AI Automatons: AI Systems Intended to Imitate Humans
Alexandra Olteanu
Solon Barocas
Su Lin Blodgett
Lisa Egede
Alicia DeVrio
Myra Cheng
There is a growing proliferation of AI systems designed to mimic people's behavior, work, abilities, likenesses, or humanness -- systems we … (voir plus)dub AI automatons. Individuals, groups, or generic humans are being simulated to produce creative work in their styles, to respond to surveys in their places, to probe how they would use a new system before deployment, to provide users with assistance and companionship, and to anticipate their possible future behavior and interactions with others, just to name a few applications. The research, design, deployment, and availability of such AI systems have, however, also prompted growing concerns about a wide range of possible legal, ethical, and other social impacts. To both 1) facilitate productive discussions about whether, when, and how to design and deploy such systems, and 2) chart the current landscape of existing and prospective AI automatons, we need to tease apart determinant design axes and considerations that can aid our understanding of whether and how various design choices along these axes could mitigate -- or instead exacerbate -- potential adverse impacts that the development and use of AI automatons could give rise to. In this paper, through a synthesis of related literature and extensive examples of existing AI systems intended to mimic humans, we develop a conceptual framework to help foreground key axes of design variations and provide analytical scaffolding to foster greater recognition of the design choices available to developers, as well as the possible ethical implications these choices might have.
2025-03-04
ArXiv (prépublication)
arxiv.org
arxiv.org
Beyond Cosine Decay: On the effectiveness of Infinite Learning Rate Schedule for Continual Pre-training
Vaibhav Singh
Paul Janson
Paria Mehrbod
Adam Ibrahim
Irina Rish
Eugene Belilovsky
Benjamin Therien
The ever-growing availability of unlabeled data presents both opportunities and challenges for training artificial intelligence systems. Whi… (voir plus)le self-supervised learning (SSL) has emerged as a powerful paradigm for extracting meaningful representations from vast amounts of unlabeled data, existing methods still struggle to adapt to the non-stationary, non-IID nature of real-world data streams without forgetting previously learned knowledge. Recent works have adopted a repeated cosine annealing schedule for large-scale continual pre-training; however, these schedules (1) inherently cause forgetting during the re-warming phase and (2) have not been systematically compared to existing continual SSL methods. In this work, we systematically compare the widely used cosine schedule with the recently proposed infinite learning rate schedule and empirically find the latter to be a more effective alternative. Our extensive empirical evaluation across diverse image and language datasets demonstrates that the infinite learning rate schedule consistently enhances continual pre-training performance compared to a repeated cosine decay without being restricted to a fixed iteration budget. For instance, in a small-scale MAE pre-training setup, it outperforms several strong baselines from the literature. We then scale up our experiments to larger MAE pre-training and autoregressive language model pre-training. Our results show that the infinite learning rate schedule remains effective at scale, surpassing repeated cosine decay for both MAE pre-training and zero-shot LM benchmarks.
2025-03-04
ArXiv (prépublication)
arxiv.org
arxiv.org
Considerations and recommendations from the <scp>ISMRM</scp> diffusion study group for preclinical diffusion <scp>MRI</scp>: Part 2—Ex vivo imaging: Added value and acquisition
Kurt G Schilling
Francesco Grussu
Andrada Ianus
Brian Hansen
Amy F. D. Howard
Rachel L. C. Barrett
Manisha Aggarwal
Stijn Michielse
Fatima Nasrallah
Warda Syeda
Nian Wang
Jelle Veraart
Alard Roebroeck
Andrew F. Bagdasarian
Cornelius Eichner
Farshid Sepehrband
Jan Zimmermann
Lucas Soustelle
Christien Bowman
Benjamin C. Tendler … (voir 38 de plus)
Andreea Hertanu
Ben Jeurissen
Marleen Verhoye
Lucio Frydman
Yohan van de Looij
David Hike
Jeff F. Dunn
Karla Miller
Bennett Landman
Noam Shemesh
Arthur Anderson
Emilie McKinnon
Shawna Farquharson
Flavio Dell’Acqua
Carlo Pierpaoli
Ivana Drobnjak
Alexander Leemans
Kevin D. Harkins
Maxime Descoteaux
Duan Xu
Hao Huang
Mathieu D. Santin
Samuel C. Grant
Andre Obenaus
Gene S. Kim
Dan Wu
Denis Le Bihan
Stephen J. Blackband
Luisa Ciobanu
Els Fieremans
Ruiliang Bai
Trygve B. Leergaard
Jiangyang Zhang
Tim B. Dyrby
G. Allan Johnson
Julien Cohen-Adad
Matthew D. Budde
Ileana O. Jelescu
2025-03-04
Magnetic Resonance in Medicine (publié)
doi.org
Considerations and recommendations from the ISMRM diffusion study group for preclinical diffusion MRI: Part 2-Ex vivo imaging: Added value and acquisition.
Kurt G Schilling
Francesco Grussu
Andrada Ianus
Brian Hansen
Amy F. D. Howard
Rachel L. C. Barrett
Fatima Nasrallah
Manisha Aggarwal
Stijn Michielse
Warda Syeda
Nian Wang
Andrew F. Bagdasarian
Jelle Veraart
Alard Roebroeck
Cornelius Eichner
Farshid Sepehrband
Jan Zimmermann
Lucas Soustelle
Christien Bowman
Benjamin C. Tendler … (voir 38 de plus)
Andreea Hertanu
Ben Jeurissen
Marleen Verhoye
Lucio Frydman
Yohan van de Looij
David Hike
Jeff F. Dunn
Karla Miller
Bennett Landman
Noam Shemesh
Arthur Anderson
Emilie McKinnon
Shawna Farquharson
Mathieu D. Santin
Flavio Dell’Acqua
Carlo Pierpaoli
Samuel C. Grant
Ivana Drobnjak
Andre Obenaus
Alexander Leemans
Kevin D. Harkins
Maxime Descoteaux
Duan Xu
Hao Huang
Gene S. Kim
Dan Wu
Denis Le Bihan
Stephen J. Blackband
Matthew D. Budde
Luisa Ciobanu
Els Fieremans
Ruiliang Bai
Trygve B. Leergaard
Jiangyang Zhang
Tim B. Dyrby
G. Allan Johnson
Julien Cohen-Adad
Ileana O. Jelescu
The value of preclinical diffusion MRI (dMRI) is substantial. While dMRI enables in vivo non-invasive characterization of tissue, ex vivo d… (voir plus)MRI is increasingly being used to probe tissue microstructure and brain connectivity. Ex vivo dMRI has several experimental advantages including higher SNR and spatial resolution compared to in vivo studies, and enabling more advanced diffusion contrasts for improved microstructure and connectivity characterization. Another major advantage of ex vivo dMRI is the direct comparison with histological data, as a crucial methodological validation. However, there are a number of considerations that must be made when performing ex vivo experiments. The steps from tissue preparation, image acquisition and processing, and interpretation of results are complex, with many decisions that not only differ dramatically from in vivo imaging of small animals, but ultimately affect what questions can be answered using the data. This work represents "Part 2" of a three-part series of recommendations and considerations for preclinical dMRI. We describe best practices for dMRI of ex vivo tissue, with a focus on the value that ex vivo imaging adds to the field of dMRI and considerations in ex vivo image acquisition. We first give general considerations and foundational knowledge that must be considered when designing experiments. We briefly describe differences in specimens and models and discuss why some may be more or less appropriate for different studies. We then give guidelines for ex vivo protocols, including tissue fixation, sample preparation, and MR scanning. In each section, we attempt to provide guidelines and recommendations, but also highlight areas for which no guidelines exist (and why), and where future work should lie. An overarching goal herein is to enhance the rigor and reproducibility of ex vivo dMRI acquisitions and analyses, and thereby advance biomedical knowledge.
2025-03-04
Magnetic Resonance in Medicine (publié)
doi.org
arxiv.org
EarthView: A Large Scale Remote Sensing Dataset for Self-Supervision
Diego Velazquez
Pau Rodriguez
Sergio Alonso
Josep M. Gonfaus
Jordi Gonzalez
Gerardo Richarte
Javier Marin
Yoshua Bengio
Alexandre Lacoste
This paper presents EarthView, a comprehensive dataset specifically designed for self-supervision on remote sensing data, intended to enhanc… (voir plus)e deep learning applications on Earth monitoring tasks. The dataset spans 15 tera pixels of global remote-sensing data, combining imagery from a diverse range of sources, including NEON, Sentinel, and a novel release of 1m spatial resolution data from Satellogic. Our dataset provides a wide spectrum of image data with varying resolutions, harnessed from different sensors and organized coherently into an accessible HuggingFace dataset in parquet format. This data spans five years, from 2017 to 2022. Accompanying the dataset, we introduce EarthMAE, a tailored Masked Autoencoder, developed to tackle the distinct challenges of remote sensing data. Trained in a self-supervised fashion, EarthMAE effectively processes different data modalities such as hyperspectral, multispectral, topographical data, segmentation maps, and temporal structure. This model helps us show that pre-training on Satellogic data improves performance on downstream tasks. While there is still a gap to fill in MAE for heterogeneous data, we regard this innovative combination of an expansive, diverse dataset and a versatile model adapted for self-supervised learning as a stride forward in deep learning for Earth monitoring.
2025-03-04
2025 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW) (publié)
doi.org
arxiv.org
Feynman-Kac Correctors in Diffusion: Annealing, Guidance, and Product of Experts
Marta Skreta
Tara Akhound-Sadegh
Viktor Ohanesian
Roberto Bondesan
Alan Aspuru-Guzik
Arnaud Doucet
Rob Brekelmans
Alexander Tong
Kirill Neklyudov
While score-based generative models are the model of choice across diverse domains, there are limited tools available for controlling infere… (voir plus)nce-time behavior in a principled manner, e.g. for composing multiple pretrained models. Existing classifier-free guidance methods use a simple heuristic to mix conditional and unconditional scores to approximately sample from conditional distributions. However, such methods do not approximate the intermediate distributions, necessitating additional 'corrector' steps. In this work, we provide an efficient and principled method for sampling from a sequence of annealed, geometric-averaged, or product distributions derived from pretrained score-based models. We derive a weighted simulation scheme which we call Feynman-Kac Correctors (FKCs) based on the celebrated Feynman-Kac formula by carefully accounting for terms in the appropriate partial differential equations (PDEs). To simulate these PDEs, we propose Sequential Monte Carlo (SMC) resampling algorithms that leverage inference-time scaling to improve sampling quality. We empirically demonstrate the utility of our methods by proposing amortized sampling via inference-time temperature annealing, improving multi-objective molecule generation using pretrained models, and improving classifier-free guidance for text-to-image generation. Our code is available at https://github.com/martaskrt/fkc-diffusion.
2025-03-04
ArXiv (prépublication)
arxiv.org
arxiv.org
Feynman-Kac Correctors in Diffusion: Annealing, Guidance, and Product of Experts
Marta Skreta
Tara Akhound-Sadegh
Viktor Ohanesian
Roberto Bondesan
Alan Aspuru-Guzik
Arnaud Doucet
Rob Brekelmans
Alexander Tong
Kirill Neklyudov
While score-based generative models are the model of choice across diverse domains, there are limited tools available for controlling infere… (voir plus)nce-time behavior in a principled manner, e.g. for composing multiple pretrained models. Existing classifier-free guidance methods use a simple heuristic to mix conditional and unconditional scores to approximately sample from conditional distributions. However, such methods do not approximate the intermediate distributions, necessitating additional `corrector' steps. In this work, we provide an efficient and principled method for sampling from a sequence of annealed, geometric-averaged, or product distributions derived from pretrained score-based models. We derive a weighted simulation scheme which we call Feynman-Kac Correctors (FKCs) based on the celebrated Feynman-Kac formula by carefully accounting for terms in the appropriate partial differential equations (PDEs). To simulate these PDEs, we propose Sequential Monte Carlo (SMC) resampling algorithms that leverage inference-time scaling to improve sampling quality. We empirically demonstrate the utility of our methods by proposing amortized sampling via inference-time temperature annealing, improving multi-objective molecule generation using pretrained models, and improving classifier-free guidance for text-to-image generation. Our code is available at https://github.com/martaskrt/fkc-diffusion.
2025-03-04
ArXiv (prépublication)
doi.org
arxiv.org
Hardware Synthesizable Exceptions using Continuations
Paul Teng
Christophe Dubach
2025-03-04
Proceedings of the 30th Asia and South Pacific Design Automation Conference (publié)
doi.org
LLM-Safety Evaluations Lack Robustness
Tim Beyer
Sophie Xhonneux
Simon Geisler
Gauthier Gidel
Leo Schwinn
Stephan Günnemann
In this paper, we argue that current safety alignment research efforts for large language models are hindered by many intertwined sources of… (voir plus) noise, such as small datasets, methodological inconsistencies, and unreliable evaluation setups. This can, at times, make it impossible to evaluate and compare attacks and defenses fairly, thereby slowing progress. We systematically analyze the LLM safety evaluation pipeline, covering dataset curation, optimization strategies for automated red-teaming, response generation, and response evaluation using LLM judges. At each stage, we identify key issues and highlight their practical impact. We also propose a set of guidelines for reducing noise and bias in evaluations of future attack and defense papers. Lastly, we offer an opposing perspective, highlighting practical reasons for existing limitations. We believe that addressing the outlined problems in future research will improve the field's ability to generate easily comparable results and make measurable progress.
2025-03-04
ArXiv (prépublication)
arxiv.org
arxiv.org
CrystalGym: A New Benchmark for Materials Discovery Using Reinforcement Learning
Prashant Govindarajan
Mathieu Reymond
Antoine Clavaud
Mariano Phielipp
Santiago Miret
Sarath Chandar
*In silico* design and optimization of new materials primarily relies on high-accuracy atomic simulators that perform density functional the… (voir plus)ory (DFT) calculations. While recent works showcase the strong potential of machine learning to accelerate the material design process, they mostly consist of generative approaches that do not use direct DFT signals as feedback to improve training and generation mainly due to DFT's high computational cost. To aid the adoption of direct DFT signals in the materials design loop through online reinforcement learning (RL), we propose **CrystalGym**, an open-source RL environment for crystalline material discovery. Using CrystalGym, we benchmark value- and policy-based reinforcement learning algorithms for designing various crystals conditioned on target properties. Concretely, we optimize for challenging properties like the band gap, bulk modulus, and density, which are directly calculated from DFT in the environment. While none of the algorithms we benchmark solve all CrystalGym tasks, our extensive experiments and ablations show different sample efficiencies and ease of convergence to optimality for different algorithms and environment settings. Our goal is for CrystalGym to serve as a test bed for reinforcement learning researchers and material scientists to address these real-world design problems with practical applications. Furthermore, we introduce a novel class of challenges for reinforcement learning methods dealing with time-consuming reward signals, paving the way for future interdisciplinary research for machine learning motivated by real-world applications.
2025-03-03
ICLR.cc/2025/Workshop/AI4MAT (spotlight)
openreview.net
openreview.net
Development and Feasibility Study of HOPE Model for Prediction of Depression Among Older Adults Using Wi-Fi-based Motion Sensor Data: Machine Learning Study.
Shayan Nejadshamsi
Vania Karami
Negar Ghourchian
Narges Armanfard
Howard Bergman
Roland Grad
Machelle Wilchesky
Vladimir Khanassov
Isabelle Vedel
Samira Abbasgholizadeh-Rahimi
BACKGROUND Depression, characterized by persistent sadness and loss of interest in daily activities, greatly reduces quality of life. Early … (voir plus)detection is vital for effective treatment and intervention. While many studies use wearable devices to classify depression based on physical activity, these often rely on intrusive methods. Additionally, most depression classification studies involve large participant groups and use single-stage classifiers without explainability. OBJECTIVE This study aims to assess the feasibility of classifying depression using nonintrusive Wi-Fi-based motion sensor data using a novel machine learning model on a limited number of participants. We also conduct an explainability analysis to interpret the model's predictions and identify key features associated with depression classification. METHODS In this study, we recruited adults aged 65 years and older through web-based and in-person methods, supported by a McGill University health care facility directory. Participants provided consent, and we collected 6 months of activity and sleep data via nonintrusive Wi-Fi-based sensors, along with Edmonton Frailty Scale and Geriatric Depression Scale data. For depression classification, we proposed a HOPE (Home-Based Older Adults' Depression Prediction) machine learning model with feature selection, dimensionality reduction, and classification stages, evaluating various model combinations using accuracy, sensitivity, precision, and F1-score. Shapely addictive explanations and local interpretable model-agnostic explanations were used to explain the model's predictions. RESULTS A total of 6 participants were enrolled in this study; however, 2 participants withdrew later due to internet connectivity issues. Among the 4 remaining participants, 3 participants were classified as not having depression, while 1 participant was identified as having depression. The most accurate classification model, which combined sequential forward selection for feature selection, principal component analysis for dimensionality reduction, and a decision tree for classification, achieved an accuracy of 87.5%, sensitivity of 90%, and precision of 88.3%, effectively distinguishing individuals with and those without depression. The explainability analysis revealed that the most influential features in depression classification, in order of importance, were "average sleep duration," "total number of sleep interruptions," "percentage of nights with sleep interruptions," "average duration of sleep interruptions," and "Edmonton Frailty Scale." CONCLUSIONS The findings from this preliminary study demonstrate the feasibility of using Wi-Fi-based motion sensors for depression classification and highlight the effectiveness of our proposed HOPE machine learning model, even with a small sample size. These results suggest the potential for further research with a larger cohort for more comprehensive validation. Additionally, the nonintrusive data collection method and model architecture proposed in this study offer promising applications in remote health monitoring, particularly for older adults who may face challenges in using wearable devices. Furthermore, the importance of sleep patterns identified in our explainability analysis aligns with findings from previous research, emphasizing the need for more in-depth studies on the role of sleep in mental health, as suggested in the explainable machine learning study.
2025-03-03
JMIR Aging (publié)
doi.org
Interval Regression: A Comparative Study with Proposed Models
Tung L. Nguyen
Toby Dylan Hocking
Regression models are essential for a wide range of real-world applications. However, in practice, target values are not always precisely kn… (voir plus)own; instead, they may be represented as intervals of acceptable values. This challenge has led to the development of Interval Regression models. In this study, we provide a comprehensive review of existing Interval Regression models and introduce alternative models for comparative analysis. Experiments are conducted on both real-world and synthetic datasets to offer a broad perspective on model performance. The results demonstrate that no single model is universally optimal, highlighting the importance of selecting the most suitable model for each specific scenario.
2025-03-03
ArXiv (prépublication)
arxiv.org
arxiv.org