Consider something you would like to create, recreate or imitate (art, photos, videos, music, stories, coding, a deepfake of a prime minister etc.), and generative AI will be happy to assist you. Does this mean that we have reached an era where saying just a few words enables you to create or replicate almost anything you can imagine (pretty much like a utopian science fiction novel)? And, for which price are we willing to have this assistance? This blog post will briefly summarise what generative AI is, and can do, its multiple types (i.e. Generative Adversarial Networks - GANS), how this differs from traditional AI, and its benefits and risks. We will conclude with a short introduction to Actor-Network Theory (ANT) by Bruno Latour as a theoretical and methodological approach to account for the relational nature of the society. Actors, in this theory, are not just humans or collectives of humans. Technical objects or artificial agents such as GANs, a class of deep neural network models able to simulate content with extreme precision, are also considered by ANT equally significant for the circulation of contents, beliefs and information inside a social system. GANs, have been primarily used to create media simulating human beings conveying politically-relevant content. What is Generative Artificial Intelligence? To what extent can we say AI can assist us in almost any task? In the face of the latest advancements in AI, the list of such tasks seems to be diminishing almost daily. Furthermore, even though we are still far away from achieving Artificial General Intelligence (AGI) or "Human-Level AI", as Kurzwei l (2005) would say, recent advancements in the sub-fields of AI, such as Natural Language Processing (giving computers the capacity to understand the text and spoken), and Computer Vision (computers see, observe, and 'understand), have shown that we are not the only ones capable of instantly and creatively generating images, videos, stories, music, or code from just a short text. This hype around generative technologies has been developing around tech giants such as Elon Musk with its DALL·E Open AI text-to-image technology. We can sub-divide generative AI's three famous models into: General Adversarial Networks (GANS) GANs are an emerging technology for both semisupervised and unsupervised learning and are composed of the i) generator and ii) the discriminator. The discriminator has access to a set of training images depicting real content. The discriminator aims to discriminate between "real" images from the training set and "fake" images generated by the generator. On the other hand, the generator generates images as similar as possible to the training set. The generator starts by generating random images and receives a signal from the discriminator whether the discriminator finds them real or fake. At equilibrium, the discriminator should not be able to tell the difference between the images generated by the generator and the actual images in the training set; hence, the generator generates images indistinguishable from the training set (Elgammal et al., 2017). AutoRegressive Convolutional Neural Networks (AR-CNN) AR-CNN is a deep convolutional network architecture for multivariate time series regression. The model is inspired by standard autoregressive (AR) models and gating mechanisms used in recurrent neural networks. It involves an AR-like weighting system, where the final predictor is obtained as a weighted sum of sub-predictors. At the same time, the weights are data-dependent functions learnt through a convolutional network. (Bińkowski et al., 2017). These transformers are designed to process sequential input data, such as natural language, with applications towards tasks such as translation and text summarisation, as in the case of Open AI with DALLE-E. Transformer-based Models A transformer is a deep learning (DL) model that adopts the self-attention mechanism, weighing the significance of each part of the input data. It is used primarily in the fields of natural language processing (NLP) (Vaswani et al., 2017) and computer vision (CV) (Brown, 1982). Where can we see generative AI? Beyond data augmentation, widely used in the medical field, generative AI can, just with a short paragraph of text, assist you with creating art and pictures ( open AI's DALL-E system that enables the creation of hyper-realistic images from text descriptions), videos ( Meta's text-to-video AI platform), AI avatars ( synthesia's text-to-speech synthesis which generates a professional video with an AI avatar reciting your text), music ( Jukebox neural net ) and much more. The magic behind most of this generative reality stands within Generative pre-trained Transformer (GPT-3) - a third-generation, autoregressive language model that uses deep learning to produce human-like texts and use the previous distinction to analyse them (Floridi, 2020). Simply speaking, we can understand it as 'text in — text out' - the model processes the text given and produces what it 'believes' should come next based on the probabilities of sequences of particular words in a sentence. Below we can see the realistic images entirely created by DALL·E from the prompt of an -> impressionist oil painting of an AI system having a drink at the beach