Moving from the computer playing chess to self-driving cars, technology continues to reinvent itself. Today, artificial intelligence, machine learning, and deep learning are everywhere!
Moving from the computer playing chess to self-driving cars, technology continues to reinvent itself. Today, artificial intelligence (AI), machine learning (ML), and deep learning (DL) are found in all sectors and at all levels. These technologies are deterministic and make it possible to predict equipment failures or even via a chatbot to interact with customers for a business, for example.
What is the history of artificial intelligence?
The concept of Artificial Intelligence (AI) has its origins in antiquity and has existed for centuries. Artificial intelligence as we know it today has more recent roots. In 1950, mathematician Alan Turing published “Computing Machines and Intelligence,” which answers the question “Can machines think?” This led to the emergence of Turing test It is a method of testing machine intelligence.
Since Turing’s time, advances in artificial intelligence have continued, especially with the development of a computer program capable of playing chess against humans. Computer scientist Arthur Samuel created this program that is able to record all the previous movements of the opponent and create his strategy. In short, the computer learns from the mistakes of the past and plays at a higher level called the intelligent level at each step. Samuel constantly improved and developed this program and in 1952 coined the term machine learning (Machine learning).
From the 1960s to the 1990s, artificial intelligence gained international fame by producing blockbuster films such as “2001: A Space Odyssey”, “Star Wars” and “Electric Dreams”. Since that time, artificial intelligence has been making great strides and competing with the big screen. In the first decade of the twenty-first century, artificial intelligence has officially spread and is popping up everywhere. Although machine learning is not very well known to the general public, it has also continued to evolve and is now one of the most popular applications of artificial intelligence.
Definition of Artificial Intelligence (AI)
Il n’existe pas de définition universellement connue de l’intelligence artificielle en revanche, elle peut être définie comme « une branche de l’informatique qui se focalise sur la construction de machines intelligentes capables d’effectuer des tâchesitlement quiténé léné human being.”
What is narrow AI and AI?
Tight AI, also known asweak artificial intelligence, to describe artificial intelligence systems that deal with a particular task that requires human intelligence. Narrow AI is only used to accomplish what is called a limited task, or one task at a time.
Narrow AI is the form we find everywhere because it is the most common form of AI: from smart assistants to facial recognition systems, search engine recommendations, or predictive maintenance models.
With regard to artificial general intelligence, also called strong artificial intelligence, it reproduces and performs the same intellectual tasks that a human can do. According to TechTalks, it is able to simulate “common sense and basic knowledgelearning transferabstraction and causation”. AI is still theoretical in nature. However, some of its applications – such as emotional analysis – rely on natural language analysis to record emotions in a text – which represents the first stage of the development of this technology.
Define machine learning?
Machine learning is part of a branch of artificial intelligence that allows computers to learn and use a lot of data andAlgorithms Organized to identify several models and then make predictions. The most indicative example of ML is Google Maps, which analyzes several data models: past traffic and current traffic and which recommends the fastest route for its user.
Where machine learning gets really exciting is with deep learning. It is a subset of machine learning that uses artificial neural networks – computer systems inspired by the human brain – to ingest and learn from structured and unstructured data. An example of deep learning in action is self-driving cars, which inherently understand the rules of the road and can interact in real time with things like a stop sign or a person crossing a street.
Deep learning is the most exciting example of machine learning. It is a subset of ML that will use some type of artificial neural network (more precisely many brain-inspired computer systems), in order to integrate and learn structured and unstructured data. The simplest example of deep learning are self-driving cars, which are created to understand the rules of the road and interact with elements in real time: a person crossing, a red light, etc…
Artificial Intelligence vs. Machine Learning vs. Deep Learning: What are the Key Differences?
Although deep learning AI and machine learning belong to the same family, they have different and unique qualities and applications.
The diagram below helps you understand the main differences:
