Ian Goodfellow
Invented generative adversarial networks (GANs) in 2014, the architecture that first made realistic image generation possible by pitting two networks against each other.
Ian Goodfellow (b. 1985) came up with generative adversarial networks during a bar conversation in 2014, then reportedly went home and implemented the first working version that same night. The idea: train two networks against each other — a generator that produces fake data and a discriminator that tries to tell fake from real — and let the resulting competition push the generator toward increasingly realistic output. No one had framed generative modeling as a two-player game before, and the GANGAN (Generative Adversarial Network)A GAN trains a generator and a discriminator against each other, the generator learning to fool the discriminator into mistaking its output for real data. became the dominant approach to realistic image generation for most of the following decade, before diffusion modelsDiffusion ModelA diffusion model learns to reverse a fixed process of gradually adding noise to data, generating new samples by denoising pure noise step by step. overtook it.
Goodfellow also co-authored 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. (2016) with 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. and Aaron Courville, for years the standard graduate-level textbook on the field, and did influential early work on adversarial examplesAdversarial ExampleAn adversarial example is an input with a small, often imperceptible perturbation deliberately crafted to make a model produce a wrong or attacker-chosen output. — small, often imperceptible perturbations to an input that reliably fool a trained model, a finding that helped launch AIAI (Artificial Intelligence)AI is the field of building systems that perform tasks normally requiring human intelligence — reasoning, perception, language, and decision-making. security as a distinct research area.
He has worked on safety and adversarial robustness at several of the major AI labs, a natural extension of research that started by showing how easily these systems can be fooled.
See also: 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: Generative Models · Wikipedia: Ian Goodfellow
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Alex Krizhevsky
Built AlexNet in 2012 with Ilya Sutskever and Geoffrey Hinton, the deep convolutional network whose ImageNet win is widely credited with kicking off the deep learning boom.
Ilya Sutskever
Co-authored AlexNet as a student, then sequence-to-sequence learning, then co-founded OpenAI and helped drive the bet that scale would produce GPT-level language models.