MIT CSAIL unsealed a special time capsule from 1999 after a self-taught programmer Belgium solved a puzzle devised by MIT professor and famed cryptographer Ron Rivest.

MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) held a special workshop with Microsoft Research to explore key challenges in creating trustworthy and robust artificial intelligence (AI) systems. The effort focused on addressing concerns about the trustworthiness of AI systems, including rising concerns with the safety, fairness, and transparency of the technologies.

We present Dense Object Nets, which build on recent developments in self-supervised dense descriptor learning, as a consistent object representation for visual understanding and manipulation.

The RePaint system reproduces paintings by combining two approaches called color-contoning and half-toning, as well as a deep learning model focused on determining how to stack 10 different inks to recreate the specific shades of color.

This work presents the design, fabrication, control, and oceanic testing of a soft robotic fish that can swim in three dimensions to continuously record the aquatic life it is following or engaging.

Systems have been limited to single color changes, i.e. changes from transparent to colored. In this paper, we present ColorMod, a method to extend this ap-proach to multi-color changes (e.g., red-to-yellow).

MIT researchers have devised a technique that “reverse engineers” complex 3-D computer-aided design (CAD) models, making them far easier for users to customize for manufacturing and 3-D printing applications.

From a single image, humans are able to perceive the full 3D shape of an object by exploiting learned shape priors from everyday life. Contemporary single-image 3D reconstruction algorithms aim to solve this task in a similar fashion, but often end up with priors that are highly biased by training classes.