Oct 14, 2020

6 Posts

Mapping Wildfires, Compressing Video, Humanizing Benchmarks, Training GANs on Small Datasets, Documenting Government AI
Oct 14, 2020

Mapping Wildfires, Compressing Video, Humanizing Benchmarks, Training GANs on Small Datasets, Documenting Government AI

My father recently celebrated a milestone: He has completed 146 online courses since 2012. His studies have spanned topics from creative writing to complexity theory. Ronald Ng is a great example of lifelong learning.
10 min read
Series of images related to a technology used to draw maps during a fight fire emergency
Oct 14, 2020

Mapping the Inferno

An AI-powered eye in the sky is helping firefighters control woodland blazes. California used maps drawn by neural networks to fight fires that threatened Yosemite National Park earlier this year.
2 min read
Series of images showing how Maxine, a media streaming platform, works
Oct 14, 2020

Data Compression By AI

In this work-from-home era, who hasn’t spent a video conference wishing they could read an onscreen document without turning their eyes from the person they’re talking with? Or simply hoping the stream wouldn’t stutter or stall? Deep learning can fill in the missing pieces.
1 min read
Screen captures of online platform Dynabench
Oct 14, 2020

Dynamic Benchmarks

Benchmarks provide a scientific basis for evaluating model performance, but they don’t necessarily map well to human cognitive abilities. Facebook aims to close the gap through a dynamic benchmarking method that keeps humans in the loop.
2 min read
Screenshots of some of the online registries storing algorithms for Amsterdam and Helsinki
Oct 14, 2020

Transparency for Smart Cities

Two European capitals launched public logs of AI systems used by the government. Amsterdam and Helsinki provide online registries that describe the algorithms that govern municipal activities, such as automated parking control and a public health chatbot.
1 min read
Examples of AI generated images
Oct 14, 2020

GANs for Smaller Data

Trained on a small dataset, generative adversarial networks (GANs) tend to generate either replicas of the training data or noisy output. A new method spurs them to produce satisfying variations.
2 min read

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