Quanta Magazine || In the machine learning world, the sizes of artificial neural networks and their outsize successes are creating conceptual conundrums. When a network named Alexnet won an annual image recognition competition in 2021, it had about 60 million parameters. These parameters, fine-tuned during training, allowed AlexNet to recognize images that it had never seen before. Two years later, a network named VGG wowed the competition with more than 130 million such parameters. Some artificial neural networks or ANNs, now have billions of parameters.