Artificial Intelligence (AI) will and is currently taking over an important role in our lives — not necessarily through intelligent robots — but a learning algorithm is implemented in many intelligent technologies. Also, Machine Learning and Deep Learning are contemporary important terms in Computer Science. But those AI-related terms are often mixed up or falsely taken as synonyms, which will be clarified in the following.

人工智能(AI)将并且目前正在我们的生活中扮演重要角色，不一定通过智能机器人，而是许多智能技术中都采用了学习算法。 此外，机器学习和深度学习也是计算机科学中的当代重要术语。 但是那些与AI相关的术语经常被混淆或错误地当作同义词，这将在下文中阐明。

The word “intelligence” alone is quite hard to define. One can try it through probably the most popular method: The Turing Test. It argues that intelligence can be identified behavior-based. For instance, regarding a chat-bot, one could say: If it is undistinguishable if the chat partner is a human or computer, the computer can be entitled as “intelligent”, even if it is just an imitation game without any awareness on the computer’s side. Consequently, intelligence has not necessarily something to do with human intelligence. The term Artificial Intelligence was primarily introduced by John McCarthy in 1956 offering a seminar with this as a title. He is therefore often referred to as the Father of AI. He stated: “The study is to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it” and therefore sets a first stone in characterizing Artificial Intelligence. Initially, Machine Learning was not separated from the field of AI, which changed in the 1990s: The scientists started to tackle practical problems and went away from symbolic approaches regarding intelligence. The term of Deep Learning appeared in the year 2000 for the first time describing an Artificial Neural Network.

仅“智能”一词很难定义。 可以尝试通过最流行的方法进行尝试：图灵测试。 它认为可以将智能识别为基于行为的。 例如，对于一个聊天机器人，人们可能会说：如果聊天伙伴是人还是计算机，如果无法区分它，则该计算机可以被称为“智能”，即使它只是一个模仿游戏，而对它却没有任何了解。电脑方面。 因此，情报不一定与人类情报有关。 人工智能一词最初是由约翰·麦卡锡(John McCarthy)在1956年提出的，当时以研讨会为标题。 因此，他经常被称为AI之父。 他说：“这项研究是基于这样的猜想，即原则上可以精确地描述学习的各个方面或智力的任何其他特征，从而可以制造出可以模拟它的机器”，因此奠定了基础。表征人工智能。 最初，机器学习并没有脱离AI领域，而AI领域在1990年代发生了变化：科学家们开始解决实际问题，并摆脱了有关智能的象征性方法。 深度学习这个词首次出现在2000年，描述了人工神经网络。

什么是人工智能？ (What is Artificial Intelligence?)

Everyone might have some applications in mind coming from utopias presented in film and fiction or media. AI is basically working with a goal, which is trying to enable machines to make decisions. As already mentioned, the word intelligence can be misleading. Therefore, we now want to go back to the basics trying to bring the fundamental idea of these technologies to you by using examples. One popular example is using oranges and apples. The goal is to teach the machine what an apple and an orange look like and enabling it to separate them. Each fruit has certain features, which have to be told to the algorithm: An apple is red or green, whereas an orange is orange. It has a bumpy surface, while the apple is smooth. Translated to English, you implement in the algorithm: “If something is red or green, is round and has a smooth texture, you call it an apple” and “If something is orange, round and has a bumpy texture you call it an orange”. And this is the first step one must take when it comes to programming an AI-powered software. If the software now recognizes the mentioned features, it can give the output “orange” or “apple”. As a code it looks like this:

每个人可能都会想到来自电影，小说或媒体中呈现的乌托邦的某些应用。 人工智能基本上是与一个目标合作，该目标试图使机器做出决策。 如前所述，“情报”一词可能会引起误解。 因此，我们现在想回到基础知识，尝试通过使用示例将这些技术的基本思想带给您。 一个流行的例子是使用橘子和苹果。 目的是教会机器苹果和橘子的外观，并使它们分开。 每个水果都有某些特征，必须告知算法：苹果是红色或绿色，而橙色是橙色。 它表面凹凸不平，苹果光滑。 翻译成英文，您可以在算法中实现：“如果某物是红色或绿色，圆形并且具有光滑的纹理，则称其为苹果”和“如果某物是橙色，圆形并且具有凹凸不平的纹理，则称其为橙色” ”。 这是编程基于AI的软件时必须采取的第一步。 如果该软件现在能够识别上述功能，则可以将输出设为“橙色”或“苹果”。 作为代码，它看起来像这样：

We can set: Artificial Intelligence means enabling a machine to think and mimic human behavior as it is now possible to take its own decisions.

我们可以设置：人工智能意味着使机器能够思考和模仿人类的行为，因为现在可以自行决定。

什么是机器学习？ (What is Machine Learning?)

The algorithm can now decide, whether it is seeing an orange or an apple, because we taught the software what the features of each fruit are and therefore enabled it to decide on its own. This is all fun and games until you cut the orange into eatable pieces. A human can still recognize the apple as apple, but how should the software be able to do it when it goes by an apple being round? It cannot. Now you give the software many pictures of apples in different angles, cut in pieces or in different color shades and add information: Those are all apples. Of course, it is the same with categorizing oranges. The software can now recognize apples and oranges in almost every shape — depending on the amount of data given. How much data is needed as minimum is depending on the application, but there are some good clues. At a bare minimum there should be around 1,000 examples. For average problems with mid complexity 10,000 to 100,000 examples are needed. Regarding the amount of data one can conject: The more the better. Machine Learning is therefore not only about decisions, but rather about being able to broaden your horizon.

该算法现在可以确定是看到橙色还是苹果，这是因为我们教了该软件每种水果的特征，因此可以自行决定。 直到您将橙子切成可食用的块，这都是有趣的游戏。 人们仍然可以将苹果识别为苹果，但是当苹果变圆时，该软件应该如何做到呢？ 这不可以。 现在，您可以为该软件提供许多不同角度的苹果图片，切成薄片或以不同的颜色阴影添加信息：这些都是苹果。 当然，对橘子进行分类也是一样。 该软件现在可以识别几乎每种形状的苹果和橙子-取决于给定的数据量。 最少需要多少数据取决于应用程序，但是有一些很好的线索。 最少应该有大约1,000个示例。 对于中等复杂度的平均问题，需要10,000至100,000个示例。 关于人们可以推测的数据量：越多越好。 因此，机器学习不仅与决策有关，而且还与拓宽视野有关。

We can set: Machine Learning is a statistical tool that enables the machine to learn from given data or experience.

我们可以设置：机器学习是一种统计工具，可使机器从给定的数据或经验中学习。

什么是深度学习？ (What is Deep Learning?)

As all important inventions of humankind, the field of AI also uses nature as a role model. Deep Learning (DL) is working through a Deep Neural Network orienting itself on the human brain. The Artificial Networks are built with different layers consisting of connected neurons. Each layer can have a special purpose, for example learning to detect patterns in the data, which enables it to recognize objects as the same ones. Another function can be looking for specific pre-set patterns, like a round, red or green object with a smooth surface and considering it as an apple. The information that this is called an apple must be given by human help. Whereas finding out that it is still an apple, even if one half is in the shadow, is something that can be learned unsupervised (without human help) by the algorithm through training data. One should mention that an Artificial Network is not nearly as complicated as a natural one and is not on a human-level intelligent or thinking human-like. Regarding the information processing there are no electrical or chemical impulses rather than a signal being zero or one. But it is indeed inspired by nature and transferred to technology: While a single neuron is not capable of doing something, a whole Neural Network is extremely complex. The behavior of the system is determined by the ways how the neurons are wired together. Each neuron reacts to the incoming signals in a specific way that can also adapt over time. DL is for example also applied in Language Processing. The Artificial Network analyses parts of the speech, like sentence structure, specific word-use or phrases. This allows the algorithm to do natural Language Processing (NLP) and puts the human-machine interaction on a whole new level. This means having a chat with a computer feels like talking to a human.

作为人类的所有重要发明，人工智能领域也将自然作为榜样。 深度学习(DL)正在通过将深度神经网络定位在人脑上的功能。 人工网络由连接神经元的不同层构成。 每一层都可以有特殊的用途，例如学习检测数据中的模式，从而使其能够将对象识别为相同的对象。 另一个功能是寻找特定的预设图案，例如表面光滑的圆形，红色或绿色物体，并将其视为苹果。 称为苹果的信息必须由人工帮助提供。 而即使有一半被发现，它仍然是一个苹果，这是算法可以通过训练数据在无监督的情况下(无需人工帮助)学习的东西。 应该提到的是，人工网络并不像自然网络那样复杂，也不是在人类层面的智能或类似人类的思维上。 关于信息处理，没有电或化学脉冲，而不是信号为零或一。 但这确实是受自然启发并转移到技术上的：虽然单个神经元无法做某事，但整个神经网络却极其复杂。 系统的行为取决于神经元如何连接在一起的方式。 每个神经元都以特定的方式对传入的信号做出React，这种方式也可以随着时间的流逝而适应。 例如，DL也应用于语言处理。 人工网络分析语音的某些部分，例如句子结构，特定的单词用法或短语。 这使算法可以进行自然语言处理(NLP)，并将人机交互提升到一个全新的水平。 这意味着与计算机聊天就像在和人聊天。

We can set: Deep Learning is enabling the machine to mimic the human brain through artificial neurons and therefore can identify important features on its own.

我们可以设置：深度学习使机器能够通过人造神经元模仿人类的大脑，因此可以自行识别重要特征。

全部连接 (It is all connected)

There is basically no common definition of Artificial Intelligence and sometimes it is hard to decide whether something can be called Artificial Intelligence or not. Each of the mentioned technologies are connected and part of each other: Deep Learning is a part of Machine Learning, which itself is a part of Artificial Intelligence.

人工智能基本上没有通用的定义，有时很难确定是否可以称为人工智能。 每种提及的技术都是相互联系的，并且是彼此的一部分：深度学习是机器学习的一部分，而机器学习本身是人工智能的一部分。

‍Going one step back, the word “intelligence” alone is quite hard to define. As explained previously, the Turing Test is a common method. It argues, that something can be identified as intelligent only due to its behavior. Regarding a chat-bot one could say that if it is indistinguishable if it is a human or computer on the other side, the behavior could be determined as “intelligent”, even if it is just an imitation game without any awareness on the computer’s side. And we remember: Even an Artificial Neural Network is only orienting on its natural role model, but is not truly comparable to one. Every intelligent outcome a computer provides is based on the training data given. As mentioned previously regarding the training data one can conject: The more relevant data, the better. This is where the field of AI is related to Data Science. Even the best software-code in the world is worth nothing without sufficient data to learn from it.

向后退一步，仅“智能”一词很难定义。 如前所述，图灵测试是一种常用方法。 它认为，只有某种事物的行为才能被认为是智能的。 关于聊天机器人，可以说，如果它是另一端是人还是计算机，如果无法区分它，则该行为可以被确定为“智能”，即使它只是一个模仿游戏，而对计算机也没有任何了解。 。 我们还记得：即使是人工神经网络也仅以其自然的榜样为导向，但并不能真正与之媲美。 计算机提供的每一个智能结果都是基于给定的训练数据。 如前所述，关于训练数据，人们可以推测：相关数据越多越好。 这就是AI领域与数据科学相关的地方。 没有足够的数据来学习，即使是世界上最好的软件代码也一文不值。

方法和问题 (‍Methods and problems)

For Deep Learning we are data-wise not talking about a bare minimum of around 1,000 examples, but rather around 100,000 to 1,000,000 examples to learn from. We are now getting into Big Data spheres in the field of Data Science. This term is also connected to the field of Artificial Intelligence in the way of AI being a part of Data Science. Since one cannot always offer this amount of data, the principle of heuristics can be pulled up. This method uses the information it has and estimates an outcome for the next step based on that. Taking chess-playing as a popular example, the program has the value and position of all figures as input and therefore can predict possible outcomes like losing a figure or winning the game. Despite this is often enough to solve a problem, it is no precise solution. The human brain uses the concept of heuristics daily, hence this is another aspect, where nature as role model can be noticed.

对于深度学习，我们在数据方面并不是在谈论至少约1,000个示例，而是要从中学习100,000至1,000,000个示例。 我们现在正在进入数据科学领域的大数据领域。 该术语也通过AI作为数据科学的一部分与人工智能领域相关联。 由于不能总是提供这么多的数据，因此启发式原理可以被提出。 该方法使用所拥有的信息，并据此估算下一步的结果。 以国际象棋为例，该程序将所有人物的价值和位置作为输入，因此可以预测可能的结果，例如失去人物或赢得比赛。 尽管这通常足以解决问题，但这并不是精确的解决方案。 人脑每天都使用启发式的概念，因此这是另一个方面，可以注意到自然作为榜样。

Besides self-driving cars and image processing of oranges and apples as an example, the state-of-the-art applications are already integrated in our daily lives. For instance, in streaming services each personal recommendation is based on AI-driven algorithms using your data. Here, another example that can be added to the picture processing of our fruit salad: The nearest neighbor classification. Here, the habits of each user are tracked: What kind of movies or series did he or her watch and how did he or her rate them? And now the algorithm compares all users to each other, assuming that people having similar consuming habits might like the same kind of content. For both applications one thing is sure: There is a huge amount of data needed — the more, the better. Hence, the less training data is available the worse is the software’s performance, which is is considered as huge problem for many applications.

除了无人驾驶汽车和橘子和苹果的图像处理以外，最先进的应用程序已经集成到我们的日常生活中。 例如，在流媒体服务中，每个个人推荐都基于使用数据的AI驱动算法。 在这里，可以添加到水果沙拉的图片处理中的另一个示例：最近邻居分类。 在此，可以跟踪每个用户的习惯：他或她观看了哪种电影或电视剧，以及他们如何评价它们？ 现在，该算法将所有用户相互比较，并假设有相似消费习惯的人们可能喜欢相同类型的内容。 对于这两个应用程序，可以肯定的是：需要大量的数据-越多越好。 因此，可用的训练数据越少，软件的性能就越差，这对于许多应用程序来说是一个巨大的问题。

渴望获得更多知识 (Hunger for more knowledge)

If you want to learn more about Artificial Intelligence and do some research on Artificial Intelligence, we can recommend a free and open AI online course developed by the University of Helsinki. It is the AI researchers’ aim to educate at least 1% of the world’s society about Artificial Intelligence.

如果您想了解有关人工智能的更多信息并进行人工智能研究，我们可以推荐由赫尔辛基大学开发的免费开放的AI在线课程 。 AI研究人员的目标是教育全世界至少1％的人工智能知识。