scholarly journals Impact of the worldwide trends on the development of the digital economy

2020 ◽  
Vol 9 (32) ◽  
pp. 81-90
Author(s):  
Valentyna H. Voronkova ◽  
Vitalina A. Nikitenko ◽  
Tatyana V. Teslenko ◽  
Vlada E. Bilohur
Keyword(s):  
Digital Economy ◽  
Technological Breakthrough ◽  
Moore’S Law ◽  
Moore's Law ◽  
Global Trends ◽  
New Model ◽  
Analysis Methodology ◽  
New Information ◽  
Scientific Technical ◽  
Economics Analysis

The aim of the article is to develop a new model of the digital economy as a new scientific direction of the philosophy of economics. Analysis methodology is the use of methods such as cross-cultural, systemic, synergetic, informational, axiological, cybernetic to develop a new model of the digital economy as a new scientific direction, in the context of which a new information space is being formed. The problems of solving the digital economy are taking place against the backdrop of new trends - globalization 4.0, Industry 4.0, Enlightenment 2.0, Agile management, in the context of which there is a transition from simple interconnection to hyperconnection and the spread of Moore's law, according to which there is a doubling of information. The results of the study. 1) The development of the digital economy as a new scientific field, which is based on a combination of concepts of informatization, digitalization, robotics, developing under the influence of global trends is studied. 2) It has been established that the digital economy contributes to technological progress and, under the pressure of global trends, develops a variety of economic models of scientific, technical and digital progress, which are based on the solution of problems of man, science, society. 3) The problematic issues of the digital economy and the conditions for its solution are identified. Conclusions. The prospects that the digital economy opens up thanks to modern technologies representing a technological breakthrough are analyzed. The digital technology network is designed so that it moves with the least loss and the smallest pieces of calculus are at the heart of this new constant flow system. All this indicates that in the context of globalization and the BIG DATA era, humanity is entering a new stage of calculus, when information doubles in accordance with Moore's law.

scholarly journals Promoting sustainable development of poverty-alleviation policies based on high-quality cultural tourism by digital economy — A case study of Chishui City in Guizhou Province

2021 ◽  
Vol 251 ◽  
pp. 02015
Author(s):  
Fawen Yang ◽  
Cheng Yang ◽  
Qian Xie
Keyword(s):  
Poverty Alleviation ◽  
Development Trend ◽  
Cultural Tourism ◽  
Digital Economy ◽  
Guizhou Province ◽  
Moore’S Law ◽  
Moore's Law ◽  
Study Case ◽  
The Development Trend

Digital economy follows three laws — the Metcalfe’s law. the Moore’s law. and the Davidow effect, which are practically in line with China’s poverty-alleviation initiative by developing cultural tourism. The Moore’s law, however, is paradoxical in given contexts, and thus, we proposed the “reverse-Moore’s law” to analyze the current cultural tourism-based poverty-alleviation policies. The features of digital economy can be employed to support the cultural tourism-based poverty-alleviation work: the development trend of digital economy also coincides with China’s cultural tourism-based poverty alleviation initiatives. With the poverty-alleviation work at Chishui City in Guizhou Province as the study case, this paper made an analysis from the perspective of digital economy to confirm the practical and surreal significance of applying digital economy to China’s poverty alleviation endeavors.

Chips with Everything

2017 ◽  
Author(s):  
David Segal
Keyword(s):  
Quantum Computing ◽  
Integrated Circuits ◽  
Critical Role ◽  
Moore’S Law ◽  
Moore's Law ◽  
Silicon Chips ◽  
History Of ◽  
Future Potential ◽  
Digital Computers ◽  
Dna Computer

Chapter 3 highlights the critical role materials have in the development of digital computers. It traces developments from the cat’s whisker to valves through to relays and transistors. Accounts are given for transistors and the manufacture of integrated circuits (silicon chips) by use of photolithography. Future potential computing techniques, namely quantum computing and the DNA computer, are covered. The history of computability and Moore’s Law are discussed.

From Moore's Law to the Carbon Law

2020 ◽  
Author(s):  
Daniel Pargman ◽  
Aksel Biørn-Hansen ◽  
Elina Eriksson ◽  
Jarmo Laaksolahti ◽  
Markus Robèrt
Keyword(s):  
Moore’S Law ◽  
Moore's Law

scholarly journals QUBO formulations for training machine learning models

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Prasanna Date ◽  
Davis Arthur ◽  
Lauren Pusey-Nazzaro
Keyword(s):  
Machine Learning ◽  
Linear Regression ◽  
Large Scale ◽  
Support Vector ◽  
Quantum Computers ◽  
Np Hard ◽  
Learning Models ◽  
Moore’S Law ◽  
Moore's Law ◽  
Machine Learning Models

AbstractTraining machine learning models on classical computers is usually a time and compute intensive process. With Moore’s law nearing its inevitable end and an ever-increasing demand for large-scale data analysis using machine learning, we must leverage non-conventional computing paradigms like quantum computing to train machine learning models efficiently. Adiabatic quantum computers can approximately solve NP-hard problems, such as the quadratic unconstrained binary optimization (QUBO), faster than classical computers. Since many machine learning problems are also NP-hard, we believe adiabatic quantum computers might be instrumental in training machine learning models efficiently in the post Moore’s law era. In order to solve problems on adiabatic quantum computers, they must be formulated as QUBO problems, which is very challenging. In this paper, we formulate the training problems of three machine learning models—linear regression, support vector machine (SVM) and balanced k-means clustering—as QUBO problems, making them conducive to be trained on adiabatic quantum computers. We also analyze the computational complexities of our formulations and compare them to corresponding state-of-the-art classical approaches. We show that the time and space complexities of our formulations are better (in case of SVM and balanced k-means clustering) or equivalent (in case of linear regression) to their classical counterparts.

Extrapolating from Moore's law

2015 ◽  
Vol 59 (1) ◽  
pp. 33-35 ◽  
Author(s):  
Michael A. Cusumano ◽  
David B. Yoffie
Keyword(s):  
Moore’S Law ◽  
Moore's Law
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