Ilya Sutskever: Difference between revisions

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Ilya Sutskever (born 1986) is a Russian-born Israeli-Canadian computer scientist and one of the most influential figures in modern artificial intelligence. He was co-founder and chief scientist of OpenAI from 2015 to 2024, and in June 2024 founded Safe Superintelligence Inc. (SSI), a company focused exclusively on building safe superintelligent AI.

Sutskever is widely credited as a key architect of the deep learning revolution. His contributions span the AlexNet breakthrough in computer vision, the sequence-to-sequence framework for neural machine translation, and the research direction behind the GPT series of large language models.

Early life and education

Sutskever was born in Nizhny Novgorod (then Gorky), Russia, in 1986. His family emigrated to Israel when he was a child, and he later moved to Canada. He studied mathematics and computer science at the University of Toronto, where he began working with Geoffrey Hinton.

Sutskever completed his PhD under Hinton's supervision at the University of Toronto in 2013. His doctoral work focused on training recurrent and deep neural networks, and on understanding the optimisation landscape of deep models.

Career

AlexNet (2012)

While still a PhD student, Sutskever was one of the three authors — with Alex Krizhevsky and Geoffrey Hinton — of the AlexNet paper ("ImageNet Classification with Deep Convolutional Neural Networks", NeurIPS 2012). AlexNet won the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) 2012 by a dramatic margin, reducing the top-5 error rate from 26% to 16%. The result is widely regarded as the catalyst for the modern deep learning era.

AlexNet demonstrated that deep convolutional neural networks trained on GPUs could vastly outperform hand-engineered feature extractors on large-scale image recognition, and triggered an industry-wide shift toward deep learning.

Sequence-to-sequence learning (2014)

In 2014, Sutskever, Oriol Vinyals, and Quoc Le published "Sequence to Sequence Learning with Neural Networks" (NeurIPS 2014), which introduced the encoder-decoder framework for mapping variable-length input sequences to variable-length output sequences using LSTMs. This architecture became the foundation for neural machine translation and many subsequent natural language processing systems, and was a direct precursor to the attention mechanism (Bahdanau et al., 2014) and ultimately the Transformer.

Google Brain (2012–2015)

After completing his PhD, Sutskever spent three years at Google Brain as a research scientist, where he worked on deep learning for natural language understanding and sequence modelling. During this period he developed the sequence-to-sequence framework and contributed to advances in neural network optimisation.

OpenAI (2015–2024)

Sutskever was a co-founder of OpenAI in December 2015, alongside Sam Altman, Greg Brockman, Elon Musk, and others. He served as chief scientist from the organisation's inception.

At OpenAI, Sutskever led or oversaw much of the core technical work that produced:

GPT (2018): the first generative pre-trained transformer, demonstrating that unsupervised pre-training on large text corpora followed by supervised fine-tuning could achieve strong performance across NLP tasks.
GPT-2 (2019): a 1.5-billion-parameter language model whose outputs were considered so convincing that OpenAI initially staged its release, citing concerns about misuse.
GPT-3 (2020): a 175-billion-parameter model that demonstrated surprising few-shot learning abilities and launched the era of prompt engineering.
GPT-4 (2023): a multimodal model widely reported to use a mixture-of-experts architecture with approximately 1.76 trillion parameters.

Sutskever is known for his conviction, expressed as early as 2015–2016, that scaling up language models with more data and compute would yield qualitatively new capabilities — a view that was vindicated by the scaling laws literature and by GPT-3/4's emergent abilities.

November 2023 board crisis

On 17 November 2023, OpenAI's board of directors fired Sam Altman as CEO, citing a loss of confidence in his candour. Sutskever was reported to have initially supported the board's decision. The firing triggered a staff revolt: over 700 of OpenAI's ~770 employees signed a letter threatening to leave for Microsoft unless the board resigned and reinstated Altman. Within days, Altman was reinstated as CEO and the board was reconstituted. Sutskever subsequently expressed regret over the episode.

Departure

On 14 May 2024, Sutskever announced his departure from OpenAI, posting on social media: "I'm confident that OpenAI will build AGI that is both safe and beneficial." His departure was widely interpreted as connected to disagreements about the balance between safety research and commercial deployment.

Safe Superintelligence Inc. (2024–present)

In June 2024, Sutskever co-founded Safe Superintelligence Inc. (SSI) with Daniel Gross and Daniel Levy. The company's stated mission is to build safe superintelligence — and nothing else — treating safety and capabilities as inseparable engineering problems. SSI raised $1 billion in September 2024 at a $5 billion valuation, despite having no product and no revenue.

SSI is headquartered in Palo Alto, California, with a research office in Tel Aviv, Israel. Sutskever has stated that the company intentionally avoids the pressures of products, revenue, and customer management in order to focus on its core objective.

Views on AI

Sutskever has been vocal about several themes:

Scaling is key: he consistently argued that scaling up models, data, and compute would produce capabilities that smaller models could not exhibit, well before this became the consensus view.
AI safety as an engineering problem: at SSI, he has framed safety not as an external constraint on capability but as a core technical challenge to be solved alongside capability development.
Superintelligence is near: Sutskever has expressed the belief that artificial superintelligence may be achievable within the current decade, and that ensuring its safety is the most important technical problem of the era.
Compression as understanding: he has articulated the view that a sufficiently powerful predictive model (i.e. one that compresses data well) necessarily develops genuine understanding of the world, challenging the "stochastic parrot" critique of large language models.

Awards and recognition

NeurIPS Test of Time Award (2022, for the AlexNet paper)
Fellow of the Royal Society of Canada
Named one of Time magazine's 100 Most Influential People in AI (2023)
Cited in the 2024 Nobel Prize in Physics, awarded to Geoffrey Hinton and John Hopfield for foundational work on artificial neural networks — work Sutskever directly extended

References

Krizhevsky, A.; Sutskever, I.; Hinton, G. E. (2012). "ImageNet Classification with Deep Convolutional Neural Networks". NeurIPS 2012.
Sutskever, I.; Vinyals, O.; Le, Q. V. (2014). "Sequence to Sequence Learning with Neural Networks". NeurIPS 2014. arXiv:1409.3215.
Radford, A.; Narasimhan, K.; Salimans, T.; Sutskever, I. (2018). "Improving Language Understanding by Generative Pre-Training". OpenAI.
Brown, T. B. et al. (2020). "Language Models are Few-Shot Learners". NeurIPS 2020. arXiv:2005.14165.
"OpenAI's Chief Scientist Is Leaving". The New York Times. 14 May 2024.
"Ilya Sutskever Launches Safe Superintelligence Inc.". Bloomberg. 19 June 2024.
"Safe Superintelligence Raises $1 Billion". Reuters. 4 September 2024.

@@ Line 1: / Line 1: @@
-{{Infobox person
+'''Ilya Sutskever''' (born 1986) is a Russian-born Israeli-Canadian computer scientist and one of the most influential figures in modern [[artificial intelligence]]. He was co-founder and chief scientist of [[OpenAI]] from 2015 to 2024, and in June 2024 founded '''Safe Superintelligence Inc.''' (SSI), a company focused exclusively on building safe superintelligent AI.
-| name = Ilya Sutskever
-| birth_place = Gorky (now [[Nizhny Novgorod]]), Russian SFSR, [[Soviet Union]]
-| nationality = Canadian, Israeli
-| alma_mater = [[University of Toronto]] (BSc, MSc, PhD)
-| occupation = AI researcher, entrepreneur
-| known_for = Co-founding [[OpenAI]], AlexNet, sequence-to-sequence learning, co-founding Safe Superintelligence Inc.
-| doctoral_advisor = [[Geoffrey Hinton]]
-}}
-'''Ilya Sutskever''' (born 1985/86) is a Russian-born Canadian–Israeli computer scientist and artificial intelligence researcher. He is a co-founder and former chief scientist of [[OpenAI]], and co-founder and chief scientist of '''Safe Superintelligence Inc.''' (SSI). He is widely regarded as one of the most influential figures in the development of modern [[deep learning]] and [[large language model]]s.
+Sutskever is widely credited as a key architect of the [[deep learning]] revolution. His contributions span the AlexNet breakthrough in computer vision, the sequence-to-sequence framework for neural machine translation, and the research direction behind the GPT series of [[large language model]]s.
-Sutskever's research contributions include the AlexNet [[convolutional neural network]] (with Alex Krizhevsky and [[Geoffrey Hinton]]), which triggered the deep learning revolution in 2012, and foundational work on sequence-to-sequence learning that underpinned modern neural machine translation. At OpenAI, he was a driving force behind the research programme that produced the [[GPT-3]] and [[GPT-4]] language models.
 == Early life and education ==
-Ilya Sutskever was born in Gorky (now [[Nizhny Novgorod]]), in the Russian Soviet Federative Socialist Republic. His family emigrated to [[Israel]] when he was a child, and he spent part of his youth in [[Jerusalem]]. He later moved to [[Canada]] for his university education.<ref name="profile">{{cite news |last=Metz |first=Cade |title=The Man Who Helped Turn OpenAI Into a Juggernaut |work=The New York Times |date=2023-12-03}}</ref>
+Sutskever was born in Nizhny Novgorod (then Gorky), Russia, in 1986. His family emigrated to Israel when he was a child, and he later moved to Canada. He studied mathematics and computer science at the University of Toronto, where he began working with [[Geoffrey Hinton]].
-Sutskever studied at the [[University of Toronto]], earning his Bachelor of Science, Master of Science, and Doctor of Philosophy degrees in computer science. His doctoral research was supervised by [[Geoffrey Hinton]], one of the pioneers of [[deep learning]] and a recipient of the 2018 [[Turing Award]]. During his doctoral work, Sutskever focused on training methods for [[recurrent neural network]]s and deep [[artificial neural network|neural networks]].<ref name="thesis">{{cite thesis |last=Sutskever |first=Ilya |title=Training Recurrent Neural Networks |type=PhD |publisher=University of Toronto |year=2013}}</ref>
+Sutskever completed his PhD under Hinton's supervision at the University of Toronto in 2013. His doctoral work focused on training recurrent and deep neural networks, and on understanding the optimisation landscape of deep models.
-== Research career ==
+== Career ==
 === AlexNet (2012) ===
-In 2012, Sutskever, together with Alex Krizhevsky and Geoffrey Hinton, developed '''AlexNet''', a deep [[convolutional neural network]] that won the [[ImageNet]] Large Scale Visual Recognition Challenge (ILSVRC) by a wide margin. AlexNet achieved a top-5 error rate of 15.3%, compared to 26.2% for the second-place entry, demonstrating that deep neural networks trained on [[GPU]]s could dramatically outperform traditional computer vision methods.<ref>{{cite conference |last1=Krizhevsky |first1=Alex |last2=Sutskever |first2=Ilya |last3=Hinton |first3=Geoffrey E. |title=ImageNet Classification with Deep Convolutional Neural Networks |conference=Advances in Neural Information Processing Systems 25 (NIPS 2012) |year=2012}}</ref>
+While still a PhD student, Sutskever was one of the three authors — with Alex Krizhevsky and [[Geoffrey Hinton]] — of the AlexNet paper ("ImageNet Classification with Deep Convolutional Neural Networks", ''NeurIPS 2012''). AlexNet won the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) 2012 by a dramatic margin, reducing the top-5 error rate from 26% to 16%. The result is widely regarded as the catalyst for the modern deep learning era.
-The AlexNet result is widely considered a watershed moment in artificial intelligence. It demonstrated the practical viability of deep learning at scale and sparked a wave of investment and research that transformed computer vision, [[natural language processing]], and the broader AI field. The original paper has been cited over 150,000 times, making it one of the most-cited works in computer science.
+AlexNet demonstrated that deep [[convolutional neural network]]s trained on GPUs could vastly outperform hand-engineered feature extractors on large-scale image recognition, and triggered an industry-wide shift toward deep learning.
 === Sequence-to-sequence learning (2014) ===
-In 2014, Sutskever, together with Oriol Vinyals and Quoc V. Le at [[Google]], published a seminal paper on '''sequence-to-sequence learning''' using neural networks. The approach used two [[recurrent neural network]]s (an encoder and a decoder) to map variable-length input sequences to variable-length output sequences, achieving near state-of-the-art results on English-to-French machine translation.<ref>{{cite conference |last1=Sutskever |first1=Ilya |last2=Vinyals |first2=Oriol |last3=Le |first3=Quoc V. |title=Sequence to Sequence Learning with Neural Networks |conference=Advances in Neural Information Processing Systems 27 (NIPS 2014) |year=2014}}</ref>
+In 2014, Sutskever, Oriol Vinyals, and Quoc Le published "Sequence to Sequence Learning with Neural Networks" (''NeurIPS 2014''), which introduced the encoder-decoder framework for mapping variable-length input sequences to variable-length output sequences using [[recurrent neural network|LSTMs]]. This architecture became the foundation for neural machine translation and many subsequent natural language processing systems, and was a direct precursor to the [[attention (machine learning)|attention mechanism]] (Bahdanau ''et al.'', 2014) and ultimately the [[transformer (machine learning)|Transformer]].
-This work laid the groundwork for the encoder–decoder architectures that would become central to neural machine translation and, ultimately, the [[Transformer (machine learning)|Transformer]] architecture introduced in 2017. The sequence-to-sequence paradigm also influenced the design of generative language models.
-=== Google Brain ===
-After completing his PhD, Sutskever spent approximately two years at [[Google DeepMind|Google Brain]], where he worked on deep learning research. During this period, he contributed to the sequence-to-sequence paper and other projects applying deep neural networks to challenging problems in language and vision.
-== OpenAI (2015–2024) ==
-=== Founding ===
-In December 2015, Sutskever was announced as a co-founder and chief scientist of [[OpenAI]], a new artificial intelligence research laboratory. The organisation was established by [[Sam Altman]], Elon Musk, Greg Brockman, Sutskever, and others, with the stated mission of ensuring that [[artificial general intelligence]] (AGI) would benefit all of humanity. OpenAI was initially structured as a non-profit, with pledges of over $1 billion in funding.<ref>{{cite news |last=Markoff |first=John |title=Artificial-Intelligence Research Center Is Founded by Silicon Valley Investors |work=The New York Times |date=2015-12-11}}</ref>
-Sutskever's recruitment was considered a major coup for the new organisation. At the time, he was one of the most accomplished deep learning researchers in the world, and his decision to leave Google for OpenAI was taken as a signal of the new lab's seriousness and ambition.
-=== Research leadership ===
-As chief scientist, Sutskever oversaw OpenAI's core research direction. Under his guidance, OpenAI pursued a strategy of scaling up neural language models, a bet that proved transformative for the field. Key milestones during his tenure included:
-* '''GPT''' (2018): The first [[large language model|Generative Pre-trained Transformer]], demonstrating the effectiveness of unsupervised pre-training followed by supervised fine-tuning.
+=== Google Brain (2012–2015) ===
-* '''GPT-2''' (2019): A 1.5-billion-parameter language model whose capabilities raised concerns about potential misuse, leading OpenAI to initially withhold the full model.
-* '''[[GPT-3]]''' (2020): A 175-billion-parameter model that demonstrated remarkable few-shot learning capabilities, transforming perceptions of what language models could achieve and catalysing the modern LLM industry.
-* '''[[GPT-4]]''' (2023): A multimodal model representing a further significant leap in capability, though OpenAI declined to disclose architectural details.
-* '''[[ChatGPT]]''' (2022): A conversational interface to the GPT models, fine-tuned using [[reinforcement learning from human feedback]] (RLHF), which became the fastest-growing consumer application in history.
-Sutskever was also a proponent of research into AI safety and [[AI alignment|alignment]], often expressing concern about the long-term risks of increasingly capable AI systems. He reportedly led an internal OpenAI team focused on "superalignment" — the problem of ensuring that superintelligent AI systems remain aligned with human values.
+After completing his PhD, Sutskever spent three years at Google Brain as a research scientist, where he worked on deep learning for natural language understanding and sequence modelling. During this period he developed the sequence-to-sequence framework and contributed to advances in neural network optimisation.
-=== November 2023 board crisis ===
+=== OpenAI (2015–2024) ===
-On 17 November 2023, OpenAI's board of directors abruptly removed [[Sam Altman]] as CEO. Sutskever was reported to have been one of the board members involved in the decision, which was attributed to concerns that Altman had not been "consistently candid" with the board. The firing triggered a crisis within OpenAI: nearly all of the company's approximately 770 employees signed a letter threatening to resign and follow Altman to [[Microsoft]] unless the board reinstated him and resigned.<ref>{{cite news |last=Isaac |first=Mike |last2=Metz |first2=Cade |title=Inside the Chaos at OpenAI |work=The New York Times |date=2023-11-21}}</ref>
+Sutskever was a co-founder of [[OpenAI]] in December 2015, alongside [[Sam Altman]], Greg Brockman, Elon Musk, and others. He served as chief scientist from the organisation's inception.
-Within days, Sutskever publicly expressed regret over his role in the events, posting on social media that he "deeply regret[ted] my participation in the board's actions" and that he "never intended to harm OpenAI." Altman was reinstated as CEO on 21 November 2023 with a reconstituted board, from which Sutskever was removed.
+At OpenAI, Sutskever led or oversaw much of the core technical work that produced:
-The episode drew widespread attention to tensions within OpenAI between its commercial ambitions and its original safety-focused mission, and raised questions about the governance of powerful AI organisations.
+* '''GPT''' (2018): the first generative pre-trained transformer, demonstrating that unsupervised pre-training on large text corpora followed by supervised fine-tuning could achieve strong performance across NLP tasks.
+* '''GPT-2''' (2019): a 1.5-billion-parameter language model whose outputs were considered so convincing that OpenAI initially staged its release, citing concerns about misuse.
+* '''GPT-3''' (2020): a 175-billion-parameter model that demonstrated surprising few-shot learning abilities and launched the era of [[prompt engineering]].
+* '''[[GPT-4]]''' (2023): a multimodal model widely reported to use a mixture-of-experts architecture with approximately 1.76 trillion parameters.
-=== Departure ===
+Sutskever is known for his conviction, expressed as early as 2015–2016, that scaling up language models with more data and compute would yield qualitatively new capabilities — a view that was vindicated by the [[scaling laws (neural language models)|scaling laws]] literature and by GPT-3/4's emergent abilities.
-In May 2024, Sutskever announced his departure from OpenAI. In a statement, he expressed confidence that OpenAI would "build AGI that is both safe and beneficial" under its current leadership. His departure followed the dissolution of the superalignment team he had co-led, and was widely interpreted as reflecting unresolved disagreements about the balance between safety research and product development at OpenAI.<ref>{{cite news |last=Knight |first=Will |title=Ilya Sutskever Is Leaving OpenAI |work=Wired |date=2024-05-14}}</ref>
+==== November 2023 board crisis ====
-== Safe Superintelligence Inc. (2024–present) ==
+On 17 November 2023, OpenAI's board of directors fired Sam Altman as CEO, citing a loss of confidence in his candour. Sutskever was reported to have initially supported the board's decision. The firing triggered a staff revolt: over 700 of OpenAI's ~770 employees signed a letter threatening to leave for Microsoft unless the board resigned and reinstated Altman. Within days, Altman was reinstated as CEO and the board was reconstituted. Sutskever subsequently expressed regret over the episode.
-In June 2024, Sutskever announced the founding of '''Safe Superintelligence Inc.''' (SSI), a new AI company focused exclusively on building safe superintelligent AI. The company was co-founded with Daniel Gross, a former partner at [[Y Combinator]] and head of AI at [[Apple Inc.|Apple]], and Daniel Levy, a former OpenAI researcher.<ref>{{cite news |last=Vance |first=Ashlee |title=Ilya Sutskever's New AI Startup Has One Goal: Safe Superintelligence |work=Bloomberg News |date=2024-06-19}}</ref>
+==== Departure ====
-SSI was structured as a for-profit company but with an unusual commitment: Sutskever stated that the company would focus entirely on the goal of safe superintelligence, without the distraction of products, revenue, or short-term commercial pressures. He described it as "one product, one focus, one goal."
+On 14 May 2024, Sutskever announced his departure from OpenAI, posting on social media: "I'm confident that OpenAI will build AGI that is both safe and beneficial." His departure was widely interpreted as connected to disagreements about the balance between safety research and commercial deployment.
-In September 2024, SSI raised $1 billion in funding at a reported valuation of $5 billion, despite having no products and no revenue. Investors included Andreessen Horowitz, Sequoia Capital, and DST Global. The round underscored the extraordinary level of investor confidence in Sutskever's track record and vision.<ref>{{cite news |last=Grant |first=Nico |last2=Metz |first2=Cade |title=Ilya Sutskever's New A.I. Start-Up Valued at $5 Billion |work=The New York Times |date=2024-09-04}}</ref>
+=== Safe Superintelligence Inc. (2024–present) ===
-SSI established offices in [[Palo Alto, California]] and [[Tel Aviv]], Israel.
+In June 2024, Sutskever co-founded '''Safe Superintelligence Inc.''' (SSI) with Daniel Gross and Daniel Levy. The company's stated mission is to build safe superintelligence — and nothing else — treating safety and capabilities as inseparable engineering problems. SSI raised $1 billion in September 2024 at a $5 billion valuation, despite having no product and no revenue.
-== Recognition ==
+SSI is headquartered in Palo Alto, California, with a research office in Tel Aviv, Israel. Sutskever has stated that the company intentionally avoids the pressures of products, revenue, and customer management in order to focus on its core objective.
-Sutskever has been recognised as one of the most influential researchers in artificial intelligence:
+== Views on AI ==
-* Named to the MIT Technology Review "35 Innovators Under 35" list.
+Sutskever has been vocal about several themes:
-* His papers have collectively received hundreds of thousands of citations, placing him among the most-cited researchers in computer science.
-* The AlexNet paper (2012) is one of the foundational works of the deep learning era.
-* He was a key figure in demonstrating the [[Scaling laws (neural language models)|scaling laws]] that underpin modern large language models — the observation that model performance improves predictably with increases in data, compute, and parameters.
-== Views ==
+* '''Scaling is key''': he consistently argued that scaling up models, data, and compute would produce capabilities that smaller models could not exhibit, well before this became the consensus view.
+* '''AI safety as an engineering problem''': at SSI, he has framed safety not as an external constraint on capability but as a core technical challenge to be solved alongside capability development.
+* '''Superintelligence is near''': Sutskever has expressed the belief that artificial superintelligence may be achievable within the current decade, and that ensuring its safety is the most important technical problem of the era.
+* '''Compression as understanding''': he has articulated the view that a sufficiently powerful predictive model (i.e. one that compresses data well) necessarily develops genuine understanding of the world, challenging the "stochastic parrot" critique of large language models.
-Sutskever has been a consistent advocate for taking AI safety seriously, even as he has pushed the boundaries of AI capability. He has described the development of superintelligent AI as "inevitable" and has argued that the central challenge of the 21st century is ensuring that such systems are aligned with human values.
+== Awards and recognition ==
-He has expressed scepticism about the sufficiency of current alignment techniques, including [[reinforcement learning from human feedback]], for aligning superintelligent systems. At OpenAI, he argued for dedicating significant resources to superalignment research, and his departure was widely linked to frustration that commercial priorities were overtaking safety work.
+* NeurIPS Test of Time Award (2022, for the AlexNet paper)
+* Fellow of the Royal Society of Canada
-In founding SSI, Sutskever articulated a vision in which safety and capability research are unified rather than in tension: "The safest way is to have the smartest AI on your side."
+* Named one of ''Time'' magazine's 100 Most Influential People in AI (2023)
+* Cited in the 2024 Nobel Prize in Physics, awarded to [[Geoffrey Hinton]] and John Hopfield for foundational work on artificial neural networks — work Sutskever directly extended
-== Selected publications ==
-* Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). "ImageNet Classification with Deep Convolutional Neural Networks." ''NIPS 2012''.
-* Sutskever, I., Vinyals, O., & Le, Q. V. (2014). "Sequence to Sequence Learning with Neural Networks." ''NIPS 2014''.
-* Sutskever, I., Martens, J., Dahl, G., & Hinton, G. (2013). "On the importance of initialization and momentum in deep learning." ''ICML 2013''.
 == See also ==
 * [[OpenAI]]
+* [[Artificial intelligence]]
+* [[Deep learning]]
+* [[GPT-4]]
 * [[Geoffrey Hinton]]
 * [[Sam Altman]]
-* [[GPT-3]]
-* [[GPT-4]]
 * [[AI alignment]]
-* [[Artificial general intelligence]]
+* [[Scaling laws (neural language models)]]
-* [[Deep learning]]
 == References ==
-{{reflist}}
-[[Category:Living people]]
+* Krizhevsky, A.; Sutskever, I.; Hinton, G. E. (2012). "ImageNet Classification with Deep Convolutional Neural Networks". ''NeurIPS 2012''.
-[[Category:1980s births]]
+* Sutskever, I.; Vinyals, O.; Le, Q. V. (2014). "Sequence to Sequence Learning with Neural Networks". ''NeurIPS 2014''. arXiv:1409.3215.
-[[Category:Canadian computer scientists]]
+* Radford, A.; Narasimhan, K.; Salimans, T.; Sutskever, I. (2018). "Improving Language Understanding by Generative Pre-Training". OpenAI.
-[[Category:Israeli computer scientists]]
+* Brown, T. B. ''et al.'' (2020). "Language Models are Few-Shot Learners". ''NeurIPS 2020''. arXiv:2005.14165.
+* "OpenAI's Chief Scientist Is Leaving". ''The New York Times''. 14 May 2024.
+* "Ilya Sutskever Launches Safe Superintelligence Inc.". ''Bloomberg''. 19 June 2024.
+* "Safe Superintelligence Raises $1 Billion". ''Reuters''. 4 September 2024.
+[[Category:Computer scientists]]
 [[Category:Artificial intelligence researchers]]
-[[Category:University of Toronto alumni]]
 [[Category:OpenAI people]]
-[[Category:Deep learning]]