It is crucial to prevent algorithms from perpetuating ideologies that disadvantage under-represented groups.

There is a gender gap in artificial intelligence (AI).

In the video above, Joanna Thelen (ENG'20) and Hung-Ching Lin (ENG'20) demonstrate Dart, the Stranger Things-inspired gripping soft robot. From the slithering of snakes to the crawling of octopuses, the field of soft robotics has often taken cues from the natural world. Boston University's Morphable Biorobotics Lab--led by Tommaso Ranzani, a College of Engineering assistant professor in mechanical, materials science, and biomedical engineering--is doing just that with "Dart," a new soft robot whose nickname is inspired by its resemblance to creatures from the popular Netflix series Stranger Things. Soft rubber robots have been able to bend, warp, and move around in unique ways, but until now, they've rarely been able to grasp objects and move them from place to place. It can not only move around on its four legs, but can grip and lift objects with its unique six-fingered mouth.

Network data can be conveniently modeled as a graph signal, where data values are assigned to nodes of a graph that describes the underlying network topology. Successful learning from network data is built upon methods that effectively exploit this graph structure. In this work, we overview graph convolutional filters, which are linear, local and distributed operations that adequately leverage the graph structure. We then discuss graph neural networks (GNNs), built upon graph convolutional filters, that have been shown to be powerful nonlinear learning architectures. We show that GNNs are permutation equivariant and stable to changes in the underlying graph topology, allowing them to scale and transfer. We also introduce GNN extensions using edgevarying and autoregressive moving average graph filters, and discuss their properties. Finally, we study the use of GNNs in learning decentralized controllers for robot swarm and in addressing the recommender system problem. I. INTRODUCTION Data generated by networks are increasingly common in power grids, robotics, biological, social and economic networks, and recommender systems among others. The irregular and complex nature of these network data poses unique challenges, therefore, making successful learning possible only by incorporating the structure into the inner-working mechanisms of the model [1]. Work in this paper is supported by NSF CCF 1717120, ARO W911NF1710438, ARL DCIST CRA W911NF-17- 2-0181, ISTC-WAS and Intel DevCloud. E. Isufi is with the Multimedia Computing Group and G. Leus is with the Circuits and Systems Group, Delft Univ. of Technology, The Netherlands.

A system created by MIT researchers could be used to automatically update factual inconsistencies in Wikipedia articles, reducing time and effort spent by human editors who now do the task manually. Wikipedia comprises millions of articles that are in constant need of edits to reflect new information. That can involve article expansions, major rewrites, or more routine modifications such as updating numbers, dates, names, and locations. Currently, humans across the globe volunteer their time to make these edits. In a paper being presented at the AAAI Conference on Artificial Intelligence, the researchers describe a text-generating system that pinpoints and replaces specific information in relevant Wikipedia sentences, while keeping the language similar to how humans write and edit.

Sight is one of our most vital senses. Biologically inspired machine vision has developed rapidly in the past decade, to the point that artificial systems can'see' in the sense of gaining valuable information from images and videos1,2, although human vision remains much more efficient. Writing in Nature, Mennel et al.3 report a design for a visual system that, rather like the brain, can be trained to classify simple images in nanoseconds. Modern image sensors such as those in digital cameras are based on semiconductor (solid-state) technology and were developed in the early 1970s; they fall into two main types, known as charge-coupled devices and active-pixel sensors4. These sensors can faithfully capture visual information from the environment, but generate a lot of redundant data.

Do you remember that scene in Walt Disney's Bambi where the titular fawn learns to stand up and walk under its own power? It's a charming vignette in the movie, showcasing a skill that plenty of baby animals -- from pigs to giraffe to, yes, deer -- pick up within minutes of their birth. Over the first few hours of life, these animals rapidly refine their motor skills until they have full control over their own locomotion. Humans, who learn to stand holding onto things at around seven months and who begin walking at 15 months, are hopelessly sluggish by comparison. Guess what the latest task that robots have beaten us at?

Do you remember that scene in Walt Disney's Bambi where the titular fawn learns to stand up and walk under its own power? It's a charming vignette in the movie, showcasing a skill that plenty of baby animals -- from pigs to giraffe to, yes, deer -- pick up within minutes of their birth. Over the first few hours of life, these animals rapidly refine their motor skills until they have full control over their own locomotion. Humans, who learn to stand holding onto things at around seven months and who begin walking at 15 months, are hopelessly sluggish by comparison. Guess what the latest task that robots have beaten us at?

Shimon the singing songwriting robot has been taught to write his own lyrics by studying tens of thousands of songs written by the musical greats. Developed by researchers from the Georgia Tech Center for Music Technology, the robot collaborates with human musicians and even has an album out in the spring. The robot was given a dataset of 50,000 lyrics covering all genres including rock, hip-hop, jazz and progressive as part of its song writing education. As well as writing the lyrics the robot can sing them and dance while performing with'his band' made up of Georgia Tech students and researchers. Professor Gil Weinberg, creator of Schimon said he works with humans to create music, they are a mixture of songs made by human and robot together.

HBO is working on a television adaptation of the bestselling video game The Last of Us. It will be written by Craig Mazin, the creator of highly acclaimed TV series Chernobyl, together with Neil Druckmann, the creative director on the game and its forthcoming sequel, according to the Hollywood Reporter. The series is set to follow the events of the original game, which takes place in a United States devastated by a deadly parasitic fungus that transforms infected victims into monsters. The action follows grizzled survivor Joel, who is hired to transport seemingly immune teenage girl Ellie out of a quarantine zone and across the country to a lab where a cure can be developed. Praised for its bleak, violent narrative, the game was a major hit on PlayStation, with a sequel due in May.