Glossary

An algorithm is a method of problem solving through a finite and non-ambiguous series of operations. More specifically, in an artificial intelligence context, it is the series of operations applied to input data to achieve the desired result.

Artificial intelligence (AI) refers to the series of techniques which allow a machine to simulate human learning, namely to learn, predict, make decisions and perceive its surroundings. In the case of a computing system, artificial intelligence is applied to digital data.

An AIS is any computing system using artificial intelligence algorithms, whether it’s software, a connected object or a robot.

A chatbot is an AI system that can converse with its user in a natural language.

DAAS refers to any computing system that can collect and record data. This data is eventually used to train AI systems or as decision-making parameters.

An AIS’s decision is justified when there exist non-trivial reasons that motivate this decision, and that these reasons can be communicated in natural language.

Deep learning is the branch of machine learning that uses artificial neuron networks on many levels. It is the technology behind the latest AI breakthroughs.

By digital activity we mean all actions taken by an individual in a digital environment, whether on a computer, a phone or any other connected object.

Digital commons are the applications or data produced by a community. Unlike material goods, they are easily shareable and do not deteriorate when used. Therefore, unlike proprietary software, open source software—which is often the result of a collaboration between programmers—are considered digital commons since their source code is open and accessible to all.

Digital disconnection refers to an individual’s temporary or permanent ceasing of online activity

An individual’s digital literacy refers to their ability to access, manage, understand, integrate, communicate, evaluate and create information safely and appropriately through digital tools and networked technologies to participate in economic and social life.

The filter bubble (or filtering bubble) expression refers to the “filtered” information which reaches an individual on the Internet. Various services such as social networks or search engines offer personalized results for their users. This can have the effect of isolating individuals (inside “bubbles”) since they no longer have access to common information.

Acronym for Generative Adversarial Network. In a GAN, two antagonist networks are placed in competition to generate an image. They can for example be used to create an image, a recording or a video that appears practically real to a human being.

An AIS is intelligible when a human being with the necessary knowledge can understand its operations, meaning its mathematical model and the processes that determine it.

Machine learning is the branch of artificial intelligence that consists of programing an algorithm so that it can learn by itself. The various techniques can be classified into three major types of machine learning:

1. 1. In supervised learning, the artificial intelligence system (AIS) learns to predict a value from entered data. This requires annotated entry-value couples during training. For example, a system can learn to recognize an object featured in a picture ;
   2. In unsupervised learning, AIS learns to find similarities among data that hasn’t been annotated, for example in order to divide them into various homogeneous partitions. A system can thereby recognize communities of social media users;
   3. Through reinforcement learning, AIS learns to act on its environment in order to maximize the reward it receives during training. This is the technique through which AIS was able to beat humans in the game of Go or the videogame Dota2.

Open data is digital data that users can access freely. For example, this is the case for most published AI research results.

Social mechanism through which technological, organizational or institutional decisions, once deemed rational but now subpar, still continue to influence decision-making. A mechanism maintained because of cognitive bias or because change would require too much money or effort. Such is the case for urban road infrastructure when it leads to traffic optimization programs, rather than considering a change to organize transportation with very low carbon emissions. This mechanism must be known when using AI for special projects, as training data in supervised learning can sometimes reinforce old organizational paradigms that are now contested.

Personal data are those that help directly or indirectly identify an individual.

The rebound effect is the mechanism through which greater energy efficiency or better environmental performance of goods, equipment and services leads to an increase in use that is more than proportional. For example, screen size increases, the number of electronic devices in a household goes up, and greater distances are travelled by car or plane. The global result is greater pressure on resources and the environment.

An AIS is reliable when it performs the task it was designed for, in expected fashion. Reliability is the probability of success that ranges between 51% and 100%, meaning strictly superior to chance. The more a system is reliable, the more its behavior is predictable.

The notion of strong environmental sustainability goes back to the idea that in order to be sustainable, the rate of natural resource consumption and polluting emissions must be compatible with planetary environmental limits, the rate of resources and ecosystem renewal, and climate stability. Unlike weak sustainability, which requires less effort, strong sustainability does not allow the substitution of the loss of natural resources with artificial capital.

Sustainable development refers to the development of human society that is compatible with the capacity of natural systems to offer the necessary resources and services to this society. It is economic and social development that fulfills current needs without compromising the existence of future generations.

Training is the machine learning process through which AIS build a model from data. The performance of AIS depends on the quality of the model, which itself depends on the quantity and quality of data used during training.