Yann LeCun
Invented the convolutional neural network in the late 1980s and deployed it commercially reading handwritten checks years before deep learning was fashionable.
Yann LeCun (b. 1960) developed the convolutional neural networkCNN (Convolutional Neural Network)A CNN is a neural network built around the convolution operation, which encodes locality and translation invariance for processing images efficiently. architecture while working with Geoffrey HintonGeoffrey HintonKnown as a godfather of deep learning — co-authored backpropagation in 1986, co-invented the Boltzmann machine and dropout, and co-authored AlexNet, the 2012 result that restarted the field. at Bell Labs in the late 1980s, building on earlier work on the neocognitron. His key idea was weight sharing: instead of learning a separate weight for every pixel position, a small kernel slides across the whole image, so the network learns the same edge- or texture-detector regardless of where it appears in frame. That single design choice made image recognition tractable with far fewer parameters than a fully connected network would need.
Unlike most of the field, LeCun's early CNN work wasn't just academic — his LeNet architecture was deployed commercially by banks in the 1990s to read handwritten digits on checks, reportedly processing a meaningful share of checks in the US at the time. That made LeCun one of the few researchers with a working, revenue-generating deep learningDeep LearningDeep learning is machine learning using multi-layer neural networks, which learn their own features from raw data instead of relying on hand-engineered ones. system a full two decades before AlexNetAlexNetAlexNet is the 2012 deep convolutional network that won the ImageNet competition by a wide margin, sparking the deep learning boom. made the approach fashionable again.
He later became the founding director of Facebook AIAI (Artificial Intelligence)AI is the field of building systems that perform tasks normally requiring human intelligence — reasoning, perception, language, and decision-making. Research (FAIR) and shared the 2018 Turing Award with Hinton and Yoshua BengioYoshua BengioHelped lay the statistical foundations of modern language modeling and co-founded the Montreal deep learning school — now a leading voice on AI safety.. He has been a vocal skeptic of claims that scaling large language modelsLLM (Large Language Model)An LLM is a large transformer trained to predict the next token on massive text corpora, then fine-tuned to follow instructions — the architecture behind GPT, Claude, Gemini, and Llama. alone will reach general intelligence, arguing instead for architectures with an explicit world model — a live disagreement with parts of the field pursuing pure scale.
See also: Geoffrey HintonGeoffrey HintonKnown as a godfather of deep learning — co-authored backpropagation in 1986, co-invented the Boltzmann machine and dropout, and co-authored AlexNet, the 2012 result that restarted the field., Yoshua BengioYoshua BengioHelped lay the statistical foundations of modern language modeling and co-founded the Montreal deep learning school — now a leading voice on AI safety.
Learn more: Computer Vision · Wikipedia: Yann LeCun
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Geoffrey Hinton
Known as a godfather of deep learning — co-authored backpropagation in 1986, co-invented the Boltzmann machine and dropout, and co-authored AlexNet, the 2012 result that restarted the field.
Yoshua Bengio
Helped lay the statistical foundations of modern language modeling and co-founded the Montreal deep learning school — now a leading voice on AI safety.