Publications

Towards Guaranteed Safe AI: A Framework for Ensuring Robust and Reliable AI Systems
David Dalrymple
David
Joar Max Viktor Skalse
Stuart Russell
Max Tegmark
Sanjit A. Seshia
Steve Omohundro
Christian Szegedy
Ben Goldhaber
Nora Ammann
Alessandro Abate
Joe Halpern
Clark Barrett
Ding Zhao
Zhi-Xuan Tan
Jeannette Wing
Joshua B. Tenenbaum
Ensuring that AI systems reliably and robustly avoid harmful or dangerous behaviours is a crucial challenge, especially for AI systems with … (voir plus)a high degree of autonomy and general intelligence, or systems used in safety-critical contexts. In this paper, we will introduce and define a family of approaches to AI safety, which we will refer to as guaranteed safe (GS) AI. The core feature of these approaches is that they aim to produce AI systems which are equipped with high-assurance quantitative safety guarantees. This is achieved by the interplay of three core components: a world model (which provides a mathematical description of how the AI system affects the outside world), a safety specification (which is a mathematical description of what effects are acceptable), and a verifier (which provides an auditable proof certificate that the AI satisfies the safety specification relative to the world model). We outline a number of approaches for creating each of these three core components, describe the main technical challenges, and suggest a number of potential solutions to them. We also argue for the necessity of this approach to AI safety, and for the inadequacy of the main alternative approaches.
Interpretability Needs a New Paradigm
Himabindu Lakkaraju
A. Chandar
The Canadian VirusSeq Data Portal & Duotang: open resources for SARS-CoV-2 viral sequences and genomic epidemiology
Erin E. Gill
Baofeng Jia
Carmen Lia Murall
Raphaël Poujol
Muhammad Zohaib Anwar
Nithu Sara John
Justin Richardsson
Ashley Hobb
Abayomi S. Olabode
Alexandru Lepsa
Ana T. Duggan
Andrea D. Tyler
Arnaud N'Guessan
Atul Kachru
Brandon Chan
Catherine Yoshida
Christina K. Yung
David Bujold
Dusan Andric
Edmund Su … (voir 50 de plus)
Emma J. Griffiths
Gary Van Domselaar
Gordon W. Jolly
Heather K. E. Ward
Henrich Feher
Jared Baker
Jared T. Simpson
Jaser Uddin
Jiannis Ragoussis
Jon Eubank
Jörg H. Fritz
José Héctor Gálvez
Karen Fang
Kim Cullion
Leonardo Rivera
Linda Xiang
Matthew A. Croxen
Mitchell Shiell
Natalie Prystajecky
Pierre-Olivier Quirion
Rosita Bajari
Samantha Rich
Samira Mubareka
Sandrine Moreira
Scott Cain
Steven G. Sutcliffe
Susanne A. Kraemer
Yann Joly
Yelizar Alturmessov
CPHLN consortium
CanCOGeN consortium
VirusSeq Data Portal Academic
Heather K. E. Ward
Marc Fiume
Terrance P. Snutch
Cindy Bell
Catalina Lopez-Correa
Julie G. Hussin
Jeffrey B. Joy
Caroline Colijn
Paul M. K. Gordon
William W. L. Hsiao
Art F. Y. Poon
Natalie C. Knox
Mélanie Courtot
Lincoln Stein
Sarah P. Otto
Guillaume Bourque
B. Jesse Shapiro
Fiona S. L. Brinkman
The COVID-19 pandemic led to a large global effort to sequence SARS-CoV-2 genomes from patient samples to track viral evolution and inform p… (voir plus)ublic health response. Millions of SARS-CoV-2 genome sequences have been deposited in global public repositories. The Canadian COVID-19 Genomics Network (CanCOGeN - VirusSeq), a consortium tasked with coordinating expanded sequencing of SARS-CoV-2 genomes across Canada early in the pandemic, created the Canadian VirusSeq Data Portal, with associated data pipelines and procedures, to support these efforts. The goal of VirusSeq was to allow open access to Canadian SARS-CoV-2 genomic sequences and enhanced, standardized contextual data that were unavailable in other repositories and that meet FAIR standards (Findable, Accessible, Interoperable and Reusable). The Portal data submission pipeline contains data quality checking procedures and appropriate acknowledgement of data generators that encourages collaboration. Here we also highlight Duotang, a web platform that presents genomic epidemiology and modeling analyses on circulating and emerging SARS-CoV-2 variants in Canada. Duotang presents dynamic changes in variant composition of SARS-CoV-2 in Canada and by province, estimates variant growth, and displays complementary interactive visualizations, with a text overview of the current situation. The VirusSeq Data Portal and Duotang resources, alongside additional analyses and resources computed from the Portal (COVID-MVP, CoVizu), are all open-source and freely available. Together, they provide an updated picture of SARS-CoV-2 evolution to spur scientific discussions, inform public discourse, and support communication with and within public health authorities. They also serve as a framework for other jurisdictions interested in open, collaborative sequence data sharing and analyses.
Piecewise Linear Parametrization of Policies: Towards Interpretable Deep Reinforcement Learning
Quantifying neurodegeneration of the cervical cord and brain in degenerative cervical myelopathy: A multicentre study using quantitative <scp>magnetic resonance imaging</scp>
Patrick Freund
Viveka Boller
Tim M. Emmenegger
Muhammad Akbar
Markus Hupp
Nikolai Pfender
Claudia A. M. Gandini Wheeler-Kingshott
Michael G. Fehlings
Armin Curt
Maryam Seif
Quantifying neurodegeneration of the cervical cord and brain in degenerative cervical myelopathy: A multicentre study using quantitative magnetic resonance imaging
Patrick Freund
Viveka Boller
Tim M. Emmenegger
Muhammad Akbar
Markus Hupp
Nikolai Pfender
Claudia A. M. Gandini Wheeler-Kingshott
Michael G. Fehlings
Armin Curt
Maryam Seif
Simultaneous assessment of neurodegeneration in both the cervical cord and brain across multiple centres can enhance the effectiveness of cl… (voir plus)inical trials. Thus, this study aims to simultaneously assess microstructural changes in the cervical cord and brain above the stenosis in degenerative cervical myelopathy (DCM) using quantitative magnetic resonance imaging (MRI) in a multicentre study.
TorchDriveEnv: A Reinforcement Learning Benchmark for Autonomous Driving with Reactive, Realistic, and Diverse Non-Playable Characters
Jonathan Wilder Lavington
Vasileios Lioutas
Matthew Niedoba
Yunpeng Liu
Dylan Green
Saeid Naderiparizi
Xiaoxuan Liang
Setareh Dabiri
Adam Ścibior
Berend Zwartsenberg
Frank N. Wood
Linking aerial hyperspectral data to canopy tree biodiversity: An examination of the spectral variation hypothesis
Anna L. Crofts
Christine I. B. Wallis
Sabine St‐Jean
Sabrina Demers‐Thibeault
Deep Inamdar
J. Pablo Arroyo‐Mora
Margaret Kalacska
Mark Vellend
Imaging spectroscopy is emerging as a leading remote sensing method for quantifying plant biodiversity. The spectral variation hypothesis pr… (voir plus)edicts that variation in plant hyperspectral reflectance is related to variation in taxonomic and functional identity. While most studies report some correlation between spectral and field‐based (i.e., taxonomic and functional) expressions of biodiversity, the observed strength of association is highly variable, and the utility in applying spectral community properties to examine environmental drivers of communities remains unknown. We linked hyperspectral data acquired by airborne imaging spectrometers with precisely geolocated field plots to examine the spectral variation hypothesis along a temperate‐to‐boreal forest gradient in southern Québec, Canada. First, we examine the degree of association between spectral and field‐based dimensions of canopy tree composition and diversity. Second, we ask whether the relationships between field‐based community properties and the environment are reproduced when using spectral community properties. We found support for the spectral variation hypothesis with the strength of association generally greater for the functional than taxonomic dimension, but the strength of relationships was highly variable and dependent on the choice of method or metric used to quantify spectral and field‐based community properties. Using a multivariate approach (comparisons of separate ordinations), spectral composition was moderately well correlated with field‐based composition; however, the degree of association increased when univariately relating the main axes of compositional variation. Spectral diversity was most tightly associated with functional diversity metrics that quantify functional richness and divergence. For predicting canopy tree composition and diversity using environmental variables, the same qualitative conclusions emerge when hyperspectral or field‐based data are used. Spatial patterns of canopy tree community properties were strongly related to the turnover from temperate‐to‐boreal communities, with most variation explained by elevation. Spectral composition and diversity provide a straightforward way to quantify plant biodiversity across large spatial extents without the need for a priori field observations. While commonly framed as a potential tool for biodiversity monitoring, we show that spectral community properties can be applied more widely to assess the environmental drivers of biodiversity, thereby helping to advance our understanding of the drivers of biogeographical patterns of plant communities.
Towards a standardized framework for AI-assisted, image-based monitoring of nocturnal insects
D. B. Roy
D. B. Roy
J. Alison
T. A. August
M. Bélisle
K. Bjerge
J. J. Bowden
M. J. Bunsen
F. Cunha
Q. Geissmann
K. Goldmann
A. Gomez-Segura
A. Jain
C. Huijbers
M. Larrivée
J. L. Lawson
H. M. Mann
M. J. Mazerolle
K. P. McFarland
L. Pasi … (voir 8 de plus)
S. Peters
N. Pinoy
D. Rolnick
G. L. Skinner
O. T. Strickson
A. Svenning
S. Teagle
T. T. Høye
Automated sensors have potential to standardize and expand the monitoring of insects across the globe. As one of the most scalable and faste… (voir plus)st developing sensor technologies, we describe a framework for automated, image-based monitoring of nocturnal insects—from sensor development and field deployment to workflows for data processing and publishing. Sensors comprise a light to attract insects, a camera for collecting images and a computer for scheduling, data storage and processing. Metadata is important to describe sampling schedules that balance the capture of relevant ecological information against power and data storage limitations. Large data volumes of images from automated systems necessitate scalable and effective data processing. We describe computer vision approaches for the detection, tracking and classification of insects, including models built from existing aggregations of labelled insect images. Data from automated camera systems necessitate approaches that account for inherent biases. We advocate models that explicitly correct for bias in species occurrence or abundance estimates resulting from the imperfect detection of species or individuals present during sampling occasions. We propose ten priorities towards a step-change in automated monitoring of nocturnal insects, a vital task in the face of rapid biodiversity loss from global threats. This article is part of the theme issue ‘Towards a toolkit for global insect biodiversity monitoring’.
Characterizing the voxel-based approaches in radioembolization dosimetry with reDoseMC.
Taehyung Peter Kim
S. Enger
BACKGROUND Yttrium-90 ( 90 Y …
A Comprehensive Dataset of Four Provincial Legislative Assembly Members
Alex B. Rivard
Marc André Bodet
Éric Montigny
This research note reports on a new dataset about legislators in four Canadian provinces since the establishment of their colonial assemblie… (voir plus)s in the eighteenth century. Over 7,000 legislators from Ontario, Quebec, New Brunswick, and Nova Scotia are included, with consolidated information drawn from multiple sources about parliamentarians’ years of birth and death, religion, electoral performance, kinship, and several other biographical indicators. We also illustrate the utility of such data with the help of a few descriptive examples drawn from the four provinces. We believe this consolidated dataset offers several opportunities for future research on representation, legislative activities and party politics.
Hierarchies define the scalability of robot swarms
Vivek Shankar Vardharajan
Karthik Soma
Sepand Dyanatkar
Pierre-Yves Lajoie
The emerging behaviors of swarms have fascinated scientists and gathered significant interest in the field of robotics. Traditionally, swarm… (voir plus)s are viewed as egalitarian, with robots sharing identical roles and capabilities. However, recent findings highlight the importance of hierarchy for deploying robot swarms more effectively in diverse scenarios. Despite nature's preference for hierarchies, the robotics field has clung to the egalitarian model, partly due to a lack of empirical evidence for the conditions favoring hierarchies. Our research demonstrates that while egalitarian swarms excel in environments proportionate to their collective sensing abilities, they struggle in larger or more complex settings. Hierarchical swarms, conversely, extend their sensing reach efficiently, proving successful in larger, more unstructured environments with fewer resources. We validated these concepts through simulations and physical robot experiments, using a complex radiation cleanup task. This study paves the way for developing adaptable, hierarchical swarm systems applicable in areas like planetary exploration and autonomous vehicles. Moreover, these insights could deepen our understanding of hierarchical structures in biological organisms.