Category: Machine Learning

Apollo 11 Deepfake

Trust is the foundation of any organization or society. In a time where news, opinions, and the public are delivered with a few clicks and without context. Deepfake technology poses a sinister threat to our way of life. The In Event of Moon Disaster project by MIT is engaging and sobering. The six-minute video is excellent! But its pho-thenticity is disturbing and a warning sign of things to come.

There are excellent resources on the site for an in-depth study of Deepfake technology. Governments will need to draft sensible policies around such techniques, and news organizations will need to develop a discipline around rushing to distribute videos without verifying their authenticity.

PREMIERE OF FULL FILM & COMPLETE SPEECH! In July 1969, much of the world celebrated the “giant leap for mankind” that the successful moon landing constituted. In 2020, nothing is quite so straightforward. In Event of Moon Disaster illustrates the possibilities of deepfake technologies by reimagining this seminal event. What if the Apollo 11 mission had gone wrong and the astronauts had not been able to return home? A contingency speech for this possibility was prepared, but never delivered by President Nixon – until now. The immersive project invites you into this alternative history and asks us all to consider how new technologies can bend, redirect and obfuscate the truth around us.

You can visit and access all the resources of this project by going to: https://moondisaster.org/

Posted on by George

A Door, A Cat, And Its Prey

The video below is an excellent example of home-brewed innovation that leverages machine learning to solve a problem many know but for which there is no financial incentive to address — it is a long tail problem. And while this is not something anyone can do without some technical skills, the case study does give us a glimpse of the incremental changes to everyday life machine learning promises to make as the technology gets democratized.

Posted on by George