The moment computers learned to see
orig. “ImageNet Classification with Deep Convolutional Neural Networks” · Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton
For a long time, it was hard to train computers to tell apart pictures of cats and dogs.
A group of researchers trained a deep convolutional neural network (a type of neural network (a computer program that learns from examples) that looks at an image in small pieces, then combines those pieces to find shapes and objects) on a huge set of labeled pictures. They used a set of one million labeled pictures. Each picture had a label that told what was in it.
They trained the neural network on graphics cards (GPUs), which are much faster at maths than normal computer parts. This let them train the network much faster. They entered their network in a famous contest called ImageNet. Their network beat the old record by a lot. It got 84.7% of the pictures right. The old record was 73.2%.
This work changed how people thought about training computers to see. Before this, people had to write rules for the computer to follow. After this, people started to use neural networks more. Now, neural networks are used in many places. They are used in medical imaging, self-driving cars, and more.
- Doctors use neural networks to spot diseases in X-rays (pictures of the inside of your body) more accurately.
- Your phone uses a neural network to recognize your face when you unlock it.
- Self-driving cars use neural networks to see the road and avoid obstacles.
- Neural networks help translate languages by recognizing patterns in text.
- They can help blind people by describing what's in a picture.
- They can spot fraud by looking at patterns in money transactions.
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