A new deep-learning algorithm studies aerial photographs after fires to identify damage.
How it works: From satellite images taken before and after the California wildfires of 2017, researchers created a data set of buildings that were either damaged or left unscathed.
The results: They tweaked a pre-trained ImageNet neural network and got it to spot damaged buildings with an accuracy of up to 85 percent.
Why it matters: After a disaster, pinpointing the hardest-hit areas could save lives and help with relief efforts. The researchers also released the data set to the public, which could improve other research that requires satellite images, like conservation and developmental aid work.
Geoffrey Hinton tells us why he’s now scared of the tech he helped build
“I have suddenly switched my views on whether these things are going to be more intelligent than us.”
ChatGPT is going to change education, not destroy it
The narrative around cheating students doesn’t tell the whole story. Meet the teachers who think generative AI could actually make learning better.
Deep learning pioneer Geoffrey Hinton has quit Google
Hinton will be speaking at EmTech Digital on Wednesday.
We are hurtling toward a glitchy, spammy, scammy, AI-powered internet
Large language models are full of security vulnerabilities, yet they’re being embedded into tech products on a vast scale.
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