A Hungarian-born startup in Copenhagen can detect if a video is fake, but no one was a buyer of their technology. With a clever change of direction and building up of the Colossyan brand, they are now trying to take the lead in an emerging market: they are producing manipulated videos themselves, but for ethical purposes. The market for synthetic media is growing, but the risk is still huge. The consortium of investors, led by the Hungarian Dayone Capital, also knows this. Exclusive excerpts from a business story that says more than anything about the age we live in.

They began working with an image and video database company, Indieframe, to whom they were able to deliver a so-called API. By 2019, others had already tried similar solutions, but this -- due to the video cards -- required an awful amount of computing capacity. "Ours was a typical startup solution, the code wasn't nice, but it worked." Lay people have to imagine this in such a way that the image database manager allowed the boys to solve their own images, and that chose which images had bad captions or other data but also examined the pixels in detail. According to Dominik, one of their strengths to this day is that they can transfer the results of scientific research and academia very well to market applications.

Causal inference deals with identifying which random variables "cause" or control other random variables. Recent advances on the topic of causal inference based on tools from statistical estimation and machine learning have resulted in practical algorithms for causal inference. Causal inference has the potential to have significant impact on medical research, prevention and control of diseases, and identifying factors that impact economic changes to name just a few. However, these promising applications for causal inference are often ones that involve sensitive or personal data of users that need to be kept private (e.g., medical records, personal finances, etc). Therefore, there is a need for the development of causal inference methods that preserve data privacy. We study the problem of inferring causality using the current, popular causal inference framework, the additive noise model (ANM) while simultaneously ensuring privacy of the users. Our framework provides differential privacy guarantees for a variety of ANM variants. We run extensive experiments, and demonstrate that our techniques are practical and easy to implement.