Dangerous Read — Deepfakes are real!
Definition as per Wikipedia :
Deepfakes (a portmanteau of “deep learning” and “fake”) are synthetic media in which a person in an existing image or video is replaced with someone else’s likeness. While the act of faking content is not new, deepfakes leverage powerful techniques from machine learning and artificial intelligence to manipulate or generate visual and audio content with a high potential to deceive. The main machine learning methods used to create deep fakes are based on deep learning and involve training generative neural network architectures, such as autoencoders or generative adversarial networks (GANs).
In simplest terms — It is exactly the opposite of what we need from large piles of data — misinformation, distorted news, abuse, image morphing, algorithms that drive you crazy.
Impact goes beyond our imagination: Face manipulation, artificial intelligence, deep learning, autoencoders, GAN, forensics, survey, lip-sync, and puppet-master.
So what are business implications?
Imagine you are looking at an eCommerce site and are continuously redirected to wrong results and that is based on your earlier searches. [Algorithm restructuring]
Imagine your private albums are made public without your knowledge, even more, harmful these images are used in different areas to hurt you. [Image/Videos Morphing]
We live in a world of information science and it’s a double-edged sword with both pros and cons.
For PMs dealing in the AI space, it is very important to take care of their algorithms to work with the framework of their existing data sets and train them properly before releasing them into open space.
Data is oil — old adage.
Data is “SOIL”. People run businesses using it.
#ProductManagement #Vigilance #Data #Privacy #DeepFakes #KPKR
