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Yann LeCun
Yann LeCun is a French‑American computer scientist and one of the most influential pioneers in artificial intelligence, particularly in deep learning and computer vision. Born on July 8, 1960, in Soisy‑sous‑Montmorency, France, he earned an engineering diploma from ESIEE Paris in 1983 and a Ph.D. in computer science from Université Pierre et Marie Curie in 1987, where he proposed an early form of the back‑propagation algorithm for neural networks. In 1988, he joined AT&T Bell Labs, where he developed **LeNet**, one of the first convolutional neural networks (CNNs), which became foundational for modern image recognition. He later contributed to technologies like the DjVu image compression format and served as the founding director of Facebook AI Research (FAIR), while also holding a professorship at New York University. In 2018, LeCun, alongside Geoffrey Hinton and Yoshua Bengio, received the Turing Award often called the “Nobel Prize of Computing” for their groundbreaking work that helped usher in the deep learning revolution.
YOLO
YOLO, short for **You Only Look Once**, is a real‑time object detection algorithm first introduced in 2015 by Joseph Redmon and colleagues. Unlike older two‑stage detectors that generate region proposals before classification, YOLO treats detection as a single regression problem, predicting bounding boxes and class probabilities in one pass through a convolutional neural network. It works by dividing an image into a grid, with each cell responsible for detecting objects whose centers fall within it, outputting coordinates, confidence scores, and class labels. This one‑stage design makes YOLO extremely fast capable of processing dozens of frames per second—while maintaining strong accuracy, which is why it’s widely used in autonomous driving, surveillance, robotics, and even medical imaging. Over the years, the YOLO family has evolved from v1 to modern versions like YOLOv8, each improving speed, accuracy, and ease of deployment for real‑world applications.