|Born||1985/1986 (age 36–37)|
|Alma mater||Stanford University|
Université de Montréal
|Known for||Generative adversarial networks, Adversarial examples|
|Institutions||Apple Inc. |
|Thesis||Deep Learning of Representations and its Application to Computer Vision (2014)|
|Doctoral advisor||Yoshua Bengio|
Ian J. Goodfellow (born 1985 or 1986) is an American computer scientist, engineer, and executive, most noted for his work on artificial neural networks and deep learning. He was previously employed as a research scientist at Google Brain and director of machine learning at Apple and has made several important contributions to the field of deep learning including the invention of the generative adversarial network (GAN). Goodfellow co-wrote the textbook Deep Learning (2016) and wrote the chapter on deep learning in the most popular textbook in the field of artificial intelligence, Artificial Intelligence: A Modern Approach (used in more than 1,500 universities in 135 countries).
Goodfellow obtained his B.S. and M.S. in computer science from Stanford University under the supervision of Andrew Ng (co-founder and head of Google Brain), and his Ph.D. in machine learning from the Université de Montréal in April 2014, under the supervision of Yoshua Bengio and Aaron Courville. Goodfellow's thesis is titled Deep learning of representations and its application to computer vision.
After graduation, Goodfellow joined Google as part of the Google Brain research team. In March 2016 he left Google to join the newly founded OpenAI research laboratory. Barely 11 months later, in March 2017, Goodfellow returned to Google Research but left again in 2019.
In 2019 Goodfellow joined Apple as director of machine learning in the Special Projects Group. He resigned from Apple in April 2022 to protest Apple's plan to require in-person work for its employees. Goodfellow then joined DeepMind as a research scientist.
Goodfellow is best known for inventing generative adversarial networks (GAN), using deep learning to generate images. This approach uses two neural networks to competitively improve an image’s quality. A “generator” network creates a synthetic image based on an initial set of images such as a collection of faces. A “discriminator” network tries to detect whether or not the generator's output is real or fake. Then the generate-detect cycle is repeated. For each iteration, the generator and the discriminator use the other's feedback to improve or detect the generated images, until the discriminator can no longer distinguish between the fakes generated by its opponent and the real thing. The ability to create high quality generated imagery has increased rapidly. Unfortunately, so has its malicious use, to create deepfakes and generate video-based disinformation.
At Google, Goodfellow developed a system enabling Google Maps to automatically transcribe addresses from photos taken by Street View cars and demonstrated security vulnerabilities of machine learning systems.
In 2017, Goodfellow was cited in MIT Technology Review's 35 Innovators Under 35. In 2019, he was included in Foreign Policy's list of 100 Global Thinkers.
- ^ Goodfellow, Ian; Bengio, Yoshua; Courville, Aaron (2016). Deep Learning. Cambridge, Massachusetts: MIT Press.
- ^ "Artificial Intelligence: A Modern Approach - The Definitive AI Book". How to Learn Machine Learning. 2020. Retrieved 19 December 2022.
- ^ Goodfellow, Ian (28 April 2020). "Chapter 21: Deep Learning". Artificial intelligence : a modern approach (PDF). By Russell, Stuart J.; Norvig, Peter (Fourth ed.). Hoboken, NJ: Pearson. ISBN 978-0134610993.
- ^ "Nobel Week Dialogue". NobelPrize.org. Retrieved 19 December 2022.
- ^ "Top 12 AI Leaders and Researchers you Should Know in 2022". Great Learning Blog: Free Resources what Matters to shape your Career!. 9 May 2022. Retrieved 19 December 2022.
- ^ La Barbera, Steve (27 March 2019). "Montreal's Yoshua Bengio Honored with the 'Nobel Prize' of Computing". Montreal in Technology. Retrieved 19 December 2022.
- ^ Goodfellow, Ian (18 February 2015). Deep learning of representations and its application to computer vision (Thesis). hdl:1866/11674.
- ^ "Ian Goodfellow PhD Defense Presentation". Universite de Montreal. Retrieved 27 October 2020.
- ^ Metz, Cade (15 February 2022). Genius Makers: The Mavericks Who Brought AI to Google, Facebook, and the World. Penguin. pp. 203–213. ISBN 978-1-5247-4269-0. Retrieved 19 December 2022.
- ^ Metz, Cade (27 April 2016). "Inside OpenAI, Elon Musk's Wild Plan to Set Artificial Intelligence Free". Wired. Retrieved 31 July 2016.
- ^ Metz, Cade (19 April 2018). "A.I. Researchers Are Making More Than $1 Million, Even at a Nonprofit". The New York Times. Retrieved 19 December 2022.
- ^ a b Novet, Jordan (5 April 2019). "Apple hires AI expert Ian Goodfellow from Google". www.cnbc.com. Retrieved 5 April 2019.
- ^ "Apple's Director of Machine Learning Resigns Due to Return to Office Work". MacRumors. Retrieved 7 May 2022.
- ^ Greene, Tristan (19 May 2022). "Losing Ian Goodfellow to DeepMind is the dumbest thing Apple's ever done". TNW | Neural. Retrieved 11 June 2022.
- ^ Waldrop, M. Mitchell (16 March 2020). "Synthetic media: The real trouble with deepfakes". Knowable Magazine. Annual Reviews. doi:10.1146/knowable-031320-1. Retrieved 19 December 2022.
- ^ Goodfellow, Ian J.; Pouget-Abadie, Jean; Mirza, Mehdi; Xu, Bing; Warde-Farley, David; Ozair, Sherjil; Courville, Aaron; Bengio, Yoshua (2014). "Generative Adversarial Networks". arXiv:1406.2661 [stat.ML].
- ^ "How Google Cracked House Number Identification in Street View". MIT Technology Review. 6 January 2014. Retrieved 31 July 2016.
- ^ Ibarz, Julian; Banerjee, Sujoy (3 May 2017). "Updating Google Maps with Deep Learning and Street View". Research Blog. Retrieved 4 May 2017.
- ^ Gershgorn, Dave (30 March 2016). "Fooling the Machine". Popular Science. Retrieved 31 July 2016.
- ^ Gershgorn, Dave (27 July 2016). "Researchers Have Successfully Tricked A.I. Into Seeing The Wrong Things". Popular Science. Retrieved 31 July 2016.
- ^ Knight, Will (16 August 2017). "Ian Goodfellow". MIT Technology Review.
- ^ "A decade of Global Thinkers". Foreign Policy. 2019.