307 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- University of TorontoCA5 papers
- Vector InstituteCA5 papers
- Material Measurement LaboratoryUS3 papers
- National Institute of Standards and TechnologyUS3 papers
- Princeton UniversityUS3 papers
- Natural Resources CanadaCA2 papers
- Structural Genomics ConsortiumCA2 papers
- University of New BrunswickCA2 papers
- University of OxfordGB2 papers
- Cognitive Neuroimaging LabFR1 paper
- Durham UniversityGB1 paper
- East China University of Political Science and LawCN1 paper
8 papers
Strategic Polysemy in AI Discourse: A Philosophical Analysis of Language, Hype, and Power
Travis LaCroix, Fintan Mallory, Sasha Luccioni
This paper examines the strategic use of language in contemporary artificial intelligence (AI) discourse, focusing on the widespread adoption of metaphorical or colloquial terms li…
Relative Principals, Pluralistic Alignment, and the Structural Value Alignment Problem
Travis LaCroix
The value alignment problem for artificial intelligence (AI) is often framed as a purely technical or normative challenge, sometimes focused on hypothetical future systems. I argue…
LLM4Mat-Bench: Benchmarking Large Language Models for Materials Property Prediction
Andre Niyongabo Rubungo, Kangming Li, Jason Hattrick-Simpers +1
Large language models (LLMs) are increasingly being used in materials science. However, little attention has been given to benchmarking and standardized evaluation for LLM-based ma…
More buck-per-shot: Why learning trumps mitigation in noisy quantum sensing
Aroosa Ijaz, C. Huerta Alderete, Frédéric Sauvage +3
Quantum sensing is one of the most promising applications for quantum technologies. However, reaching the ultimate sensitivities enabled by the laws of quantum mechanics can be a c…
Probing out-of-distribution generalization in machine learning for materials
Kangming Li, Andre Niyongabo Rubungo, Xiangyun Lei +5
Scientific machine learning (ML) endeavors to develop generalizable models with broad applicability. However, the assessment of generalizability is often based on heuristics. Here,…
Efficient first principles based modeling via machine learning: from simple representations to high entropy materials
Kangming Li, Kamal Choudhary, Brian DeCost +2
High-entropy materials (HEMs) have recently emerged as a significant category of materials, offering highly tunable properties. However, the scarcity of HEM data in existing densit…
Managing extreme AI risks amid rapid progress
Yoshua Bengio, Geoffrey Hinton, Andrew Yao +22
Artificial Intelligence (AI) is progressing rapidly, and companies are shifting their focus to developing generalist AI systems that can autonomously act and pursue goals. Increase…
On the redundancy in large material datasets: efficient and robust learning with less data
Kangming Li, Daniel Persaud, Kamal Choudhary +3
Extensive efforts to gather materials data have largely overlooked potential data redundancy. In this study, we present evidence of a significant degree of redundancy across multip…