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We introduce you to the complexity and workings of generative Artificial Intelligence, methodically breaking down its algorithmic nature, revealing how it is able to create unique and varied content by automating the creative process.
Definition of generative AI
What is generative AI?
Generative artificial intelligence, or IA generative, is a branch of artificial intelligence that uses models to generate new data that mimics existing reference data. This new information can take various forms, such as a imageThe model is then used to create a new type of content, such as music or text, depending on the field of application and the data the model has been trained on. Generative AI is thus able to create original content that is consistent with training data.
Differences between generative AI and discriminative AI
Generative AI differs from discriminative AI in the way it approaches problems. Whereas discriminative AI seeks to categorize data by finding decision boundaries, generative AI seeks to understand data at a deeper level. Generative AI tries to model how data is produced, by understanding the latent variables that contribute to the formation of that data.
Fundamentals of generative AI
Statistical models
In generative AI, statistical models are used to learn the distribution of training data. These models can be simple or complex, ranging from Gaussian to Markov models. Thanks to this statistics-based approach, generative AI can generate new data that resembles real data.
Density estimation
Density estimation is a fundamental concept in generative AI. It is a statistical technique whose aim is to construct a function that is a good model of the distribution of the data it was trained on. The aim is to learn how to generate new, similar data from this distribution.
Likelihood maximization
La maximisation de vraisemblance est une autre technique clé en IA générative. Elle sert à estimer les paramètres des modèles génératifs en maximisant la probabilité des données observées, en fonction des paramètres. Elle permet de rendre le modèle le plus “vraisemblable” possible compte tenu des données observées.
Models commonly used in generative AI
Networks of antagonistic generators (GAN)
Adversarial generator networks, also known as GANs, are a popular type of generative AI model. A GAN consists of two elements: a generator and a discriminator. The generator tries to create new data that looks real, while the discriminator tries to differentiate real data from generated data.
Variable auto-encoder (VAE)
Another common type of model in generative AI is the variational auto-encoder, or VAE. A VAE is based on the encoding and decoding of data. It attempts to compress data into a latent representation, and then generate new data from that same representation.
Reinforcement learning
Reinforcement learning is a machine learning paradigm in which an agent learns to perform a task by performing actions and receiving rewards or punishments for these actions. It has also been used successfully in generative AI.
Generative AI applications
Creating generative art and music
Thanks to generative AI, art and music can be created in an entirely new way. Paintings, drawings and musical compositions can be created automatically by these models, offering infinite possibilities for creativity.
Video game development
In the field of video game development, generative AI can help create scenarios, levels and characters that are both realistic and innovative. These models can dynamically generate content, enabling richer and more varied gaming experiences.
Data simulation for scientific research
Generative AI can also be used to simulate data for scientific research. For example, it can generate simulations of physical or biological systems, help analyze genetic data or model the climate.
Creation of realistic content for film and television
In the film and TV industry, generative AI can help create special effects or generate realistic environments. The technology can also be used to create digital doubles of real actors, or to make difficult or dangerous scenes safer to film.
Operation of networks of antagonistic generators (GAN)
GAN structure
A GAN consists of two elements, a generator and a discriminator. The generator is a type of neural network that produces new data, while the discriminator is another neural network that attempts to discriminate between real data and that produced by the generator.
GAN formation process
GAN training is an optimization process in which the generator and discriminator are trained simultaneously. The generator tries to improve its ability to produce realistic data, while the discriminator strives to better distinguish real data from generated data. This creates an adversarial game in which both elements seek to outperform the other.
Examples of GAN use
GANs have been used in a variety of fields. For example, they have been used to generate realistic images of human faces, to create digital art, to improve image quality, to synthesize text from images, and even to generate music.
Variable auto-encoder (VAE) operation
VAE structure
A VAE is a type of auto-encoder that uses probabilistics and information theory to encode and decode data. It consists of two components: an encoder that transforms input data into a latent representation, and a decoder that generates new data from this representation.
VAE training process
VAE training differs from that of a traditional auto-encoder. It uses a loss function that takes into account both data reconstruction and KL divergence, a measure of the difference between two probability distributions. This generates more varied and higher quality data.
Examples of VAE use
VAEs have been used in various fields such as image generation, sequence prediction, modeling of the natural languageand other signal and image processing applications.
How reinforcement learning works
Principles of reinforcement learning
Reinforcement learning is a machine learning paradigm in which an agent learns to perform a task by performing actions and receiving rewards or punishments based on the results of these actions. The aim is to maximize the sum of rewards over the long term.
Structure of an AI based on reinforcement learning
An AI based on reinforcement learning generally consists of an agent, an environment, a set of possible actions, and a reward function. The agent interacts with the environment by performing actions, receives feedback on these actions in the form of rewards or punishments, and uses this information to update its knowledge and improve its future actions.
Examples of reinforcement learning in generative AI
Reinforcement learning has been used in generative AI for robot control, text generation, strategy learning in games, and many other applications.
Advantages and disadvantages of generative AI
Benefits of using generative AI
Generative AI offers a wide range of benefits. It enables new data to be created and hypotheses to be generated, which can benefit scientific research, product development and other fields. It can help visualize multi-dimensional data in a comprehensible way, and it enables new forms of art and music to be explored.
Limits and challenges of generative AI
Despite its advantages, generative AI also presents challenges. The process of training generative models can be complex and resource-intensive. In addition, generative models can be difficult to evaluate and interpret. There are also ethical concerns, as generative AI can be used to create misleading or malicious information.
The future of generative AI
Generative AI and deep fakes
L’IA générative a fait avancer la technologie des “deep fakes”, où des vidéos et des images peuvent être falsifiées pour ressembler à de vraies personnes. Bien que cela puisse avoir des applications légitimes comme la post-production cinématographique ou le développement de jeux vidéo, cela soulève également de sérieuses préoccupations en matière d’éthique et de désinformation.
Generative AI and the creation of new drugs
Generative AI is revolutionizing pharmaceutical research. It can be used to generate new molecular structures, predict drug efficacy and toxicity, and accelerate the drug discovery process.
Research prospects in generative AI
The prospects for research in generative AI are vast and promising. Efforts are underway to improve the quality of generations, to make model learning more efficient and robust, to develop more accurate evaluation techniques, and to better understand how generative models work.
Additional resources for understanding generative AI
Books on generative AI
There are many books available to help you understand generative AI. These books cover various aspects of generative AI, from the underlying theory to practical implementation techniques.
Online courses and tutorials for learning generative AI
Numerous online courses and tutorials are available for those wishing to learn generative AI. These resources allow you to learn at your own pace, and offer a variety of difficulty levels to suit everyone's needs.
Conferences and events on generative AI
There are also many conferences and events around generative AI. These events provide opportunities to learn from thought leaders, meet professionals in the field, and stay up-to-date on the latest advances and trends in generative AI.
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