人工智能（AI）、机器学习（ML）和深度学习（DL）：有什么区别？
AI 人工智能

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> 我们经常交替使用人工智能（AI）、机器学习（ML）和深度学习（DL）这些术语，尽管我们几乎每天都阅读或听到它们。本文解释了这些技术是如何演变的以及它们有何不同。
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![AI ML and DL What’s the Difference](/data/attachment/album/202208/11/144748ym6u45jaocza0vvv.jpg)


<ruby> 人工智能 <rt>  Artificial Intelligence </rt></ruby>（AI）、<ruby> 机器学习 <rt>  Machine Learning </rt></ruby>（ML）和<ruby> 深度学习 <rt>  Deep Learning </rt></ruby>（DL）通常可以互换使用。但是，它们并不完全相同。人工智能是最广泛的概念，它赋予机器模仿人类行为的能力。机器学习是将人工智能应用到系统或机器中，帮助其自我学习和不断改进。最后，深度学习使用复杂的算法和深度神经网络来重复训练特定的模型或模式。


让我们看看每个术语的演变和历程，以更好地理解人工智能、机器学习和深度学习实际指的是什么。


#### 人工智能


自过去 70 多年以来，人工智能已经取得了长足的进步。无论我们是否知道，也不管喜欢与否，，它已经渗透到了我们生活的方方面面。在过去十年中，机器学习和深度学习的进步已经在各种规模的行业和组织中创造了人工智能热潮。云服务提供商通过开发免费的开源服务和提供新的场景进一步推动的这种势头。


![Figure 1: Overview of AI, ML and DL](/data/attachment/album/202208/11/144748rgglstfewfttyp4l.jpg)


人工智能可能是自 1956 年以来最受关注的概念。到 2015 年，GPU 的广泛使用使并行处理更快、更强大、更便宜。而愈加廉价的存储可以大规模地存储大数据（从纯文本到图像、映射等）。这产生了对数据分析的需求，它被更普遍地称为<ruby> 数据科学 <rt>  data science </rt></ruby>，导致机器学习发展为实现人工智能的方法。


#### 机器学习


机器学习是使用算法来处理、学习和理解或预测可用数据的模式。最近，软件开发的低代码和无代码概念被用作机器学习中的自学习过程，它给出了完成特定任务的特定指令。通过使用数据和算法对机器进行“训练”，使其能够学习如何执行任务，更重要的是，将学习应用到不断发展的过程中。


![Figure 2: Evolution of AI, ML and DL](/data/attachment/album/202208/11/144748ubaeuufn7jcan5fu.jpg)


机器学习是在开发者社区专注于 AI 时发展起来的，然后发展了算法决策树学习、逻辑编程、聚类、并行处理和强化学习。这些都是朝着正确方向迈出的良好一步，但不足以解决世界感兴趣的场景。


#### 深度学习


深度学习是神经网络和机器学习的进化，是人工智能社区的创意。它学习了人类思维在特定场景中的工作方式，然后在这项工作上比人类做得更好！例如，IBM 的 Watson 与自己下国际象棋，并在游戏中取得了很大进步，最终击败了世界冠军。谷歌的 AlphaGo 也学会了如何玩围棋游戏，一遍又一遍地玩它以提高自己，并成为冠军。


人工智能、机器学习和深度学习正在不断发展。参与数据科学的每个人都希望推进这些概念以改善我们的日常生活。而开源社区、私营企业、科学家和政府机构都在为此共同努力。


![Figure 3: Types of AI, ML and DL](/data/attachment/album/202208/11/144749ki40q1x2vg1p1ikp.jpg)


总而言之，虽然 AI 有助于创建智能机器，但机器学习有助于构建 AI 驱动的应用。深度学习是机器学习的一个子集。它通过利用复杂算法处理大量数据来训练特定模型。由于狭义 AI 极难开发，机器学习正在通过刚性计算解决这一领域的机遇。至少对于实现通用 AI，深度学习有助于将 AI 和机器学习结合在一起。




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via: <https://www.opensourceforu.com/2022/08/ai-ml-and-dl-whats-the-difference/>


作者：[Bala Kalavala](https://www.opensourceforu.com/author/bala-kalavala/) 选题：[lkxed](https://github.com/lkxed) 译者：[geekpi](https://github.com/geekpi) 校对：[wxy](https://github.com/wxy)


本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出


OK
*We often use the terms artificial intelligence, machine learning and deep learning interchangeably, even though we read or hear about them almost each day. This article explains how these technologies evolved and in what ways they differ.*

Artificial intelligence (AI), machine learning (ML), and deep learning (DL) are often used interchangeably; however, they are not quite the same things. AI is the broadest concept of all, and gives a machine the ability to imitate human behaviour. ML is the application of AI into a system or machine, which helps it to self-learn and improve continually. Lastly, DL uses complex algorithms and deep neural networks to repetitively train a specific model or pattern.

Let’s look at the evolution and journey of each term to get a better understanding of what AI, ML and DL actually refer to.

### Artificial intelligence

AI has a come a long way since the last 70-odd years, infiltrating into every aspect of our life, whether we know it, and like it or not. Advancements in machine learning and deep learning over the last decade have created an AI boom across industries and organisations of all sizes. Cloud service providers have added to the momentum by developing open source services that are available for free and by offering new use cases.

![Overview of AI, ML and DL](https://www.opensourceforu.com/wp-content/uploads/2022/06/Figure-1-Overview-of-AI-ML-and-DL.jpg)


![Overview of AI, ML and DL](https://www.opensourceforu.com/wp-content/uploads/2022/06/Figure-1-Overview-of-AI-ML-and-DL.jpg)

AI is perhaps the most worked upon concept since 1956. By 2015, the wide availability of GPUs made parallel processing faster, powerful and cheaper. Cheaper options led to humongous storage of Big Data (plain text to images, to mapping, etc). This created the need for data analytics, more popularly known as data science, leading to the evolution of machine learning as an approach to achieving artificial intelligence.

### Machine learning

ML is the use of algorithms to process, learn and make sense or predict the pattern of available data. More recently, the low-code and no-code concepts of software development are being used in machine learning as self-learning processes that give specific instructions to accomplish particular tasks. The machine is ‘trained’ by using data and algorithms, giving it the ability to learn how to perform the task and, more importantly, apply the learning to evolve continuously.

![Evolution of AI, ML and DL](https://www.opensourceforu.com/wp-content/uploads/2022/06/Figure-2-Evolution-of-AI-ML-and-DL.jpg)


![Evolution of AI, ML and DL](https://www.opensourceforu.com/wp-content/uploads/2022/06/Figure-2-Evolution-of-AI-ML-and-DL.jpg)

ML was evolved when the developer community focused on AI, and then developed algorithmic decision-tree learning, logic programming, clustering, parallel processing and reinforcement learning. ML was evolved when the developer community focused on AI, and then developed algorithmic decision-tree learning, logic programming, clustering, parallel processing and reinforcement learning. These were all good steps in the right direction but not enough to solve use cases that were of interest to the world.

### Deep learning

DL is an evolution of neural networks and machine learning, and the brainchild of the AI community. It learns about how the human mind works in specific scenarios, and then gets better at that job than humans! As an example, IBM’s Watson played chess against itself and improved at the game so much to eventually beat the world champion. Google’s AlphaGo also learnt how to play the Go board game by playing it over and over to better itself, and became the champion.

AI, ML and DL are evolving continuously. It’s the intent of everyone involved with data science to advance these concepts to better our daily lives. The good thing is that the open source community, private enterprises, scientists, and government agencies are all working together for this.

![Types of AI, ML and DL](https://www.opensourceforu.com/wp-content/uploads/2022/06/Figure-3-Types-of-AI-ML-and-DL-590x293.jpg)


![Types of AI, ML and DL](https://www.opensourceforu.com/wp-content/uploads/2022/06/Figure-3-Types-of-AI-ML-and-DL-590x293.jpg)

To conclude, while AI helps to create smart intelligent machines, ML helps to build AI-driven applications. DL is a subset of ML; it trains a specific model by leveraging complex algorithms for large volumes of data. As narrow AI is extremely difficult to develop, ML is addressing the opportunities in this space with rigid computing. DL helps to bring AI and ML together, at least for realising general AI.