Matthew Kenney

Jin Hyun Cheong, Eshin Jolly, Tiankang Xie et al.

Studying facial expressions is a notoriously difficult endeavor. Recent advances in the field of affective computing have yielded impressive progress in automatically detecting facial expressions from pictures and videos. However, much of this work has yet to be widely disseminated in social science domains such as psychology. Current state of the art models require considerable domain expertise that is not traditionally incorporated into social science training programs. Furthermore, there is a notable absence of user-friendly and open-source software that provides a comprehensive set of tools and functions that support facial expression research. In this paper, we introduce Py-Feat, an open-source Python toolbox that provides support for detecting, preprocessing, analyzing, and visualizing facial expression data. Py-Feat makes it easy for domain experts to disseminate and benchmark computer vision models and also for end users to quickly process, analyze, and visualize face expression data. We hope this platform will facilitate increased use of facial expression data in human behavior research.

Matthew Kenney

Artificial intelligence agents are increasingly capable of performing complex tasks across various domains. As these agents advance, there is a growing need to accurately measure and benchmark their capabilities, particularly in accelerating AI research and development. Current benchmarks focus on general machine learning tasks, but lack comprehensive evaluation methods for assessing AI agents' abilities in tackling research-level problems and competition-level challenges in the field of AI. We present the ML Research Benchmark (MLRB), comprising 7 competition-level tasks derived from recent machine learning conference tracks. These tasks span activities typically undertaken by AI researchers, including model training efficiency, pretraining on limited data, domain specific fine-tuning, and model compression. This paper introduces a novel benchmark and evaluates it using agent scaffolds powered by frontier models, including Claude-3 and GPT-4o. The results indicate that the Claude-3.5 Sonnet agent performs best across our benchmark, excelling in planning and developing machine learning models. However, both tested agents struggled to perform non-trivial research iterations. We observed significant performance variations across tasks, highlighting the complexity of AI development and the challenges in creating versatile agent scaffolds. While current AI agents can successfully navigate complex instructions and produce baseline results, they fall short of the capabilities required for advanced AI research. The ML Research Benchmark provides a valuable framework for assessing and comparing AI agents on tasks mirroring real-world AI research challenges.