Generative AI is a type of artificial intelligence technology that can produce various kinds of content, including text, images, sound, and synthetic data. For several months now, generative AI has been a hot subject of conversation. The cause? The fact that it’s accessible to everyone, simply and, more often than not, free of charge. In this article, discover everything you ever needed to know about generative AI!
The history of generative AI goes back to the early 50s, with the first attempts at generating language via machines. However, it’s only very recently that deep neural networks have given rise to significant advances. In 2014, GANs (generative adversarial networks) emerged, enabling the generation of realistic data. In 2018, OpenAI introduced the GPT model, capable of creating coherent text. Since then, generative AI has conquered art, music, and literature, demonstrating its creative potential, but also raising questions about the ethics and relevance of machine-generated creations.
How generative AI works
Generative AI is a branch of artificial intelligence. It aims to create realistic content, such as text, images, or music, using machine learning models. The most common model is the generative adversarial network (GAN).
A GAN consists of two distinct parts: the generator and the discriminator. The generator creates data (e.g. images) from random noise. The discriminator evaluates whether the data is real or generated. The strength of generative AI? Both parts (generator and discriminator) improve continuously via an iterative process. The generator adjusts its output to challenge the discriminator, while the discriminator improves its ability to distinguish reality from generation. Over time, the generator succeeds in creating increasingly convincing data. GANs have evolved to generate text (like GPT) or other forms of content, revolutionizing creativity, art, design, and simulation.
How can you benefit from generative AI?
Companies can benefit from generative AI in a variety of ways. It can stimulate creativity by producing innovative ideas, designs, and concepts. Generative AI improves personalization by tailoring products and services to individual customer preferences. It accelerates design by automating the creation of prototypes and visual elements. In addition, it optimizes decision-making by generating simulations and scenarios to inform strategic choices. It can also facilitate the automation of creative tasks, increasing operational efficiency. However, ethical and quality considerations must be taken into account to ensure that the results generated are relevant and comply with established standards.
What are the risks of using generative AI?
Concerns about Artificial Intelligence have their roots in science fiction. But should we deny the dangers associated with the indiscriminate use of generative AI? Certainly not, because the risks are very real! Indeed, the quality of the content (images, texts, videos…) generated by generative AI is such that it can lead to incorrect… but credible information. In addition, there are risks associated with copyright infringement or plagiarism, as generative models can reproduce existing content without authorization and, above all, without mentioning their sources. But the main threat associated with the use of generative AI is linked to the disclosure of misleading information (also known as Fake news), to the production of deepfakes. Finally, strong ethical concerns are emerging around the automation of artistic creation, questioning the authenticity and artistic value of creations generated in this way.
What are the most inspiring use cases for generative AI?
Improving customer experience
Generative AI has revolutionized the customer experience in a variety of industries. This is notably the case in e-commerce, which leverages generative AI to make product recommendations based on past preferences. In healthcare, it proposes personalized care models based on medical data, thereby improving patient care. In customer relations, it generates automated, contextual responses for fast, efficient customer service based on autonomy.
Personalizing customer paths
As the notion of the customer journey becomes ever more central in a digitalized world, generative AI makes it possible to deliver relevant answers more quickly. In marketing, it generates targeted messages tailored to targets’ interests to boost engagement. On e-learning platforms, for example, generative AI can tailor study plans to the skills and needs of each learner. Tourism can also benefit from generative AI by offering unique itineraries based on traveler preferences.
Creation of new products/services
By harnessing the creativity of generative models, AI helps create new offerings while optimizing the resources and time invested. By accelerating time-to-market, generative AI can propose virtual prototypes, speeding up the development process. In product design, it elaborates innovative concepts based on market needs.
Preparing sales pitches
Once the products and services have been created, they need to be supported in their marketing by making the case for both their strengths and the promises they hold for their intended targets. Here again, generative AI can make a valuable contribution by facilitating the preparation of sales pitches. It does this by analyzing target market data and highlighting competitive advantages, illustrating usage scenarios, and reinforcing the credibility of the product or service.
