Machine learning is a subset of artificial intelligence. Various technology companies are utilizing Machine learning and artificial intelligence technologies to offer automated solution predictions to their customers. Sometimes these two technologies are used interchangeably, but they are different in terms of their applications and use cases. In this article we will discuss the difference between Machine Learning vs Artificial Intelligence.
What is the Difference between Machine Learning and Artificial Intelligence
Ultimate goal of AI is to create machines that can think and behave like humans. Whereas machine learning (ML) is a subset of AI that enable machines to learn from data without being explicitly programmed. To understand the difference between machine learning and artificial intelligence in detail, first we will understand what these technologies are?
What is Machine Learning?
Machine learning is a subset of artificial intelligence that provides machines or computer programs ability to learn from their own experience or available data without being explicitly programmed.. ML algorithms work by analyzing and identifying a pattern in data and doing predictions accordingly. Following three types of machine learning techniques are used to do predictions:
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
How Machine Learning system Works?
To understand this we will consider an example of working of ML algorithms to predict if given image is a car or not. A machine learning system works in following steps.
- Splitting Data
- Extracting Features
- Cross Validation Testing
- Prediction
Step-1 : Splitting Data Set
First Step is to split available data in the following three different categories.
- Training Set
- Cross validation Test
- Test Set
Step-2 : Extracting Features using Test Set Data
In the next step labeled data (known output) is provided to the machine learning algorithm for training. ML program extracts features from it and tries to identify a pattern between them.
Step-3 : Cross-Validation and testing of ML Algorithm
After the training of ML algorithm, Next step is running Machine learning algorithm on validation set to ensure if the selected ML algorithm is a right choice for given problem.
Step-4 : Prediction
After training and validation is finished. When unknown input is provided to the ML algorithm. It can identify the pattern and do predictions according to previous learning. In this way Machine learning algorithms learn from previous experience without being explicitly programmed.
What is Artificial Intelligence?
Artificial Intelligence is a broader term that provides machines an ability to understand, think and behave like humans. Machine learning, Deep Learning, machine vision, robotics are subsets of artificial intelligence. Artificial Intelligence can be classified into following types.
- Type 1
- Narrow AI
- General AI
- Strong AI
- Type 2
- Reactive Machines
- Limited Memory
- Theory of Mind
- Self Awareness
Ultimate goal of AI is to enable machines to think like humans. Similar to human, AI enabled machines can do the following tasks:
- Understand speech in the way humans listen.
- Process images in the way humans see.
- Read text and if required to speak the text in the way humans do.
- Learn from their Mistakes.
In this way Artificial Intelligent enabled systems can listen, sense and convey information, learn from mistakes like the way we humans do. For example, AI enabled Self driving cars are improving with every driving hour experience. Self driving cars utilizes machine learning, deep learning, sensors, cloud computing, data science, internet of things and robotics technologies to drive a car without a driver. All of these technologies are components of AI.
Machine Learning vs Artificial Intelligence
Following are the key differences between Machine learning (ML) and Artificial Intelligence (AI).
| Description | Machine Learning | Artificial Intelligence |
|---|---|---|
| Definition | Allow machines to learn from its own experience and available data without programming explicitly. | AI provides machines an ability to understand and think like humans. |
| Scope | Deep Learning is a subset of Machine Learning. | Robotics, Machine Learning, Machine Vision are subset of Artificial Intelligence. |
| Goal | Ultimate goal of ML is make machines that learns from their experience. | Ultimate goal of AI is to enable machines think like humans. |
| Data Input | ML utilizes data sets to acquire knowledge and experience. | AI utilizes ML, cloud computing, data science, IoT, data science and robotic technologies. |
| Applications | ML enabled system provides insight to its user. Following are the Applications of machine learning :
| AI enabled system can take decisions on their own. Following are the applications of Artificial Intelligence:
|
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