We are seeing all the rage of Artificial intelligence and machine learning these days. With all the noise swirling around the tech trends, many of us are unaware of the difference between these buzz words. When the hype is created, often the reality got hide inner the layers of fantasies and anticipations. Sometimes the trends become so common that nobody focuses on the basics and what matter to them is the job’s getting done. For instance, since an algorithm is involved in finding the information, people think it necessarily would involve AI or ML. since the algorithm is the foundation of both artificial intelligence and machine learning, it contributes to the better understanding of both concepts. In order to differentiate between Artificial intelligence and machine learning, lets first understand what an algorithm is.
What Is an Algorithm?
An Algorithm is defined as a set of rules to solve a certain problem.
This set of rules or algorithm is followed while solving a problem. The findings can be based on a very simple or complexed set of rules. Regardless of the complexity or simplicity, an algorithm should be able to provide a correct answer in an efficient manner.
Or otherwise, what is the need of an algorithm if it doesn’t deliver accuracy or takes as long as a human to analyze a problem.
An algorithm is gradually trained to learn to process and classify information efficiently. The accuracy and efficiency largely depend on how well an algorithm is trained to process and deliver results.
However, the use of an algorithm doesn’t always declare the involvement of machine learning or artificial intelligence. Since all squares are rectangles, not every rectangle is square.
Let’s understand both of these terms individually first to get a clear idea.
What Is Artificial Intelligence?
AI is the science of making machines able to think and decide just as human do.
The development of Artificial intelligence includes complex AI algorithms. With Artificial intelligence, machine and robots can be used in a diverse range of fields including agriculture, robotics, healthcare, business, marketing, analytics and more.
Before moving ahead on AI, let’s first understand Machine Learning and how does it relate AI.
What Is Machine Learning?
An algorithm works by taking input and then use logic and mathematics to deliver the result. While on the other hand, an AI Algorithm works by taking a combination of both inputs and outputs concurrently so to learn the data and deliver results when new inputs are given.
The process of enabling machines to learn from data is known as Machine Learning.
So we come to know that Machine Learning is a sub-field of Artificial Intelligence, in which we bring AI into the equation by learning from the data.
Artificial Intelligence vs Machine Learning
These days, we see that the terms Artificial intelligence and Machine language are used interchangeably, so as to indicate the use of an algorithm for data analyzation and result. However, using an algorithm to predict the result is not something defined as machine learning. It is rather the use of an outcome of a prediction in order to improve future prediction.
Consequently, Artificial intelligence and machine learning are not the same things. So, it is suggestible for tech lovers and followers to understand how these two different domains can be applied in different ways.
To put it simply, artificial intelligence is a broader domain than machine learning. The concept of AI involves the use of machines to mimic the cognitive ability of humans. When machines perform a task based on algorithms in an “intelligent” manner, this is referred to as AI.
Alternatively, Machine learning is a sub-domain of artificial intelligence and focuses on a machine’s ability to take a set of data and information to learn for themselves. On learning more about the information they receive and process, change algorithms.
Use of Neural Networks in Machine Learning
To train computers to be able to mimic the cognitive skills of humans, neural networks are used. Neural networks are a series of algorithms imitating the system of the human brain. It recognizes patterns and helps the computer to categorize and classify information.
Neural networks do the same job for computers as a brain does for a human. Benefits of neural networks include extracting meaningful information from complex data and detecting patterns which are too complicated for humans to do. Using neural networks also enables learning by example. And last not but not least, it gives speed advantages.