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Difference Between AI, Machine Learning, and Deep Learning
The most trending technical topics nowadays are artificial intelligence, machine learning, and deep learning. All these technologies are being implemented nowadays in different machines, software applications, etc. In this article, we will discuss the difference between artificial intelligence, machine learning, and deep learning.
What is Artificial Intelligence?
Artificial intelligence is a technology in which a list of rules is used by machines and they act in the same way as humans. AI can be defined as the training of machines by using different algorithms so that they can act in the same way as humans. AI has the parts which include the following:
- Learning
- Self-correction
- Reasoning
All these three parts help in increasing the efficiency of a machine up to the maximum.
Examples of AI
The examples of AI are as follows ?
- Speech recognition
- Personalized recommendations
- Predictive maintenance
- Medical diagnosis
- Autonomous vehicles
- Virtual Personal Assistants
- Autonomous vehicles
- Fraud detection
- Image recognition
- Natural language processing
- Predictive analytics
- Game-playing AI
What is Machine Learning?
Machine learning can be said to be a study or a process which helps computers to learn different things automatically. Developers develop different types of programs which help computers to access data without any human intervention.
Examples of Machine Learning
The examples of machine learning are as follows ?
- Image recognition
- Speech recognition
- Natural language processing (NLP)
- Recommendation systems
- Sentiment analysis
- Predictive maintenance
- Spam filters in email
- Recommendation systems
- Predictive maintenance
- Credit risk assessment
- Customer segmentation
- Fraud detection
What is Deep Learning?
Deep learning is a subset of machine learning. Neural networks are used by deep learning which helps the machines to mimic in the same way as human brains. Neural networks work in the same way as neurons in the human brain.
Examples of Deep Learning
The examples of deep learning are as follows ?
- Image and video recognition
- Generative models
- Autonomous vehicles
- Image classification
- Speech recognition
- Natural language processing
- Recommender systems
- Fraud detection
- Game-playing AI
- Time series forecasting
Difference between AI, Machine Learning, and Deep Learning
AI, Machine Learning, and Deep Learning have many differences which can be found in the table below ?
| AI | Machine Learning | Deep Learning |
|---|---|---|
| Artificial Intelligence is a study or a process which is used by different machines to work in the same way as humans. Machines can do this with the help of some algorithms. | Machine learning uses statistical methods which help machines to improve their experience. | Deep Learning uses Neural Networks which act in the same way as a human brain. |
| Machine learning and deep learning are the components of artificial intelligence. | Machine learning is a part of artificial intelligence. | Deep learning is a part of machine learning. |
| Artificial intelligence is a computer algorithm which uses decision-making to promote intelligence. | Machine learning is an algorithm of artificial intelligence. | Deep learning is an algorithm of machine learning which is used to develop neural networks for data analysis. The output is the result of this analysis. |
| AI consists of the most complicated mathematics. | Complex data can be visualized easily if the logic is clear. | Complex functionalities can be broken into simple ones by adding more layers. |
| AI has the aim to achieve success but there may not be any accuracy. | Machine learning focuses on accuracy rather than success. | Deep learning is trained by using a lot of data so the results are more accurate than machine learning. |
The different categories of AI are ?
|
The categories of machine learning are ?
|
Deep learning consists of different architectures which are ?
|
| ML and DL provide efficiency to AI. | The efficiency of ML is less than DL and it is not suitable if the amount of data is large. | DL is much powerful as it has the ability to handle a large amount of data. |
| AI systems can be data-driven, knowledge-based, or rule-based. | Machin learning consists of learning the error with the help of trial and error. Feedback will be available in the form of punishments or rewards. | DL networks depend on neurons which are interconnected. Hierarchical methodology is used to process the data. |
Conclusion
Artificial intelligence is a broad concept and machine learning and deep learning are its subparts. Deep learning is a part of machine learning. All three technologies are being used to train the machines so that they can mimic humans. Artificial intelligence is used to train machines so that they can work in the same way as humans.
Machine learning is a process in which different types of programs are developed to help computers learn different things automatically. Deep learning uses neural networks which work in the same way as neurons in a human brain.
FAQs on AI Vs. ML Vs. DL
1. Which of the technologies in AI, ML, DL is the broadest?
AI or Artificial Intelligence is the broadest technology as machine learning and deep learning are its parts. Deep learning is a part of machine learning. In the case of AI, there are a set of rules which machines have to follow to work in the same way as humans.
2. What are the categories of Artificial Intelligence?
The categories of AI are ?
- Artificial Narrow Intelligence (ANI)
- Artificial General Intelligence (AGI)
- Artificial Super Intelligence (ASI)
3. Which technology is the most efficient.?
Deep learning is the most efficient as it can handle a large amount of data. DL depends on neural networks which enhances its efficiency.
4. What is the aim of all the three technologies?
Artificial intelligence focuses on success while machine learning focuses on accuracy. The results provided by deep learning are more accurate than machine learning as it uses neural networks.
5. Which technology depends on neural networks?
Deep learning depends on neural networks whose working is similar to that of neurons in a human brain. The results provided by DL are the most accurate in comparison to AL and ML.
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