In 2012, a quiet revolution occurred. The AlexNet neural network won the ImageNet competition, recognizing images with 10% higher accuracy than all previous systems. This was the moment when deep learning ceased to be a theory and became a reality. Since then, AI has transformed medicine, science, art, and everyday life.
The essence of this breakthrough lies in the architecture of convolutional neural networks (CNNs). They imitate the work of the brain’s visual cortex: they identify edges, shapes, and textures, and assemble them into a coherent whole. But unlike humans, AI can learn on millions of images in hours.
Today, AI diagnoses skin cancer more accurately than dermatologists. It analyzes tomograms, predicts epileptic seizures, and develops drugs. In 2020, DeepMind’s AlphaFold solved protein folding—a problem biologists had been struggling with for 50 years. This accelerated drug development exponentially.
But AI isn’t magic. It learns from data. And if the data is biased, AI will discriminate. For example, facial recognition systems perform worse on darker skin because the training sets were mostly Caucasian. This isn’t a technological failure. It’s a reflection of society.
