scholarly journals Les laboratoires des entreprises vidéoludiques installées au Québec et les partenariats avec l’université

Loading ◽  
2021 ◽  
Vol 14 (23) ◽  
pp. 55-76
Author(s):  
Maude Bonenfant ◽  
Jonathan Bonneau
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Video Game ◽  
Social Dynamics ◽  
Future Perspectives ◽  
Research Partnerships ◽  
Research Theme ◽  
Large Production ◽  
The University ◽  
Gaming Communities

Given the large production of video games in Quebec, the province has been able to develop an exceptional context of research partnerships between video game companies and university laboratories, each of which has developed an expertise specific to their field. In this article, the following question will first be asked: what kind of research is carried out in companies? The objective is not to make a systematic survey of the various forms of research carried out within all companies located in Quebec, but rather to identify the main realities experienced in gaming companies in order to answer a second question: what kind of research is not carried out those companies? The answer will be used to illustrate possible partnerships with researchers interested in gaming practices and in gaming communities, a research theme that is not often addressed by companies. Among the university gaming laboratories in Montreal, the example of the laboratory of the Université du Québec à Montréal will be briefly presented in order to situate researches that explicitly aims to understand identification, communication and social dynamics of gaming communities. The article concludes with an exposition of some of the future perspectives of research in this field, mainly related to the development of artificial intelligence and machine learning.

Machine Learning as Grounded Theory: Human-Centered Interfaces for Social Network Research through Artificial Intelligence

2018 ◽  
Vol 62 (1) ◽  
pp. 1252-1256
Author(s):  
Lorenzo Barberis Canonico ◽  
Nathan J. McNeese ◽  
Chris Duncan
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Grounded Theory ◽  
Social Network ◽  
Social Dynamics ◽  
Wide Spectrum ◽  
Social Phenomena ◽  
Unique Challenge ◽  
Grounded Theory Research ◽  
Effective Models

Internet technologies have created unprecedented opportunities for people to come together and through their collective effort generate large amounts of data about human behavior. With the increased popularity of grounded theory, many researchers have sought to use ever-increasingly large datasets to analyze and draw patterns about social dynamics. However, the data is simply too big to enable a single human to derive effective models for many complex social phenomena. Computational methods offer a unique opportunity to analyze a wide spectrum of sociological events by leveraging the power of artificial intelligence. Within the human factors community, machine learning has emerged as the dominant AI-approach to deal with big data. However, along with its many benefits, machine learning has introduced a unique challenge: interpretability. The models of macro-social behavior generated by AI are so complex that rarely can they translated into human understanding. We propose a new method to conduct grounded theory research by leveraging the power of machine learning to analyze complex social phenomena through social network analysis while retaining interpretability as a core feature.

scholarly journals The Evolution of Artificial Intelligence and the Possibility of its Application in Cyber Games

2020 ◽  
Vol 9 (28) ◽  
pp. 123-129 ◽  
Author(s):  
D. Yu. Eliseeva ◽  
A. Yu. Fedosov ◽  
D. V. Agaltsova ◽  
O. L. Mnatsakanyan ◽  
K. K. Kuchmezov
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Video Game ◽  
Handwriting Recognition ◽  
Historical Context ◽  
Unmanned Vehicles ◽  
Active Process ◽  
Economic Profit ◽  
New Methods ◽  
Intelligence Development

Artificial intelligence, as a separate field of research, is currently experiencing a boom - new methods of machine learning and hardware are emerging and improving, and the results achieved change the life of society. Machine translation, handwriting recognition, speech recognition are changing our reality. The work of creating unmanned vehicles, voice assistants and other devices using these technologies is in an active process. The article examines the historical context of the artificial intelligence development, it evaluates the possibilities of its introduction into cyber games, as a safe and effective platform for testing new methods of machine learning. The promotion of such projects can increase the reputation of development companies, ensure increased user confidence in other products and, with a competent marketing strategy, cause a significant public resonance among video game fans, providing the developer with economic profit.

scholarly journals Artificial Intelligence in Dermatopathology: New Insights and Perspectives

2021 ◽  
Vol 8 (3) ◽  
pp. 418-425
Author(s):  
Gerardo Cazzato ◽  
Anna Colagrande ◽  
Antonietta Cimmino ◽  
Francesca Arezzo ◽  
Vera Loizzi ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Differential Diagnosis ◽  
State Of The Art ◽  
The State ◽  
Embryonic Stage ◽  
Future Perspectives ◽  
Review Of The Literature ◽  
Melanocytic Lesions ◽  
Modern Era

In recent years, an increasing enthusiasm has been observed towards artificial intelligence and machine learning, involving different areas of medicine. Among these, although still in the embryonic stage, the dermatopathological field has also been partially involved, with the attempt to develop and train algorithms that could assist the pathologist in the differential diagnosis of complex melanocytic lesions. In this article, we face this new challenge of the modern era, carry out a review of the literature regarding the state of the art and try to determine promising future perspectives.

5th European conference on principles of knowledge discovery in databases

2002 ◽  
Vol 17 (2) ◽  
pp. 197-203
Author(s):  
FRANS COENEN
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Knowledge Discovery ◽  
Knowledge Discovery In Databases ◽  
Surrounding Area ◽  
European Conference ◽  
Form Part ◽  
Lecture Notes ◽  
University Buildings ◽  
The University

1 OpeningPKDD 2001, the 5th European Conference on Principles of Knowledge Discovery in Databases (PKDD), was held in Freiburg, Baden-Württemberg, Germany, this year (Monday 3 to Thursday 7 September), and co-located with the 12th European Conference on Machine Learning (ECML 2001). The proceedings comprised two volumes, one for PKDD (De Raedt & Siebes, 2001) and one for ECML (De Raedt & Flach, 2001); and form part of the Springer Lecture Notes on Artificial Intelligence (LNAI) series. The conference was held in the University buildings in the centre of the old town. Freiburg and the surrounding area were for many years part of the Austro-Hungarian empire and thus the university was described to us as being one of the oldest Austrian Universities.

Nature: A Tool Resulting from the Union of Artificial Intelligence and Natural Language Processing for Searching Research Projects in Colombia

2021 ◽  
Vol 12 (04) ◽  
pp. 01-21
Author(s):  
Felipe Cujar-Rosero ◽  
David Santiago Pinchao Ortiz ◽  
Silvio Ricardo Timarán Pereira ◽  
Jimmy Mateo Guerrero Restrepo
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Virtual Environment ◽  
Language Processing ◽  
Machine Learning Algorithms ◽  
Research Projects ◽  
Machine Learning Model ◽  
The University

This paper presents the final results of the research project that aimed for the construction of a tool which is aided by Artificial Intelligence through an Ontology with a model trained with Machine Learning, and is aided by Natural Language Processing to support the semantic search of research projects of the Research System of the University of Nariño. For the construction of NATURE, as this tool is called, a methodology was used that includes the following stages: appropriation of knowledge, installation and configuration of tools, libraries and technologies, collection, extraction and preparation of research projects, design and development of the tool. The main results of the work were three: a) the complete construction of the Ontology with classes, object properties (predicates), data properties (attributes) and individuals (instances) in Protegé, SPARQL queries with Apache Jena Fuseki and the respective coding with Owlready2 using Jupyter Notebook with Python within the virtual environment of anaconda; b) the successful training of the model for which Machine Learning algorithms were used and specifically Natural Language Processing algorithms such as: SpaCy, NLTK, Word2vec and Doc2vec, this was also performed in Jupyter Notebook with Python within the virtual environment of anaconda and with Elasticsearch; and c) the creation of NATURE by managing and unifying the queries for the Ontology and for the Machine Learning model. The tests showed that NATURE was successful in all the searches that were performed as its results were satisfactory.

Machine Learning Applications in Nanomedicine and Nanotoxicology

2019 ◽  
Vol 4 (1) ◽  
pp. 1-7
Author(s):  
Gerardo M. Casañola-Martin ◽  
Hai Pham-The
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Cloud Computing ◽  
Machine Learning Algorithms ◽  
Learning Approaches ◽  
Future Perspectives ◽  
Computational Tools ◽  
Machine Learning Applications ◽  
Nanoparticles Characterization

The development of machine learning algorithms together with the availability of computational tools nowadays have given an increase in the application of artificial intelligence methodologies in different fields. However, the use of these machine learning approaches in nanomedicine remains still underexplored in certain areas, despite the development in hardware and software tools. In this review, the recent advances in the conjunction of machine learning with nanomedicine are shown. Examples dealing with biomedical properties of nanoparticles, characterization of nanomaterials, text mining, and image analysis are also presented. Finally, some future perspectives in the integration of nanomedicine with cloud computing, deep learning and other techniques are discussed.

scholarly journals Artificial intelligence in drug design: algorithms, applications, challenges and ethics

2021 ◽  
pp. FDD59
Author(s):  
Alya A Arabi
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Drug Discovery ◽  
Drug Design ◽  
Chemical Properties ◽  
Binding Affinities ◽  
Future Perspectives ◽  
Ethical Considerations ◽  
Brain Barrier Permeability ◽  
And Chemical Properties

The discovery paradigm of drugs is rapidly growing due to advances in machine learning (ML) and artificial intelligence (AI). This review covers myriad faces of AI and ML in drug design. There is a plethora of AI algorithms, the most common of which are summarized in this review. In addition, AI is fraught with challenges that are highlighted along with plausible solutions to them. Examples are provided to illustrate the use of AI and ML in drug discovery and in predicting drug properties such as binding affinities and interactions, solubility, toxicology, blood–brain barrier permeability and chemical properties. The review also includes examples depicting the implementation of AI and ML in tackling intractable diseases such as COVID-19, cancer and Alzheimer’s disease. Ethical considerations and future perspectives of AI are also covered in this review.

Machine Learning in Agriculture

2018 ◽  
Vol 8 (6) ◽  
pp. 26 ◽  
Author(s):  
Matthew N. O. Sadiku ◽  
Chandra M. M Kotteti ◽  
Sarhan M. Musa
Keyword(s):  
Climate Change ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Disease Diagnosis ◽  
Automated Detection ◽  
Disease Prediction ◽  
Paper Briefly ◽  
Agriculture Sector ◽  
Modern Agriculture ◽  
Crop Disease

Machine learning is an emerging field of artificial intelligence which can be applied to the agriculture sector. It refers to the automated detection of meaningful patterns in a given data.  Modern agriculture seeks ways to conserve water, use nutrients and energy more efficiently, and adapt to climate change.  Machine learning in agriculture allows for more accurate disease diagnosis and crop disease prediction. This paper briefly introduces what machine learning can do in the agriculture sector.

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