Artificial Intelligence vs Machine Learning vs Deep Learning: What’s the difference between them?
AI is the broader field; ML is a subset; Deep Learning uses deep neural networks. How do you tell the three apart, and where does each apply in practice?
The concepts of Artificial Intelligence, Machine Learning, and Deep Learning are often used by people as if they had the same meaning; however, there are some fundamental differences between them.
In this article, we will dive deeper into what those differences are and the main applications of these technologies, especially for data science.
What is Artificial Intelligence?
Are machines capable of thinking?
This question, originally documented in an article by Alan Turing in 1950, was one of the factors that helped drive major innovations in computer science, especially when it comes to Artificial Intelligence technology, whose core goal is to apply the rational elements of the human mind to robots and other computer systems.
The history of AI development has been marked by a series of setbacks, advances, delays, and waves of high investment and investment losses, but in the last two decades it has seen remarkable progress, with developments in robotics, the internet, digital media, and less complex programming languages.
This is still a rapidly developing field with virtually infinite application possibilities. What was once extremely difficult to understand or find real applications for is now part of people’s daily lives.
For example: software that creates images and digital artwork or even videos from natural language descriptions; highly intelligent digital assistants; translation software capable of interpreting figurative contexts in texts and adapting them from one language to another; among other simple or complex applications. The way AIs work depends on how they are programmed, but they can be divided into three main groups:
Supervised: In this type of artificial intelligence, the final outcome is directly defined by the programmer. For example: AIs that are taught to recognize and indicate items, objects, people, animals, or other elements programmed into them, and are eventually able to do so autonomously based on the parameters they initially learned.
Semi-supervised: In this case, it compiles the information it has learned to indicate the expected result, according to what the person responsible for its learning process expects.
Unsupervised: Here, AIs are able to independently draw parallels between different items, objects, or samples, without the person responsible for training specifying what is expected. Within the context of Artificial Intelligence, there are Machine Learning and Deep Learning technologies. In other words: both are contained within the broader AI field, even though they are distinct concepts.
What is Machine Learning?
Machine Learning is a field dedicated to studying and developing techniques and methodologies based on algorithms that can improve machines’ ability to perform certain functions autonomously. In general, they can be used across many fields of knowledge, from speech recognition (as in Amazon’s Echo Dot—the famous Alexa) to the creation of accurate statistical models and scenario prediction, based on mathematical optimization and data mining.
These applications can be used to improve data performance and indicate improvements across all business segments and areas, making this a highly relevant field of study for entrepreneurs.
What is Deep Learning?
Within the field of Machine Learning, we have Deep Learning, which is the process of teaching computers to think by association and learn by example—the way human beings naturally acquire knowledge.
Through it, computers learn to recognize and classify tasks through pattern recognition, such as voice, images, videos, and text messages. Some examples of Deep Learning seen in everyday life are: driverless cars, digital assistants on mobile devices, aerospace defense, object identification in space satellites, and even medical research for genetic markers and cancer detection.
In conclusion…
The advancement of Artificial Intelligence technology is a field that generates great interest and sparks everyone’s curiosity. In recent decades, we have seen accelerated development of this technology, which has also enabled the development of specialized areas within the AI scope, such as Machine Learning, and subsequently Deep Learning.
Although many people confuse all these concepts, they are distinct fields of research within the scope of Artificial Intelligence, and their applications have had an increasingly greater impact on everyday life, such as increasingly intelligent smartphones and mobile devices, safer vehicles, household tools that make routines easier, and medical advances that help improve people’s quality of life.
Each of these fields presents infinite potential for expansion, especially for businesses and the creation of more data-driven companies.
To learn more about applying these technologies in business, visit the Erathos blog, where we frequently publish content exploring how to build a data-driven organization.
The concepts of Artificial Intelligence, Machine Learning, and Deep Learning are often used by people as if they had the same meaning; however, there are some fundamental differences between them.
In this article, we will dive deeper into what those differences are and the main applications of these technologies, especially for data science.
What is Artificial Intelligence?
Are machines capable of thinking?
This question, originally documented in an article by Alan Turing in 1950, was one of the factors that helped drive major innovations in computer science, especially when it comes to Artificial Intelligence technology, whose core goal is to apply the rational elements of the human mind to robots and other computer systems.
The history of AI development has been marked by a series of setbacks, advances, delays, and waves of high investment and investment losses, but in the last two decades it has seen remarkable progress, with developments in robotics, the internet, digital media, and less complex programming languages.
This is still a rapidly developing field with virtually infinite application possibilities. What was once extremely difficult to understand or find real applications for is now part of people’s daily lives.
For example: software that creates images and digital artwork or even videos from natural language descriptions; highly intelligent digital assistants; translation software capable of interpreting figurative contexts in texts and adapting them from one language to another; among other simple or complex applications. The way AIs work depends on how they are programmed, but they can be divided into three main groups:
Supervised: In this type of artificial intelligence, the final outcome is directly defined by the programmer. For example: AIs that are taught to recognize and indicate items, objects, people, animals, or other elements programmed into them, and are eventually able to do so autonomously based on the parameters they initially learned.
Semi-supervised: In this case, it compiles the information it has learned to indicate the expected result, according to what the person responsible for its learning process expects.
Unsupervised: Here, AIs are able to independently draw parallels between different items, objects, or samples, without the person responsible for training specifying what is expected. Within the context of Artificial Intelligence, there are Machine Learning and Deep Learning technologies. In other words: both are contained within the broader AI field, even though they are distinct concepts.
What is Machine Learning?
Machine Learning is a field dedicated to studying and developing techniques and methodologies based on algorithms that can improve machines’ ability to perform certain functions autonomously. In general, they can be used across many fields of knowledge, from speech recognition (as in Amazon’s Echo Dot—the famous Alexa) to the creation of accurate statistical models and scenario prediction, based on mathematical optimization and data mining.
These applications can be used to improve data performance and indicate improvements across all business segments and areas, making this a highly relevant field of study for entrepreneurs.
What is Deep Learning?
Within the field of Machine Learning, we have Deep Learning, which is the process of teaching computers to think by association and learn by example—the way human beings naturally acquire knowledge.
Through it, computers learn to recognize and classify tasks through pattern recognition, such as voice, images, videos, and text messages. Some examples of Deep Learning seen in everyday life are: driverless cars, digital assistants on mobile devices, aerospace defense, object identification in space satellites, and even medical research for genetic markers and cancer detection.
In conclusion…
The advancement of Artificial Intelligence technology is a field that generates great interest and sparks everyone’s curiosity. In recent decades, we have seen accelerated development of this technology, which has also enabled the development of specialized areas within the AI scope, such as Machine Learning, and subsequently Deep Learning.
Although many people confuse all these concepts, they are distinct fields of research within the scope of Artificial Intelligence, and their applications have had an increasingly greater impact on everyday life, such as increasingly intelligent smartphones and mobile devices, safer vehicles, household tools that make routines easier, and medical advances that help improve people’s quality of life.
Each of these fields presents infinite potential for expansion, especially for businesses and the creation of more data-driven companies.
To learn more about applying these technologies in business, visit the Erathos blog, where we frequently publish content exploring how to build a data-driven organization.