Machine learning is the discipline of artificial intelligence where artificial intelligence is broader and covers the major systems working intelligently similar to humans. The terminologies machine learning and artificial intelligence are differentiated by the fact that artificial intelligence is the design and synthesis of the useful, intelligent inventions imitating human intelligence. On the other hand, the machine learning emphasis on the learning mechanism of the machines and systems in which there is no programming is done to perform some certain task on a machine instead the machine learns by itself from the previous knowledge and data.
Content: Machine Learning Vs Artificial Intelligence
Comparison Chart
| Basis for comparison | Machine Learning | Artificial Intelligence |
|---|---|---|
| Basic | Attainment of knowledge or skill. | Ability to gain and apply knowledge. |
| Objective | To enhance the accuracy | To increase the probability of success |
| Implementation | The machine takes data and learns from that data. | The computer system does a smart work similar to human. |
| Application | Helps in the generation of learning algorithms. | Systems that mimic human responses according to a circumstance. |
| Obtained solution | Any solution | Optimal |
| Leads to | Knowledge | Intelligence or wisdom |
Definition of Machine Learning
Machine learning is a subdivision of artificial intelligence. It revolves around the computer systems that analyse the data and learn from it. The machine learning utilises the algorithms that consistently learn from data. For example, if there is a task to filter spam emails, then how successful machine learning will be implemented. In the above-given case, the machine is provided with a set of spam emails which are then analysed by the machine. After that, if any spam email arrives the machine first checks it by comparing it from the spam email lists, and if the arriving email is spam, it is deleted and sent to the trash folder.
The above-given example just shows a very limited application of machine learning, but it can be used in an extremely vast field. This knowledge learning helps in discovering patterns and making predictions. The machine learning and artificial intelligence are somehow related terms as machine learning comes under artificial intelligence, means that machine learning is a part of artificial intelligence. Now, this emerges a question, why do we need machine learning? The two main reasons for the necessity of machine learning is the complexity of the problem and the need for adaptivity.
There are three variations of machine learning that are – supervised learning, unsupervised learning and reinforcement learning.
- Supervised learning – Systems are capable of predicting the future results according to the past data. This type of learning requires a training model for accomplishing any of task.
- Unsupervised learning – The hidden patterns are determined by exploring it from the provided input data without requiring any training.
- Reinforcement learning – This type learning method follows the trial and error method in which high reward yielding algorithm is determined.
Definition of Artificial Intelligence
Artificial intelligence is the concept evolved to imitate the human brain. In simplistic terms, it is a way of developing expert systems that can perform tasks that are at present humans can do better. The natural and artificial intelligence would work in the same manner, but the main difference between them is that one is natural another is man-made. Let us understand it by a real-life example, a large wave is producing due to an earthquake in the sea or landslide would be known as tsunami but, if the same wave is created by blasting in the sea or using graphics would also be called as a tsunami.
The main goal of Artificial Intelligence is to understand the concept that makes intelligent behaviour functions in the natural and artificial system. This is achieved by following the below-given steps.
- Natural and artificial agents are analysed at first.
- The hypothesis of the construction of the intelligent agent is planned and tested.
- Then the computational system that performs operations is designed, constructed and analysed.
Key Differences Between Machine Learning and Artificial Intelligence
- Machine learning can be determined by the acquirement of knowledge and skills. As against, artificial intelligence is the capability of gaining and applying knowledge.
- The machine learning intended to improve the accuracy which maximises the performance of the machine on some specific task through learning. Conversely, artificial intelligence aims to increase the chance of success rather than accuracy which result in the simulation of natural intelligence to solve complex problems.
- Machine learning is implemented on the system that needs to analyse the data and learn from the data such as building learning algorithms. On the contrary, the artificial intelligence is employed in the systems performing complex tasks that are performed by the humans in a superior way such as natural language processing, self-driving cars, navigation systems, chatbots, etcetera.
- The solution obtained by artificial intelligence is optimal while which are achieved by machine learning are need not to be optimal always.
Conclusion
The machine learning involves systems that learn things without being programmed to perform such operation. On the other hand, artificial intelligence involves intelligent machines that think and act like human beings.
Raghava says
Hey really great post on the difference between Machine Learning and Artificial Intelligence, I enjoyed reading it a lot. We all know its important in today’s and future world.