A Cognitive Science Based Machine Learning Architecture

In an attempt to illustrate the application of cognitive science principles to hard AI problems in machine learning we propose the LIDA technology, a cognitive science based architecture capable of more human-like learning. A LIDA based software agent or cognitive robot will be capable of three fundamental, continuously active, humanlike learning mechanisms:\ud 1) perceptual learning, the learning of new objects, categories, relations, etc.,\ud 2) episodic learning of events, the what, where, and when,\ud 3) procedural learning, the learning of new actions and action sequences with which to accomplish new tasks. The paper argues for the use of modular components, each specializing in implementing individual facets of human and animal cognition, as a viable approach towards achieving general intelligence

Assessment of tertiary students’ learning of statistical modelling

In this paper, we report on the learning of statistical modelling in a second-year statistics module through the assessment of a problem that required a Monte Carlo simulation. On the forefront of the 4th industrial revolution is science, technology, engineering and mathematics subjects, where mathematical statistics plays a key role in topics such as machine learning and predictive analysis. Students often find statistical modelling difficult, where obstructions in the modelling process could lead towards a dead-end. For this reason, assessment of learning, for learning and as learning in statistics education seems necessary. General pillars of good assessment practice is considered in this study, as well as guidelines for the development of students’ conceptual understanding of the content, such as, statistical reasoning, statistical thinking and statistical literacy. Therefore, this study was conducted to provide educators with information of student achievement of desired student learning outcomes. Based on an analysis of the reports collected individually and through voluntary group work, descriptive statistics are presented. These results are discussed in relation with assessment measures and provides a basis for teaching and learning statistics.Institute for Science and Technology Education (ISTE

Combining statistical and machine learning methods to explore German students’ attitudes towards ICT in PISA

In our age of big data and growing computational power, versatility in data analysis is important. This study presents a flexible way to combine statistics and machine learning for data analysis of a large-scale educational survey. The authors used statistical and machine learning methods to explore German students’ attitudes towards information and communication technology (ICT) in relation to mathematical and scientific literacy measured by the Programme for International Student Assessment (PISA) in 2015 and 2018. Implementations of the random forest (RF) algorithm were applied to impute missing data and to predict students’ proficiency levels in mathematics and science. Hierarchical linear models (HLM) were built to explore relationships between attitudes towards ICT and mathematical and scientific literacy with the focus on the nested structure of the data. ICT autonomy was an important variable in RF models, and associations between this attitude and literacy scores in HLM were significant and positive, while for other ICT attitudes the associations were negative (ICT in social interaction) or non-significant (ICT competence and ICT interest). The need for further research on ICT autonomy is discussed, and benefits of combining statistical and machine learning approaches are outlined

An overview of machine learning in health related areas: pitfalls and opportunities / Uma visão geral do aprendizado de máquina em áreas relacionadas à saúde: armadilhas e oportunidades

Machine learning techniques are on the spotlight in current scientific literature and these methods are gaining prominence in the health field. However, there are a few considerations that must be taken before conducting a study with machine learning techniques. This paper aims to provide an overview of machine learning methods applied to studies of health related areas. Additionally, this article will discuss important points about data preparation that may influence on the prediction outcome; comparison with statistical analysis; and potential applications. A literature search was carried out, using IEEE xplore and Pubmed, of publications from the last 10 years. Undoubtedly machine learning is becoming more and more present in science. However, the unfamiliarity with this technology may hinder or jeopardize its application. As any scientific tool, machine learning presents positive points along with limitations and both aspects should be considered in every analysis. The researcher must select the most adequate method and consider all repercussions of data preparation on the predictive model. A special attention should be given towards distance based techniques. ML techniques are full with potential applications; however these methods did not replace classical statistical analysis and, yet, they will continue to be an important tool to in health areas

Review of Neural Network Algorithms

The artificial neural network is the core tool of machine learning to realize intelligence. It has shown its advantages in the fields of sound, image, sound, picture, and so on. Since entering the 21st century, the progress of science and technology and people\u27s pursuit of artificial intelligence have introduced the research of artificial neural networks into an upsurge. Firstly, this paper introduces the application background and development process of the artificial neural network in order to clarify the research context of neural networks. Five branches and related applications of single-layer perceptron, linear neural network, BP neural network, Hopfield neural network, and depth neural network are analyzed in detail. The analysis shows that the development trend of the artificial neural network is developing towards a more general, flexible, and intelligent direction. Finally, the future development of the artificial neural network in training mode, learning mode, function expansion, and technology combination has prospected

Critical Media, Information, and Digital Literacy: Increasing Understanding of Machine Learning Through an Interdisciplinary Undergraduate Course

Widespread use of Artificial Intelligence in all areas of today’s society creates a unique problem: algorithms used in decision-making are generally not understandable to those without a background in data science. Thus, those who use out-of-the-box Machine Learning (ML) approaches in their work and those affected by these approaches are often not in a position to analyse their outcomes and applicability. Our paper describes and evaluates our undergraduate course at the University of Minnesota Morris, which fosters understanding of the main ideas behind ML. With Communication, Media & Rhetoric and Computer Science faculty expertise, students from a variety of majors, most with no prior background in data science or computing, reviewed the scope of applicability of algorithms and became aware of possible biases, ‘politics’ and pitfalls. After discussing articles on societal attitudes towards technology, explaining key concepts behind ML algorithms (training and dependence on data), and constructing a decision tree as an example of an algorithm, we attempted to develop guidelines for ‘best practices’ for use of algorithms. Students presented a ‘case analysis’ capstone paper on an application of machine learning in society. Paper topics included: use of algorithms by child protection services, ‘deepfake’ videos, genetic testing. The level of papers was indicative of students’ strong interest in the subject and their ability to understand key terms and ideas behind algorithms, societal perception and misconceptions of use of algorithms, and their ability to identify good and problematic practices in use of algorithms

Integrating Machine Learning and Multiscale Modeling: Perspectives, Challenges, and Opportunities in the Biological, Biomedical, and Behavioral Sciences

Fueled by breakthrough technology developments, the biological, biomedical, and behavioral sciences are now collecting more data than ever before. There is a critical need for time- and cost-efficient strategies to analyze and interpret these data to advance human health. The recent rise of machine learning as a powerful technique to integrate multimodality, multifidelity data, and reveal correlations between intertwined phenomena presents a special opportunity in this regard. However, classical machine learning techniques often ignore the fundamental laws of physics and result in ill-posed problems or non-physical solutions. Multiscale modeling is a successful strategy to integrate multiscale, multiphysics data and uncover mechanisms that explain the emergence of function. However, multiscale modeling alone often fails to efficiently combine large data sets from different sources and different levels of resolution. We show how machine learning and multiscale modeling can complement each other to create robust predictive models that integrate the underlying physics to manage ill-posed problems and explore massive design spaces. We critically review the current literature, highlight applications and opportunities, address open questions, and discuss potential challenges and limitations in four overarching topical areas: ordinary differential equations, partial differential equations, data-driven approaches, and theory-driven approaches. Towards these goals, we leverage expertise in applied mathematics, computer science, computational biology, biophysics, biomechanics, engineering mechanics, experimentation, and medicine. Our multidisciplinary perspective suggests that integrating machine learning and multiscale modeling can provide new insights into disease mechanisms, help identify new targets and treatment strategies, and inform decision making for the benefit of human health