This is part 2 of a three-part series examining the effects of robots and automation on employment, based on new research from economist and Institute Professor Daron Acemoglu. 

This is part 1 of a three-part series examining the effects of robots and automation on employment, based on new research from economist and Institute Professor Daron Acemoglu.  

By Benjamin Eysenbach and Abhishek Gupta

This post is cross-listed on the CMU ML blog.

The history of machine learning has largely been a story of increasing abstraction. In the dawn of ML, researchers spent considerable effort engineering features. As deep learning gained popularity, researchers then shifted towards tuning the update rules and learning rates for their optimizers. Recent research in meta-learning has climbed one level of abstraction higher: many researchers now spend their days manually constructing task distributions, from which they can automatically learn good optimizers. What might be the next rung on this ladder? In this post we introduce theory and algorithms for unsupervised meta-learning, where machine learning algorithms themselves propose their own task distributions. Unsupervised meta-learning further reduces the amount of human supervision required to solve tasks, potentially inserting a new rung on this ladder of abstraction.

In this episode, Audrow Nash speaks with Janet Vertessi, Assistant Professor of Sociology at Princeton, on her book Seeing Like a Rover: How Robots, Teams, and Images Craft Knowledge of Mars. The book is written about her experience living and working with NASA’s Mars Rover team, and includes her observations about the team’s leadership and their relationship with their robot millions of miles away on Mars. She also gives some advice from her findings for teams.

Looking at the Open Source COVID-19 Medical Supplies production tally of handcrafted masks and faceshields, we’re trying to answer that question in our weekly discussions about ‘COVID-19, robots and us’. We talked to  Rachel ‘McCrafty’ Sadd has been building systems and automation for COVID mask making, as the founder of Project Mask Making and #distillmyheart projects in the SF Bay Area, an artist and also as Executive Director of Ace Monster Toys makerspace/studio. Rachel has been organizing volunteers and automating workflows to get 1700 cloth masks hand sewn and distributed to people at risk before the end of April. “Where’s my f*king robot!” was the theme of her short presentation.

Health care workers are not the only unwilling essential services frontline workers at increased risk of COVID-19. According to the Washington Post on April 12, “At least 41 grocery workers have died of the coronavirus and thousands more have tested positive in recent weeks”. At the same time, grocery stores are seeing a surge in demand and are currently hiring. The food industry is also seeing increasing adoption of robots in both the back end supply chain and in the food retail and food service sectors.

Community, Art and the Vernacular in Technological Ecosystems

COVID-19, robots and us – weekly discussion from March 31 2020

In this episode, Lilly interviews Kajal Gada on her work at BrainCorp, the San Diego-based company behind BrainOS, a technology stack for autonomous solutions. Kajal discusses BrainCorp’s cloud-connected operating system and their floor cleaning, vacuuming, and warehouse delivery robots. She also articulates some of the challenges in becoming a software engineer and developing commercial solutions.