This article talks about technologies used to produce human-like text, and "deep-fake" video and audio. The article argues that such generative AI can be used b

This article talks about technologies used to produce human-like text, and "deep-fake" video and audio. The article argues that such generative AI can be used by criminals to commit fraud- but at the same time, AI can also be used on the other side to detect and prevent fraud! You might be interested to know that the technological breakthrough that made today's realistic image and audio generation possible was in fact a form of this very idea: one AI creates fake but realistic data, while another AI attempts to detect the fake data. The two systems fight against each other in a game theoretical back-and-forth contest until both the fraudster and the fraud detector are almost perfect. The idea, the Generative Adversarial Network, was published by Goodfellow et al. [1] in 2014 and has spread widely since then. My favorite introductory explanation of GANs is [2], which describes the generator as a money counterfeiter attempting to fraudulently submit fake dollar bills, and the discriminator as a police officer who is inspecting each dollar bill to determine whether it is real or counterfeit. Given infinite time and memory, the mathematical result is always the same: the fraudster's output becomes perfect, and the police can only guess at random. [1] https://arxiv.org/abs/1406.2661 https://arxiv.org/abs/1406.2661 [2] https://medium.com/@ageitgey/abusing-generative-adversarial-networks-to-make-8-bit-pixel-art-e45d9b96cee7 https://medium.com/@ageitgey/abusing-generative-adversarial-...