With the aim of simulating human intelligence, artificial intelligence has emerged since the early 2010s, driven by deep learning, big data, and the explosion of computing power.
Artificial intelligence (AI) refers to “an application that is able to more satisfactorily process tasks currently performed by humans in that they involve higher-level mental processes such as perceiving learning, organizing memory, and critical thinking.” This is how the American scientist Marvin Lee Minsky, the father of artificial intelligence, defines this concept. It was in 1956 during a meeting of scholars in Dartmouth (south Boston) organized to consider creating thinking machines that he was able to persuade his audience to accept the term.
After early work, especially with regard to expert systems, artificial intelligence emerged later. In 1989, the French Yann Lecun developed the first neural network capable of recognizing handwritten numbers. But it will be necessary to wait until 2019 for his research and that of Canadians Jeffrey Hinton and Joshua Bengio to be crowned with the Turing Prize. why ? Because to work, deep learning faces two obstacles. First, the computing power needed to train neural networks. The advent of graphics processors in 2010 provides a solution to the problem. Then, it is clear that learning involves having huge amounts of data. In this regard, Gafam has since pulled out of the game, but datasets have also been published in open source such as ImagiNET.
Before embarking on the deployment of AI, it is clear that it will be necessary to integrate the vocabulary of AI, as well as the capabilities and limitations of the main methods of machine learning: supervised learning, unsupervised learning, semi-supervised learning, and reinforcement. learning, etc.
Similarly, many machine learning algorithms are available from the simplest to the most complex: regression, decision tree, random forest, support vector machine, and neural network (read our article What AI Is For You?). Depending on the problem to be solved and the quality of the training data set, each will result in predictions of more or less accuracy.
Machine learning infrastructures or libraries, deep learning, automated machine learning environments, data science studio… Tools abound in the field of artificial intelligence. Hence the importance of comparing the strengths and weaknesses of each to make the right decision.
Who uses artificial intelligence?
Automobiles, banking finance, logistics, energy, industry… No sector of activity is escaping the rise of artificial intelligence. And for good reason, machine learning algorithms are available at all levels depending on business issues.
What are the benefits of artificial intelligence?
In the automotive industry, AI drives autonomous vehicles via deep learning models (or neural networks). In banking finance, you estimate the risk of an investment or trade. In transportation, it calculates the best paths and optimizes the flows within the warehouses. In both energy and retail, you anticipate customer consumption with the goal of optimizing inventory and distribution. Finally, in industry, it makes it possible to anticipate equipment failures (whether for a robot on an assembly line, a computer server, an elevator, etc.) even before they happen. Objective: To perform preventive maintenance operations.
On a daily basis, AI is also used to implement smart assistants (chatbot, callbot, voicebot) or smartphone cameras to take a snapshot in all conditions.
It is clear that the digital giants did not wait to exploit the full potential that artificial intelligence can offer them. Since volumes of personal data have never been reached in history, they compete in innovation in using detailed learning algorithms about psychographic segmentation to meet the most diverse needs: search, advertising targeting, talent discovery, voice interface…
Artificial intelligence has given rise to a range of new skill profiles. The first of them is none other than a data scientist. We expect him to have skills in big data, algorithms, statistics, data visualization, and also experience in business.
A guide to artificial intelligence
With the rise of AI projects comes a new profile to support the public data scientist: the machine learning engineer. This is a dedicated data scientist whose job is to cover the full lifecycle of a learning model, from its design and training to its monitoring, explicitly including its deployment (read the Machine Learning Engineer article: A New Stellar Job in Data Science).