Feb 24, 2021

6 Posts

Face Datasets Under Fire, Baking With AI, Human Disabilities Baffle Algorithms, Ginormous Transformers
Feb 24, 2021

Face Datasets Under Fire, Baking With AI, Human Disabilities Baffle Algorithms, Ginormous Transformers

AI-enabled automation is often portrayed as a binary on-or-off: A process is either automated or not. But in practice, automation is a spectrum, and AI teams have to choose where on this spectrum to operate. It’s important to...
11 min read
Dozens of different faces shown in a series of images
Feb 24, 2021

Cutting Corners to Recognize Faces

Datasets for training face recognition models have ballooned in size — while slipping in quality and respect for privacy. In a survey of 130 datasets compiled over the last four decades, researchers traced how the need for increasing quantities of data led researchers to relax their standards.
2 min read
Person in wheelchair, person in side profile, person wearing a hoodie
Feb 24, 2021

Human Disabilities Baffle Algorithms

Facebook’s content moderation algorithms block many advertisements aimed at disabled people. The social media platform’s automated systems regularly reject ads for clothing designed for people with physical disabilities.
2 min read
Model predicting ingredients in a recipe and woman cooking
Feb 24, 2021

Cake + Cookie = Cakie

AI may help revolutionize the human diet – or dessert, at least.What’s new: Google applied AI engineer Dale Markowitz and developer advocate Sara Robinson trained a model to predict whether a recipe is a
1 min read
System Oscar+ working
Feb 24, 2021

Sharper Eyes For Vision+Language

Models that interpret the interplay of words and images tend to be trained on richer bodies of text than images. Recent research worked toward giving such models a more balanced knowledge of the two domains.
2 min read
Different graphs showing switch transformer data
Feb 24, 2021

Bigger, Faster Transformers

Performance in language tasks rises with the size of the model — yet, as a model’s parameter count rises, so does the time it takes to render output. New work pumps up the number of parameters without slowing down the network.
2 min read

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