Machine learning and artificial intelligence are two of the most spoken words when it comes to the Big Data or Data analysis industries. Even though the two bare different meanings, they have been used interchangeably by most people. In a nutshell, the artificial intelligence is a much broader term than machine learning. Machine learning is an application of artificial intelligence, which is a rather new concept as opposes to artificial intelligence, which has been spoken of for many decades.
Artificial intelligence has been spoken of even during the Greek times where myths have been heard about there being mechanical men, who performed work similar to that of real men. Earlier, during the development of this technology, making computers do complex calculations and statistics was taken under consideration. With the ongoing time, however, it was thought that it would be a better idea for developing technology so as to mimic the human brain in order to enable decision making and to replace the tasks carried out by humans in a better way. Thus, the artificial intelligence, as the name suggests, is something which is made up by humans but with a developed ability to think and understand. Some consider artificial intelligence as a system. However, it is not a system, rather it is something that is implemented in a particular system. In short, it is the way in which you can train computers efficiently so that they can function or are capable of performing tasks that humans do.
The artificial intelligence devices are made to perform and act intelligently. They have been classified into two groups viz. the applied and the general AI.
- Applied artificial intelligence: The applied group of AI is much more common than the general AI group. The systems are designed to perform tasks like trading shares and stocks, to operate an autonomous vehicle and so on.
- Generalized artificial intelligence: These are the systems which are developed in such a manner to understand and handle any sort of task assigned to it. These systems are not as common as the applied AI ones but have a very wide scope for development. This is the source which has given rise to the machine learning and artificial intelligence. Thus, machine learningcan be regarded as a subset of the generalized type of artificial intelligence.
Now that the concept of artificial intelligence has been discussed, it is time to discuss about machine learning.
Machine learning is a science that has been developed in the year 1959, by Arthur Samuel. Machine learning is regarded as the steering wheel of the artificial intelligence, which has taken the science ahead with great speed. There were two vital components that made machine learning a success:
- The thought by Arthur Samuel, that rather than making a technology where the computers will learn everything we teach them, in order to carry out various tasks, let the computers be their own teachers so as to learn and understand.
- Due to the internet, immense information and data were made readily available in order to be used, stored and new data to be generated.
Upon understanding the grave importance of both these points, innovations towards building the perfect technology started taking place.
Machine learning applications have an ability to read a given text and to analyze whether the person involved in writing it is complementing or commenting. The applications can also understand the mood of a person when a particular song is been played by him.
Differences between machine learning and artificial intelligence:
| Artificial Intelligence | Machine Learning |
| Artificial intelligence or AI is defined as the man-made technology developed with an aim to think, understand and make proper decisions. | Machine learning or ML is a technology based on the fact that the system or machine can learn or understand on its own to make proper decisions. |
| The main aim of the AI lies in the fact that it has more to do with increasing the chance of success but can compromise on the level of accuracy. | The main aim of using machine learning is to enable a high level of accuracy, regardless of the rate of success. |
| It simply works on the given data as a regular computer program designed to perform smart work. | It not only reads the data but also tries to understand the same and then perform over the given task. |
| The aim is to solve a complex problem by means of natural intelligence. | The aim is to actually learn from the task in order to maximize the performance of the machine. |
| AI is based on decision making. | ML is more to do with allowing the system to learn from the available data. |
| It enables development of a system to mimic responses like that of a human. | It creates self-learning algorithms. |
| It leads to wisdom or intelligence. | It leads to knowledge. |
The worlds of machine learning and artificial intelligence are thus quite different from one another and very crucial in the betterment of future.