Aimed at simulating human intelligence, artificial intelligence has been emerging since the beginning of 2010, driven by deep learning, big data and the explosion in computing power.
Artificial Intelligence, What Is It?
Artificial intelligence (AI) refers to “an application capable of processing tasks that are currently performed more satisfactorily by human beings insofar as they involve high-level mental processes such as perceptual learning, memory organization and critical thinking”. This is how the American scientist Marvin Lee Minsky, considered to be the father of AI, defines the concept. It was in 1956 at 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 in particular around expert systems, AI emerges much later. In 1989, the Frenchman Yann Lecun developed the first neural network capable of recognizing handwritten numbers. But it was not until 2019 that his research and that of Canadians Geoffrey Hinton and Yoshua Bengio were awarded the Turing Prize. Why is this? Because deep learning faces two obstacles. First, the computing power needed to train neural networks. The emergence of graphics processors in the 2010’s brings a solution to the problem. Secondly, learning obviously involves massive volumes of data. In this respect, the Gafams have since been successful, but data sets have also been published in open source such as ImagiNET.
How To Start An AI Project?
Before launching into the deployment of an AI, it will obviously be necessary to integrate the vocabulary of artificial intelligence, as well as the potential and constraints of the main methods of machine learning: supervised learning, unsupervised learning, semi-supervised learning, learning by reinforcement…
Similarly, many machine learning algorithms are available from the simplest to the most complex: regression, decision tree, random forest, support vector machine, neural network (read our article What artificial intelligence is right for you?). Depending on the problem to be solved and the quality of the training data set, each will lead to predictions with a more or less accurate 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 one to make the right choice.
How Is AI Revolutionizing The Economy?
Automotive, banking-finance, logistics, energy, industry… No sector of activity is spared by the rise of artificial intelligence. And with good reason: machine learning algorithms are being used at every level, depending on the business issues involved.
In the automotive industry, they motorize autonomous vehicles via deep learning models (or neural networks). In banking and finance, they estimate investment or trading risks. In logistics, they calculate the best routes and optimize flows within warehouses. In energy and retail, they forecast customer consumption in order to optimize distribution and sales volumes. Finally, in industry, they anticipate equipment failures (whether for a robot on an assembly line, a computer server, an elevator, etc.) even before they occur. Objective: to carry out maintenance operations in a preventive way.
Obviously, the digital giants did not wait to exploit the full potential that artificial intelligence can bring them. With volumes of personal data never before reached in history, they are rivaling each other in inventiveness in the use of learning algorithms based on psychographic segmentation to meet the most diverse needs: search, advertising targeting, talent detection, voice interface…
Artificial Intelligence has given rise to a whole host of new skill profiles. The first of them is none other than the data scientist. He is expected to have skills in big data, algorithms, statistics, data visualization, but also business expertise.
With the rise in importance of AI projects, a new profile has emerged to support the generalist data scientist: the machine learning engineer. This is a specialized data scientist whose mission is to cover the entire lifecycle of a learning model, from its design and training to its monitoring, and of course its deployment.
Our Perspective And Integration
Our experienced team can help with your Artificial Intelligence projects. We know how AI can improve your business process and how you can automate your workflows. Contact us today.