In 2019, the Association for Computing Machinery awarded the 2018 Turing Award — often called the Nobel Prize of computing — to Geoffrey Hinton, Yann LeCun and Yoshua Bengio for conceptual and engineering breakthroughs that made deep neural networks a critical component of computing. Often called the “godfathers of deep learning”, the three kept working on neural networks through decades when most of the field had moved on. Their persistence underpins today’s AI boom.

Geoffrey Hinton

Born in London in 1947, Hinton studied experimental psychology at Cambridge and earned a PhD in artificial intelligence at Edinburgh. He spent most of his career at the University of Toronto.

Key contributions:

  • the 1986 Nature paper with David Rumelhart and Ronald Williams that popularized backpropagation for training multi-layer networks;
  • Boltzmann machines and deep belief networks, which revived interest in deep networks in the mid-2000s;
  • AlexNet (2012), developed with his students Alex Krizhevsky and Ilya Sutskever, which won the ImageNet competition by a wide margin and triggered the deep learning revolution in computer vision;
  • dropout, a widely used technique against overfitting.

Hinton’s company DNNresearch was acquired by Google in 2013, and he worked at Google until 2023, when he left so he could speak freely about AI’s risks. In 2024, he shared the Nobel Prize in Physics with John Hopfield for foundational discoveries enabling machine learning with artificial neural networks.

Yann LeCun

Born in France in 1960, LeCun worked at Bell Labs, where he developed convolutional neural networks (CNNs) trained with backpropagation and applied them to reading handwritten digits — technology that was used to process checks in the 1990s. CNNs became the foundation of modern computer vision.

LeCun became a professor at New York University and in 2013 founded Facebook AI Research (FAIR), later serving as Meta’s chief AI scientist; in 2025 he announced he would leave Meta to start a company focused on world models. He has been a strong advocate of open research and open-weight models, and argues that current large language models lack key ingredients of intelligence, such as understanding the physical world, persistent memory and planning. He has proposed alternative architectures based on learning “world models”.

Yoshua Bengio

Born in Paris in 1964 and raised in Montreal, Bengio is a professor at the Université de Montréal and founder of Mila, the Quebec AI institute, one of the world’s largest academic deep learning research centers.

Key contributions:

  • neural probabilistic language models (2003), an early example of learning word representations with neural networks — an ancestor of today’s language models;
  • work on attention mechanisms for machine translation with Dzmitry Bahdanau and Kyunghyun Cho, which influenced the Transformer;
  • generative adversarial networks, co-authored with Ian Goodfellow and others in his lab;
  • influential textbooks and training of many researchers.

Bengio is among the most cited computer scientists in the world. He chaired the international scientific report on the safety of advanced AI commissioned after the 2023 Bletchley Park AI Safety Summit.

Three views on AI risk

Despite sharing the award, the three now hold different views:

  • Hinton warns that AI could surpass human intelligence and pose existential risks, and has called for more safety research and regulation.
  • Bengio has also become a prominent voice on catastrophic risks, advocating safety research, regulation and international coordination.
  • LeCun considers existential risk fears overblown, argues that open research makes AI safer, and believes current systems are far from human-level intelligence.

Their public disagreement mirrors the wider debate in AI between those focused on long-term risks and those who emphasize near-term benefits and openness.

What to watch next

  • Hinton’s and Bengio’s roles in AI safety advocacy and policy.
  • LeCun’s work on world models and non-LLM architectures.
  • Mila’s research and AI safety initiatives.
  • Ongoing debate about open-weight models and risk.

Key takeaways

  • Hinton, LeCun and Bengio received the 2018 Turing Award for deep learning breakthroughs.
  • Hinton popularized backpropagation, co-created AlexNet and won the 2024 Nobel Prize in Physics.
  • LeCun developed convolutional neural networks and founded Facebook AI Research.
  • Bengio pioneered neural language models and founded Mila.
  • The three now disagree publicly about the risks of advanced AI.

Frequently asked questions

Why are they called the godfathers of deep learning?

Because they developed core techniques and kept advancing neural networks when the approach was unpopular, laying the foundations for modern AI.

Did Hinton win a Nobel Prize?

Yes. In 2024 he shared the Nobel Prize in Physics with John Hopfield for foundational work on artificial neural networks.

Why did Hinton leave Google?

He left in 2023 so that he could speak freely about the risks of AI without considering how his statements affected Google.