There is a lot of confusion about machine learning and artificial intelligence. Most people are generally sure that these two terms refer to the same thing. There are situations when companies sell their solutions and deliberately ignore the differences between the terms to achieve a more resounding effect in advertising.
To find out the difference, let’s consider the main features of artificial intelligence and machine learning.
What is Artificial Intelligence (AI)
AI is the ability of computer systems to perform creative and intelligent functions that were traditionally considered the domain of humans. Moreover, the main idea of AI is to teach machines to complete tasks faster, more efficiently, and better than humans.
Artificial intelligence combines several scientific areas: neural networks, machine learning, natural language processing, cognitive computing, and computer vision.
Artificial intelligence is usually divided into three categories:
- Artificial Narrow Intelligence. Also known as weak AI due to its limited abilities. It came about in the first phase of AI. An AI with weak or narrow intelligence is better at completing the specific tasks for which it is designed. It cannot be used to assist in other areas outside of its predefined areas of work.
- General artificial intelligence. You may have seen an advanced version of AI in sci-fi movies where machines perform human functions. General intelligence is a category of AI in which machines perform any intellectual task with the efficiency of a human.
- Artificial superintelligence. It is what people usually think of when on the topic of AI. The world of robots and machines surpasses the human mind and people. AI scientists and companies are working to improve the features and intelligence of AI applications. Thus, in the future, machines will be more intelligent, more competent, and creative.
What is Machine Learning (ML)
Machine learning is a branch of AI that is traditionally defined as: “the science of computer algorithms that automatically improve through experience.” Machine learning relies on working with large datasets by examining and comparing data to find common patterns and learn nuances.
For example, if you provide a machine learning program with many X-ray images that include corresponding symptoms, it can simplify or even automate the analysis of X-ray images in the future. The algorithm will compare all these different images and find common patterns in images that have been tagged with similar symptoms. In addition, if more images are added, the algorithm will compare the content against the established patterns and be able to determine the likelihood of having the symptoms it has learned.
Three components are vital to the concept of machine learning.
- Datasets. Sets of data that are used by machines for prediction, analysis, and calculations. Datasets can include images, text, or any other type of data.
- Functions. In machine learning, multiple inputs are required to make a decision. Characteristics are individual variables that are independent in nature and act as input to the prediction system.
- Algorithms. An algorithm is a procedure that uses data to create a machine learning model and produce a result. Different algorithms can be used to solve the same problem. Sometimes it is better to combine different algorithms to achieve better and higher performance.
Key differences between Artificial Intelligence and Machine Learning
To understand the difference between AI and ML, consider the following points:
- A programmer develops a program that can learn. During the initial stage, the algorithm does nothing.
- After that, the programmer adds ML techniques to improve the program.
- The program is then able to pass the training and can be considered an AI.
Today machine learning is the only possible option for creating AI since any modern technology is the result of computer training.
AI includes not only Machine learning but also various computing powers, programs, data, etc.
Let’s say you want to classify images into two categories (cats and dogs). When using machine learning, you need to represent these pictures and the structured data. So, you have to label images of dogs and cats so that the algorithm can determine the characteristics of each animal species. This type of information will be enough for the training. After that, the algorithm will continue to work based on the initial information.
AI, in its turn, will use a different approach to solve the problem. There is no need to label the images since the technology will independently determine the specific features of each image. After processing the data, AI will find the corresponding identifiers in the images and be able to classify them.
Conclusion
AI refers to devices that mimic human intelligence in one form or another. There are many methods of artificial intelligence, and machine learning is one of them. Deep learning is another critical component that should not be dismissed. It is a subset of ML and uses multilayer neural networks to complete complex tasks.
Today, technology is applied to problems that classical programming cannot solve. Therefore, many areas such as face recognition, image classification, and natural language processing, which have remained stagnant for a long time, are undergoing huge leaps in development. This has led to a resurgence in the popularity of artificial intelligence in recent years, and with it, machine learning is now more in demand than ever.
