Artificial Intelligence, or AI, has become an important and integral part of our modern society. According to Forbes, two years ago, in 2017, 51% of modern enterprises have already implemented AI, and the industry itself was estimated at $16 billion. According to forecasts, this indicator will grow exponentially, reaching 190 billion US dollars in 2025.
Often the terms artificial intelligence and machine learning are used unsystematic and are considered to be interchangeable, but, in fact, there are differences between them.
AI has become a kind of generic term that can mean several things, including machine learning. This creates some confusion about the fact that many people associate AI with independent thinking. Nevertheless, from the definition of machine learning, it follows that this is an action that a person could perform, and it requires a certain level of intelligence. Perhaps these actions do not need a high level of intelligence, but they fall under the definition of AI.
Today we are going to speak about the difference between these two terms!
A little bit of theory
What is Artificial Intelligence?
Artificial Intelligence (AI) — various technological and scientific solutions and methods that help make programs in the likeness of human intelligence. Artificial intelligence includes many tools, algorithms, and systems, among which are also all components of Data Science and Machine learning.
Today, lots of artificial intelligence systems are used in almost any application that uses data, such as management software, recommendation algorithms, media analysis, or even voice assistants. In fact, even simple tracking apps now use AI. As practice shows, if there is a rather complicated process of completing a task that must be performed regularly without direct human intervention, it will most likely contain AI. In general, artificial intelligence systems can be divided into three groups:
- limited artificial intelligence (Narrow AI)
- general artificial intelligence (AGI)
- superintelligent artificial intelligence.
AI functionality is widely in demand in all sectors, especially for question-and-answer systems that can be used in the provision of legal assistance, patent search, risk communication, and medical research. Other uses for AI are presented below:
What is Machine Learning?
Machine learning is the use of algorithms for analyzing data, drawing conclusions, and making decisions or predicting in relation to something. Instead of creating programs manually using a special set of commands to perform a specific task, the machine is trained using a large amount of data and
algorithms that enable it to learn how to perform this task. That is, the machine can find a pattern in complex and multi-parameter problems (which the human brain cannot solve), thus finding more accurate answers. As a result, correct forecasting.
Therefore, instead of routine programs for manual coding with a certain set of instructions for performing a specific task, the machine “learns” using a large amount of data and algorithms that enable it to learn how to complete the task.
Machine learning arose directly from AI practices, and algorithmic approaches over the years included learning the decision tree, inductive logical programming, clustering, gain training, and the Bayesian network.
There is only exist a subset of machine learning — Deep Learning, where algorithms are created and operate similarly to machine learning, but there are many levels of these algorithms, each of which provides a different interpretation of the data that it conveys. Such a network of algorithms is called artificial neural networks. In simple words, it resembles the neural connections that exist in the human brain.
AI and ML
Here we prepare a comparative table to show the distinctive features of those two great definitions:
To sum up
Although artificial intelligence and machine learning can be used interchangeably for many common applications, it is important to note that machine learning has one very distinctive characteristic: adaptation. This means that ML is always studying. The system can make many initial mistakes, unlike the previously created AI, but it is designed to learn from them, build from them and, ultimately, use all this to solve the tasks for which it was created.
Have any questions? Looking forward to answering!