Generative Adversarial Networks (GANs) Will Change Content Creation
Generative Adversarial Networks (GANs) have shown great promise by yielding some impressive results when it comes to generating new data from existing datasets. A Generative Adversarial Networks (GAN) is a machine learning technique that enables computer programs to create and modify data in many forms. They can be used to generate realistic-looking faces, for example, or to modify the audio of a recorded speech.
GANs will be of great interest to businesses because they can make AI systems far more efficient at generating high-quality fake images. But this technology will also enable deepfakes that are impossibly sophisticated for any human to spot without electronic assistance, for instance, it will be getting a lot better at forging banknotes or creating fake people.
On a more positive note, we will also be able to acquire data more efficiently and at a cheaper cost than we can today as GANs can be used to create high-quality “fakes” at a fraction of the data previously required. This will change the way we use technology and in the long run, have a lasting impact on how technology helps our businesses.
