ai透视按键_透视人工智能

ai透视按键

by Rishal Hurbans

由Rishal Hurbans

透视人工智能 (Artificial Intelligence in Perspective)

The buzz words of today: artificial intelligence (AI), machine learning, and deep learning. Business circles, corporations, startups, developers, and the average person have heard about these terms and seen them appear more and more often in news and online chatter. But what do they really mean?

今天的流行语是：人工智能(AI)，机器学习和深度学习。 商界，公司，初创公司，开发人员和一般人都已经听说过这些术语，并在新闻和在线聊天中越来越频繁地看到它们。 但是，它们的真正含义是什么？

The concepts and methodologies behind artificial intelligence are not new. Known techniques are used in different ways to achieve new and extraordinary things. It’s made possible today by some key factors in the advancement of technology and business, namely:

人工智能背后的概念和方法学并不是新事物。 已知技术以不同的方式用于实现新奇的事物。 今天，通过推动技术和业务发展的一些关键因素，使之成为可能：

Computational hardware advancement: Over the last two decades, technological advancement in computational hardware has drastically improved, allowing for general access to powerful hardware at cheaper costs. Contrary to popular belief, AI algorithms utilize the GPU (Graphic Processing Unit) in a computer, not the CPU (Central Processing Unit). Historically, the GPU was required for playing the latest games, however, the architecture is well suited for algorithms that make AI possible.

计算硬件的进步 ：在过去的二十年中，计算硬件的技术进步得到了极大的改善，从而使人们能够以更便宜的价格通用使用强大的硬件。 与流行的看法相反，AI算法利用计算机中的GPU(图形处理单元)而不是CPU(中央处理单元)。 过去，玩最新游戏需要使用GPU，但是该架构非常适合使AI成为可能的算法。

Lots of data to work with: The catalyst that makes artificial intelligence and machine learning possible is data. The more data an algorithm has to work with, the more refined it can become. The terms big data, and data mining boomed in the recent past. Data collected via various mechanisms on various things, and uncovering insights on that data, provides a solid foundation for artificial intelligence and machine learning. With that said, understanding, cleansing, and preparing data is crucial step in implementing most artificial intelligence algorithms successfully.

大量数据可供使用 ：使人工智能和机器学习成为可能的催化剂是数据。 一个算法必须处理的数据越多，它就可以变得越完善。 在过去，“大数据”和“数据挖掘”一词蓬勃发展。 通过各种机制针对各种事物收集的数据以及对这些数据的了解，为人工智能和机器学习奠定了坚实的基础。 话虽如此，理解，清理和准备数据是成功实施大多数人工智能算法的关键步骤。

Business opportunities: Businesses strive to make a profit. If an initiative adds no value to the business and does not contribute in some way or another towards increasing profit, a business won’t adopt it. Given the amount of data businesses have acquired, new use cases and opportunities have emerged with the potential to make profit. This makes AI a feasible area to experiment in, even if it’s simply a tool used to understand a business, its offerings, and its customers.

商业机会 ：企业努力赚钱。 如果一项举措没有给企业带来任何价值，并且没有以某种方式对增加利润做出贡献，那么企业就不会采用它。 考虑到企业已获取的数据量，出现了新的用例和机会，并有可能获利。 这使AI成为了一个可以进行试验的可行领域，即使它只是用于了解业务，其产品和客户的工具。

什么是AI？ (What is AI?)

Before we understand how AI works, what algorithms exist, and where they are useful, we need an understanding of what it is. By definition, “artificial” means something that is simulated, not organic, and typically created by humans.

在我们了解AI的工作原理，存在哪些算法以及它们在哪里有用之前，我们需要了解它的含义。 根据定义，“人工”是指模拟的东西，不是有机的，通常是人类创造的东西 。

What about intelligence? Intelligence is a somewhat subjective and philosophical matter. Is the ability to understand and implement the process of making toast an example of intelligence? Is making a decision about whether someone has enough money in their account for a transaction an example of intelligence? Is beating a chess master at a game of chess an example of intelligence? Intelligence is a philosophical question, and can be highly subjective.

智力呢？ 智力是有点主观和哲学的事情。 理解和实施烤面包过程的能力是否是智能的榜样？ 决定某人的账户中是否有足够的钱来进行交易是否是明智的例子？ 在国际象棋比赛中击败国际象棋大师是智力的例子吗？ 智力是一个哲学问题，可以高度主观。

什么是智力？ (What is Intelligence?)

By definition, “intelligence” means to acquire and apply knowledge and skills. This is still very vague. What is knowledge? By definition, “knowledge” is an acquired understanding about a concept. This is still not something tangible.

根据定义，“智能”是指获得和应用知识和技能 。 这仍然很模糊。 什么是知识？ 根据定义，“知识” 是对概念的一种后天理解 。 这仍然不是切实的东西。

As humans, we believe that we’re intelligent since we dominate the world. We have evolved into a species that endures, adapts, and innovates the way we live. However, we see many examples of what could be classified as intelligent behavior from seemingly unintelligent organisms. Ants exhibit complex intelligent behavior when navigating terrain and transporting food. Birds flock together for protection against predators and their environment.

作为人类，我们相信自己是聪明的，因为我们统治着世界。 我们已经发展成为一个可以忍受，适应和创新我们生活方式的物种。 但是，我们看到了许多看似不智能的生物可以将其归为智能行为的例子。 蚂蚁在地形和运输食物时表现出复杂的智能行为。 鸟类聚集在一起，以保护它们免受捕食者及其环境的侵害。

So, the question remains, what is intelligence?

因此，问题仍然是，什么是智力？

Is something that learns intelligent? If something is continually aware of knowledge acquired in the past and is able to apply it going forward, is it intelligent? As humans, we make the same mistakes over and over again, even though we have knowledge of the outcome from past experience, yet it still happens. Does this make us unintelligent?

是学到聪明的东西吗？ 如果某人不断意识到过去获得的知识并且能够将其应用于未来，那么它是否明智？ 作为人类，即使我们从过去的经验中学到了结果，我们也会一遍又一遍地犯同样的错误，但仍然会发生。 这会使我们变得不聪明吗？

Is something that reasons intelligent? If something acquires knowledge in various areas, and strings that knowledge together to form an opinion or way of thinking, is it intelligent?

有什么理由聪明吗？ 如果某物在各个领域获得知识，并将这些知识串在一起形成观点或思维方式，这是否明智？

Is something that creates intelligent? Creativity is strange. It’s a special kind of cognitive activity that is very difficult to quantify. Many humans struggle with creativity because of the unique way different people think. Even the most prolific people can draw a blank when it comes to creativity.

是创造智慧的东西吗？ 创造力很奇怪。 这是一种特殊的认知活动，很难量化。 由于不同的人思考的独特方式，许多人与创造力作斗争。 在创造力方面，即使是最有产力的人也可以空白。

As humans, our sensory system, and survival instinct impacts how we learn, reason, and create. For now, let’s agree that we look at ourselves when understanding what intelligence is and assume that we’re intelligent because we’re the most dominant species. Call it arrogance, but we don’t know any better, and it’s more than likely that we’re far from the pinnacle of true intelligence.

作为人类，我们的感觉系统和生存本能会影响我们的学习，推理和创造方式。 现在，让我们同意，我们在理解什么是智能时会看着自己，并假设我们是智能的，因为我们是最主要的物种。 称其为自大，但是我们没有更好的了解，而且很可能我们离真正智慧的顶峰还很远。

人类进步 (Human Advancement)

This is what we care about. We want to continually improve, as people, teams, communities, and as a species. Let’s take a look at the past. The industrial revolution happened in the 1800’s, and the first digital computer was invented in the 1900’s. That’s about a hundred years apart. The time between the first digital computer, to the first human in space was less than a hundred years. The time between the first human in space, and the first personal computer was a couple of decades. The time between the first personal computer and mobile phones was just several years.

这就是我们所关心的。 我们希望作为人员，团队，社区和物种不断发展。 让我们看看过去。 工业革命发生在1800年代，第一台数字计算机是在1900年代发明的。 相距大约一百年。 从第一台数字计算机到太空中的第一个人之间的时间不到一百年。 从太空中的第一个人到第一台个人计算机的时间是几十年。 第一台个人计算机和移动电话之间的时间只有几年。

This exponential technological advancement is an example of human advancement. We create things that change the way we live, and interact with the world. We rapidly change economies by this advancement. From looking at the past, it is clear that the driving factors for advancement is money, power, improvement, and sometimes curiosity…

这种指数级的技术进步是人类进步的一个例子。 我们创造的事物改变了我们的生活方式，并与世界互动。 我们通过这一进步Swift改变了经济。 从过去来看，很明显，进步的驱动因素是金钱，力量，进步，有时还有好奇心……

Given this, it’s highly unlikely that we would create something of the likes of the terminator, if it does not benefit our advancement. However, malicious people will do malicious things with whatever they have at their disposal. To understand more about what we need to control, let’s have a look at different categorizations of artificial intelligence.

鉴于此，如果它不能使我们的进步受益，我们极不可能创造出类似终结者的东西。 但是，恶意人员会利用自己掌握的一切来做恶意的事情。 为了更多地了解我们需要控制的内容，让我们看一下人工智能的不同分类。

人工智能(ANI) (Artificial Narrow Intelligence (ANI))

This categorization of AI includes implementations that focus on something very specific. It may be solving a specific problem, learning something very specific, or making decisions on something specific. Examples include; a program that makes smart decisions in the game of chess, a program that predicts the shelf life of products, a program that understand speech, etc.

AI的这种分类包括专注于非常具体的实现的实现。 它可能是解决一个特定的问题，学习非常特定的东西，或者对特定的东西做出决定。 例子包括； 在国际象棋游戏中做出明智决策的程序，预测产品保质期的程序，理解语音的程序等。

These are typically applications of AI that focus on a narrow domain. This is not to say that multiple narrow intelligence implementations can’t be integrated to work together. Although multiple ANI implementations may be integrated together, this is not considered artificial general intelligence.

这些通常是专注于狭窄领域的AI应用。 这并不是说多个集成的智能实现无法集成在一起工作。 尽管可以将多个ANI实现集成在一起，但这不被认为是人工智能。

人工智能(AGI) (Artificial General Intelligence (AGI))

This is a huge jump from narrow intelligence. This categorization of AI is essentially human-like. If we think about how we think, things get complicated. We have gained so much knowledge that we’re not consciously aware of. We have a bias in the way we think that we don’t consciously know about. It’s not as simple as stringing a bunch of narrow intelligence units together.

与狭narrow的情报相比，这是一个巨大的飞跃。 AI的这种分类本质上是类似于人类的。 如果我们考虑自己的想法，事情就会变得复杂。 我们已经获得了很多知识，而我们没有意识到。 我们认为自己没有意识地了解自己的方式存在偏见。 这并不像将一堆狭窄的情报部门串在一起那样简单。

人工智能(ASI) (Artificial Super Intelligence (ASI))

Super intelligence. This is where things could get scary. We don’t know what could be more intelligent than us. Theories suggest that if we achieve artificial super intelligence, it will surpass our intelligence in a matter of seconds. This is eerie since we may not even be able to comprehend it.

超级情报。 这是事情可能会令人恐惧的地方。 我们不知道还有什么比我们更聪明的。 理论表明，如果我们实现了人工智能，它将在几秒钟内超越我们的智能。 这太诡异了，因为我们甚至可能无法理解它。

Until we understand exactly how our brains work and are able to quantify how we think and what makes us think that way, we may never understand what true intelligence really is.

除非我们确切地了解我们的大脑如何工作，并且能够量化我们的思维方式和使我们如此思考的方式，否则我们可能永远无法理解真正的智力到底是什么。

道德与责任 (Ethics and Responsibility)

There is a question of ethics around artificial intelligence and its applications. Could super intelligent machines rebel and rule over us? If an intelligent car injures someone, who’s responsible? This isn’t about the computers and AI, it’s about people.

关于人工智能及其应用存在伦理问题。 超级智能机器可以反抗并统治我们吗？ 如果智能汽车伤害了某人，谁应该负责？ 这与计算机和AI无关，而与人有关。

Whether we like it or not, some people will be malicious for self benefit and utilize anything they can towards it. The question of ethics is a philosophical one as well. What makes us feel bad when we do something considered bad? Can we simulate that in a machine? It comes down to malevolent and mature decision making from people in leadership or decision making roles.

无论我们是否喜欢，有些人都会出于自身利益而恶意使用并竭尽所能。 道德问题也是一个哲学问题。 当我们做被认为不好的事情时，什么使我们感到难过？ 我们可以在机器上模拟吗？ 它取决于领导或决策角色中的恶意和成熟的决策。

If the intentions are positive, and the impacts are analyzed, we will be less likely to create technology that hurts us as a species. This isn’t something new, most technological breakthroughs could have been widely used for malicious intentions, but they weren’t. I believe that as a species, we exhibit behavior for collective advancement, even though there may be a few bad apples in the batch.

如果意图是积极的，并分析了影响，我们将不太可能创造出对我们物种有害的技术。 这不是什么新鲜事物，大多数技术突破本可以广泛用于恶意目的，但事实并非如此。 我相信，作为一个物种，即使批次中可能有一些坏苹果，我们也会表现出集体进步的行为。

With regards to AI replacing existing jobs, it’s natural progress. The internet boom hurt physical newspaper sales, but industries such as SEO (Search engine optimization), and social media management wouldn’t exist without it. Technological progress hurts some occupations, but inevitably creates more. We are unable to tell what new industries AI will create until it actually happens.

关于AI替代现有工作，这是自然的进步。 互联网的繁荣损害了实体报纸的销售，但是没有它，诸如SEO(搜索引擎优化)和社交媒体管理之类的行业将不复存在。 技术进步会伤害某些职业，但不可避免地会创造更多职业。 在实际发生之前，我们无法确定AI将创造什么新行业。

AI可以做什么？ (What Can AI Do?)

Okay. Enough with philosophy and ethics. Let’s look at where AI can be applied to add value. As mentioned previously, multiple techniques and intelligent units can be used together, but independently. Current implementations of AI usually do one of the following.

好的。 具有足够的哲学和道德观。 让我们看看可以在哪里应用AI来增加价值。 如前所述，多种技术和智能单元可以一起使用，但可以独立使用。 AI的当前实现通常执行以下操作之一。

做出有限的决定 (Make a finite decision)

This has historically been used in AI that play games or work within a finite set of rules. Given a current state with known data, an algorithm is able to determine the best decision in its context.

从历史上讲，这已用于在有限的规则集内玩游戏或工作的AI中。 给定具有已知数据的当前状态，算法能够确定其上下文中的最佳决策。

An example is a game of chess. By evaluating the moves that have happened and as many possible future moves, an algorithm can determine the best possible decision for the next move to make. Or given a set of images, an algorithm that can classify which are pictures of people.

一个例子是下棋。 通过评估已经发生的动作以及尽可能多的未来动作，算法可以确定下一步做出的最佳决策。 或者给定一组图像，该算法可以对哪些图像进行分类。

Currently, decisions made by machine learning algorithms usually happen under something called supervised learning which is elaborated on later.

当前，由机器学习算法做出的决策通常在称为监督学习的情况下发生，稍后将对此进行详细介绍。

做出预测或推荐 (Make a prediction or recommendation)

Making a prediction requires recognizing patterns, and calculating probabilities to find trends. It can also form a model that may not have been known upfront.

进行预测需要识别模式，并计算发现趋势的概率。 它也可以形成一个可能尚未预先知道的模型。

This is very similar to making a decision, however, the possible outcome may not have even been an option at the start. An example is, given a picture, categorize the physical objects in the picture whilst considering other pictures.

这与做出决定非常相似，但是可能的结果甚至在一开始都不是一个选择。 给定一个图片的例子是，在考虑其他图片的同时对图片中的物理对象进行分类。

In machine learning, these non-finite outcomes fall under the category of unsupervised learning.

在机器学习中，这些非确定性的结果属于无监督学习的范畴。

推理 (Reasoning)

As humans, our brains form connections between different pieces of knowledge which creates the concept of reasoning. By definition, “reasoning” means to think about something in a logical manner. However, with regards to intelligence, it’s a complex cognitive trait that is difficult to understand.

作为人类，我们的大脑在不同的知识之间形成联系，从而形成了推理的概念。 根据定义，“推理”是指以逻辑方式思考某件事。 但是，关于智力，这是一个很难理解的复杂认知特征。

As humans, we form opinions and conclusions based on how we reason without consciously trying to reason. It’s a hidden language that ties the strings of knowledge together in our minds.

作为人类，我们在不自觉地尝试推理的情况下基于我们的推理方式形成意见和结论。 这是一门隐藏的语言，它将我们的思想链联系在一起。

So can machines reason? Yes. It’s happened already. An example being Google Translate. It is able to translate phrases between two different languages without using an intermediary language understood by humans. We will likely never understand the reasoning that happens for it to achieve this.

那么机器可以推理吗？ 是。 已经发生了 一个例子是谷歌翻译。 它能够在两种不同语言之间翻译短语，而无需使用人类可以理解的中间语言。 我们可能永远无法理解实现它的原因。

This form of intelligence emerges more when concepts of deep learning are applied and implemented over time on a large dataset.

当在大型数据集上随着时间的推移应用和实施深度学习的概念时，这种形式的智能就会出现。

人工智能如何实现这一目标？ (How Does AI Achieve This?)

From a technical perspective, the terms artificial intelligence, machine learning, and deep learning tend to be confusing and one is sometimes unsure about how they relate. Artificial intelligence encompasses different techniques to synthesize intelligence.

从技术角度来看，人工智能，机器学习和深度学习这两个术语容易混淆，有时人们不确定它们之间的关系。 人工智能包含各种合成智能的技术。

The following sections describe different types of approaches where each approach uses specific principles to achieve a goal. The respective approach is selected based on the data available, the goal that’s trying to be achieved, and the nature of the problem. There are many other approaches, but these are most popular ones used today.

以下各节描述了不同类型的方法，其中每种方法都使用特定的原理来实现目标。 根据可用的数据，要实现的目标以及问题的性质来选择相应的方法。 还有许多其他方法，但是这些方法是当今最流行的方法。

进化算法 (Evolutionary Algorithms)

These algorithms are based on concepts of biological evolution. From scientific studies, we have observed the process and outcomes of reproduction, mutation, and individual selection in natural organisms.

这些算法基于生物进化的概念。 从科学研究中，我们观察了自然生物繁殖，变异和个体选择的过程和结果。

Essentially, these algorithms are based on the premise that organisms reproduce to create more organisms, the children of the original organisms are comprised of a combination of the genetic make-up of them. However, there are slight variants in the children; this is called mutation. Given the mixed genetic make-up of the children and their mutations, they could potentially be “better” than their parents even in the case that their parents as individuals are considered “inferior”. Individuals are selected to live on based on their fitness which is derived from how “good” they are. This is the general process that most living organisms have followed over millions of years to be what they are today, including us humans.

本质上，这些算法是基于这样的前提：生物繁殖以产生更多的生物，原始生物的子代由它们的遗传组成的组合组成。 但是，孩子们的情况略有不同。 这称为变异。 鉴于孩子的遗传组成和他们的突变混合在一起，即使父母被视为“劣等”，他们也可能比父母“更好”。 根据个人的适应能力来选择个人，他们的适应能力来自于他们的“好”程度。 这是大多数生物体(包括我们人类)已经经历了数百万年的发展的总过程。

Evolutionary algorithms are suited for problems where a single result is comprised of permutations of finite things. These algorithms are geared towards finding incrementally better solutions, but cannot guarantee finding the most optimal solution.

进化算法适用于单个结果由有限事物的排列组成的问题。 这些算法旨在寻找渐进的更好的解决方案，但不能保证找到最佳的解决方案。

As an example; consider the problem of optimizing package delivery by drones from warehouses to customers where there are constraints on weight that the drones can carry. Each action for a specific drone is finite — let’s call this a gene. Permutations of sequences of possible actions across all drones can be generated — let’s call this a chromosome. And each chromosome will have a different performance — let’s call this fitness.

举个例子; 考虑优化无人驾驶飞机从仓库到客户的包裹运输的问题，因为无人驾驶飞机可以携带重量。 特定无人机的每个动作都是有限的-我们称其为基因 。 可以在所有无人机上生成可能的动作序列的排列-我们称其为染色体 。 每个染色体都有不同的性能-我们称这种适应性 。

These chromosomes are generated, reproduce new sequences, and the fitness of each is evaluated to determine which should live on. This happens for a number of generations or a specified stopping condition is reached. The most fit chromosome is then used as the most optimal solution. This means that an optimal sequence of actions for drones will eventually emerge.

生成这些染色体，复制新的序列，并评估每个染色体的适合度，以确定应该存活的染色体。 这种情况发生了很多代，或者达到了指定的停止条件。 然后，将最适合的染色体用作最优化的解决方案。 这意味着无人机的最佳动作序列将最终出现。

机器学习 (Machine Learning)

The underlying algorithms used for machine learning are essentially based around statistics. Machine learning is similar to the concepts around data mining. An algorithm attempts to find patterns in data to classify, predict, or uncover meaningful trends. Machine learning is only useful if enough data is available, and if the data has been prepared correctly.

用于机器学习的基础算法基本上是基于统计的。 机器学习类似于围绕数据挖掘的概念。 一种算法试图在数据中找到模式以分类，预测或发现有意义的趋势。 机器学习仅在有足够的数据可用且数据准备正确时才有用。

As a toy example, consider that evaluation of password strength depends on the length of the password, if it contains numbers, and if it contains special characters. Let’s also assume that we have a list of passwords and their respective strength. Simply using the raw textual representation of the password for a machine learning algorithm to learn what makes a password strong or not will not work.

作为一个玩具示例，请考虑对密码强度的评估取决于密码的长度，密码是否包含数字以及密码是否包含特殊字符。 还假设我们有一个密码列表及其各自的强度。 仅将密码的原始文本表示形式用于机器学习算法，以了解使密码变强或变弱的原因是行不通的。

Extraction of metadata such as the number of characters, the number of special characters, and the number of numeric digits is required before a machine learning algorithm can learn any trends. This metadata and the process of preparing it is imperative to successful machine learning.

在机器学习算法可以学习任何趋势之前，需要提取元数据，例如字符数，特殊字符数和数字位数。 此元数据及其准备过程对于成功的机器学习至关重要。

Machine learning consists of two categories, namely supervised learning, and unsupervised learning.

机器学习包括两类，即监督学习和无监督学习。

Supervised Learning: Most practical solutions use supervised learning. Supervised learning encompasses approaches to satisfy the need to classify things into categories — known as classification. It also includes approaches to address the need to provide variable real-value solutions such as weight or height — known as regression.

监督学习 ：大多数实际的解决方案都使用监督学习。 监督学习包括满足将事物分类到类别(称为分类)的需求的方法 。 它还包括解决提供可变实值解决方案(例如重量或身高)的需求的方法(称为回归) 。

Unsupervised Learning: The goal of this type of learning is to model data and uncover trends that are not obvious in its original state. This type of learning is used to learn about data.

无监督学习 ：这种学习的目的是对数据建模并发现在其原始状态下不明显的趋势。 这种类型的学习用于学习数据。

There are no answers that the algorithm tries to guess. It discovers “hidden” structures and correlations that are not apparent at face value. This is useful for finding groups of data that are similar — known as clustering. It is also useful for discovering rules that govern portions of the data — known as association.

该算法尝试猜测没有答案。 它发现了从表面上看并不明显的“隐藏”结构和相关性。 这对于查找相似的数据组(称为集群 )很有用。 这对于发现控制数据部分的规则(称为关联 )也很有用。

深度学习和神经网络 (Deep Learning and Neural Networks)

Deep learning is a term that sounds very mysterious and complex, and it is to an extent. It is similar to machine learning in that it classifies things and discovers patterns in data, however, deep learning algorithms constantly improve their knowledge on what they have already learned in the past. These algorithms may consist of chaining a number of different AI approaches to achieve its goal.

深度学习是一个听起来非常神秘和复杂的术语，而且在一定程度上。 它与机器学习类似，因为它可以对事物进行分类并发现数据中的模式，但是，深度学习算法会不断提高他们对过去已经学过的知识的知识。 这些算法可能包括链接许多不同的AI方法以实现其目标。

As an example; consider that a large image database exists and there is a need for an algorithm to describe the objects in pictures. Using deep learning, an algorithm is able to find similar objects in different pictures and group them. After a human labels that group, the algorithm understands what that object is going forward, however, it can create further subgroups within that object for different variants. If a grouping of cars is discovered, the algorithm may find different variations of cars such as sedans, hatchbacks, SUVs, etc. Given enough data, these subtle variants can be discovered.

举个例子; 考虑到存在一个大型图像数据库，并且需要一种算法来描述图片中的对象。 使用深度学习，算法可以在不同图片中找到相似的对象并将其分组。 在人类标记了该组之后，该算法可以了解该对象的前进方向，但是，它可以在该对象内为不同的变体创建更多的子组。 如果发现了一组汽车，该算法可能会发现不同的汽车变型，例如轿车，掀背车，SUV等。如果有足够的数据，则可以发现这些微妙的变型。

Neural network algorithms are heavily used in deep learning due to their adaptive nature. Neural networks are based on our understanding of how the human brain and nervous system works. It is the concept of a layered hierarchy of neurons that accept an input, influences the input, and then directs the result to other neurons based on the weightings of the neuron.

神经网络算法由于具有自适应性，因此在深度学习中大量使用。 神经网络基于我们对人脑和神经系统工作原理的理解。 这是神经元分层层次结构的概念，它接受输入，影响输入，然后根据神经元的权重将结果定向到其他神经元。

The weighting on each neuron changes over time as the network becomes better at classifying the input. A higher-weighted neuron will have more influence on the input it receives and thus could strongly impact the outcome of the network. Neural networks are useful for classification problems where classification can change or be refined in the future.

随着网络对输入的分类变得更好，每个神经元的权重会随着时间而变化。 权重较高的神经元将对其接收的输入产生更大的影响，因此可能强烈影响网络的结果。 对于将来可能会更改或改进分类的分类问题，神经网络很有用。

结论 (Conclusion)

Artificial intelligence is an exciting concept that will shake industries and the way we live. It’s unlikely that we will create human hating robots that go bananas and destroy us, if we focus on the benevolent uses of it. This piece attempts to make AI concepts more clear to you and demystify the buzzwords. Equipped with this knowledge, I challenge you to learn more about AI, and find valuable practical uses for it in your work and everyday life.

人工智能是一个令人兴奋的概念，它将动摇行业和我们的生活方式。 如果我们专注于仁慈的使用，我们不太可能创造出让人讨厌的机器人来摧毁我们。 本文试图使您更清楚地了解AI概念，并揭开流行语的神秘面纱。 掌握了这些知识后，我将挑战您以更多地了解AI，并在您的工作和日常生活中找到有价值的实际用途。

翻译自: https://www.freecodecamp.org/news/artificial-intelligence-in-perspective-6331dc384685/

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