Over the past ten years, Artificial Intelligence (AI) and Machine Learning (ML) have steadily crept into the Art Industry. From Deepfakes to DALL·E, the impact of these new technologies can be longer be ignored, and many communities are now on the edge of a reckoning. On one side, the potential for modern AIs to generate and edit both images and videos is opening new job opportunities for millions; but on the other is also threatening a sudden and disruptive change across many industries.
The purpose of this long article is to serve as an introduction to the complex topic of AI Art: from the technologies that are powering this revolution, to the ethical and legal issues they have unleashed. While this is still an ongoing conversation, I hope it will serve as a primer for anyone interested in better understanding these phenomena—especially journalists who are keen to learn more about the benefits, changes and challenges that that AI will inevitably bring into our own lives. And since the potential of these technologies—and the best way to use them—are still being explored, there will likely be more questions and tentative suggestions, rather than definite answers.
In this article I will try to keep a positive outlook, as I feel is important to show and inspire people on how to better harness this technology, rather than just demonising it. And while predicting the future is beyond the scope of this article, there will be plenty of examples of how new art practices and technologies have impacted art communities in the past.
This online course provides a theoretical and practical guide to the use of face-swap technology. In the past few months, deep neural networks have been wildly used to digital insert actor Nicolas Cage into several movie scenes. These so-called deepfakes have generated a lot of discussion on the ethics of Machine Learning. This second lesson will focus on the potential applications that face-swap technology can offer, and on how to use it properly.
If you are interested in understanding not only how deekfakes are generated, but also to create your own, this is the tutorial you have been looking for. And if you have been using face-swap technology already, I hope this first post will help you become more aware of why and how this technology should (and shouldn’t) be used.
This tutorial will cover the theory and practice of creating deepfakes: videos in which faces have been swapped using Machine Learning and Deep Neural Networks. If you are interested in learning more about this novel technique, this is the course for you.
After a theoretical introduction, this course will focus on how to make the most out of the popular applications FakeApp and faceswap; most of the deepfakes you will find online (such as the ones featuring Nicolas Cage) have been created using them.
Noise is everywhere. Whether you’re sampling accelerometer data for a mobile game or trying to measure the temperature of a room, noise will be there. Even if you could remove all the noise from an input device, you’ll still have a certain degree of uncertainty. If a player has tapped on the screen, where did they really wanted to tap? All these scenarios forces to re-think about how we gather and preprocess data.
The previous post in this series, Understanding Deep Dreams, explained what deep dreams are, and what they can be used for. In this second post you’ll learn how to create them, with a step by step guide.