Generative Artificial Intelligence: understanding the concept

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Generative Artificial Intelligence

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In this article, we look at the notion ofgenerative artificial intelligencea category of algorithms for creating original content from existing data.

Generative artificial intelligence opens the way to a wide range of applications in fields such as audiovisual, the arts and the design of physical objects.

Artificial Intelligence categories

Artificial intelligence (IA) is a vast field that can be divided into several sub-categories:

• Deterministic AI These algorithms are designed to solve very specific problems using pre-established rules. They do not develop new knowledge and are not capable of learning.
• Adaptive AI On the contrary, they are able to learn from past experience and continually improve.
• Generative AI This is the central theme of our article. These artificial intelligences have the ability to create artistic things, in original and sometimes even unprecedented ways.

As a result generative artificial intelligence is a particularly emerging branch of AI, based on automatic and deep learning techniques (machine learning and deep learning) to generate innovative content from existing data.

How do generative AIs work?

To better understand the concept, it is useful to look at the methods used by generative artificial intelligences. Among them, two approaches frequently stand out:

1. Generative models  They are used to create large, complex data representations from a smaller input.
2. Generative Adversarial Networks (GAN) This technique involves competition between two neural networks to produce high-quality results.

Generative models: a probabilistic approach

This method is based on the idea that the observed data is the result of a hidden generation process, which can be modelled using probabilities. Generative models seek to learn the underlying structure of data in order to propose new instantiations. Types of generative models include :

• • Probabilistic graphical modelssuch as Markov fields or Bayesian networks, which offer a formalisation of causal hypotheses about the data.
  • Latent variable modelssuch as Boltzmann networks and auto-encoders, which seek to discover hidden representations of data.

Generative Adversarial Networks (GAN): the art of competition

The generative adversarial networks (GAN) was introduced in 2014 by Ian Goodfellow, a researcher specialising in deep learning. This technique pits two neural networks against each other:

• • The network generatorThe aim is to create data that resembles the reference data as closely as possible.
  • The network discriminatorwhich must differentiate between counterfeits produced by the generator and genuine data.

This competition drives the generator to continually improve its ability to create convincing data, while the discriminator also improves its ability to detect fakes. All in all, the GAN method delivers highly effective results in the creation of original, realistic content.

Examples of applications of generative AI

Generative artificial intelligence has applications in a wide range of fields:

Visual arts

Artistic works such as paintings or drawings can be generated using generative AI. For example, algorithms reproduce the style of famous painters to create new works in their tradition.

Audiovisual arts

Generative algorithms are also capable of composing music, writing film scripts and even categorising and editing videos to create original montages.

Literature

AIs have been created to write poems or stories in a variety of literary styles. They can also help with writing by suggesting ideas for dialogues, characters or plots based on existing texts.

Design of physical objects

In the fields of industrial design and architecture, generative AI can be used to imagine original shapes for objects or buildings. The algorithms are fed with data such as technical, environmental, aesthetic and cultural constraints.

Generative AI: challenges and prospects

However, the development and use of generative artificial intelligence raises a number of questions:

• Originality To what extent are the creations of generative AI truly innovative? Aren't they simply a reflection of the learning data provided?
• Responsibility Who is responsible for the creations generated by an AI? The designer of the algorithm, the owner of the data, or nobody, since the AI is "independent"?
• Ethics How can we prevent algorithms from reproducing and amplifying the stereotypes, prejudices and discrimination present in the initial data?

Finally, future developments in generative artificial intelligence could incorporate new dimensions, such as taking account of human emotions and self-awareness, all of which are likely to enrich the creations offered by these technologies.