scholarly journals Rutas de acceso al léxico en un entorno lexicográfico

2021 ◽  
Vol 16 (1) ◽  
pp. 63
Author(s):  
Ricardo Mairal-Usón ◽  
Pamela Faber
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Text Mining ◽  
Lexical Entry ◽  
Base Word ◽  
Lexical Resources ◽  
Lexical Knowledge ◽  
Digital Era ◽  
Conceptual Organization ◽  
Vast Range

This paper examines a set of lexicographic projects with innovative routes of access to lexical knowledge, and which constitute a first step towards a more intelligent dictionary, These projects include: (1) collocation dictionaries that specify the relations between a base word and its collocate; (2) dictionaries that make explicit the semantic and lexical restrictions between a predicate and its arguments; (3) lexical resources that describe the linguistic properties of a lexical entry within the context of its frame or frames of activation; (4) dictionaries that provide a conceptual organization of the definiens, instead of the definiendum. Without a doubt, the Digital Era (artificial intelligence, data and text mining, and machine learning) has opened up a vast range of possibilities, which will lead to intelligent lexicographic resources that are more intelligent and interconnected. This chapter concludes with some ideas and proposals about the characteristics of a dictionary 5.0 of the future.

scholarly journals Social aspects of Artificial Intelligence – selected issues

2021 ◽  
pp. 279-294
Author(s):  
Marcin Kowalczyk
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Free Will ◽  
Biometric Identification ◽  
Surveillance Systems ◽  
Web Browsers ◽  
Human Beings ◽  
Digital Era ◽  
Dating Apps ◽  
On Line

The paper presents findings regarding AI and Machine Learning and how “thinking machines” differ from human beings? In the next part the paper presents the issue of AI and Machine Learning’s impact on day-to-day activities in the following areas: 1. Microtargetting and psychometrics – with the examples from the business and politics; 2. Surveillance systems, biometric identification, COVID 19 tracing apps etc. – the issue of privacy in the digital era; 3. The question of choice optimization (AI-driven Web browsers and dating apps, chatbots and virtual assistants etc.); whether free will still exist in the AI supported on-line environment? The article is summed up with conclusions.

Convergence of AI, ML, and DL for Enabling Smart Intelligence

2021 ◽  
pp. 180-195
Author(s):  
Revathi Rajendran ◽  
Arthi Kalidasan ◽  
Chidhambara Rajan B.
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Smart Grids ◽  
Web Applications ◽  
Heterogeneous Data ◽  
Learning From Experience ◽  
Digital Era ◽  
Huge Data ◽  
Viable Solution

The evolution of digital era and improvements in technology have enabled the growth of a number of devices and web applications leading to the unprecedented generation of huge data on a day-to-day basis from many applications such as industrial automation, social networking cites, healthcare units, smart grids, etc. Artificial intelligence acts as a viable solution for the efficient collection and analyses of the heterogeneous data in large volumes with reduced human effort at low time. Machine learning and deep learning subspaces of artificial intelligence are used for the achievement of smart intelligence in machines to make them intelligent based on learning from experience automatically. Machine learning and deep learning have become two of the most trending, groundbreaking technologies that enable autonomous operations and provide decision making support for data processing systems. The chapter investigates the importance of machine learning and deep learning algorithms in instilling intelligence and providing an overview of machine learning, deep learning platforms.

scholarly journals Penyuluhan Machine Learning dan Quantum Artificial Intelligence di Era Industri 4.0

Servirisma ◽  
2021 ◽  
Vol 1 (1) ◽  
pp. 23-35
Author(s):  
Hendra Bunyamin ◽  
Teddy Marcus Zakaria ◽  
Andreas Widjaja ◽  
Natanael Halim ◽  
Vania Sarwoko
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Poor Performance ◽  
Work Force ◽  
Business Leaders ◽  
Doctoral Graduates ◽  
Digital Era ◽  
Job Openings ◽  
Youtube Videos ◽  
Catch Up

The Digital Era 4.0 has started since 2016 and two Southeast Asia countries such as Malaysia and Singapore have already adapted to the era; unfortunately, Indonesia has been struggling to adapt the era and, therefore, needs to catch up the digital competitiveness of its neighbouring countries. According to IMD World Digital Competitiveness 2020, Indonesia placed 56th of 63 countries in the digital competitiveness measurement. Despite its poor performance, Indonesia can catch up with other countries by starting from universities’ environment where Indonesia’s next generations study. Universities are prominent education institutions which prepare next generations for world digital competitiveness. According to BPS Indonesia, the unemployment of bachelor, master, and doctoral graduates reach a total number of 737.000, or 5,67% of 13 millions work force. One of the causes is the lack of technological knowledge, specifically, Artificial Intelligence (AI), from the graduates. Particularly, when they become business leaders, they are not fully prepared to create new job openings because mostly their mindsets are to find suitable jobs after study. The two webinars are results of collaboration between several universities which form NUNI (Jejaring Universitas Nusantara) whose purpose is to equip students with the knowledge of AI. Our method of counselling whose format is two webinars with both titles are Interpretable Machine Learning and Quantum Artificial Intelligence has gained appreciation in the form of average participation score which approaches excellent score (4,60 of 5,00). Additionally, these two webinars are publicly available in web blogs and Youtube videos.  

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.

Press “A” for Artificial Intelligence in Agriculture: A Review

2020 ◽  
Vol 4 (3) ◽  
Author(s):  
Yogesh Awasthi
Keyword(s):  
Artificial Intelligence ◽  
Image Analysis ◽  
Significant Role ◽  
Pattern Analysis ◽  
Cost Effective ◽  
Predictive Analysis ◽  
Accurate Information ◽  
Digital Era ◽  
Learning Techniques ◽  
Crucial Information

Agriculture is the backbone of the developing country. In old era agriculture was based on the experience which was shared by people to people but in this digital era technology play a very important and significant role in agriculture. Now agriculture become a business hub therefore farmers are focusing on precision farming. They introduced the technology in agriculture to define the accurate information about seed, soil, weather, disease and all factors which affecting the farming. Artificial Intelligence uses predictive analysis, image analysis, learning techniques and Pattern analysis to declare the best cost effective and maximum gain for the agriculturist. The aim of this paper is to provide the crucial information with the help of technology which a farmers can use to harvest the variety of crops as per the demand in world so that they can get maximum benefits.

Development of innovative hygienic technologies for preservation of the workers’ reproductive health

2020 ◽  
pp. 792-792
Author(s):  
M. A. Fesenko ◽  
G. V. Golovaneva ◽  
A. V. Miskevich
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Reproductive Health ◽  
Reproductive System ◽  
Preventive Measures ◽  
Learning Algorithms ◽  
Reproductive Function ◽  
Machine Learning Algorithms ◽  
New Model

The new model «Prognosis of men’ reproductive function disorders» was developed. The machine learning algorithms (artificial intelligence) was used for this purpose, the model has high prognosis accuracy. The aim of the model applying is prioritize diagnostic and preventive measures to minimize reproductive system diseases complications and preserve workers’ health and efficiency.

Sign in / Sign up

Export Citation Format

Share Document