Ten Years of AI in Review

The last decade has been a thrilling and eventful ride for the field of artificial intelligence (AI). Modest explorations of the potential of deep learning turned into an explosive proliferation of a field that now includes everything from recommender systems in e-commerce to object detection for autonomous vehicles and generative models that can create everything from realistic images to coherent text. In this article, we’ll take a walk down memory lane and revisit some of the key breakthroughs that got us to where we are today. Whether you are a seasoned AI practitioner or simply interested in the latest developments in the field, this article will provide you with a comprehensive overview of the remarkable progress that led AI to become a household name. <h2>2013: AlexNet and Variational Autoencoders</h2> The year 2013 is widely regarded as the “coming-of-age” of deep learning, initiated by major advances in computer vision. According to a recent interview of Geoffrey Hinton, by 2013 “pretty much all the computer vision research had switched to neural nets”. This boom was primarily fueled by a rather surprising breakthrough in image recognition one year earlier. In September 2012, AlexNet, a deep convolutional neural network (CNN), pulled off a record-breaking performance in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), demonstrating the potential of deep learning for image recognition tasks. It achieved a top-5 error of 15.3%, which was 10.9% lower than that of its nearest competitor. <a href="https://towardsdatascience.com/ten-years-of-ai-in-review-85decdb2a540">Visit Now</a>