Ian Goodfellow

Last updated

Ian Goodfellow
Ian Goodfellow.jpg
Goodfellow, 2017
Born1987 (age 36–37) [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 is a research scientist at Google DeepMind, [2] 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) [3] and wrote the chapter on deep learning in the authoritative textbook of the field of artificial intelligence, Artificial Intelligence: A Modern Approach [4] [5] (used in more than 1,500 universities in 135 countries). [6]

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),[ citation needed ] and his Ph.D. in machine learning from the Université de Montréal in February 2015, under the supervision of Yoshua Bengio and Aaron Courville. [7] [8] Goodfellow's thesis is titled Deep learning of representations and its application to computer vision. [7] [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] Shortly after, Goodfellow then joined Google DeepMind as a research scientist. [2] [15] [16]

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. [17] [18]

At Google, Goodfellow developed a system enabling Google Maps to automatically transcribe addresses from photos taken by Street View cars [19] [20] and demonstrated security vulnerabilities of machine learning systems. [21] [22]

Recognition

In 2017, Goodfellow was cited in MIT Technology Review's 35 Innovators Under 35. [23] In 2019, he was included in Foreign Policy's list of 100 Global Thinkers. [24]

Related Research Articles

Artificial intelligence (AI), in its broadest sense, is intelligence exhibited by machines, particularly computer systems. It is a field of research in computer science that develops and studies methods and software that enable machines to perceive their environment and use learning and intelligence to take actions that maximize their chances of achieving defined goals. 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 computer scientist (born 1947)

Geoffrey Everest Hinton is a British-Canadian computer scientist, cognitive scientist, cognitive psychologist, known for his work on artificial neural networks which earned him the title as the "Godfather of AI".

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

Yann André LeCun is a French-American 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 a subset of machine learning that focuses on utilizing neural networks to perform tasks such as classification, regression, and representation learning. The field takes inspiration from biological neuroscience and is centered around stacking artificial neurons into layers and "training" them to process data. 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 (ML), feature learning or representation learning is a set of techniques that allow 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 that served as the sole AI branch of Google before being incorporated under the newer umbrella of Google AI, a research division at Google dedicated to artificial intelligence. Formed in 2011, it combined open-ended machine learning research with information systems and large-scale computing resources. It created tools such as TensorFlow, which allow neural networks to be used by the public, and multiple internal AI research projects, and aimed to create research opportunities in machine learning and natural language processing. It was merged into former Google sister company DeepMind to form Google DeepMind in April 2023.

Adversarial machine learning is the study of the attacks on machine learning algorithms, and of the defenses against such attacks. A survey from May 2020 exposes the fact that practitioners report a dire need for better protecting machine learning systems in industrial applications.

<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 artificial intelligence. The concept was initially developed by Ian Goodfellow and his colleagues 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.

<span class="mw-page-title-main">Artificial intelligence art</span> Visual media created with AI

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

<span class="mw-page-title-main">Mila (research institute)</span> Research laboratory in Montreal, Canada

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, Mila is part of the Pan-Canadian Artificial Intelligence Strategy.

Artificial neural networks (ANNs) are models created using machine learning to perform a number of tasks. Their creation was inspired by biological neural circuitry. While some of the computational implementations ANNs relate to earlier discoveries in mathematics, the first implementation of ANNs was by psychologist Frank Rosenblatt, who developed the perceptron. Little research was conducted on ANNs in the 1970s and 1980s, with the AAAI calling this period an "AI winter".

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.

<span class="mw-page-title-main">Samy Bengio</span> Canadian computer scientist (born 1965)

Samy Bengio is a Canadian computer scientist, senior director of Artificial Intelligence and Machine Learning Research at Apple, adjunct professor at École Polytechnique Fédérale de Lausanne, 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, 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.

<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 a subset of artificial intelligence that uses generative models to produce text, images, videos, or other forms of data. These models learn the underlying patterns and structures of their training data and use them to produce new data based on the input, which often comes in the form of natural language prompts.

In deep learning, weight initialization describes the initial step in creating a neural network. A neural network contains trainable parameters that are modified during training: weight initialization is the pre-training step of assigning initial values to these parameters.

<span class="mw-page-title-main">Sasha Luccioni</span> Ukrainian computer scientist (born 1990)

Alexandra Sasha Luccioni is a computer scientist specializing in the intersection of artificial intelligence (AI) and climate change. Her work focuses on quantifying the environmental impact of AI technologies and promoting sustainable practices in machine learning development.

References

  1. 1 2 "Goodfellow, Ian". Katalog der Deutschen Nationalbibliothek (in German). German National Library . Retrieved September 4, 2024.
  2. 1 2 Gurman, Mark (May 18, 2022). "Apple Executive Who Left Over Return-to-Office Policy Joins Google AI Unit". Yahoo Finance . Bloomberg News . Retrieved September 4, 2024.
  3. ↑ Goodfellow, Ian; Bengio, Yoshua; Courville, Aaron (2016). Deep Learning. Cambridge, Massachusetts: MIT Press.
  4. ↑ "Artificial Intelligence: A Modern Approach - The Definitive AI Book". How to Learn Machine Learning. 2020. Retrieved December 19, 2022.
  5. ↑ Goodfellow, Ian (April 28, 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.
  6. ↑ "Nobel Week Dialogue". Nobel Foundation. 2022. Retrieved December 19, 2022.
  7. 1 2 Goodfellow, Ian (April 2014). Deep learning of representations and its application to computer vision (Thesis). Université de Montréal. hdl: 1866/11674 . Retrieved September 4, 2024.
  8. ↑ La Barbera, Steve (March 27, 2019). "Montreal's Yoshua Bengio Honored with the 'Nobel Prize' of Computing". Montreal in Technology. Archived from the original on December 19, 2022. Retrieved December 19, 2022.
  9. ↑ Ian Goodfellow PhD Defense Presentation. September 3, 2014. Retrieved October 27, 2020– via YouTube.
  10. ↑ Metz, Cade (February 15, 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 December 19, 2022.
  11. ↑ Metz, Cade (April 27, 2016). "Inside OpenAI, Elon Musk's Wild Plan to Set Artificial Intelligence Free". Wired . Retrieved July 31, 2016.
  12. ↑ Metz, Cade (April 19, 2018). "A.I. Researchers Are Making More Than $1 Million, Even at a Nonprofit". The New York Times . Retrieved December 19, 2022.
  13. 1 2 Novet, Jordan (April 5, 2019). "Apple hires AI expert Ian Goodfellow from Google". CNBC . Retrieved April 5, 2019.
  14. ↑ Bove, Tristan (May 10, 2022). "Apple's chief of machine learning quits over return-to-office policy". Fortune . Retrieved September 4, 2024.
  15. ↑ Greene, Tristan (May 19, 2022). "Losing Ian Goodfellow to DeepMind is the dumbest thing Apple's ever done". TNW | Neural. Retrieved June 11, 2022.
  16. ↑ Goodfellow, Ian [@goodfellow_ian] (July 6, 2022). "I'm excited to announce that I've joined DeepMind! I'll be a research scientist in @OriolVinyalsML's Deep Learning team" (Tweet) – via Twitter.
  17. ↑ Waldrop, M. Mitchell (March 16, 2020). "Synthetic media: The real trouble with deepfakes". Knowable Magazine. Annual Reviews. doi: 10.1146/knowable-031320-1 . S2CID   215882738 . Retrieved December 19, 2022.
  18. ↑ 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].
  19. ↑ "How Google Cracked House Number Identification in Street View". MIT Technology Review . January 6, 2014. Retrieved July 31, 2016.
  20. ↑ Ibarz, Julian; Banerjee, Sujoy (May 3, 2017). "Updating Google Maps with Deep Learning and Street View". Research Blog. Retrieved May 4, 2017.
  21. ↑ Gershgorn, Dave (March 30, 2016). "Fooling the Machine". Popular Science . Retrieved July 31, 2016.
  22. ↑ Gershgorn, Dave (July 27, 2016). "Researchers Have Successfully Tricked A.I. Into Seeing The Wrong Things". Popular Science . Retrieved July 31, 2016.
  23. ↑ Knight, Will (August 16, 2017). "Ian Goodfellow". MIT Technology Review . Retrieved September 4, 2024.
  24. ↑ "A Decade of Global Thinkers". Foreign Policy . 2019. Retrieved September 4, 2024.
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.