Portrait of Yoshua Bengio

Yoshua Bengio

Core Academic Member
Canada CIFAR AI Chair
Full Professor, Université de Montréal, Department of Computer Science and Operations Research Department
Founder and Scientific Advisor, Leadership Team
Research Topics
Causality
Computational Neuroscience
Deep Learning
Generative Models
Graph Neural Networks
Machine Learning Theory
Medical Machine Learning
Molecular Modeling
Natural Language Processing
Probabilistic Models
Reasoning
Recurrent Neural Networks
Reinforcement Learning
Representation Learning
Website
Google Scholar
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Biography

*For media requests, please write to medias@mila.quebec.

For more information please contact Marie-Josée Beauchamp, Administrative Assistant at marie-josee.beauchamp@mila.quebec.

Yoshua Bengio is recognized worldwide as a leading expert in AI. He is most known for his pioneering work in deep learning, which earned him the 2018 A.M. Turing Award, “the Nobel Prize of computing,” with Geoffrey Hinton and Yann LeCun.

Bengio is a full professor at Université de Montréal, and the founder and scientific advisor of Mila – Quebec Artificial Intelligence Institute. He is also a senior fellow at CIFAR and co-directs its Learning in Machines & Brains program, serves as special advisor and founding scientific director of IVADO, and holds a Canada CIFAR AI Chair.

In 2019, Bengio was awarded the prestigious Killam Prize and in 2022, he was the most cited computer scientist in the world by h-index. He is a Fellow of the Royal Society of London, Fellow of the Royal Society of Canada, Knight of the Legion of Honor of France and Officer of the Order of Canada. In 2023, he was appointed to the UN’s Scientific Advisory Board for Independent Advice on Breakthroughs in Science and Technology.

Concerned about the social impact of AI, Bengio helped draft the Montréal Declaration for the Responsible Development of Artificial Intelligence and continues to raise awareness about the importance of mitigating the potentially catastrophic risks associated with future AI systems.

Current Students

Jamal Abou Haibeh
Collaborating Alumni - McGill University
Github
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Mohammed Abukalam
Collaborating Alumni - Université de Montréal
Github
Berkes Anaïs
Collaborating researcher - Cambridge University
Principal supervisor :
David Rolnick
Website
Github
Google Scholar
Rim Assouel
PhD - Université de Montréal
Junyeob BAEK
Independent visiting researcher - KAIST
Website
Github
Google Scholar
Stefan Bauer
Independent visiting researcher
Co-supervisor :
Guillaume Lajoie
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Paul Bertin
PhD - Université de Montréal
Shahana Chatterjee
Collaborating researcher - N/A
Principal supervisor :
David Rolnick
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Xiaoyin Chen
PhD - Université de Montréal
Sanghyeok Choi
Collaborating researcher - KAIST
Website
Github
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Tristan Deleu
PhD - Université de Montréal
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Aniket Didolkar
PhD - Université de Montréal
Website
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Abdessamad EL KABID
Research Intern - Université de Montréal
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Eric Elmoznino
PhD - Université de Montréal
Co-supervisor :
Guillaume Lajoie
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Github
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Léna Ezzine
PhD - Université de Montréal
Github
Jean-Pierre Falet
PhD - Université de Montréal
Co-supervisor :
Guillaume Lajoie
Website
Github
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Leo Feng
PhD - Université de Montréal
leo.feng@mila.quebec
Website
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Ivan Grega
Research Intern - Université de Montréal
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Pietro Greiner
PhD
Google Scholar
Mohsin Hasan
PhD - Université de Montréal
mohsin.hasan@mila.quebec
Website
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Edward Hu
PhD - Université de Montréal
Moksh Jain
PhD - Université de Montréal
moksh.jain@mila.quebec
Website
Github
Google Scholar
Thomas Jiralerspong
PhD - Université de Montréal
Principal supervisor :
Guillaume Lajoie
Website
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Younesse Kaddar
Collaborating Alumni - Université de Montréal
Website
Github
Hyeonah Kim
Postdoctorate - Université de Montréal
Principal supervisor :
Alex Hernandez
Yaroslav KIVVA
Collaborating researcher - Université de Montréal
Github
Google Scholar
Salem Lahlou
Collaborating Alumni - Université de Montréal
Github
Tabitha Edith Lee
Postdoctorate - Université de Montréal
Principal supervisor :
Glen Berseth
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Github
Google Scholar
Seanie Lee
Collaborating Alumni - Université de Montréal
Website
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Google Scholar
Chenghao Liu
Collaborating Alumni
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Google Scholar
Zhen Liu
Collaborating Alumni - Université de Montréal
Principal supervisor :
Liam Paull
Kanika Madan
PhD - Université de Montréal
Nikolay Malkin
Collaborating Alumni - Université de Montréal
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Github
Google Scholar
Cristian Dragos Manta
PhD - Université de Montréal
Co-supervisor :
Dhanya Sridhar
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Sören Mindermann
Collaborating researcher - Université de Montréal
Website
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Sarthak Mittal
PhD - Université de Montréal
Principal supervisor :
Guillaume Lajoie
Website
Github
Google Scholar
Jama Mohamud
PhD - Université de Montréal
Principal supervisor :
Mirco Ravanelli
Website
Github
Google Scholar
Cyrus Neary
Postdoctorate - Université de Montréal
Principal supervisor :
Glen Berseth
Website
Github
Google Scholar
Phong Nguyen
Independent visiting researcher - Université de Montréal
Padideh Nouri
PhD - Université de Montréal
Principal supervisor :
Doina Precup
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Google Scholar
Ali Parviz
Collaborating researcher - Ying Wu Coll of Computing
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Lena Podina
PhD - University of Waterloo
Principal supervisor :
David Rolnick
Github
Google Scholar
Nassim Rahaman
Collaborating Alumni - Max-Planck-Institute for Intelligent Systems
Amine RAZIG
Research Intern - Université de Montréal
Website
Jarrid Rector-Brooks
PhD - Université de Montréal
Danyal REHMAN
Postdoctorate - Université de Montréal
Github
James Requeima
Independent visiting researcher - Université de Montréal
Oli RICHARDSON
Postdoctorate - Université de Montréal
Website
Github
Google Scholar
Camille Rochefort-Boulanger
PhD - Université de Montréal
Principal supervisor :
Julie Hussin
Github
Victor Schmidt
Collaborating Alumni - Université de Montréal
Website
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Google Scholar
Luca Scimeca
Postdoctorate - Université de Montréal
Website
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Google Scholar
Dragos Secrieru
Master's Research - Université de Montréal
Marcin Sendera
Collaborating Alumni - Université de Montréal
Github
Google Scholar
Vedant Shah
Master's Research - Université de Montréal
Website
Github
Google Scholar
Divya Sharma
Postdoctorate
Website
Github
Marco Stock
Independent visiting researcher - Technical University of Munich
marco.stock@tum.de
Github
Mélisande Astrid Crystal Teng
PhD - Université de Montréal
Co-supervisor :
Hugo Larochelle
Website
Github
Alex Tong
Postdoctorate - Université de Montréal
alexander.tong@mila.quebec
Website
Github
Google Scholar
Donna Vakalis
Postdoctorate - Université de Montréal
Co-supervisor :
David Rolnick
Website
Github
Google Scholar
Siddarth Venkatraman
PhD - Université de Montréal
Principal supervisor :
Glen Berseth
Website
Github
Google Scholar
Leah WAIRIMU
Collaborating researcher - Université de Montréal
Github
Omar G. Younis
Collaborating researcher
Website
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Google Scholar
Taeyoung YUN
Research Intern - Université de Montréal
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Tianyu Zhang
PhD - Université de Montréal
Website
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Nicole Zhang
PhD - McGill University
Principal supervisor :
Mathieu Blanchette
Github
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Dinghuai Zhang
PhD - Université de Montréal
Principal supervisor :
Aaron Courville
Website
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Harry Zhao
PhD - McGill University
Principal supervisor :
Doina Precup
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Publications

Efficient Diversity-Preserving Diffusion Alignment via Gradient-Informed GFlowNets
Zhen Liu
Tim Z. Xiao
Weiyang Liu
Yoshua Bengio
Dinghuai Zhang
While one commonly trains large diffusion models by collecting datasets on target downstream tasks, it is often desired to align and finetun… (see more)e pretrained diffusion models on some reward functions that are either designed by experts or learned from small-scale datasets. Existing methods for finetuning diffusion models typically suffer from lack of diversity in generated samples, lack of prior preservation, and/or slow convergence in finetuning. Inspired by recent successes in generative flow networks (GFlowNets), a class of probabilistic models that sample with the unnormalized density of a reward function, we propose a novel GFlowNet method dubbed Nabla-GFlowNet (abbreviated as
2024-12-10
ArXiv (preprint)
doi.org
arxiv.org
Efficient Diversity-Preserving Diffusion Alignment via Gradient-Informed GFlowNets
Zhen Liu
Tim Z. Xiao
Weiyang Liu
Yoshua Bengio
Dinghuai Zhang
While one commonly trains large diffusion models by collecting datasets on target downstream tasks, it is often desired to align and finetun… (see more)e pretrained diffusion models on some reward functions that are either designed by experts or learned from small-scale datasets. Existing methods for finetuning diffusion models typically suffer from lack of diversity in generated samples, lack of prior preservation, and/or slow convergence in finetuning. Inspired by recent successes in generative flow networks (GFlowNets), a class of probabilistic models that sample with the unnormalized density of a reward function, we propose a novel GFlowNet method dubbed Nabla-GFlowNet (abbreviated as
2024-12-10
ArXiv (preprint)
arxiv.org
arxiv.org
BigDocs: An Open and Permissively-Licensed Dataset for Training Multimodal Models on Document and Code Tasks
Juan Rodriguez
Xiangru Jian
Siba Smarak Panigrahi
Tianyu Zhang
Aarash Feizi
Abhay Puri
Akshay Kalkunte
Franccois Savard
Ahmed Masry
Shravan Nayak
Rabiul Awal
Mahsa Massoud
Amirhossein Abaskohi
Zichao Li
Suyuchen Wang
Pierre-Andre Noel
M. L. Richter
Saverio Vadacchino
Shubbam Agarwal
Sanket Biswas … (see 23 more)
Sara Shanian
Ying Zhang
Noah Bolger
Kurt MacDonald
Simon Fauvel
Sathwik Tejaswi
Srinivas Sunkara
Joao Monteiro
Krishnamurthy Dj Dvijotham
Torsten Scholak
Nicolas Chapados
Sepideh Kharagani
Sean Hughes
M. Özsu
Siva Reddy
Marco Pedersoli
Yoshua Bengio
Chris Pal
Issam Hadj Laradji
Spandanna Gella
Perouz Taslakian
David Vazquez
Sai Rajeswar
Multimodal AI has the potential to significantly enhance document-understanding tasks, such as processing receipts, understanding workflows,… (see more) extracting data from documents, and summarizing reports. Code generation tasks that require long-structured outputs can also be enhanced by multimodality. Despite this, their use in commercial applications is often limited due to limited access to training data and restrictive licensing, which hinders open access. To address these limitations, we introduce BigDocs-7.5M, a high-quality, open-access dataset comprising 7.5 million multimodal documents across 30 tasks. We use an efficient data curation process to ensure our data is high-quality and license-permissive. Our process emphasizes accountability, responsibility, and transparency through filtering rules, traceable metadata, and careful content analysis. Additionally, we introduce BigDocs-Bench, a benchmark suite with 10 novel tasks where we create datasets that reflect real-world use cases involving reasoning over Graphical User Interfaces (GUI) and code generation from images. Our experiments show that training with BigDocs-Bench improves average performance up to 25.8% over closed-source GPT-4o in document reasoning and structured output tasks such as Screenshot2HTML or Image2Latex generation. Finally, human evaluations showed a preference for outputs from models trained on BigDocs over GPT-4o. This suggests that BigDocs can help both academics and the open-source community utilize and improve AI tools to enhance multimodal capabilities and document reasoning. The project is hosted at https://bigdocs.github.io .
2024-12-05
ArXiv (preprint)
arxiv.org
arxiv.org
BigDocs: An Open and Permissively-Licensed Dataset for Training Multimodal Models on Document and Code Tasks
Juan Rodriguez
Xiangru Jian
Siba Smarak Panigrahi
Tianyu Zhang
Aarash Feizi
Abhay Puri
Akshay Kalkunte
Franccois Savard
Ahmed Masry
Shravan Nayak
Rabiul Awal
Mahsa Massoud
Amirhossein Abaskohi
Zichao Li
Suyuchen Wang
Pierre-Andre Noel
Mats Leon Richter
Saverio Vadacchino
Shubbam Agarwal
Sanket Biswas … (see 23 more)
Sara Shanian
Ying Zhang
Noah Bolger
Kurt MacDonald
Simon Fauvel
Sathwik Tejaswi
Srinivas Sunkara
Joao Monteiro
Krishnamurthy Dj Dvijotham
Torsten Scholak
Nicolas Chapados
Sepideh Kharaghani
Sean Hughes
M. Özsu
Siva Reddy
Marco Pedersoli
Yoshua Bengio
Chris Pal
Issam Hadj Laradji
Spandanna Gella
Perouz Taslakian
David Vazquez
Sai Rajeswar
Multimodal AI has the potential to significantly enhance document-understanding tasks, such as processing receipts, understanding workflows,… (see more) extracting data from documents, and summarizing reports. Code generation tasks that require long-structured outputs can also be enhanced by multimodality. Despite this, their use in commercial applications is often limited due to limited access to training data and restrictive licensing, which hinders open access. To address these limitations, we introduce BigDocs-7.5M, a high-quality, open-access dataset comprising 7.5 million multimodal documents across 30 tasks. We use an efficient data curation process to ensure our data is high-quality and license-permissive. Our process emphasizes accountability, responsibility, and transparency through filtering rules, traceable metadata, and careful content analysis. Additionally, we introduce BigDocs-Bench, a benchmark suite with 10 novel tasks where we create datasets that reflect real-world use cases involving reasoning over Graphical User Interfaces (GUI) and code generation from images. Our experiments show that training with BigDocs-Bench improves average performance up to 25.8% over closed-source GPT-4o in document reasoning and structured output tasks such as Screenshot2HTML or Image2Latex generation. Finally, human evaluations showed a preference for outputs from models trained on BigDocs over GPT-4o. This suggests that BigDocs can help both academics and the open-source community utilize and improve AI tools to enhance multimodal capabilities and document reasoning. The project is hosted at https://bigdocs.github.io .
2024-12-05
ArXiv (preprint)
arxiv.org
arxiv.org
BigDocs: An Open and Permissively-Licensed Dataset for Training Multimodal Models on Document and Code Tasks
Juan Rodriguez
Xiangru Jian
Siba Smarak Panigrahi
Tianyu Zhang
Aarash Feizi
Abhay Puri
Akshay Kalkunte
Franccois Savard
Ahmed Masry
Shravan Nayak
Rabiul Awal
Mahsa Massoud
Amirhossein Abaskohi
Zichao Li
Suyuchen Wang
Pierre-Andre Noel
Mats Leon Richter
Saverio Vadacchino
Shubbam Agarwal
Sanket Biswas … (see 23 more)
Sara Shanian
Ying Zhang
Noah Bolger
Kurt MacDonald
Simon Fauvel
Sathwik Tejaswi
Srinivas Sunkara
Joao Monteiro
Krishnamurthy Dj Dvijotham
Torsten Scholak
Nicolas Chapados
Sepideh Kharaghani
Sean Hughes
M. Özsu
Siva Reddy
Marco Pedersoli
Yoshua Bengio
Chris Pal
Issam Hadj Laradji
Spandanna Gella
Perouz Taslakian
David Vazquez
Sai Rajeswar
Multimodal AI has the potential to significantly enhance document-understanding tasks, such as processing receipts, understanding workflows,… (see more) extracting data from documents, and summarizing reports. Code generation tasks that require long-structured outputs can also be enhanced by multimodality. Despite this, their use in commercial applications is often limited due to limited access to training data and restrictive licensing, which hinders open access. To address these limitations, we introduce BigDocs-7.5M, a high-quality, open-access dataset comprising 7.5 million multimodal documents across 30 tasks. We use an efficient data curation process to ensure our data is high-quality and license-permissive. Our process emphasizes accountability, responsibility, and transparency through filtering rules, traceable metadata, and careful content analysis. Additionally, we introduce BigDocs-Bench, a benchmark suite with 10 novel tasks where we create datasets that reflect real-world use cases involving reasoning over Graphical User Interfaces (GUI) and code generation from images. Our experiments show that training with BigDocs-Bench improves average performance up to 25.8% over closed-source GPT-4o in document reasoning and structured output tasks such as Screenshot2HTML or Image2Latex generation. Finally, human evaluations showed a preference for outputs from models trained on BigDocs over GPT-4o. This suggests that BigDocs can help both academics and the open-source community utilize and improve AI tools to enhance multimodal capabilities and document reasoning. The project is hosted at https://bigdocs.github.io .
2024-12-05
ArXiv (preprint)
doi.org
arxiv.org
BigDocs: An Open and Permissively-Licensed Dataset for Training Multimodal Models on Document and Code Tasks
Juan Rodriguez
Xiangru Jian
Siba Smarak Panigrahi
Tianyu Zhang
Aarash Feizi
Abhay Puri
Akshay Kalkunte
Franccois Savard
Ahmed Masry
Shravan Nayak
Rabiul Awal
Mahsa Massoud
Amirhossein Abaskohi
Zichao Li
Suyuchen Wang
Pierre-Andre Noel
Mats Leon Richter
Saverio Vadacchino
Shubbam Agarwal
Sanket Biswas … (see 23 more)
Sara Shanian
Ying Zhang
Noah Bolger
Kurt MacDonald
Simon Fauvel
Sathwik Tejaswi
Srinivas Sunkara
Joao Monteiro
Krishnamurthy Dj Dvijotham
Torsten Scholak
Nicolas Chapados
Sepideh Kharaghani
Sean Hughes
M. Özsu
Siva Reddy
Marco Pedersoli
Yoshua Bengio
Chris Pal
Issam Hadj Laradji
Spandanna Gella
Perouz Taslakian
David Vazquez
Sai Rajeswar
Multimodal AI has the potential to significantly enhance document-understanding tasks, such as processing receipts, understanding workflows,… (see more) extracting data from documents, and summarizing reports. Code generation tasks that require long-structured outputs can also be enhanced by multimodality. Despite this, their use in commercial applications is often limited due to limited access to training data and restrictive licensing, which hinders open access. To address these limitations, we introduce BigDocs-7.5M, a high-quality, open-access dataset comprising 7.5 million multimodal documents across 30 tasks. We use an efficient data curation process to ensure our data is high-quality and license-permissive. Our process emphasizes accountability, responsibility, and transparency through filtering rules, traceable metadata, and careful content analysis. Additionally, we introduce BigDocs-Bench, a benchmark suite with 10 novel tasks where we create datasets that reflect real-world use cases involving reasoning over Graphical User Interfaces (GUI) and code generation from images. Our experiments show that training with BigDocs-Bench improves average performance up to 25.8% over closed-source GPT-4o in document reasoning and structured output tasks such as Screenshot2HTML or Image2Latex generation. Finally, human evaluations showed a preference for outputs from models trained on BigDocs over GPT-4o. This suggests that BigDocs can help both academics and the open-source community utilize and improve AI tools to enhance multimodal capabilities and document reasoning. The project is hosted at https://bigdocs.github.io .
2024-12-05
ArXiv (preprint)
doi.org
arxiv.org
BigDocs: An Open and Permissively-Licensed Dataset for Training Multimodal Models on Document and Code Tasks
Juan Rodriguez
Xiangru Jian
Siba Smarak Panigrahi
Tianyu Zhang
Aarash Feizi
Abhay Puri
Akshay Kalkunte
Franccois Savard
Ahmed Masry
Shravan Nayak
Rabiul Awal
Mahsa Massoud
Amirhossein Abaskohi
Zichao Li
Suyuchen Wang
Pierre-Andre Noel
M. L. Richter
Saverio Vadacchino
Shubbam Agarwal
Sanket Biswas … (see 23 more)
Sara Shanian
Ying Zhang
Noah Bolger
Kurt MacDonald
Simon Fauvel
Sathwik Tejaswi
Srinivas Sunkara
Joao Monteiro
Krishnamurthy Dj Dvijotham
Torsten Scholak
Nicolas Chapados
Sepideh Kharagani
Sean Hughes
M. Özsu
Siva Reddy
Marco Pedersoli
Yoshua Bengio
Chris Pal
Issam Hadj Laradji
Spandanna Gella
Perouz Taslakian
David Vazquez
Sai Rajeswar
Multimodal AI has the potential to significantly enhance document-understanding tasks, such as processing receipts, understanding workflows,… (see more) extracting data from documents, and summarizing reports. Code generation tasks that require long-structured outputs can also be enhanced by multimodality. Despite this, their use in commercial applications is often limited due to limited access to training data and restrictive licensing, which hinders open access. To address these limitations, we introduce BigDocs-7.5M, a high-quality, open-access dataset comprising 7.5 million multimodal documents across 30 tasks. We use an efficient data curation process to ensure our data is high-quality and license-permissive. Our process emphasizes accountability, responsibility, and transparency through filtering rules, traceable metadata, and careful content analysis. Additionally, we introduce BigDocs-Bench, a benchmark suite with 10 novel tasks where we create datasets that reflect real-world use cases involving reasoning over Graphical User Interfaces (GUI) and code generation from images. Our experiments show that training with BigDocs-Bench improves average performance up to 25.8% over closed-source GPT-4o in document reasoning and structured output tasks such as Screenshot2HTML or Image2Latex generation. Finally, human evaluations showed a preference for outputs from models trained on BigDocs over GPT-4o. This suggests that BigDocs can help both academics and the open-source community utilize and improve AI tools to enhance multimodal capabilities and document reasoning. The project is hosted at https://bigdocs.github.io .
2024-12-05
ArXiv (preprint)
arxiv.org
arxiv.org
Imagining and building wise machines: The centrality of AI metacognition
Samuel G. B. Johnson
Amir-Hossein Karimi
Yoshua Bengio
Nick Chater
Tobias Gerstenberg
Kate Larson
Sydney Levine
Melanie Mitchell
Iyad Rahwan
Bernhard Schölkopf
Igor Grossmann
Recent advances in artificial intelligence (AI) have produced systems capable of increasingly sophisticated performance on cognitive tasks. … (see more)However, AI systems still struggle in critical ways: unpredictable and novel environments (robustness), lack of transparency in their reasoning (explainability), challenges in communication and commitment (cooperation), and risks due to potential harmful actions (safety). We argue that these shortcomings stem from one overarching failure: AI systems lack wisdom. Drawing from cognitive and social sciences, we define wisdom as the ability to navigate intractable problems - those that are ambiguous, radically uncertain, novel, chaotic, or computationally explosive - through effective task-level and metacognitive strategies. While AI research has focused on task-level strategies, metacognition - the ability to reflect on and regulate one's thought processes - is underdeveloped in AI systems. In humans, metacognitive strategies such as recognizing the limits of one's knowledge, considering diverse perspectives, and adapting to context are essential for wise decision-making. We propose that integrating metacognitive capabilities into AI systems is crucial for enhancing their robustness, explainability, cooperation, and safety. By focusing on developing wise AI, we suggest an alternative to aligning AI with specific human values - a task fraught with conceptual and practical difficulties. Instead, wise AI systems can thoughtfully navigate complex situations, account for diverse human values, and avoid harmful actions. We discuss potential approaches to building wise AI, including benchmarking metacognitive abilities and training AI systems to employ wise reasoning. Prioritizing metacognition in AI research will lead to systems that act not only intelligently but also wisely in complex, real-world situations.
2024-11-04
ArXiv (preprint)
doi.org
arxiv.org
Imagining and building wise machines: The centrality of AI metacognition
Samuel G. B. Johnson
Amir-Hossein Karimi
Yoshua Bengio
Nick Chater
Tobias Gerstenberg
Kate Larson
Sydney Levine
Melanie Mitchell
Iyad Rahwan
Bernhard Schölkopf
Igor Grossmann
Recent advances in artificial intelligence (AI) have produced systems capable of increasingly sophisticated performance on cognitive tasks. … (see more)However, AI systems still struggle in critical ways: unpredictable and novel environments (robustness), lack of transparency in their reasoning (explainability), challenges in communication and commitment (cooperation), and risks due to potential harmful actions (safety). We argue that these shortcomings stem from one overarching failure: AI systems lack wisdom. Drawing from cognitive and social sciences, we define wisdom as the ability to navigate intractable problems - those that are ambiguous, radically uncertain, novel, chaotic, or computationally explosive - through effective task-level and metacognitive strategies. While AI research has focused on task-level strategies, metacognition - the ability to reflect on and regulate one's thought processes - is underdeveloped in AI systems. In humans, metacognitive strategies such as recognizing the limits of one's knowledge, considering diverse perspectives, and adapting to context are essential for wise decision-making. We propose that integrating metacognitive capabilities into AI systems is crucial for enhancing their robustness, explainability, cooperation, and safety. By focusing on developing wise AI, we suggest an alternative to aligning AI with specific human values - a task fraught with conceptual and practical difficulties. Instead, wise AI systems can thoughtfully navigate complex situations, account for diverse human values, and avoid harmful actions. We discuss potential approaches to building wise AI, including benchmarking metacognitive abilities and training AI systems to employ wise reasoning. Prioritizing metacognition in AI research will lead to systems that act not only intelligently but also wisely in complex, real-world situations.
2024-11-04
ArXiv (preprint)
doi.org
arxiv.org
Imagining and building wise machines: The centrality of AI metacognition
Samuel G. B. Johnson
Amir-Hossein Karimi
Yoshua Bengio
Nick Chater
Tobias Gerstenberg
Kate Larson
Sydney Levine
Melanie Mitchell
Iyad Rahwan
Bernhard Schölkopf
Igor Grossmann
2024-11-01
arXiv (published)
doi.org
arxiv.org
Trajectory Flow Matching with Applications to Clinical Time Series Modeling
Xi Zhang
Yuan Pu
Yuki Kawamura
Andrew Loza
Yoshua Bengio
Dennis Shung
Alexander Tong
Modeling stochastic and irregularly sampled time series is a challenging problem found in a wide range of applications, especially in medici… (see more)ne. Neural stochastic differential equations (Neural SDEs) are an attractive modeling technique for this problem, which parameterize the drift and diffusion terms of an SDE with neural networks. However, current algorithms for training Neural SDEs require backpropagation through the SDE dynamics, greatly limiting their scalability and stability. To address this, we propose Trajectory Flow Matching (TFM), which trains a Neural SDE in a simulation-free manner, bypassing backpropagation through the dynamics. TFM leverages the flow matching technique from generative modeling to model time series. In this work we first establish necessary conditions for TFM to learn time series data. Next, we present a reparameterization trick which improves training stability. Finally, we adapt TFM to the clinical time series setting, demonstrating improved performance on three clinical time series datasets both in terms of absolute performance and uncertainty prediction.
2024-10-28
ArXiv (preprint)
doi.org
arxiv.org
Trajectory Flow Matching with Applications to Clinical Time Series Modeling
Xi Zhang
Yuan Pu
Yuki Kawamura
Andrew Loza
Yoshua Bengio
Dennis Shung
Alexander Tong
Modeling stochastic and irregularly sampled time series is a challenging problem found in a wide range of applications, especially in medici… (see more)ne. Neural stochastic differential equations (Neural SDEs) are an attractive modeling technique for this problem, which parameterize the drift and diffusion terms of an SDE with neural networks. However, current algorithms for training Neural SDEs require backpropagation through the SDE dynamics, greatly limiting their scalability and stability. To address this, we propose Trajectory Flow Matching (TFM), which trains a Neural SDE in a simulation-free manner, bypassing backpropagation through the dynamics. TFM leverages the flow matching technique from generative modeling to model time series. In this work we first establish necessary conditions for TFM to learn time series data. Next, we present a reparameterization trick which improves training stability. Finally, we adapt TFM to the clinical time series setting, demonstrating improved performance on three clinical time series datasets both in terms of absolute performance and uncertainty prediction.
2024-10-28
ArXiv (preprint)
doi.org
arxiv.org