (AI) and to imitate the way people learn.”

“事实证明，该解决方案不仅可以模仿人类行为(AI)并模仿人们的学习方式。”

Consider how you have learned to read. Before you picked up your first book, you did not sit and learn orthotics and grammar. You read basic books, which over time, became more complicated. You learned the laws of orthography and grammar (and exceptions) from your lecture. In other words, you have processed and learned a lot of information.

考虑一下您如何学习阅读。 在您拿起第一本书之前，您没有坐下来学习矫正和语法。 您阅读基础书籍，随着时间的流逝，基础书籍变得越来越复杂。 您从您的讲座中学到了拼字法和语法法(和例外)。 换句话说，您已经处理并学习了很多信息。

This is the idea of machine learning precisely. Feed a lot of data into an algorithm (unlike the brain) and let things figure out. Feed an algorithm a lot of financial transaction information, tell them which financial transactions are fraudulent, and make them work out what is fraudulent so that they can predict fraud in the future. Or supply your customer base information and let it know how to segment it best. Read more about machine learning here.

这就是机器学习的思想。 将大量数据输入到算法中(不同于大脑)，然后让事情解决。 向算法提供大量金融交易信息，告诉他们哪些金融交易是欺诈性的，并使它们确定什么是欺诈性的，以便他们可以预测将来的欺诈行为。 或者提供您的客户群信息，并让其知道如何对其进行最好的细分。 在此处阅读有关机器学习的更多信息。

With the development of these algorithms, several problems could be dealt. But certain things people find simple (e.g., understanding of speech or writing) were still difficult for machines.

随着这些算法的发展，可以解决几个问题。 但是，人们发现某些简单的事情(例如，对语音或写作的理解)对于机器仍然很困难。

The notion that artificial neurons are the key elements of your brain was for some time to be used (neurons, bound by synapses). And software simulated neural networks began to be used for specific issues. They have been successful, and they can solve complex problems that other algorithms can not answer.

人造神经元是大脑关键要素的观念已经使用了一段时间(神经元，由突触结合)。 并且软件模拟的神经网络开始用于特定问题。 他们已经取得了成功，并且可以解决其他算法无法解决的复杂问题。

特定机器如何学习？ (How can a particular machine learn?)

We will need these three components to “educate” the machine or make the computer learn:

我们将需要这三个组件来“教育”机器或使计算机学习：

Datasets:A data set consists of collecting similar entities and values within a single body that can be viewed individually or as an entire item and structured with a data structure. Typically, building a successful dataset requires a lot of time and effort.

数据集：数据集包括在单个主体内收集相似的实体和值，这些实体和值可以单独查看，也可以作为一个整体查看，并使用数据结构进行结构化。 通常，构建成功的数据集需要大量时间和精力。

Two ways to collect data for your Model:1. Rely on open-source data2. Collect your data in the right way

收集模型数据的两种方法：1。 依靠开源数据2。 以正确的方式收集数据

Features:Features are essential data objects, which are the key to the task’s solution. They are individual measures or characteristics of a phenomenon being observed. The selection of informative, discriminatory, and independent features is crucial for effective pattern recognition, classification, and regression algorithms. They display the machine what to pay attention to.

功能：功能是必不可少的数据对象，这是任务解决方案的关键。 它们是观察到的现象的单独度量或特征。 信息性，区分性和独立性的选择对于有效的模式识别，分类和回归算法至关重要。 他们向机器显示要注意什么。

Algorithm:An algorithm is a component that allows systems to learn and develop experience automatically without being programmed explicitly. There are extensive use and description of algorithms like linear regression, deep learning, coevolutionary networks, and recommendation systems. The accuracy or speed of the results will vary depending on the algorithm.

算法：算法是允许系统自动学习和发展经验而无需进行显式编程的组件。 诸如线性回归，深度学习，协同进化网络和推荐系统之类的算法已得到广泛使用和描述。 结果的准确性或速度会因算法而异。

学习类型 (Types of Learning)

There are four types of machine learning:

机器学习有四种类型：

• Supervised Learning: Training data contains optimal outcomes (also known as inductive learning). Learning is tracked in this method. Some famous examples of supervised machine learning algorithms are Linear regression for regression problems.

  监督学习 ：培训数据包含最佳结果(也称为归纳学习)。 通过这种方法跟踪学习。 监督机器学习算法的一些著名示例是用于回归问题的线性回归。

• Unsupervised Learning: There are not the desired outputs in the training results. Clustering is an example. It is impossible to know what is and what is not good learning.

  无监督学习：培训结果中没有所需的输出。 集群就是一个例子。 不可能知道什么是什么，什么不是很好的学习。

• Semi-supervised Learning: A few desired outputs are included in the training data.

  半监督学习：训练数据中包含一些期望的输出。

• Reinforcement Learning: Rewards are given after a sequence of actions. In a given case, it is a matter of taking appropriate steps to maximize compensation. It is the most ambitious method of learning in AI.

  强化学习：奖励是在一系列行动之后给出的。 在给定的情况下，必须采取适当的步骤来最大化补偿。 这是AI中最雄心勃勃的学习方法。

深度学习 (DEEP LEARNING)

Deep learning is a subset of machine learning in artificial intelligence (AI) with networks capable of learning unsupervised from unstructured or unlabeled data. Also known as deep neural learning or deep neural network. Deep learning algorithms use hierarchical multi-level neural networks, in which the abstraction level slowly increases through non-linear input data transformations.

深度学习是人工智能(AI)中机器学习的一个子集，其网络能够从非结构化或未标记的数据中不受监督地学习。 也称为深度神经学习或深度神经网络。 深度学习算法使用分层的多级神经网络，其中抽象级别通过非线性输入数据转换缓慢增加。

它实际上如何运作？ (How does it actually work?)

The digital era, which contributed to the proliferation of data in all forms, has been transformed into profound learning from all the corners of the world. Such data, also known as big data, come from outlets such as social media, Internet search engines, electronic commerce, and online videos. This massive amount of data can be easily accessed and exchanged through fintech applications, such as cloud computing.

推动各种形式的数据扩散的数字时代已经转变为世界各个角落的深刻学习。 这些数据也称为大数据，来自社交媒体，互联网搜索引擎，电子商务和在线视频等渠道。 通过金融技术应用程序(例如云计算)可以轻松访问和交换大量数据。

The typically unstructured data, however, is so large that it may take decades for people to grasp it and extract relevant information. Companies understand the enormous potential that this wealth of information can bring about and are increasingly adapting for automated support to AI systems.

但是，通常非结构化的数据是如此之大，以至于人们可能需要数十年才能掌握它并提取相关信息。 公司了解大量信息可以带来的巨大潜力，并且越来越适应于对AI系统的自动支持。

The specific number of neurons is called bias. In addition to the weights, this bias is added to the weighted amount of inputs to the neuron. When the neuron is activated, the outcome of the task decides. Each activated neuron passes the following layers. It goes on to the last second sheet. The final layer generating program outputs is the output layer in an artificial neural network.

神经元的具体数量称为偏见。 除了权重，此偏差还会添加到神经元输入的加权数量上。 当神经元被激活时，任务的结果将决定。 每个激活的神经元都经过以下层。 继续到最后第二张纸。 生成程序输出的最后一层是人工神经网络中的输出层。

Deep learning algorithms are very hype today, but a boundary between deep and not as deep algorithms.

如今，深度学习算法非常受炒作，但深度算法与非深度算法之间存在界限。