Artificial Intelligence

VGGNet is a famous deep learning model used in computer vision—essentially, teaching computers to understand images. It was created by researchers at the Visual Geometry Group (VGG) at the University of Oxford. Since its debut in 2014, VGGNet has become one of the key models that helped advance how machines see and recognize objects in photos. At its core, VGGNet is designed to look at images and decide what is in them.

LeNet-5 is an early and very influential type of convolutional neural network (CNN) developed by Yann LeCun and his colleagues in 1998, designed mainly to recognize handwritten digits like those in the MNIST dataset. What makes LeNet-5 special is how it combines several clever ideas that allow it to efficiently and accurately understand images despite their complexity—ideas that were crucial stepping stones for today’s deep learning revolution.

The journey of pioneers in Machine Learning (ML) and Artificial Intelligence (AI) is a remarkable tale of innovation, collaboration, and the relentless pursuit of knowledge.

Gran Turismo Sophy is an advanced AI racing agent developed through a collaboration between Sony AI, Polyphony Digital, and Sony Interactive Entertainment. This groundbreaking technology utilizes deep reinforcement learning to master the complexities of competitive racing in the Gran Turismo Sport simulator. Initially starting as an AI that struggled to navigate tracks, Sophy has evolved into a formidable competitor capable of challenging top human drivers by mastering racing tactics, etiquette, and vehicle control.

Temporal Difference (TD) Learning is a fundamental concept in the field of reinforcement learning, which is a subfield of artificial intelligence (AI). It is particularly powerful for problems where an agent must learn to make decisions over time based on its interactions with an environment. Unlike traditional supervised learning, where a model learns from a fixed dataset, TD Learning enables agents to learn directly from experience, making it well-suited for dynamic and uncertain environments.