Machine Learning is Part of Artificial Intelligence, What's the Difference?


Machine learning and artificial intelligence are very popular terms in the technology world. These two terms are often used interchangeably. But basically, artificial intelligence and machine learning have differences in terms of algorithms. Machine learning is part of artificial intelligence which refers to software and hardware that can imitate human intelligence. Machine learning requires algorithms to collect and analyze large amounts of data, volumes, and scales. Currently, the amount and type of data is increasing so that humans need a computational process to extract the data into useful, inexpensive, and easy-to-understand information. Such computing can be discovered by combining artificial intelligence with machine learning. The combination of the two will result in a model that can analyze complex and large data automatically. In addition, the analysis results obtained will be faster and more accurate. 

According to Andrew Moore, former dean of the Faculty of Computer Science at Carnegie Mellon University who is now the head of Google Cloud AI "Artificial intelligence is the science and engineering of making computers behave in ways that, until recently, we thought required human intelligence". The behavior in question is the decision-making process, image and speech recognition, problem solving, and translation. One example of technology that utilizes artificial intelligence and machine learning is the iRobot vacuum cleaner robot named Roomba. This robot can scan the size of the room, identify where items are located, and find the most efficient cleaning route. Even the latest version of this robot can make a map of the house and know the floor plan. From this example, It is clear that in this modern and technology-based era, artificial intelligence and machine learning are very important for human life. Although machine learning is part of artificial intelligence, machine learning is still different from artificial intelligence. Then what's the difference? DQLab has summarized the difference between artificial intelligence and machine learning. Want to know anything? Let's read this article to the end!

1. What is Machine Learning?

Machine learning is a branch of artificial intelligence and according to Tom M. Mitchell, a computer scientist and pioneer of machine learning, machine learning is the study of computer algorithms that can improve the performance of computer programs automatically through previous data. How machine learning works is to collect, examine, and compare small to large data to find patterns and explore differences. Machine learning is divided into three types, namely supervised learning, unsupervised learning, and reinforcement learning. 

Supervised learning is an algorithm used to model relationships and dependencies between predicted output targets and inputs so that we can predict output values ​​for new data based on previously established relationships and dependencies. Unsupervised learning is a machine learning algorithm whose main functions are pattern detection and descriptive modeling. This algorithm does not have output categories or labels on the data (does not have training data and test data). While reinforcement learning is an algorithm that aims to take actions that will maximize output and minimize risk by observing and collecting interactions with the environment. 

2. What is Artificial Intelligence?

The term artificial intelligence is a combination of the words artificial and intelligence which means the ability to think artificially. In general, artificial intelligence can be defined as a field of computer science that has an emphasis on creating intelligent machines that can work like humans. Examples of the application of artificial intelligence in technology are Google Home, Siri, and Alexa which are currently better known as human "intelligent assistant robots". The term artificial intelligence emerged in 1956 when it was coined by a group of researchers, including Allen Newell and Herbert A. Simon. Since then, the artificial intelligence industry has continued to experience fluctuating developments. 

Artificial intelligence is divided into two types, namely narrow artificial intelligence and Artificial General Intelligence. Narrow artificial intelligence or also known as weak artificial intelligence is a simulation of human intelligence. Narrow artificial intelligence is focused on doing one task well. Although including intelligent machines, this type of artificial intelligence still works under human intelligence. Narrow artificial intelligence is all around us and is the most successful type of artificial intelligence today. Some examples of Narrow artificial intelligence are Google search, Image recognition software, Siri, Alexa, Self-driving cars, and IBM's Watson. Artificial General Intelligence (AGI), also known as strong artificial intelligence, is a type of artificial intelligence that we usually see in movies, such as robots from Westworld and Star Trek: The Next Generation. Artificial general intelligence is a machine that is programmed the same as general intelligence that humans have. Just like humans, this type of artificial intelligence can also solve any problem. 

3. Difference between Machine Learning and Artificial Intelligence

The main purpose of artificial intelligence is to increase the chances of success whereas machine learning is more focused on accuracy than the chances of success of a system. Another goal of artificial intelligence is to stimulate natural intelligence to solve complex problems while another goal of machine learning is to study data for a specific task so as to maximize machine performance. Generally, artificial intelligence is used for decision making while machine learning is used to help the system to learn from previous experiences. Artificial intelligence develops systems to imitate humans so that the system can respond and do something, while machine learning helps algorithms to work automatically. 

Machine learning, artificial intelligence, and data science are interrelated sciences. Similar to artificial intelligence and machine learning, data science can be used in various sectors. This science is a combination of statistics, mathematics, and computer science. The increase in data production is directly proportional to the increasing job vacancies as data scientists. One of the competencies that a data scientist must have is understanding machine learning and artificial intelligence. It's no wonder that someone who wants to explore data science must also explore artificial intelligence and machine learning. Uniquely, data science can be studied by anyone, from students to professors. In fact, currently data science can be studied by anyone with any educational background,

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