Swarms of simple, interacting robots have the potential to unlock stealthy abilities for accomplishing complex tasks. Getting these robots to achieve a true-hive like mind of coordination, though, has still proved to be a hurdle.
This talk covers work being done in the Retail and Consumer Products industries, where there is a need to track and trace live goods including plants and food. The technical aspects of how to implement this will be covered including where Internet of Things (IoT) sensors are leveraged to provide location, status, and state to know previous, current, and future provenance of products materials, and consumer goods. Blockchain, sensors, and analytics will be covered in specifically how they are used in various examples.
“There’s a growing concern about machine-generated fake text, and for a good reason,” says CSAIL PhD student Tal Schuster, lead author on a new paper on their findings. “I had an inkling that something was lacking in the current approaches to identifying fake information by detecting auto-generated text — is auto-generated text always fake? Is human-generated text always real?”
Existing efforts to detect IP hijacks tend to look at specific cases when they’re already in process. But what if we could predict these incidents in advance by tracing things back to the hijackers themselves?
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.
A few years ago, the idea of tricking a computer vision system by subtly altering pixels in an image or hacking a street sign seemed like more of a hypothetical threat than anything to seriously worry about.
A novel system developed by MIT researchers automatically “learns” how to schedule data-processing operations across thousands of servers — a task traditionally reserved for imprecise, human-designed algorithms. Doing so could help today’s power-hungry data centers run far more efficiently.
A new machine learning model allows for increased predictive analytics and a higher accuracy for diagnosing and treating patients with vocal cord disorders.
An automated system developed by MIT researchers designs and 3-D prints complex robotic parts called actuators that are optimized according to an enormous number of specifications. In short, the system does automatically what is virtually impossible for humans to do by hand.
