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A Look Back at the 1960s PLATO Computing System

In the 1960s, researchers at the University of Illinois Urbana—Champaign developed a computer system that they hoped would expand access to education. They envisioned instructors usingthe system to build lessons, and students stationing themselves at machines—whose touchscreen plasma displays had a distinct orange glow—to complete coursework.

Sleeping In Rooms With Even a Little Light Can Increase Risk of Depression, Study Finds

Why Doing Good Makes It Easier to Be Bad

In a recent paper, economists at the University of Chicago reported that working for a socially responsible company motivated employees to act immorally. In one experiment, people were hired to transcribe images of short German texts and paid 10 percent upfront, with the remaining payment being delivered if they completed the transcriptions, or if they declared the documents too illegible to transcribe. When they were told that, for every job completed or marked illegible, 5 percent of their wages would be donated to Unicef’s educational programs, the instances of cheating rose by 25 percent, compared to where no charitable donation was offered.

3 reasons to question the use of emotion-tracking AI in recruiting

The autonomous “selfie drone”

Skydio, founded by three MIT alumni, is commercializing an autonomous video-capturing camera drone, called R1 and dubbed the “selfie drone,” that tracks and films a subject, while freely navigating any environment.

Larry Page’s autonomous air taxi ‘Cora’ flies in New Zealand

PyTorch – Internal Architecture Tour

This post is a tour around the PyTorch codebase, it is meant to be a guide for the architectural design of PyTorch and its internals. My main goal is to provide something useful for those who are interested in understanding what happens beyond the user-facing API and show something new beyond what was already covered in other tutorials.

Building Windows: 4 million commits, 10 million work items

Microsoft’s switch to using Git as the version control system for Windows’ development has resulted in many challenges. Git wasn’t really built for a 300GB repository with 3.5 million files, and the engineering effort to make Git scale in this way continues.

Semantic Image Segmentation with DeepLab in Tensorflow

Today, we are excited to announce the open source release of our latest and best performing semantic image segmentation model, DeepLab-v3+ [1], implemented in Tensorflow. This release includes DeepLab-v3+ models built on top of a powerful convolutional neural network (CNN) backbone architecture [2, 3] for the most accurate results, intended for server-side deployment. As part of this release, we are additionally sharing our Tensorflow model training and evaluation code, as well as models already pre-trained on the Pascal VOC 2012 and Cityscapes benchmark semantic segmentation tasks.