scholarly journals Roundtable Discussion III: The Development and Uses of Artificial Intelligence in Medicine: A Work in Progress

2020 ◽  
Vol 4 ◽  
pp. 247028971989870 ◽  
Author(s):  
Marianne J. Legato ◽  
Francoise Simon ◽  
James E. Young ◽  
Tatsuya Nomura ◽  
Ibis Sánchez-Serrano
Keyword(s):  
Artificial Intelligence ◽  
Arabic Numeral ◽  
Official Journal ◽  
Roundtable Discussion ◽  
Desktop Computer ◽  
Modern Computer ◽  
Quantum Leap ◽  
The Difference ◽  
Problem Solve ◽  
Classical Computer

Humans have devised machines to replace computation by individuals since ancient times: The abacus predated the written Hindu–Arabic numeral system by centuries. We owe a quantum leap in the development of machines to help problem solve to the British mathematician Charles Babbage who built what he called the Difference Engine in the mid-19th century. But the Turing formula created in 1936 is the foundation for the modern computer; it produced printed symbols on paper tape that listed a series of logical instructions. Three decades later, Olivetti manufactured the first mass-marketed desktop computer (1964), and by 1981, IBM had developed the first personal computer. Computing machines have become more and more powerful, culminating recently in Google’s claim that it had achieved quantum supremacy in developing a system that can complete a task in 200 seconds that it would take the most powerful type of classical computer available 10 000 years to achieve. In short, we are in a period of human history in which we are creating more and more powerful and complex machines potentially capable of duplicating human intelligence and indeed surpassing/expanding its power. We are solidly in the age of artificial intelligence (AI). Increasing interest in the development of AI and its application to human health at all levels makes a roundtable discussion by experts a valuable project for publication in our journal, Gender and the Genome, the official journal of the Foundation for Gender-Specific Medicine and the International Society of Gender Medicine.

scholarly journals On the Juridical Relevance of the Phenomenological Notion of Person in Max Scheler and Edith Stein

2021 ◽  
Author(s):  
Francesco Galofaro
Keyword(s):  
Artificial Intelligence ◽  
Edmund Husserl ◽  
Phenomenological Analysis ◽  
Machine Learning Algorithms ◽  
Decision Makers ◽  
Edith Stein ◽  
Max Scheler ◽  
Human Decision ◽  
The Difference ◽  
Social Units

AbstractThe paper presents a semiotic interpretation of the phenomenological debate on the notion of person, focusing in particular on Edmund Husserl, Max Scheler, and Edith Stein. The semiotic interpretation lets us identify the categories that orient the debate: collective/individual and subject/object. As we will see, the phenomenological analysis of the relation between person and social units such as the community, the association, and the mass shows similarities to contemporary socio-semiotic models. The difference between community, association, and mass provides an explanation for the establishment of legal systems. The notion of person we inherit from phenomenology can also be useful in facing juridical problems raised by the use of non-human decision-makers such as machine learning algorithms and artificial intelligence applications.

scholarly journals Deep learning-enabled medical computer vision

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Andre Esteva ◽  
Katherine Chou ◽  
Serena Yeung ◽  
Nikhil Naik ◽  
Ali Madani ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Computer Vision ◽  
Deep Learning ◽  
Medical Imaging ◽  
Real World ◽  
Recent Progress ◽  
Medical Applications ◽  
Modern Computer ◽  
Medical Computer

AbstractA decade of unprecedented progress in artificial intelligence (AI) has demonstrated the potential for many fields—including medicine—to benefit from the insights that AI techniques can extract from data. Here we survey recent progress in the development of modern computer vision techniques—powered by deep learning—for medical applications, focusing on medical imaging, medical video, and clinical deployment. We start by briefly summarizing a decade of progress in convolutional neural networks, including the vision tasks they enable, in the context of healthcare. Next, we discuss several example medical imaging applications that stand to benefit—including cardiology, pathology, dermatology, ophthalmology–and propose new avenues for continued work. We then expand into general medical video, highlighting ways in which clinical workflows can integrate computer vision to enhance care. Finally, we discuss the challenges and hurdles required for real-world clinical deployment of these technologies.

scholarly journals Study of Multimedia Delivery over Software Defined Networks

2016 ◽  
Vol 7 (4) ◽  
pp. 37 ◽  
Author(s):  
Jose Miguel Jimenez ◽  
Oscar Romero ◽  
Albert Rego ◽  
Avinash Dilendra ◽  
Jaime Lloret
Keyword(s):  
Artificial Intelligence ◽  
Personal Computer ◽  
Network Protocols ◽  
Performance Comparison ◽  
Distributed Artificial Intelligence ◽  
Software Defined Networks ◽  
Multimedia Delivery ◽  
Real Network ◽  
Multimedia Streams ◽  
The Difference

Software Defined Networks (SDN) have become a new way to make dynamic topologies. They have great potential in both the creation and development of new network protocols and the inclusion of distributed artificial intelligence in the network. There are few emulators, like Mininet, that allow emulating a SDN in a single personal computer, but there is lack of works showing its performance and how it performs compared with real cases. This paper shows a performance comparison between Mininet and a real network when multimedia streams are being delivered. We are going to compare them in terms of consumed bandwidth (throughput), delay and jitter. Our study shows that there are some important differences when these parameters are compared. We hope that this research will be the basis to show the difference with real deployments when Mininet is used.

scholarly journals Prediksi Pemakaian Listrik Kelompok Tarif Menggunakan Jaringan Syaraf Tiruan dan ARIMA

2013 ◽  
Vol 7 (2) ◽  
pp. 189
Author(s):  
Silviani E Rumagit ◽  
Azhari SN
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Statistical Method ◽  
Moving Average ◽  
Electric Energy ◽  
Backpropagation Neural Network ◽  
The Difference ◽  
The Government ◽  
Increasing Demand

AbstrakLatar Belakang penelitian ini dibuat dimana semakin meningkatnya kebutuhan listrik di setiap kelompok tarif. Yang dimaksud dengan kelompok tarif dalam penelitian ini adalah kelompok tarif sosial, kelompok tarif rumah tangga, kelompok tarif bisnis, kelompok tarif industri dan kelompok tarif pemerintah. Prediksi merupakan kebutuhan penting bagi penyedia tenaga listrik dalam mengambil keputusan berkaitan dengan ketersediaan energi listik. Dalam melakukan prediksi dapat dilakukan dengan metode statistik maupun kecerdasan buatan.            ARIMA merupakan salah satu metode statistik yang banyak digunakan untuk prediksi dimana ARIMA mengikuti model autoregressive (AR) moving average (MA). Syarat dari ARIMA adalah data harus stasioner, data yang tidak stasioner harus distasionerkan dengan differencing. Selain metode statistik, prediksi juga dapat dilakukan dengan teknik kecerdasan buatan, dimana dalam penelitian ini jaringan syaraf tiruan backpropagation dipilih untuk melakukan prediksi. Dari hasil pengujian yang dilakukan selisih MSE ARIMA, JST dan penggabungan ARIMA, jaringan syaraf tiruan tidak berbeda secara signifikan. Kata Kunci— ARIMA, jaringan syaraf tiruan, kelompok tarif.  AbstractBackground this research was made where the increasing demand for electricity in each group. The meaning this group is social, the household, business, industry groups and the government fare. Prediction is an important requirement for electricity providers in making decisions related to the availability of electric energy. In doing predictions can be made by statistical methods and artificial intelligence.            ARIMA is a statistical method that is widely used to predict where the ARIMA modeled autoregressive (AR) moving average (MA). Terms of ARIMA is the data must be stationary, the data is not stationary should be stationary  use differencing. In addition to the statistical method, predictions can also be done by artificial intelligence techniques, which in this study selected Backpropagation neural network to predict. From the results of tests made the difference in MSE ARIMA, ANN and merging ARIMA, artificial neural networks are not significantly different. Keyword—ARIMA, neural network, tarif groups

scholarly journals AUTOMATED DETECTION SYSTEM FOR LUNG MASSES AS A STAGE OF ARTIFICIAL INTELLIGENCE DEVELOPMENT IN THE DIAGNOSTICS OF LUNG CANCER

2018 ◽  
pp. 62-68 ◽  
Author(s):  
B. M. Moiseenko ◽  
A. A. Meldo ◽  
L. V. Utkin ◽  
I. Yu. Prokhorov ◽  
M. A. Ryabinin ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Lung Cancer ◽  
Industrial Revolution ◽  
Detection System ◽  
Lung Nodule ◽  
Cancer Diagnostics ◽  
Digital Information ◽  
Intellectual Activity ◽  
Domestic Literature ◽  
Modern Computer

In the century of the fourth industrial revolution, there is a rapid progress of technological developments in medicine. Possibilities of collecting large amounts of digital information and the modern computer capacity growth are reasons for the increased attention to artificial intelligence (AI) and its role in the diagnostics and the prediction of diseases. In the diagnostics, AI aims to model the human intellectual activity, providing assistance to a practicing doctor in the processing of big data. Development of AI can be considered as a way for implementation and ensuring of national political and economic interests in the health care improvement. Lung cancer is on the first position of cancer incidences. This implies that the development and implementation of computed-aided systems for lung cancer diagnostic is very urgent and important. The article presents the results concerning the development of a computed-aided system for the lung nodule detection, which is based on the processing of computed tomography data. Perspectives of the AI application to the lung cancer diagnostics are discussed. There is a few information about a role of Russian developments in this area in foreign and domestic literature.

scholarly journals The next step for Artificial Intelligence in a globally connected economy

2021 ◽  
Vol 129 ◽  
pp. 04001
Author(s):  
Dumitru Alexandru Bodislav ◽  
Florina Bran ◽  
Carol Cristina Gombos ◽  
Amza Mair
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Human Factor ◽  
Value Added ◽  
Point Of View ◽  
Business Analytics ◽  
Economic Point ◽  
Technological Breakthrough ◽  
Big Picture ◽  
The Difference

Research background: This research paper represents an overview of what artificial intelligence is, what are its roots, and what is the next big thing regarding the domain. In this paper we try to highlight how the domain is growing and what is the difference between the ideology, the business factor and the human factor. We try to create a big picture on the entire phenomenon by creating a parallel between machine learning, artificial intelligence and the influence of technological breakthrough from a hardware perspective. Purpose of the article: The paper is built as a tool in understanding technology, globalization and the pathway to success and scientific glory for what can be seen as the industry of artificial intelligence. The tools presented in the research have the purpose to create an easier path to how we can develop this domain by accelerating theoretical processing and business analytics that come together to form the next level of machine learning/artificial intelligence; research and development, everything being filtered from an economic point of view. Methods: The used research method is based on fundamental analysis of the artificial intelligence domain and its purpose in the complexity of globalization and economic development. Findings & Value added: The paper tries to offer a tool for building a better understanding of the next decade in the domain of artificial intelligence.

Rethinking Software Protection

2021 ◽  
pp. 341-362
Author(s):  
Peter R Slowinski
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Evolutionary Algorithms ◽  
Future Development ◽  
Computer Programs ◽  
Software Protection ◽  
Computing Power ◽  
The Core ◽  
Quantum Leap ◽  
Core Part

The core of artificial intelligence (AI) applications is software of one sort or another. But while available data and computing power are important for the recent quantum leap in AI, there would not be any AI without computer programs or software. Therefore, the rise in importance of AI forces us to take—once again—a closer look at software protection through intellectual property (IP) rights, but it also offers us a chance to rethink this protection, and while perhaps not undoing the mistakes of the past, at least to adapt the protection so as not to increase the dysfunctionality that we have come to see in this area of law in recent decades. To be able to establish the best possible way to protect—or not to protect—the software in AI applications, this chapter starts with a short technical description of what AI is, with readers referred to other chapters in this book for a deeper analysis. It continues by identifying those parts of AI applications that constitute software to which legal software protection regimes may be applicable, before outlining those protection regimes, namely copyright and patents. The core part of the chapter analyses potential issues regarding software protection with respect to AI using specific examples from the fields of evolutionary algorithms and of machine learning. Finally, the chapter draws some conclusions regarding the future development of IP regimes with respect to AI.

6. Heuristic computing

2016 ◽  
pp. 104-118
Author(s):  
Subrata Dasgupta
Keyword(s):  
Artificial Intelligence ◽  
Common Sense ◽  
Heuristic Search ◽  
Heuristic Algorithms ◽  
Heuristic Rules ◽  
Sense Experience ◽  
Everyday Activities ◽  
The Difference

Many ordinary problems and everyday activities are not conducive to algorithmic solutions. Yet, people do perform these tasks and solve such problems, so what other computational means are available to perform such tasks? The answer is to resort to a mode of computing that deploys heuristics—rules, precepts, principles, hypotheses based on common sense, experience, judgement, analogies, informed guesses, etc., which offer promise but are not guaranteed to solve problems. Heuristic computing encompasses both heuristic search and heuristic algorithms. ‘Heuristic computing’ explains a meta-heuristic called ‘satisficing’; the difference between exact and heuristic algorithms; how heuristics is used in artificial intelligence; weak and strong methods; and how to interpret heuristic rules.

Reform and innovation of artificial intelligence technology for information service in university physical education

2020 ◽  
pp. 1-11
Author(s):  
Jianye Zhang
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Physical Education ◽  
Information Service ◽  
Management Decision ◽  
Negative Effects ◽  
Education Management ◽  
Depth Analysis ◽  
Artificial Intelligence Technology ◽  
The Difference

This article analyzes the reform of information services in university physical education based on artificial intelligence technology and conducts in-depth and innovative research on it. In-depth analysis of the relationship between big data and the development and application of information technology such as the Internet, Internet of Things, cloud computing, to clarify the difference and connection between big data, informatization and intelligence. Artificial intelligence will bring opportunities for changes in data collection, management decision-making, governance models, education and teaching, scientific research services, evaluation and evaluation of physical education in our university. At the same time, big data education management in colleges and universities faces many challenges such as the balance of privacy and freedom, data hegemony, data junk, data standards, and data security, and they have many negative effects. In accordance with the requirements of educational modernization, centering on the goal of intelligent and humanized education management, it aims existing issues in college physical education management.

scholarly journals Real-Time Guitar Amplifier Emulation with Deep Learning

2020 ◽  
Vol 10 (3) ◽  
pp. 766 ◽  
Author(s):  
Alec Wright ◽  
Eero-Pekka Damskägg ◽  
Lauri Juvela ◽  
Vesa Välimäki
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Real Time ◽  
Reference Signal ◽  
Network Models ◽  
Neural Network Models ◽  
Desktop Computer ◽  
The Neural Network ◽  
Highly Nonlinear ◽  
The Difference

This article investigates the use of deep neural networks for black-box modelling of audio distortion circuits, such as guitar amplifiers and distortion pedals. Both a feedforward network, based on the WaveNet model, and a recurrent neural network model are compared. To determine a suitable hyperparameter configuration for the WaveNet, models of three popular audio distortion pedals were created: the Ibanez Tube Screamer, the Boss DS-1, and the Electro-Harmonix Big Muff Pi. It is also shown that three minutes of audio data is sufficient for training the neural network models. Real-time implementations of the neural networks were used to measure their computational load. To further validate the results, models of two valve amplifiers, the Blackstar HT-5 Metal and the Mesa Boogie 5:50 Plus, were created, and subjective tests were conducted. The listening test results show that the models of the first amplifier could be identified as different from the reference, but the sound quality of the best models was judged to be excellent. In the case of the second guitar amplifier, many listeners were unable to hear the difference between the reference signal and the signals produced with the two largest neural network models. This study demonstrates that the neural network models can convincingly emulate highly nonlinear audio distortion circuits, whilst running in real-time, with some models requiring only a relatively small amount of processing power to run on a modern desktop computer.

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