scholarly journals Machine intelligence today: applications, methodology, and technology

2021 ◽  
Vol 44 (2) ◽  
pp. 104-114
Author(s):  
Bernhard G. Humm ◽  
Hermann Bense ◽  
Michael Fuchs ◽  
Benjamin Gernhardt ◽  
Matthias Hemmje ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Best Practices ◽  
Machine Intelligence ◽  
Strong Impact ◽  
Industrial Manufacturing ◽  
Knowledge Based ◽  
Current Trends ◽  
Culture Education

AbstractMachine intelligence, a.k.a. artificial intelligence (AI) is one of the most prominent and relevant technologies today. It is in everyday use in the form of AI applications and has a strong impact on society. This article presents selected results of the 2020 Dagstuhl workshop on applied machine intelligence. Selected AI applications in various domains, namely culture, education, and industrial manufacturing are presented. Current trends, best practices, and recommendations regarding AI methodology and technology are explained. The focus is on ontologies (knowledge-based AI) and machine learning.

scholarly journals Government by Algorithm: Artificial Intelligence in Federal Administrative Agencies, a Case of USA

2021 ◽  
Vol 5 (1) ◽  
pp. 1-15
Author(s):  
Rubina Shaheen ◽  
Mir Kasi
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Thematic Analysis ◽  
Image Data ◽  
Workplace Health ◽  
Current Trends ◽  
Safety And Health ◽  
Administrative Agencies ◽  
Conventional Machine ◽  
The Government

The report gives a presents use of artificial intelligence in few administrative agencies. In-depth thematic analysis of some institution, have been conducted to review the current trends. In thematic analysis, 12 institutions have been selected and described the details of the institutions using artificial intelligence in different departments. These analyses yielded five major findings. First, the government has a wide application of Artificial Intelligence toolkit traversing the federal administrative and state. Almost half of the federal agencies evaluated (45%) has used AI and associated machine learning (ML) tools. Also, AI tools are already enhancing agency strategies in  the full span of governance responsibilities, such as keeping regulatory assignments bordering on market efficiency, safety in workplace, health care, and protection of the environmental, protecting the privileges and benefits of the government ranging from intellectual properties to disability, accessing, verifying and analyzing all risks to public  safety and health, Extracting essential data from the data stream of government including complaints by consumer and the communicating with citizens on their rights, welfare, asylum seeking and business ownership. AI toolkit owned by government span the complete scope of Artificial Intelligence techniques, ranging from conventional machine learning to deep learning including natural language and image data. Irrespective of huge acceptance of AI, much still has to be done in this area by the government. Recommendations also discussed at the end.

Predictive Models in Urology

2013 ◽  
Vol 80 (1) ◽  
pp. 42-45 ◽  
Author(s):  
Andrea Cestari
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Predictive Modeling ◽  
Predictive Models ◽  
Machine Intelligence ◽  
Health Records ◽  
Knowledge Based ◽  
Current State ◽  
Computer Based ◽  
Future Direction

Predictive modeling is emerging as an important knowledge-based technology in healthcare. The interest in the use of predictive modeling reflects advances on different fronts such as the availability of health information from increasingly complex databases and electronic health records, a better understanding of causal or statistical predictors of health, disease processes and multifactorial models of ill-health and developments in nonlinear computer models using artificial intelligence or neural networks. These new computer-based forms of modeling are increasingly able to establish technical credibility in clinical contexts. The current state of knowledge is still quite young in understanding the likely future direction of how this so-called ‘machine intelligence’ will evolve and therefore how current relatively sophisticated predictive models will evolve in response to improvements in technology, which is advancing along a wide front. Predictive models in urology are gaining progressive popularity not only for academic and scientific purposes but also into the clinical practice with the introduction of several nomograms dealing with the main fields of onco-urology.

Artificial Intelligence in Medical Education: Best Practices Using Machine Learning to Assess Surgical Expertise in Virtual Reality Simulation

2019 ◽  
Vol 76 (6) ◽  
pp. 1681-1690 ◽  
Author(s):  
Alexander Winkler-Schwartz ◽  
Vincent Bissonnette ◽  
Nykan Mirchi ◽  
Nirros Ponnudurai ◽  
Recai Yilmaz ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Medical Education ◽  
Virtual Reality ◽  
Best Practices ◽  
Virtual Reality Simulation ◽  
Surgical Expertise

scholarly journals Deep learning, past present and future: An odyssey

2021 ◽  
Author(s):  
Anwaar Ulhaq
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Deep Neural Networks ◽  
Review Paper ◽  
Complex Problems ◽  
Current Trends

Machine learning has grown in popularity and effectiveness over the last decade. It has become possible to solve complex problems, especially in artificial intelligence, due to the effectiveness of deep neural networks. While numerous books and countless papers have been written on deep learning, new researchers want to understand the field's history, current trends and envision future possibilities. This review paper will summarise the recorded work that resulted in such success and address patterns and prospects.

Artificial Intelligence and Investing

2011 ◽  
pp. 237-240 ◽  
Author(s):  
Roy Rada
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Risk Management ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Asset Valuation ◽  
Stock Valuation ◽  
Knowledge Based ◽  
Processing Techniques

The techniques of artificial intelligence include knowledgebased, machine learning, and natural language processing techniques. The discipline of investing requires data identification, asset valuation, and risk management. Artificial intelligence techniques apply to many aspects of financial investing, and published work has shown an emphasis on the application of knowledge-based techniques for credit risk assessment and machine learning techniques for stock valuation. However, in the future, knowledge-based, machine learning, and natural language processing techniques will be integrated into systems that simultaneously address data identification, asset valuation, and risk management.

scholarly journals Best Practices for Artificial Intelligence in Life Sciences Research

2020 ◽  
Author(s):  
Vladimir Makarov ◽  
Terry Stouch ◽  
Brandon Allgood ◽  
Christopher Willis ◽  
Nick Lynch
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Best Practices ◽  
Life Sciences ◽  
Organizational Management ◽  
Biotechnology Research

We describe 11 best practices for the successful use of Artificial Intelligence and Machine Learning in the pharmaceutical and biotechnology research, on the data, technology, and organizational management levels.

scholarly journals Neuro-symbolic A.I. for the smart city

2021 ◽  
Vol 2042 (1) ◽  
pp. 012018
Author(s):  
Gilles Morel
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Smart City ◽  
Smart Building ◽  
Knowledge Based ◽  
Relevant Today ◽  
Cross Domain ◽  
Source Data ◽  
Symbolic Ai ◽  
Standard Software

Abstract Smart building and smart city specialists agree that complex, innovative use cases, especially those using cross-domain and multi-source data, need to make use of Artificial Intelligence (AI). However, today’s AI mainly concerns machine learning and artificial neural networks (deep learning), whereas the first forty years of the discipline (the last decades of the 20th century) were essentially focused on a knowledge-based approach, which is still relevant today for some tasks. In this article we advocate a merging of these two AI trends – an approach known as neuro-symbolic AI – for the smart city, and point the way towards a complete integration of the two technologies, compatible with standard software.

scholarly journals MACHINE INTELLIGENCE. ESSAYS ON THE THEORY OF MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE

2019 ◽  
Author(s):  
Сергей Шумский ◽  
Sergey Shumskiy
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Scientific Revolution ◽  
Machine Intelligence ◽  
General Intelligence ◽  
New Science ◽  
Artificial General Intelligence ◽  
Science Of Mind ◽  
Artificial Brains

This book is about the nature of mind, both human and artificial, from the standpoint of the theory of machine learning. It addresses the problem of creating artificial general intelligence. The author shows how one can use the basic mechanisms of our brain to create artificial brains of future robots. How will this ever-stronger artificial intelligence fit into our lives? What awaits us in the next 10-15 years? How can someone who wants to take part in a new scientific revolution, participate in developing a new science of mind?

scholarly journals Artificial intelligence approaches and mechanisms for big data analytics: a systematic study

2021 ◽  
Vol 7 ◽  
pp. e488
Author(s):  
Amir Masoud Rahmani ◽  
Elham Azhir ◽  
Saqib Ali ◽  
Mokhtar Mohammadi ◽  
Omed Hassan Ahmed ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Optimization Theory ◽  
Artificial Intelligence Techniques ◽  
Knowledge Based ◽  
Complete Survey ◽  
The Internet Of Things

Recent advances in sensor networks and the Internet of Things (IoT) technologies have led to the gathering of an enormous scale of data. The exploration of such huge quantities of data needs more efficient methods with high analysis accuracy. Artificial Intelligence (AI) techniques such as machine learning and evolutionary algorithms able to provide more precise, faster, and scalable outcomes in big data analytics. Despite this interest, as far as we are aware there is not any complete survey of various artificial intelligence techniques for big data analytics. The present survey aims to study the research done on big data analytics using artificial intelligence techniques. The authors select related research papers using the Systematic Literature Review (SLR) method. Four groups are considered to investigate these mechanisms which are machine learning, knowledge-based and reasoning methods, decision-making algorithms, and search methods and optimization theory. A number of articles are investigated within each category. Furthermore, this survey denotes the strengths and weaknesses of the selected AI-driven big data analytics techniques and discusses the related parameters, comparing them in terms of scalability, efficiency, precision, and privacy. Furthermore, a number of important areas are provided to enhance the big data analytics mechanisms in the future.

scholarly journals Best practices for authors of healthcare-related artificial intelligence manuscripts

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Sujay Kakarmath ◽  
Andre Esteva ◽  
Rima Arnaout ◽  
Hugh Harvey ◽  
Santosh Kumar ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Best Practices ◽  
Training Data ◽  
Health State ◽  
Fitness Level ◽  
The Past ◽  
Digital Medicine ◽  
Disproportionate Number ◽  
Limited Generalizability

Abstract Since its inception in 2017, npj Digital Medicine has attracted a disproportionate number of manuscripts reporting on uses of artificial intelligence. This field has matured rapidly in the past several years. There was initial fascination with the algorithms themselves (machine learning, deep learning, convoluted neural networks) and the use of these algorithms to make predictions that often surpassed prevailing benchmarks. As the discipline has matured, individuals have called attention to aberrancies in the output of these algorithms. In particular, criticisms have been widely circulated that algorithmically developed models may have limited generalizability due to overfitting to the training data and may systematically perpetuate various forms of biases inherent in the training data, including race, gender, age, and health state or fitness level (Challen et al. BMJ Qual. Saf. 28:231–237, 2019; O’neil. Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy, Broadway Book, 2016). Given our interest in publishing the highest quality papers and the growing volume of submissions using AI algorithms, we offer a list of criteria that authors should consider before submitting papers to npj Digital Medicine.

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