Yann LeCun     ML, CV, mobile robotics and computational neuroscience scientist

· Employed at: Facebook
· Expertise: Image Recognition

Yann André LeCun (/ləˈkʌn/ lə-KUN, French: [ləkœ̃]; originally spelled Le Cun) born 8 July 1960,   is a Turing Award winning French computer scientist working primarily in the fields of machine learning, computer vision, mobile robotics and computational neuroscience. He is the Silver Professor of the Courant Institute of Mathematical Sciences at New York University and Vice-President, Chief AI Scientist at Meta.

He is well known for his work on optical character recognition and computer vision using convolutional neural networks (CNN), and is a founding father of convolutional nets. He is also one of the main creators of the DjVu image compression technology (together with Léon Bottou and Patrick Haffner). He co-developed the Lush programming language with Léon Bottou.

LeCun received the 2018 Turing Award (often referred to as the "Nobel Prize of Computing"), together with Yoshua Bengio and Geoffrey Hinton, for their work on deep learning. The three are sometimes referred to as the "Godfathers of AI" and "Godfathers of Deep Learning".

Yann LeCun is a French computer scientist hailed as one of the pioneers in the realm of deep learning. His contributions to the field of artificial intelligence have been profound and extensive. Here are some significant projects and contributions he has made:

01 Convolutional Neural Networks (CNN)

Yann LeCun is one of the pioneers of Convolutional Neural Networks. In the early 1990s, he collaborated on the development of LeNet-5, a CNN used for handwritten digit recognition. CNN's successful applications in computer vision, such as image classification, object detection, and image generation, have made it one of the fundamental models of deep learning.

02 Long Short-Term Memory (LSTM)

While not its founder, LeCun's work in neural network sequence modeling is related to LSTM. LSTM is a type of recurrent neural network specifically designed for handling and predicting time-series data, such as speech recognition and natural language processing tasks.

03 GloVe Word Embeddings

LeCun collaborated with other researchers to develop the GloVe (Global Vectors for Word Representation) algorithm, used to generate high-quality word embeddings. These embeddings are widely used in natural language processing to represent words as vectors and extract semantic information in text processing tasks.