Explore the concept of texture bias in convolutional neural networks through this 26-minute Launchpad video. Delve into the origins and prevalence of this phenomenon, examining its potential problems and impact on model performance. Investigate how datasets influence texture or shape bias, and analyze the effects of training objectives and hyperparameters. Compare shape bias in various models, including high-performing ImageNet models and more "brain-like" architectures. Gain insights into the layers of AlexNet and the implications of different pre-processing techniques. Understand the interplay between shape and texture information in neural networks, and consider how this knowledge can be applied to improve model design and performance.