A comprehensive review of digital twin—part 1: modeling and twinning enabling technologies

A Thelen, X Zhang, O Fink, Y Lu, S Ghosh… - Structural and …, 2022‏ - Springer
As an emerging technology in the era of Industry 4.0, digital twin is gaining unprecedented
attention because of its promise to further optimize process design, quality control, health …

Biological underpinnings for lifelong learning machines

D Kudithipudi, M Aguilar-Simon, J Babb… - Nature Machine …, 2022‏ - nature.com
Biological organisms learn from interactions with their environment throughout their lifetime.
For artificial systems to successfully act and adapt in the real world, it is desirable to similarly …

Unifying large language models and knowledge graphs: A roadmap

S Pan, L Luo, Y Wang, C Chen… - IEEE Transactions on …, 2024‏ - ieeexplore.ieee.org
Large language models (LLMs), such as ChatGPT and GPT4, are making new waves in the
field of natural language processing and artificial intelligence, due to their emergent ability …

A definition of continual reinforcement learning

D Abel, A Barreto, B Van Roy… - Advances in …, 2023‏ - proceedings.neurips.cc
In a standard view of the reinforcement learning problem, an agent's goal is to efficiently
identify a policy that maximizes long-term reward. However, this perspective is based on a …

Intelligent computing: the latest advances, challenges, and future

S Zhu, T Yu, T Xu, H Chen, S Dustdar, S Gigan… - Intelligent …, 2023‏ - spj.science.org
Computing is a critical driving force in the development of human civilization. In recent years,
we have witnessed the emergence of intelligent computing, a new computing paradigm that …

Deep learning for change detection in remote sensing: a review

T Bai, L Wang, D Yin, K Sun, Y Chen… - Geo-spatial Information …, 2023‏ - Taylor & Francis
ABSTRACT A large number of publications have incorporated deep learning in the process
of remote sensing change detection. In these Deep Learning Change Detection (DLCD) …

Symbolic knowledge distillation: from general language models to commonsense models

P West, C Bhagavatula, J Hessel, JD Hwang… - arxiv preprint arxiv …, 2021‏ - arxiv.org
The common practice for training commonsense models has gone from-human-to-corpus-to-
machine: humans author commonsense knowledge graphs in order to train commonsense …

Artificial intelligence and machine learning

N Kühl, M Schemmer, M Goutier, G Satzger - Electronic Markets, 2022‏ - Springer
Within the last decade, the application of “artificial intelligence” and “machine learning” has
become popular across multiple disciplines, especially in information systems. The two …

A comprehensive study of knowledge editing for large language models

N Zhang, Y Yao, B Tian, P Wang, S Deng… - arxiv preprint arxiv …, 2024‏ - arxiv.org
Large Language Models (LLMs) have shown extraordinary capabilities in understanding
and generating text that closely mirrors human communication. However, a primary …

Dynabench: Rethinking benchmarking in NLP

D Kiela, M Bartolo, Y Nie, D Kaushik, A Geiger… - arxiv preprint arxiv …, 2021‏ - arxiv.org
We introduce Dynabench, an open-source platform for dynamic dataset creation and model
benchmarking. Dynabench runs in a web browser and supports human-and-model-in-the …