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.

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.

scholarly journals An efficient classification of flower images with convolutional neural networks

2017 ◽  
Vol 7 (1.1) ◽  
pp. 384 ◽  
Author(s):  
M V.D. Prasad ◽  
B JwalaLakshmamma ◽  
A Hari Chandana ◽  
K Komali ◽  
M V.N. Manoja ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Recognition Rate ◽  
Image Data ◽  
Image Database

Machine learning is penetrating most of the classification and recognition tasks performed by a computer. This paper proposes the classification of flower images using a powerful artificial intelligence tool, convolutional neural networks (CNN). A flower image database with 9500 images is considered for the experimentation. The entire database is sub categorized into 4. The CNN training is initiated in five batches and the testing is carried out on all the for datasets. Different CNN architectures were designed and tested with our flower image data to obtain better accuracy in recognition. Various pooling schemes were implemented to improve the classification rates. We achieved 97.78% recognition rate compared to other classifier models reported on the same dataset.

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.

Using Artificial Intelligence Methods for Dental Image Analysis: State-of-the-Art Reviews

2020 ◽  
Vol 10 (11) ◽  
pp. 2532-2542
Author(s):  
Junho Ahn ◽  
Thi Kieu Khanh Ho ◽  
Jaeyong Kang ◽  
Jeonghwan Gwak
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Image Analysis ◽  
Deep Learning ◽  
Cone Beam Ct ◽  
Dual Energy ◽  
Cone Beam ◽  
Learning Approaches ◽  
Conventional Machine ◽  
Aided Diagnosis

A large number of studies that use artificial intelligence (AI) methodologies to analyze medical imaging and support computer-aided diagnosis have been conducted in the biomedical engineering domain. Owing to the advances in dental diagnostic X-ray systems such as panoramic radiographs, periapical radiographs, and dental computed tomography (CT), especially, dual-energy cone beam CT (CBCT), dental image analysis now presents more opportunities to discover new results and findings. Recent researches on dental image analysis have been increasingly incorporating analytics that utilize AI methodologies that can be divided into conventional machine learning and deep learning approaches. This review first covers the theory on dual-energy CBCT and its applications in dentistry. Then, analytical methods for dental image analysis using conventional machine learning and deep learning methods are described. We conclude by discussing the issues and suggesting directions for research in future.

Current Trends in Deep Learning Frameworks With Opportunities and Future Prospectus

2020 ◽  
pp. 63-77
Author(s):  
Chitra A. Dhawale ◽  
Krtika Dhawale
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Real Time ◽  
End Users ◽  
The Other ◽  
Qualitative Review ◽  
Current Trends ◽  
Real Time Applications ◽  
Learning Frameworks

Artificial Intelligence (AI) is going through its golden era by playing an important role in various real-time applications. Most AI applications are using Machine learning and it represents the most promising path to strong AI. On the other hand, Deep Learning (DL), which is itself a kind of Machine Learning (ML), is becoming more and more popular and successful at different use cases, and is at the peak of developments. Hence, DL is becoming a leader in this domain. To foster the growth of the DL community to a greater extent, many open source frameworks are available which implemented DL algorithms. Each framework is based on an algorithm with specific applications. This chapter provides a brief qualitative review of the most popular and comprehensive DL frameworks, and informs end users of trends in DL Frameworks. This helps them make an informed decision to choose the best DL framework that suits their needs, resources, and applications so they choose a proper career.

scholarly journals Machine and Deep Learning Approaches for Flooding Prevention in Distillation and Extraction Columns

2020 ◽  
Author(s):  
Jonas Oeing ◽  
Laura Neuendorf ◽  
Lukas Bittorf ◽  
Waldemar Krieger ◽  
Norbert Kockmann
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Image Data ◽  
High Potential ◽  
Process Industry ◽  
Sensor Data ◽  
Learning Approaches ◽  
Separation Columns ◽  
Module Type

Machine Learning (ML) algorithms can be combined with the modular automation protocol (MTP) and recognize the flooding behavior of laboratory fluids separation columns. Hence, artificial intelligence (AI) tools with deep learning (DL) offer a high potential for the process industry and allow to capture operating states that are otherwise difficult to detect or model. However, the advanced methods are only hesitantly applied in practice. This article provides an overview on how artificial intelligence-based algorithms can be implemented in existing laboratory plants. Process sensor data as well as image data are used to model the flooding behavior of distillation and extraction columns and the system is adapted to the existing modular automation standard of the Module Type Package (MTP).

Becoming a Digital Dictatorship

2020 ◽  
pp. 163-178
Author(s):  
Jennifer Pan
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Digital Technologies ◽  
Classification Systems ◽  
Political Order ◽  
Chinese Government ◽  
Computing Power ◽  
The Government ◽  
Available Information

The conclusion considers how China’s pursuit of political order through preemptive control changes in a digital context of rapidly growing data, computing power, and advances in machine learning (e.g., deep learning, artificial intelligence / “AI”). Digital advances help the Chinese government collect more information about the entire population, and to do so in ways that are less detectable. However, new digital technologies do not alter China’s goal of preemptive control or the predictive surveillance that underpins this goal. Digital technologies will likely enable the government to identify more potential threats, but because digital technologies will not eliminate error altogether and because there is always a tradeoff between precision and recall in machine classification systems, the dramatic expansion of available information may expand the number of people trapped in programs of preemptive control.

IR 4.0. IN PARLIAMENT: CONCEPTUALISING THE APPLICATION OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING IN THE PARLIAMENT OF MALAYSIA’S PARLIAMENTARY QUESTIONS

2020 ◽  
Vol 5 (20) ◽  
pp. 124-137
Author(s):  
Muthanna Saari
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Industrial Revolution ◽  
Emerging Technologies ◽  
Participant Observation ◽  
Research Data ◽  
Human Values ◽  
Structured Interviews ◽  
Fourth Industrial Revolution ◽  
The Government

The Fourth Industrial Revolution (IR 4.0.) offers significant opportunities to humankind in revitalising human values through which the emerging technologies inevitably seam into daily societal life. Legislatures face ever-increasing challenges in fulfilling their duties in such a complicated society which subsequently entails complex legislations. Parliamentary questions (PQs) as one of the traditional tools utilised by parliamentarians provide a quintessential mechanism to achieve the oversight functions of parliament. However, there are still immense undiscovered potentials of PQs, yet many previous studies have not looked into the content of the questions and the consequences of the response to the conduct of governments. This paper set out to examine the usefulness of IR 4.0. namely, artificial intelligence (AI) and machine learning towards improving the efficiency, transparency, and accountability of parliament and the government. The research data of this exploratory and interpretative study is drawn from three main sources: literature studies, semi-structured interviews, and participant observation of the existing PQs processing in the Dewan Rakyat, Parliament of Malaysia. This study has found that generally, the approval of such technologies introduction to the parliamentary businesses is contingent upon its ability to capture complex considerations in the existing environment.

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