Deep Neural Networks as Scientific Models Cichy, Radoslaw M.; Kaiser, Daniel
Trends in cognitive sciences,
April 2019, 2019-04-00, 20190401, Volume:
23, Issue:
4
Journal Article
Peer reviewed
Open access
Artificial deep neural networks (DNNs) initially inspired by the brain enable computers to solve cognitive tasks at which humans excel. In the absence of explanations for such cognitive phenomena, in ...turn cognitive scientists have started using DNNs as models to investigate biological cognition and its neural basis, creating heated debate. Here, we reflect on the case from the perspective of philosophy of science. After putting DNNs as scientific models into context, we discuss how DNNs can fruitfully contribute to cognitive science. We claim that beyond their power to provide predictions and explanations of cognitive phenomena, DNNs have the potential to contribute to an often overlooked but ubiquitous and fundamental use of scientific models: exploration.
Neurally inspired deep neural networks (DNNs) have recently emerged as powerful computer algorithms tackling real-world tasks on which humans excel, such as object recognition, speech processing, and cognitive planning.
In the absence of scientific explanations regarding how humans solve such tasks, some cognitive scientists have turned to DNNs as models of human brain responses and behaviour.
In visual and auditory processing, DNNs were found to predict human brain responses and behaviour better than other models.
The use of DNNs as models in cognitive science has created a heated debate about their scientific value: in particular, are DNNs only valuable as predictive tools or do they also offer useful explanations of the phenomena investigated?
Involving students in scientific modeling practice is one of the most effective approaches to achieving the next generation science education learning goals. Given the complexity and ...multirepresentational features of scientific models, scoring student‐developed models is time‐ and cost‐intensive, remaining one of the most challenging assessment practices for science education. More importantly, teachers who rely on timely feedback to plan and adjust instruction are reluctant to use modeling tasks because they could not provide timely feedback to learners. This study utilized machine learning (ML), the most advanced artificial intelligence (AI), to develop an approach to automatically score student‐drawn models and their written descriptions of those models. We developed six modeling assessment tasks for middle school students that integrate disciplinary core ideas and crosscutting concepts with the modeling practice. For each task, we asked students to draw a model and write a description of that model, which gave students with diverse backgrounds an opportunity to represent their understanding in multiple ways. We then collected student responses to the six tasks and had human experts score a subset of those responses. We used the human‐scored student responses to develop ML algorithmic models (AMs) and to train the computer. Validation using new data suggests that the machine‐assigned scores achieved robust agreements with human consent scores. Qualitative analysis of student‐drawn models further revealed five characteristics that might impact machine scoring accuracy: Alternative expression, confusing label, inconsistent size, inconsistent position, and redundant information. We argue that these five characteristics should be considered when developing machine‐scorable modeling tasks.
In order to continuously improve the construction capacity of power grid projects, improve the level of construction technology, and promote the transformation and upgrading of construction ...enterprises, power companies actively promote the mechanized construction of transmission lines. On the basis of summarizing the existing calculation rules of mechanized construction area, through on-site investigation and extensive collection of funds, research and excavate indicators that adapt to the new situation and new regulations. According to the actual area composition of the mechanized construction site, scientific calculation rules are formulated by modeling analysis and other methods. Compare and verify the calculated area of the new rule with the actual compensation area and the calculated area of the original rule, and propose a complete set of calculation rules for mechanized construction area, which has the conditions and value for popularization and application.
Ontology-Driven Computer Systems Mykola Petrenko; Ellen Cohn; Oleksandr Shchurov ...
South African computer journal = Suid-Afrikaanse rekenaartydskrif,
12/2023, Volume:
35, Issue:
2
Journal Article
Peer reviewed
Open access
This article delves into the evolving frontier of ontology-driven natural language information processing. Through an in-depth examination, we put forth a novel linguistic processor architecture, ...uniquely integrating linguistic and ontological paradigms during semantic analysis. Distancing from conventional methodologies, our approach showcases a profound merger of knowledge extraction and representation techniques. A central highlight of our research is the development of an ontology-driven information system, architected with an innate emphasis on self-enhancement and adaptability. The system’s salient capability lies in its adept handling of elementary knowledge, combined with its dynamic aptitude to foster innovative concepts and relationships. A particular focus is accorded to the system’s application in scientific information processing, signifying its potential in revolutionising knowledge-based applications within scientific domains. Through our endeavours, we aim to pave the way for more intuitive, precise, and expansive ontology-driven tools in the realm of knowledge extraction and representation.
The purpose of this article is to provide an overview of the nature of models and their uses in the science classroom based on a theoretical review of literature. The ideas that science philosophers ...and science education researchers have in common about models and modelling are scrutinised according to five subtopics: meanings of a model, purposes of modelling, multiplicity of scientific models, change in scientific models and uses of models in the science classroom. First, a model can be defined as a representation of a target and serves as a 'bridge' connecting a theory and a phenomenon. Second, a model plays the roles of describing, explaining and predicting natural phenomena and communicating scientific ideas to others. Third, multiple models can be developed in science because scientists may have different ideas about what a target looks like and how it works and because there are a variety of semiotic resources available for constructing models. Fourth, scientific models are tested both empirically and conceptually and change along with the process of developing scientific knowledge. Fifth, in the science classroom, not only teachers but also students can take advantage of models as they are engaged in diverse modelling activities. The overview presented in this article can be used to educate science teachers and encourage them to utilise scientific models appropriately in their classrooms.
This article delves into the evolving frontier of ontology-driven natural language information processing. Through an in-depth examination, we put forth a novel linguistic processor architecture, ...uniquely integrating linguistic and ontological paradigms during semantic analysis. Distancing from conventional methodologies, our approach showcases a profound merger of knowledge extraction and representation techniques. A central highlight of our research is the development of an ontology-driven information system, architected with an innate emphasis on self-enhancement and adaptability. The system’s salient capability lies in its adept handling of elementary knowledge, combined with its dynamic aptitude to foster innovative concepts and relationships. A particular focus is accorded to the system’s application in scientific information processing, signifying its potential in revolutionising knowledge-based applications within scientific domains. Through our endeavours, we aim to pave theway for more intuitive, precise, and expansive ontology-driven tools in the realm of knowledge extraction and representation.
Today environmental scientists heavily rely on geospatial web services (GWS). However, many online facilities are under-utilized by the environmental modelling community because accessing the ...disparate service interfaces requires highly specialized technical expertise. This paper proposes a Simple Universal Interface for Services (SUIS) framework which is a client framework for accessing heterogeneous services via a single unified interface to simplify service access. The supported services including Open Geospatial Consortium (OGC), Simple Object Access Protocol (SOAP) and Representational State Transfer (REST) services. SUIS relieves modellers from having to learn the details of service technologies such as protocols, bindings, and schemas. SUIS4j, a Java implementation of the SUIS framework, is developed and tested to combine multiple operational GWS to demonstrate geoprocessing workflows in agricultural drought monitoring and coastal ocean modelling. The results confirm the expected benefits. SUIS is demonstrated to support simplified use of geospatial cyberinfrastructure for ad-hoc environmental model integration.
•New simple and universal client framework to reduce complexity and unleash the full power of geospatial web services.•Understandable and descriptive interface for environmental modellers/scientists.•SUIS hides technical terminologies in network communications from scientists.•SUIS enables simple and effective composition of web services to perform agricultural drought and coastal ocean modelling.•SUIS add negligible time cost (<10 ms) into service performance.
We present the results of a study of Chilean students' understanding of scientific models, using a Spanish-adapted version of the Students' Understanding of Models in Science instrument (Treagust, D. ...F., Chittleborough, G., & Mamiala, T. L. (2002). Students understanding of the role of scientific models in learning science. International Journal of Science Education, 24(4), 357-368. doi:10.1080/09500690110066485). The study covered 290 students in three schools in different parts of Chile. Results showed a Cronbach's alpha of 0.86 and medium correlation between the dimensions of the instrument. A confirmatory factor analysis showed that the five dimensions' theoretical structure is supported by the data, and one-way ANOVA and a t-test showed a balanced, non-discriminatory instrument that is suited for use in the Spanish language. The instrument also showed that Chilean students most need improvement in two of the dimensions - 'understanding of scientific models in terms of exact replicas' and the 'use of scientific models'. This reflects the original data analysed by Treagust et al. (
2002
) and suggests that these factors are common issues in the teaching and learning of scientific models. Future research will review the data by year and group to determine how students develop the idea of a scientific model and relate this information to science curricula. It will also investigate the link between students' understanding of scientific models and Anderson & Krathwohl's (2001) cognitive taxonomy.
This study analyses the evolution of school-level scientific models proposed by prospective pre-primary teachers (PPTs) (a) about the water molecule and its intermolecular bonds, and (b) representing ...the different states of water molecule aggregation at a microscopic level. The data were acquired from the PPTs' responses at the beginning and at the end of the intervention to two open-ended questions. Given the qualitative and interpretive nature of the study, the responses were analysed and categorised by combining inter- and intra-rater evaluation methods. In general, positive evolution was observed in the models for both questions, progressing from representations that were symbolic, macroscopic, or without differentiating the constituents of the molecule to molecular representations that were more coherent. They ended up presenting a more proportionate ratio between the sizes of the two types of atom involved, considering the approximate angle between the bonds, and even differentiating the intra- from the intermolecular hydrogen bonds characteristic of water to describe the states of water molecule aggregation. The conclusion was that, with interventions of this kind, it is possible to initiate PPTs in the practice of scientific modelling as part of their training as science teachers at the initial levels of education.
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