AI adoption in America: Who, what, and where

K McElheran, JF Li, E Brynjolfsson… - … of Economics & …, 2024 - Wiley Online Library
We study the early adoption and diffusion of five artificial intelligence (AI)‐related
technologies (automated‐guided vehicles, machine learning, machine vision, natural …

Mental-llm: Leveraging large language models for mental health prediction via online text data

X Xu, B Yao, Y Dong, S Gabriel, H Yu… - Proceedings of the …, 2024 - dl.acm.org
Advances in large language models (LLMs) have empowered a variety of applications.
However, there is still a significant gap in research when it comes to understanding and …

Automated annotation with generative ai requires validation

N Pangakis, S Wolken, N Fasching - arXiv preprint arXiv:2306.00176, 2023 - arxiv.org
Generative large language models (LLMs) can be a powerful tool for augmenting text
annotation procedures, but their performance varies across annotation tasks due to prompt …

VisAlign: dataset for measuring the alignment between AI and humans in visual perception

J Lee, S Kim, S Won, J Lee… - Advances in …, 2024 - proceedings.neurips.cc
AI alignment refers to models acting towards human-intended goals, preferences, or ethical
principles. Analyzing the similarity between models and humans can be a proxy measure for …

A shared journey: Experiential perspective and empirical evidence of virtual social robot ChatGPT's priori acceptance

A Abadie, S Chowdhury, SK Mangla - Technological Forecasting and Social …, 2024 - Elsevier
Due to recent technological advancements, social robots are becoming increasingly
prevalent in the consumer space. ChatGPT, a virtual social robot, has captured significant …

Detectors for safe and reliable llms: Implementations, uses, and limitations

S Achintalwar, AA Garcia, A Anaby-Tavor… - arXiv preprint arXiv …, 2024 - arxiv.org
Large language models (LLMs) are susceptible to a variety of risks, from non-faithful output
to biased and toxic generations. Due to several limiting factors surrounding LLMs (training …

Measuring the success of diffusion models at imitating human artists

S Casper, Z Guo, S Mogulothu, Z Marinov… - arXiv preprint arXiv …, 2023 - arxiv.org
Modern diffusion models have set the state-of-the-art in AI image generation. Their success
is due, in part, to training on Internet-scale data which often includes copyrighted work. This …

VisAlign: Dataset for Measuring the Degree of Alignment between AI and Humans in Visual Perception

J Lee, S Kim, S Won, J Lee, M Ghassemi… - arXiv preprint arXiv …, 2023 - arxiv.org
AI alignment refers to models acting towards human-intended goals, preferences, or ethical
principles. Given that most large-scale deep learning models act as black boxes and cannot …

Transparent AI-assisted chemical engineering process: Machine learning modeling and multi-objective optimization for integrating process data and molecular-level …

W Xu, Y Wang, D Zhang, Z Yang, Z Yuan, Y Lin… - Journal of Cleaner …, 2024 - Elsevier
Thoroughly utilizing the first principles of chemical processes, industrial big data, and
artificial intelligence algorithms has been a deterministic trend in process modeling …

[HTML][HTML] ANNOTE: Annotation of time-series events

R Groh, JY Li, NYK Li-Jessen, AM Kist - Software Impacts, 2024 - Elsevier
Supervised training of machine learning models heavily relies on accurate annotations.
However, data annotation, such as in the case of time-series signals, poses a labor …