Publications

Noam Kolt

Peter Wills

Usman Anwar

Christian Schroeder de Witt

Lewis Hammond

David M. Krueger

Lennart Heim

Markus Anderljung

2024-06-16

ArXiv (prépublication)

IDs for AI Systems

Noam Kolt

Peter Wills

Usman Anwar

Christian Schroeder de Witt

Lewis Hammond

David M. Krueger

Lennart Heim

Markus Anderljung

2024-06-16

ArXiv (prépublication)

IDs for AI Systems

Noam Kolt

Peter Wills

Usman Anwar

Christian Schroeder de Witt

Lewis Hammond

David M. Krueger

Lennart Heim

Markus Anderljung

2024-06-16

ArXiv (prépublication)

IDs for AI Systems

Noam Kolt

Peter Wills

Usman Anwar

Christian Schroeder de Witt

Lewis Hammond

David M. Krueger

Lennart Heim

Markus Anderljung

AI systems are increasingly pervasive, yet information needed to decide whether and how to engage with them may not exist or be accessible. … (voir plus)A user may not be able to verify whether a system has certain safety certifications. An investigator may not know whom to investigate when a system causes an incident. It may not be clear whom to contact to shut down a malfunctioning system. Across a number of domains, IDs address analogous problems by identifying particular entities (e.g., a particular Boeing 747) and providing information about other entities of the same class (e.g., some or all Boeing 747s). We propose a framework in which IDs are ascribed to instances of AI systems (e.g., a particular chat session with Claude 3), and associated information is accessible to parties seeking to interact with that system. We characterize IDs for AI systems, provide concrete examples where IDs could be useful, argue that there could be significant demand for IDs from key actors, analyze how those actors could incentivize ID adoption, explore a potential implementation of our framework for deployers of AI systems, and highlight limitations and risks. IDs seem most warranted in settings where AI systems could have a large impact upon the world, such as in making financial transactions or contacting real humans. With further study, IDs could help to manage a world where AI systems pervade society.

2024-06-16

ArXiv (prépublication)

IDs for AI Systems

Noam Kolt

Peter Wills

Usman Anwar

Christian Schroeder de Witt

Lewis Hammond

David M. Krueger

Lennart Heim

Markus Anderljung

2024-06-16

ArXiv (prépublication)

IDs for AI Systems

Noam Kolt

Peter Wills

Usman Anwar

Christian Schroeder de Witt

Lewis Hammond

David M. Krueger

Lennart Heim

Markus Anderljung

AI systems are increasingly pervasive, yet information needed to decide whether and how to engage with them may not exist or be accessible. … (voir plus)A user may not be able to verify whether a system has certain safety certifications. An investigator may not know whom to investigate when a system causes an incident. It may not be clear whom to contact to shut down a malfunctioning system. Across a number of domains, IDs address analogous problems by identifying particular entities (e.g., a particular Boeing 747) and providing information about other entities of the same class (e.g., some or all Boeing 747s). We propose a framework in which IDs are ascribed to instances of AI systems (e.g., a particular chat session with Claude 3), and associated information is accessible to parties seeking to interact with that system. We characterize IDs for AI systems, provide concrete examples where IDs could be useful, argue that there could be significant demand for IDs from key actors, analyze how those actors could incentivize ID adoption, explore a potential implementation of our framework for deployers of AI systems, and highlight limitations and risks. IDs seem most warranted in settings where AI systems could have a large impact upon the world, such as in making financial transactions or contacting real humans. With further study, IDs could help to manage a world where AI systems pervade society.

2024-06-16

ArXiv (prépublication)

IDs for AI Systems

Noam Kolt

Peter Wills

Usman Anwar

Christian Schroeder de Witt

Lewis Hammond

David M. Krueger

Lennart Heim

Markus Anderljung

2024-06-16

ArXiv (prépublication)

IDs for AI Systems

Noam Kolt

Peter Wills

Usman Anwar

Christian Schroeder de Witt

Lewis Hammond

David M. Krueger

Lennart Heim

Markus Anderljung

AI systems are increasingly pervasive, yet information needed to decide whether and how to engage with them may not exist or be accessible. … (voir plus)A user may not be able to verify whether a system has certain safety certifications. An investigator may not know whom to investigate when a system causes an incident. It may not be clear whom to contact to shut down a malfunctioning system. Across a number of domains, IDs address analogous problems by identifying particular entities (e.g., a particular Boeing 747) and providing information about other entities of the same class (e.g., some or all Boeing 747s). We propose a framework in which IDs are ascribed to instances of AI systems (e.g., a particular chat session with Claude 3), and associated information is accessible to parties seeking to interact with that system. We characterize IDs for AI systems, provide concrete examples where IDs could be useful, argue that there could be significant demand for IDs from key actors, analyze how those actors could incentivize ID adoption, explore a potential implementation of our framework for deployers of AI systems, and highlight limitations and risks. IDs seem most warranted in settings where AI systems could have a large impact upon the world, such as in making financial transactions or contacting real humans. With further study, IDs could help to manage a world where AI systems pervade society.

2024-06-16

ArXiv (prépublication)

IDs for AI Systems

Noam Kolt

Peter Wills

Usman Anwar

Christian Schroeder de Witt

Lewis Hammond

David M. Krueger

Lennart Heim

Markus Anderljung

AI systems are increasingly pervasive, yet information needed to decide whether and how to engage with them may not exist or be accessible. … (voir plus)A user may not be able to verify whether a system has certain safety certifications. An investigator may not know whom to investigate when a system causes an incident. It may not be clear whom to contact to shut down a malfunctioning system. Across a number of domains, IDs address analogous problems by identifying particular entities (e.g., a particular Boeing 747) and providing information about other entities of the same class (e.g., some or all Boeing 747s). We propose a framework in which IDs are ascribed to instances of AI systems (e.g., a particular chat session with Claude 3), and associated information is accessible to parties seeking to interact with that system. We characterize IDs for AI systems, provide concrete examples where IDs could be useful, argue that there could be significant demand for IDs from key actors, analyze how those actors could incentivize ID adoption, explore a potential implementation of our framework for deployers of AI systems, and highlight limitations and risks. IDs seem most warranted in settings where AI systems could have a large impact upon the world, such as in making financial transactions or contacting real humans. With further study, IDs could help to manage a world where AI systems pervade society.

2024-06-16

ArXiv (prépublication)

IDs for AI Systems

Noam Kolt

Peter Wills

Usman Anwar

Christian Schroeder de Witt

Lewis Hammond

David M. Krueger

Lennart Heim

Markus Anderljung

AI systems are increasingly pervasive, yet information needed to decide whether and how to engage with them may not exist or be accessible. … (voir plus)A user may not be able to verify whether a system has certain safety certifications. An investigator may not know whom to investigate when a system causes an incident. It may not be clear whom to contact to shut down a malfunctioning system. Across a number of domains, IDs address analogous problems by identifying particular entities (e.g., a particular Boeing 747) and providing information about other entities of the same class (e.g., some or all Boeing 747s). We propose a framework in which IDs are ascribed to instances of AI systems (e.g., a particular chat session with Claude 3), and associated information is accessible to parties seeking to interact with that system. We characterize IDs for AI systems, provide concrete examples where IDs could be useful, argue that there could be significant demand for IDs from key actors, analyze how those actors could incentivize ID adoption, explore a potential implementation of our framework for deployers of AI systems, and highlight limitations and risks. IDs seem most warranted in settings where AI systems could have a large impact upon the world, such as in making financial transactions or contacting real humans. With further study, IDs could help to manage a world where AI systems pervade society.

2024-06-16

ArXiv (prépublication)

Improving Molecular Modeling with Geometric GNNs: an Empirical Study

Fragkiskos D. Malliaros

Alexandre AGM Duval

2024-06-16

ICML.cc/2024/Workshop/ML4LMS (poster)

openreview.net

Joint Multimodal Transformer for Emotion Recognition in the Wild

Paul Waligora

Muhammad Haseeb Aslam

Muhammad Osama Zeeshan

Soufiane Belharbi

Alessandro Lameiras Koerich

Marco Pedersoli

Simon Bacon

Eric Granger

Multimodal emotion recognition (MMER) systems typically outperform unimodal systems by leveraging the inter-and intra-modal relationships be… (voir plus)tween, e.g., visual, textual, physiological, and auditory modalities. This paper proposes an MMER method that relies on a joint multi-modal transformer (JMT) for fusion with key-based cross-attention. This framework can exploit the complementary nature of diverse modalities to improve predictive accuracy. Separate backbones capture intra-modal spatiotemporal dependencies within each modality over video sequences. Subsequently, our JMT fusion architecture integrates the individual modality embeddings, allowing the model to effectively capture inter- and intra-modal relationships. Extensive experiments on two challenging expression recognition tasks – (1) dimensional emotion recognition on the Affwild2 dataset (with face and voice) and (2) pain estimation on the Biovid dataset (with face and biosensors) – indicate that our JMT fusion can provide a cost-effective solution for MMER. Empirical results show that MMER systems with our proposed fusion allow us to outperform relevant baseline and state-of-the-art methods. Code is available at: https://github.com/PoloWlg/Joint-Multimodal-Transformer-6th-ABAW

2024-06-16

2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (publié)