The rise in popularity of social media platforms, has resulted in millions of new, content pieces being created every day. This surge in content creation underscores the need to pay attention to our design choices as they can greatly impact how long content remains relevant. In today's landscape where regularly recommending new content is crucial, particularly in the absence of detailed information, a variety of factors such as UI features, algorithms and system settings contribute to shaping the journey of content across the platform. While previous research has focused on how new content affects users' experiences, this study takes a different approach by analyzing these decisions considering the content itself. Through a series of carefully crafted experiments we explore how seemingly small decisions can influence the longevity of content, measured by metrics like Content Progression (CVP) and Content Survival (CSR). We also emphasize the importance of recognizing the stages that content goes through underscoring the need to tailor strategies for each stage as a one size fits all approach may not be effective. Additionally we argue for a departure from traditional experimental setups in the study of content lifecycles, to avoid potential misunderstandings while proposing advanced techniques, to achieve greater precision and accuracy in the evaluation process.

AI is constantly in the news these days, identifying prospects for the technology doing both good and bad. One topic that's generating a lot of buzz is the use of AI for creating "deepfakes," a term originally coined in 2017. Deepfakes uses neural networks to combine and superimpose existing images and videos onto source images or videos using a deep learning technique known as generative adversarial networks (GANs). Three of the most common deepfakes techniques are known as "lip-sync," "face swap," and "puppet-master." These techniques, however, can create a disconnect that may be uncovered by a clever algorithm as a way to combat deepfakes.

MIT's fleet of robotic boats has been updated with new capabilities to "shapeshift," by autonomously disconnecting and reassembling into a variety of configurations, to form floating structures in Amsterdam's many canals. The autonomous boats -- rectangular hulls equipped with sensors, thrusters, microcontrollers, GPS modules, cameras, and other hardware -- are being developed as part of the ongoing "Roboat" project between MIT and the Amsterdam Institute for Advanced Metropolitan Solutions (AMS Institute). The project is led by MIT professors Carlo Ratti, Daniela Rus, Dennis Frenchman, and Andrew Whittle. In the future, Amsterdam wants the roboats to cruise its 165 winding canals, transporting goods and people, collecting trash, or self-assembling into "pop-up" platforms -- such as bridges and stages -- to help relieve congestion on the city's busy streets. In 2016, MIT researchers tested a roboat prototype that could move forward, backward, and laterally along a preprogrammed path in the canals.