Artificial intelligence is more and more present in our lives and it remodels permanently the world we live in. For this reason, we need to know what is meant artificial intelligence and how it works.
What is Artificial Intelligence?
At first, artificial intelligence seemed to be just a science-fiction subject: talking machines, machines that can think, machines that can feel. However, scientists have recently made significant advances in this field of computer science research.
Over the years, much has been said about artificial intelligence, car learning, and deep learning. What difference does it make among these three terms, which is quite difficult to understand, and what is the connection between them?
Artificial Intelligence (AI)
is a branch of computer science with the aim of building intelligent machines and shaping human intelligence.
is a branch of the artificial intelligence science that aims to give the machines the ability to “learn”. This is done by using algorithms that identify patterns based on received data, so machines can make decisions and make predictions that is, become “smart.” In this way, it is no longer necessary for the machine to be programmed specifically for each individual action.
As Arthur Samuel stated in 1959, machine learning is “the science that gives computers the ability to learn without being explicitly programmed.”
On the other hand, Deep Learning is a branch of the machine learning science that represents the most advanced field of artificial intelligence. It has the main aim of giving cars the ability to learn and think as much as humans.
The picture below illustrates perfectly the relationship between artificial intelligence, machine learning, and deep learning.
The term “artificial intelligence” appeared in 1959, and as Raymond Kurzweil, a pioneer in the field of artificial intelligence, artificial intelligence is “the art of creating cars that would perform intelligence functions if they were fulfilled by people”.
The progress made in this area of informatics has sparked heated debates about the threat that artificial intelligence poses to humanity, whether we are talking about a physical threat (there are voices claiming humanity can be exterminated by robots endowed with artificial intelligence), or Economic (to prevent this danger, the basic universal income, which is currently being tested in some countries) has been proposed.
Machine learning methods allow the elimination of the need for machine programming for each action in a list of possibilities and determine how the machine intelligence should react to each of them.
From 1949 until the late 1960s, American engineer Arthur Samuel worked intensively on the development of artificial intelligence, from the mere recognition of models to learning from experience. He used a chess game for his research while working with IBM, and his work later influenced the programming of early IBM computers.
Current machine learning methods are becoming more and more sophisticated, integrating them into a range of complex medical applications such as genome analysis, in a disease prevention effort, diagnosing depression based on speech patterns, or identifying individuals with suicidal tendencies.
Deep learning requires a complex architecture that imitates neural networks of the human brain to give meaning to models even when details are lacking, data available is insufficient, or when they can create confusion. Although the possibilities offered by deep learning are vast, its requirements are: we need a lot of data and extraordinary computing power.
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