Article
Engineering, Multidisciplinary
Joshua Bongard, Michael Levin
Summary: The applicability of computational models to the biological world is being debated, with the suggestion to adopt an observer-dependent view rather than strict boundaries between categories. Living systems are polycomputing, simultaneously performing multiple functions in the same place, which challenges prediction and control. Understanding and harnessing polycomputing can have significant impacts on fields like regenerative medicine, robotics, and computer engineering.
Review
Biology
Abhaya Bhardwaj, Shristi Kishore, Dhananjay K. Pandey
Summary: Artificial intelligence has the potential to improve various fields of biology, such as medicine, agriculture, and bio-based industry, by providing more precise diagnosis, cost-effective treatment, increased output, and decreased waste.
Article
Neurosciences
Thomas Jochmann, Marc S. Seibel, Elisabeth Jochmann, Sheraz Khan, Matti S. Haemaelaeinen, Jens Haueisen
Summary: This study investigates a convolutional neural network that detects sex from clinical EEG and finds that electrocardiac artifacts leak into the classifier. However, even after removing these artifacts, the sex can still be determined from the EEG, with topographies being critical but waveforms and frequencies not important for sex detection.
HUMAN BRAIN MAPPING
(2023)
Review
Biology
Douglas S. Glazier
Summary: Various phenotypic traits show allometric scaling relationships with the size of living systems. The causes of these relationships are widely debated. This study focuses on the body-mass scaling of biological processes and life-history events, and argues that a time perspective may be equally or even more important than an energy perspective.
Article
Engineering, Industrial
Xin Hu, Ang Liu, Xiaopeng Li, Yun Dai, Masayuki Nakao
Summary: AI can improve customer segmentation in product development, but the lack of transparency often leads designers to doubt its predictions. Explainable AI (XAI) is a new paradigm that provides humanly understandable explanations about AI predictions. The use of XAI explanations, based on features and data, can enhance AI performance and foster trust in AI among designers. A new framework is proposed and validated through an experiment, showing that XAI can enhance AI performance by facilitating feature selection and identifying high-value datasets.
CIRP ANNALS-MANUFACTURING TECHNOLOGY
(2023)
Article
Medicine, General & Internal
Diego Morena, Jesus Fernandez, Carolina Campos, Maria Castillo, Guillermo Lopez, Maria Benavent, Jose Luis Izquierdo
Summary: The study uses artificial intelligence to analyze the clinical characteristics and treatment management of patients with idiopathic pulmonary fibrosis. Data from the Castilla-La Mancha Regional Healthcare Service in Spain between 2012 and 2020 were collected using the Savana Manager 3.0 AI platform and natural language processing. The results show that 64.8% of the 897 included subjects were men, with a mean age of 72.9 years, and 35.2% were women, with a mean age of 76.8 years. 45% of patients received antifibrotic therapy.
JOURNAL OF CLINICAL MEDICINE
(2023)
Article
Computer Science, Cybernetics
A. Pandiaraj, C. Sundar, S. Pavalarajan
Summary: This paper introduces a new sentiment analysis model for article reviews from newspapers, which has been proven to effectively recognize sentiments in newspaper articles.
Article
Education & Educational Research
Yu-Yin Wang, Yu-Wei Chuang
Summary: With the development of AI applications, it is important to understand individual's perceived self-efficacy and subsequent behaviors towards AI. However, a suitable scale for measuring AI self-efficacy has yet to be developed. This research aims to investigate and validate an AI self-efficacy scale (AISES).
EDUCATION AND INFORMATION TECHNOLOGIES
(2023)
Article
Health Care Sciences & Services
Alessandra Panico, Gianluca Gatta, Antonio Salvia, Graziella Di Grezia, Noemi Fico, Vincenzo Cuccurullo
Summary: Breast cancer is the most common non-skin cancer in women and is influenced by habits and heredity. Regular screening, particularly through mammography, is crucial for early detection and increased chances of survival. Innovative techniques using artificial intelligence, such as radiomics, have shown promise in improving the quality of diagnosis for breast cancer.
JOURNAL OF PERSONALIZED MEDICINE
(2023)
Article
Multidisciplinary Sciences
Peng Deng, Yiming Gao, Li Mu, Xiangang Hu, Fubo Yu, Yuying Jia, Zhenyu Wang, Baoshan Xing
Summary: By building an NP-plant database and using machine learning, this study predicts the response and uptake/transport of nanoparticles by plants. It reveals that plant responses are driven by various factors such as NP exposure dose, duration, plant age at exposure, and NP size and zeta potential. According to the prediction, Africa is a suitable area for nanoenabled agriculture.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
(2023)
Editorial Material
Biochemical Research Methods
Chenyang Zhang, Minjie Mou, Ying Zhou, Wei Zhang, Xichen Lian, Shuiyang Shi, Mingkun Lu, Huaicheng Sun, Fengcheng Li, Yunxia Wang, Zhenyu Zeng, Zhaorong Li, Bing Zhang, Yunqing Qiu, Feng Zhu, Jianqing Gao
Summary: This study conducted a comprehensive assessment and analysis on the biological activities of DIGs in drug formulations, providing the first comprehensive activities for both DIGs and DFMs to the pharmaceutical community. The biological targets of each DIG and formulation were fully referenced to available databases, and popular artificial intelligence techniques were used to evaluate the predictive potential of DIGs' activity data. The findings of this work are expected to have significant implications for the future practice of drug discovery and precision medicine.
BRIEFINGS IN BIOINFORMATICS
(2022)
Article
Engineering, Mechanical
Nico Herwig, Zhongxiao Peng, Pietro Borghesani
Summary: Neural networks have been widely studied for automated condition monitoring assessment. The use of explainable AI methods, such as Layer-wise Relevance Propagation (LRP), can provide insights into the features used by the network for classification. This study applies LRP to a convolutional neural network (CNN) for tribological image analysis and shows that the CNN can classify images based on similar guidelines as a domain expert. The output of the method provides a list of defining features for each severity class, allowing domain experts to assess the meaningfulness of the network's classification.
TRIBOLOGY INTERNATIONAL
(2023)
Article
Gastroenterology & Hepatology
Eun Jeong Gong, Chang Seok Bang, Jae Jun Lee, Gwang Ho Baik, Hyun Lim, Jae Hoon Jeong, Sung Won Choi, Joonhee Cho, Deok Yeol Kim, Kang Bin Lee, Seung-Il Shin, Dick Sigmund, Byeong In Moon, Sung Chul Park, Sang Hoon Lee, Ki Bae Bang, Dae-Soon Son
Summary: This study aimed to develop and validate a deep learning-based clinical decision support system for the automated detection and classification of gastric neoplasms in real-time endoscopy. The results showed high accuracy in lesion detection and classification.
Review
Cardiac & Cardiovascular Systems
Arnold von Eckardstein, Borge G. Nordestgaard, Alan T. Remaley, Alberico L. Catapano
Summary: Previous interest in HDL focused on its role in atherosclerotic cardiovascular disease, but recent studies show that HDL has broader physiological functions and is associated with various diseases.
EUROPEAN HEART JOURNAL
(2023)
Article
Chemistry, Multidisciplinary
Leonor Fernandes, Vera Migueis, Ivo Pereira, Eduardo Oliveira
Summary: This paper introduces a hybrid recommender system that combines four independent systems to improve the accuracy and personalization of recommendations using transactional and portfolio information.
APPLIED SCIENCES-BASEL
(2023)
Article
Computer Science, Artificial Intelligence
Sebastian Risi, Joel Lehman, David B. D'Ambrosio, Ryan Hall, Kenneth O. Stanley
IEEE TRANSACTIONS ON COMPUTATIONAL INTELLIGENCE AND AI IN GAMES
(2016)
Article
Multidisciplinary Sciences
Joel Lehman, Risto Miikkulainen
Article
Computer Science, Artificial Intelligence
Joel Lehman, Jeff Clune, Dusan Misevic, Christoph Adami, Lee Altenberg, Julie Beaulieu, Peter J. Bentley, Samuel Bernard, Guillaume Beslon, David M. Bryson, Nick Cheney, Patryk Chrabaszcz, Antoine Cully, Stephane Doncieux, Fred C. Dyer, Kai Olav Ellefsen, Robert Feldt, Stephan Fischer, Stephanie Forrest, Antoine Frenoy, Christian Gagn, Leni Le Goff, Laura M. Grabowski, Babak Hodjat, Frank Hutter, Laurent Keller, Carole Knibbe, Peter Krcah, Richard E. Lenski, Hod Lipson, Robert MacCurdy, Carlos Maestre, Risto Miikkulainen, Sara Mitri, David E. Moriarty, Jean-Baptiste Mouret, Anh Nguyen, Charles Ofria, Marc Parizeau, David Parsons, Robert T. Pennock, William F. Punch, Thomas S. Ray, Marc Schoenauer, Eric Shulte, Karl Sims, Kenneth O. Stanley, Francois Taddei, Danesh Tarapore, Simon Thibault, Richard Watson, Westley Weimer, Jason Yosinski
Proceedings Paper
Computer Science, Artificial Intelligence
Adrien Ecoffet, Joel Lehman
Summary: The ambitious goal of machine learning is to create agents that behave ethically, expanding the context in which autonomous agents can be practically deployed. However, there is widespread disagreement about the nature of morality, leading to the proposal that ethical behavior requires acting under moral uncertainty. This paper translates insights from moral philosophy to reinforcement learning, proposing training methods that address competing desiderata and highlighting potential technical complications in grounding moral philosophy in RL.
INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 139
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Tanmay Gangwani, Joel Lehman, Qiang Liu, Jian Peng
35TH UNCERTAINTY IN ARTIFICIAL INTELLIGENCE CONFERENCE (UAI 2019)
(2020)
Proceedings Paper
Computer Science, Artificial Intelligence
Rui Wang, Joel Lehman, Jeff Clune, Kenneth O. Stanley
PROCEEDINGS OF THE 2019 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE (GECCO'19)
(2019)
Article
Computer Science, Interdisciplinary Applications
Pd-Chi Huang, Luis Sentis, Joel Lehman, Chien-Liang Fok, Aloysius K. Mok, Risto Miikkulainen
ACM TRANSACTIONS ON CYBER-PHYSICAL SYSTEMS
(2019)
Proceedings Paper
Computer Science, Artificial Intelligence
Joel Lehman, Jeff Clune, Dusan Misevic
2018 CONFERENCE ON ARTIFICIAL LIFE (ALIFE 2018)
(2018)
Proceedings Paper
Computer Science, Artificial Intelligence
Joel Lehman, Kenneth O. Stanley
2018 CONFERENCE ON ARTIFICIAL LIFE (ALIFE 2018)
(2018)
Proceedings Paper
Computer Science, Theory & Methods
Henok Mengistu, Joel Lehman, Jeff Clune
GECCO'16: PROCEEDINGS OF THE 2016 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE
(2016)
Article
Robotics
Joel Lehman, Bryan Wilder, Kenneth O. Stanley
FRONTIERS IN ROBOTICS AND AI
(2016)
Proceedings Paper
Computer Science, Theory & Methods
Jacob Schrum, Joel Lehman, Sebastian Risi
PROCEEDINGS OF THE 2016 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE (GECCO'16 COMPANION)
(2016)
Proceedings Paper
Computer Science, Theory & Methods
Pei-Chi Huang, Luis Sentis, Joel Lehman, Chien-Liang Fok, Aloysius K. Mok, Risto Miikkulainen
2015 IEEE 36TH REAL-TIME SYSTEMS SYMPOSIUM (RTSS 2015)
(2015)
Proceedings Paper
Computer Science, Artificial Intelligence
Joel Lehman, Risto Miikkulainen
GECCO'15: PROCEEDINGS OF THE 2015 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE
(2015)