- Google’s neural network, software which uses statistics to approximate how the brain learns, taught itself to detect the shapes of cats and humans with more than 70% accuracy.
- “Over the last five years it’s been pretty incredible, the progress of deep [neural] nets,” says Grant Van Horn, lead competition organizer and graduate student at California Institute of Technology.
- Van Horn says this latest Google competition differs from ImageNet, which forces algorithms to identify a wide variety of objects like cars and houses and boats, because iNat requires AI to examine the “nitty-gritty details” that separate one species from another.
- On a scale from general image recognition (ImageNet) to specific (facial recognition,where most faces generally look the same and only slight variations matter), iNat lies somewhere in the middle, Van Horn says.
- Van Horn, who has specialized in building AI that distinguishes differences between birds, said that the iNat competition illustrates how AI is beginning to help people learn about the world around them, rather than just help them organize their photos, for instance.
In 2012, Google made a breakthrough: It trained its AI to recognize cats in YouTube videos. Google’s neural network, software which uses statistics to approximate how the brain learns, taught itself to detect the shapes of cats and humans with more than 70% accuracy. It was a 70% improvement over any other machine learning at the time. Five years later,…
Continue reading “Five years ago, artificial intelligence was struggling to identify cats. Now it’s trying to tackle 5000 species — Quartz”