人工智能：人工智能基本上是通过一组规则(算法)将人类智能融入机器的机制。 AI 是两个词的组合：“人工”表示由人类或非自然事物制造的东西，“智能”表示相应地理解或思考的能力。另一个定义可能是“人工智能基本上是训练你的机器(计算机)来模仿人脑及其思维能力的研究” 。 AI 专注于 3 个主要方面(技能)：学习、推理和自我纠正，以尽可能获得最大效率。
机器学习：机器学习基本上是一种研究/过程，它使系统(计算机)通过它所拥有的经验自动学习，并在没有明确编程的情况下相应地改进。 ML 是 AI 的应用程序或子集。 ML 专注于程序的开发，以便它可以访问数据以供自己使用。整个过程对数据进行观察，以确定正在形成的可能模式，并根据提供给他们的示例做出更好的未来决策。 ML 的主要目标是让系统通过经验自行学习，无需任何人工干预或帮助。
深度学习：深度学习基本上是更广泛的机器学习家族的一个子部分，它利用神经网络(类似于我们大脑中工作的神经元)来模仿人类大脑的行为。 DL 算法专注于信息处理模式机制，可以像人脑一样识别模式并相应地对信息进行分类。与机器学习相比，深度学习处理更大的数据集，预测机制由机器自我管理。
下表列出了人工智能、机器学习和深度学习之间的差异：
| Artificial Intelligence | Machine Learning | Deep Learning |
|---|---|---|
| AI stands for Artificial Intelligence, and is basically the study/process which enables machines to mimic human behaviour through particular algorithm. | ML stands for Machine Learning, and is the study that uses statistical methods enabling machines to improve with experience. | DL stands for Deep Learning, and is the study that makes use of Neural Networks(similar to neurons present in human brain) to imitate functionality just like a human brain. |
| AI is the broader family consisting of ML and DL as it’s components. | ML is the subset of AI. | DL is the subset of ML. |
| AI is a computer algorithm which exhibits intelligence through decision making. | ML is an AI algorithm which allows system to learn from data. | DL is a ML algorithm that uses deep(more than one layer) neural networks to analyze data and provide output accordingly. |
| Search Trees and much complex math is involved in AI. | If you have a clear idea about the logic(math) involved in behind and you can visualize the complex functionalities like K-Mean, Support Vector Machines, etc., then it defines the ML aspect. | If you are clear about the math involved in it but don’t have idea about the features, so you break the complex functionalities into linear/lower dimension features by adding more layers, then it defines the DL aspect. |
| The aim is to basically increase chances of success and not accuracy. | The aim is to increase accuracy not caring much about the success ratio. | It attains the highest rank in terms of accuracy when it is trained with large amount of data. |
| Three broad categories/types Of AI are: Artificial Narrow Intelligence (ANI), Artificial General Intelligence (AGI) and Artificial Super Intelligence (ASI) | Three broad categories/types Of ML are: Supervised Learning, Unsupervised Learning and Reinforcement Learning | DL can be considered as neural networks with a large number of parameters layers lying in one of the four fundamental network architectures: Unsupervised Pre-trained Networks, Convolutional Neural Networks, Recurrent Neural Networks and Recursive Neural Networks |
| The efficiency Of AI is basically the efficiency provided by ML and DL respectively. | Less efficient than DL as it can’t work for longer dimensions or higher amount of data. | More powerful than ML as it can easily work for larger sets of data. |
| Examples of AI applications include: Google’s AI-Powered Predictions, Ridesharing Apps Like Uber and Lyft, Commercial Flights Use an AI Autopilot, etc. | Examples of ML applications include: Virtual Personal Assistants: Siri, Alexa, Google, etc., Email Spam and Malware Filtering. | Examples of DL applications include: Sentiment based news aggregation, Image analysis and caption generation, etc. |