Ian Goodfellow

American computer scientist who, as a doctoral student of Yoshua BengioYoshua Bengio, prop. n.: A Canadian computer scientist who helped found deep learning, including ideas behind today's language models, and who has become a leading voice on the risks of advanced AI. and Aaron Courville at the Université de Montréal, invented generative adversarial networksgenerative adversarial network, n.: Two neural networks trained as rivals: one forges fake images, the other tries to spot the fakes. As both improve, the forger's output becomes realistic enough to fool people. (GANs, 2014): a generator learns to produce samples that a discriminator cannot distinguish from real data. GANs led image synthesis until diffusion modelsdiffusion model, n.: An AI that learns to create images, sound or video by practicing noise removal: shown pictures buried under static, it learns to clean them up, then builds new ones starting from pure static. overtook them and underlay early deepfakesdeepfake, n.: A fake but realistic video, photo or voice recording made with AI that puts words in a real person's mouth or shows them in a scene that never happened.. He co-authored the paper that named adversarial examplesadversarial example, n.: An input altered on purpose, often too slightly for people to notice, so that an AI model gets it badly wrong — like a panda photo that a classifier confidently calls a gibbon. and demonstrated them in deep neural networks (Christian Szegedy et al., 2013) and introduced the fast gradient sign methodfast gradient sign method, n.: A quick way to make an image that fools a neural network: nudge every pixel slightly in whichever direction most increases the network's error. (2014), which made adversarial trainingadversarial training, n.: Toughening an AI model by deliberately showing it attacks during training — the tricky inputs that fool it — along with the right responses, so it learns to resist them. practical and helped found the field of adversarial machine learningadversarial machine learning, n.: The study of how machine-learning systems can be fooled, corrupted or spied on by someone working deliberately against them, and of how to build systems that hold up.. He co-wrote the textbook 'Deep Learning' (2016). He has worked at Google Brain, OpenAI, Apple, where he directed machine learning in its Special Projects Group (2019–2022), and Google DeepMind, which he joined in 2022.