Artificial intelligence: definition, advice, comparisons, testimonials…

Aiming to simulate human intelligence, artificial intelligence has been emerging since the early 2010s, driven by deep learning, big data and the explosion of computing power.

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Artificial intelligence (AI) refers to “an application capable of processing tasks which are currently performed more satisfactorily by human beings insofar as they involve high-level mental processes such as learning perception, memory organization and critical thinking”. This is how the American scientist Marvin Lee Minsky, considered the father of AI, defines this concept. It was in 1956 during a meeting of scientists in Dartmouth (south of Boston) organized to consider the creation of thinking machines that he managed to convince his audience to accept the term.

Following initial work, particularly around expert systems, AI emerges much later. In 1989, Frenchman Yann Lecun developed the first neural network capable of recognizing handwritten digits. But it will be necessary to wait until 2019 for his research and that of the Canadians Geoffrey Hinton and Yoshua Bengio to be crowned with the Turing Prize. Why ? Because to work, deep learning faces two obstacles. First, the computing power needed to train the neural networks. The emergence of graphics processors in the 2010s provides a solution to the problem. Then, learning obviously involves having massive volumes of data. On this plan, the Gafam have since pulled out of the game, but data sets have also been published in open source such as ImagiNET.

Before embarking on the deployment of an AI, it will obviously be necessary to integrate artificial intelligence vocabularyas well as the potential and constraints of the main machine learning methods: supervised learning, unsupervised learning, semi-supervised learning, reinforcement learning

Similarly, many machine learning algorithms are available from the simplest to the most complex: regression, decision treerandom forest, support vector machine, neural network (read our article Which artificial intelligence is made for you?). Depending on the problem to be solved and the quality of the training data set, each will result in predictions with a more or less precise accuracy score.

Infrastructures or libraries for machine learning, 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 choice.

Who uses artificial intelligence?

Automotive, banking-finance, logistics, energy, industry… No sector of activity is spared by 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 AI?

In the automotive industry, artificial intelligence drives autonomous vehicles via deep learning models (or neural networks). In banking-finance, it estimates investment or trading risks. In transport, it calculates the best routes and optimizes flows within warehouses. In both energy and retail, it forecasts customer consumption with a view to optimizing stocks and distribution. Finally, in industry, it makes it possible to anticipate equipment breakdowns (whether for a robot on an assembly line, a computer server, an elevator, etc.) even before they occur. Objective: carry out preventive maintenance operations.

On a daily basis, artificial intelligence is also used to implement intelligent assistants (chatbot, callbot, voicebot) or smartphone cameras to take a snapshot in all circumstances.

Obviously, the digital giants have not waited to exploit the full potential that artificial intelligence can bring them. With volumes of personal data never reached in history, they compete in inventiveness in the use of learning algorithms based around segmentation psychographic to meet the most diverse needs: research, advertising targeting, talent detection, voice interface…

Artificial Intelligence has given rise to a host of new skill profiles. The first of them is none other than the data scientist. Skills are expected of him in big dataalgorithms, statistics, data visualization, but also business expertise.

guide to artificial intelligence

With the rise of AI projects, a new profile comes to support the generalist data scientist: the machine learning engineer. This is a specialized data scientist whose mission is to cover the entire life cycle of a learning model, from its design and training to its monitoring, obviously including its deployment (read the article Machine learning engineer: new star job in data science).

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Artificial intelligence: definition, advice, comparisons, testimonials…

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