Ian Goodfellow

Last updated

Ian Goodfellow
Ian Goodfellow.jpg
Born1987 [1]
Nationality American
Alma mater Stanford University
Université de Montréal
Known for Generative adversarial networks, Adversarial examples
Scientific career
Fields Computer science
Institutions Apple Inc.
Google Brain
OpenAI
DeepMind
Google DeepMind
Thesis Deep Learning of Representations and its Application to Computer Vision  (2014)
Doctoral advisor Yoshua Bengio
Aaron Courville
Website www.iangoodfellow.com

Ian J. Goodfellow (born 1987 [1] ) 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, as the first author, the textbook Deep Learning (2016) [2] and wrote the chapter on deep learning in the authoritative textbook of the field of artificial intelligence, Artificial Intelligence: A Modern Approach [3] [4] (used in more than 1,500 universities in 135 countries). [5]

Contents

Education

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. [6] [7] Goodfellow's thesis is titled Deep learning of representations and its application to computer vision. [8] [9]

Career

After graduation, Goodfellow joined Google as part of the Google Brain research team. [10] In March 2016 he left Google to join the newly founded OpenAI research laboratory. [11] Barely 11 months later, in March 2017, Goodfellow returned to Google Research [12] but left again in 2019. [13]

In 2019 Goodfellow joined Apple as director of machine learning in the Special Projects Group. [13] He resigned from Apple in April 2022 to protest Apple's plan to require in-person work for its employees. [14] Goodfellow then joined DeepMind as a research scientist. [15]

Research

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. [16] [17]

At Google, Goodfellow developed a system enabling Google Maps to automatically transcribe addresses from photos taken by Street View cars [18] [19] and demonstrated security vulnerabilities of machine learning systems. [20] [21]

Recognition

In 2017, Goodfellow was cited in MIT Technology Review's 35 Innovators Under 35. [22] In 2019, he was included in Foreign Policy's list of 100 Global Thinkers. [23]

Related Research Articles

<span class="mw-page-title-main">Artificial intelligence</span> Intelligence of machines or software

Artificial intelligence (AI) is the intelligence of machines or software, as opposed to the intelligence of humans or animals. It is a field of study in computer science which develops and studies intelligent machines. Such machines may be called AIs.

<span class="mw-page-title-main">Jürgen Schmidhuber</span> German computer scientist

Jürgen Schmidhuber is a German computer scientist noted for his work in the field of artificial intelligence, specifically artificial neural networks. He is a scientific director of the Dalle Molle Institute for Artificial Intelligence Research in Switzerland. He is also director of the Artificial Intelligence Initiative and professor of the Computer Science program in the Computer, Electrical, and Mathematical Sciences and Engineering (CEMSE) division at the King Abdullah University of Science and Technology (KAUST) in Saudi Arabia.

<span class="mw-page-title-main">Geoffrey Hinton</span> British-Canadian computer scientist and psychologist (born 1947)

Geoffrey Everest Hinton is a British-Canadian computer scientist and cognitive psychologist, most noted for his work on artificial neural networks. From 2013 to 2023, he divided his time working for Google and the University of Toronto, before publicly announcing his departure from Google in May 2023, citing concerns about the risks of artificial intelligence (AI) technology. In 2017, he co-founded and became the chief scientific advisor of the Vector Institute in Toronto.

<span class="mw-page-title-main">Yann LeCun</span> French computer scientist (born 1960)

Yann André LeCun 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.

<span class="mw-page-title-main">Deep learning</span> Branch of machine learning

Deep learning is the subset of machine learning methods based on artificial neural networks with representation learning. The adjective "deep" refers to the use of multiple layers in the network. Methods used can be either supervised, semi-supervised or unsupervised.

<span class="mw-page-title-main">Feature learning</span> Set of learning techniques in machine learning

In machine learning, feature learning or representation learning is a set of techniques that allows a system to automatically discover the representations needed for feature detection or classification from raw data. This replaces manual feature engineering and allows a machine to both learn the features and use them to perform a specific task.

Google Brain was a deep learning artificial intelligence research team under the umbrella of Google AI, a research division at Google dedicated to artificial intelligence. Formed in 2011, Google Brain combined open-ended machine learning research with information systems and large-scale computing resources. The team has created tools such as TensorFlow, which allow for neural networks to be used by the public, with multiple internal AI research projects. The team aims to create research opportunities in machine learning and natural language processing. The team was merged into former Google sister company DeepMind to form Google DeepMind in April 2023.

<span class="mw-page-title-main">Yoshua Bengio</span> Canadian computer scientist

Yoshua Bengio is a Canadian computer scientist, most noted for his work on artificial neural networks and deep learning. He is a professor at the Department of Computer Science and Operations Research at the Université de Montréal and scientific director of the Montreal Institute for Learning Algorithms (MILA).

<span class="mw-page-title-main">Generative adversarial network</span> Deep learning method

A generative adversarial network (GAN) is a class of machine learning frameworks and a prominent framework for approaching generative AI. The concept was initially developed by Zhuowen Tu in 2007. It was popularized Ian Goodfellow and his colleagues, who re-implemented the idea using deep networks in June 2014. In a GAN, two neural networks contest with each other in the form of a zero-sum game, where one agent's gain is another agent's loss.

Wojciech Zaremba is a Polish computer scientist, a founding team member of OpenAI (2016–present), where he leads both the Codex research and language teams. The teams actively work on AI that writes computer code and creating successors to GPT-3 respectively. The mission of OpenAI is to build safe artificial intelligence (AI), and ensure that its benefits are as evenly distributed as possible.

<span class="mw-page-title-main">Russ Salakhutdinov</span> Canadian AI researcher

Ruslan "Russ" Salakhutdinov is a Canadian researcher of Tatar origin working in the field of artificial intelligence.

<span class="mw-page-title-main">Artificial intelligence art</span> Machine application of knowledge of human aesthetic expressions

Artificial intelligence art is any visual artwork created through the use of artificial intelligence (AI) programs.

Mila - Quebec AI Institute is a research institute in Montreal, Quebec, focusing mainly on machine learning research. Approximately 1000 students and researchers and 100 faculty members, were part of Mila in 2022. Along with Alberta's Amii and Toronto's Vector Institute (Canada), Mila is part of the Pan-Canadian Artificial Intelligence Strategy.

Synthetic media is a catch-all term for the artificial production, manipulation, and modification of data and media by automated means, especially through the use of artificial intelligence algorithms, such as for the purpose of misleading people or changing an original meaning. Synthetic media as a field has grown rapidly since the creation of generative adversarial networks, primarily through the rise of deepfakes as well as music synthesis, text generation, human image synthesis, speech synthesis, and more. Though experts use the term "synthetic media," individual methods such as deepfakes and text synthesis are sometimes not referred to as such by the media but instead by their respective terminology Significant attention arose towards the field of synthetic media starting in 2017 when Motherboard reported on the emergence of AI altered pornographic videos to insert the faces of famous actresses. Potential hazards of synthetic media include the spread of misinformation, further loss of trust in institutions such as media and government, the mass automation of creative and journalistic jobs and a retreat into AI-generated fantasy worlds. Synthetic media is an applied form of artificial imagination.

An audio deepfake is a type of artificial intelligence used to create convincing speech sentences that sound like specific people saying things they did not say. This technology was initially developed for various applications to improve human life. For example, it can be used to produce audiobooks, and also to help people who have lost their voices to get them back. Commercially, it has opened the door to several opportunities. This technology can also create more personalized digital assistants and natural-sounding text-to-speech as well as speech translation services.

Samy Bengio is a Canadian computer scientist, Senior Director of AI and Machine Learning Research at Apple, and a former long-time scientist at Google known for leading a large group of researchers working in machine learning including adversarial settings. Bengio left Google shortly after the company fired his report, Timnit Gebru, without first notifying him. At the time, Bengio said that he had been "stunned" by what happened to Gebru. He is also among the three authors who developed Torch in 2002, the ancestor of PyTorch, one of today's two largest machine learning frameworks.

Meta AI is an artificial intelligence laboratory that belongs to Meta Platforms Inc. Meta AI intends to develop various forms of artificial intelligence, improving augmented and artificial reality technologies. Meta AI is an academic research laboratory focused on generating knowledge for the AI community. This is in contrast to Facebook's Applied Machine Learning (AML) team, which focuses on practical applications of its products.

<span class="mw-page-title-main">Text-to-image model</span> Machine learning model

A text-to-image model is a machine learning model which takes an input natural language description and produces an image matching that description.

<span class="mw-page-title-main">Generative artificial intelligence</span> AI system capable of generating content in response to prompts

Generative artificial intelligence is artificial intelligence capable of generating text, images, or other media, using generative models. Generative AI models learn the patterns and structure of their input training data and then generate new data that has similar characteristics.

Comparison of user features of chatbots refers to a comparison of the general user features of major chatbot applications or web interfaces, in a narrative format.It is a comparison of basic roles and the most prominent features. It does not encompass a full exhaustive comparison or description of all technical details of all chatbots. It also includes the most important features of the chatbots origins, historical development, and role.

References

  1. 1 2 "Katalog der Deutschen Nationalbibliothek". portal.dnb.de.
  2. ↑ Goodfellow, Ian; Bengio, Yoshua; Courville, Aaron (2016). Deep Learning. Cambridge, Massachusetts: MIT Press.
  3. ↑ "Artificial Intelligence: A Modern Approach - The Definitive AI Book". How to Learn Machine Learning. 2020. Retrieved 19 December 2022.
  4. ↑ 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.
  5. ↑ "Nobel Week Dialogue". NobelPrize.org. Retrieved 19 December 2022.
  6. ↑ "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.
  7. ↑ La Barbera, Steve (27 March 2019). "Montreal's Yoshua Bengio Honored with the 'Nobel Prize' of Computing". Montreal in Technology. Retrieved 19 December 2022.
  8. ↑ Goodfellow, Ian (18 February 2015). Deep learning of representations and its application to computer vision (Thesis). hdl: 1866/11674 .
  9. ↑ "Ian Goodfellow PhD Defense Presentation". Universite de Montreal. Retrieved 27 October 2020.
  10. ↑ 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.
  11. ↑ Metz, Cade (27 April 2016). "Inside OpenAI, Elon Musk's Wild Plan to Set Artificial Intelligence Free". Wired. Retrieved 31 July 2016.
  12. ↑ 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.
  13. 1 2 Novet, Jordan (5 April 2019). "Apple hires AI expert Ian Goodfellow from Google". www.cnbc.com. Retrieved 5 April 2019.
  14. ↑ "Apple's Director of Machine Learning Resigns Due to Return to Office Work". MacRumors. Retrieved 7 May 2022.
  15. ↑ Greene, Tristan (19 May 2022). "Losing Ian Goodfellow to DeepMind is the dumbest thing Apple's ever done". TNW | Neural. Retrieved 11 June 2022.
  16. ↑ Waldrop, M. Mitchell (16 March 2020). "Synthetic media: The real trouble with deepfakes". Knowable Magazine. Annual Reviews. doi: 10.1146/knowable-031320-1 . S2CID   215882738 . Retrieved 19 December 2022.
  17. ↑ 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].
  18. ↑ "How Google Cracked House Number Identification in Street View". MIT Technology Review. 6 January 2014. Retrieved 31 July 2016.
  19. ↑ Ibarz, Julian; Banerjee, Sujoy (3 May 2017). "Updating Google Maps with Deep Learning and Street View". Research Blog. Retrieved 4 May 2017.
  20. ↑ Gershgorn, Dave (30 March 2016). "Fooling the Machine". Popular Science. Retrieved 31 July 2016.
  21. ↑ Gershgorn, Dave (27 July 2016). "Researchers Have Successfully Tricked A.I. Into Seeing The Wrong Things". Popular Science. Retrieved 31 July 2016.
  22. ↑ Knight, Will (16 August 2017). "Ian Goodfellow". MIT Technology Review.
  23. ↑ "A decade of Global Thinkers". Foreign Policy. 2019.
This page is based on this Wikipedia article
Text is available under the CC BY-SA 4.0 license; additional terms may apply.
Images, videos and audio are available under their respective licenses.