The Concept of Integrating Artificial Intelligence Technologies Into Human Resources in a Digital Paradigm

2020 ◽  
Vol 9 (2) ◽  
pp. 5-9
Author(s):  
Oksana Chulanova
Keyword(s):  
Artificial Intelligence ◽  
Computer Vision ◽  
Natural Language Processing ◽  
Decision Support ◽  
Speech Recognition ◽  
Human Resources ◽  
Natural Language ◽  
Language Processing

The article discusses the capabilities of artificial intelligence technologies - technologies based on the use of artificial intelligence, including natural language processing, intellectual decision support, computer vision, speech recognition and synthesis, and promising methods of artificial intelligence. The results of the author's study and the analysis of artificial intelligence technologies and their capabilities for optimizing work with staff are presented. A study conducted by the author allowed us to develop an author's concept of integrating artificial intelligence technologies into work with personnel in the digital paradigm.

scholarly journals An Overview of Image Caption Generation Methods

2020 ◽  
Vol 2020 ◽  
pp. 1-13 ◽  
Author(s):  
Haoran Wang ◽  
Yue Zhang ◽  
Xiaosheng Yu
Keyword(s):  
Artificial Intelligence ◽  
Computer Vision ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Rapid Development ◽  
Evaluation Criteria ◽  
Arduous Task ◽  
Image Caption Generation ◽  
Image Caption

In recent years, with the rapid development of artificial intelligence, image caption has gradually attracted the attention of many researchers in the field of artificial intelligence and has become an interesting and arduous task. Image caption, automatically generating natural language descriptions according to the content observed in an image, is an important part of scene understanding, which combines the knowledge of computer vision and natural language processing. The application of image caption is extensive and significant, for example, the realization of human-computer interaction. This paper summarizes the related methods and focuses on the attention mechanism, which plays an important role in computer vision and is recently widely used in image caption generation tasks. Furthermore, the advantages and the shortcomings of these methods are discussed, providing the commonly used datasets and evaluation criteria in this field. Finally, this paper highlights some open challenges in the image caption task.

scholarly journals Review of Artificial Intelligence Adversarial Attack and Defense Technologies

2019 ◽  
Vol 9 (5) ◽  
pp. 909 ◽  
Author(s):  
Shilin Qiu ◽  
Qihe Liu ◽  
Shijie Zhou ◽  
Chunjiang Wu
Keyword(s):  
Artificial Intelligence ◽  
Computer Vision ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Physical World ◽  
Research Progress ◽  
Testing Stage ◽  
Adversarial Attack ◽  
Attack And Defense

In recent years, artificial intelligence technologies have been widely used in computer vision, natural language processing, automatic driving, and other fields. However, artificial intelligence systems are vulnerable to adversarial attacks, which limit the applications of artificial intelligence (AI) technologies in key security fields. Therefore, improving the robustness of AI systems against adversarial attacks has played an increasingly important role in the further development of AI. This paper aims to comprehensively summarize the latest research progress on adversarial attack and defense technologies in deep learning. According to the target model’s different stages where the adversarial attack occurred, this paper expounds the adversarial attack methods in the training stage and testing stage respectively. Then, we sort out the applications of adversarial attack technologies in computer vision, natural language processing, cyberspace security, and the physical world. Finally, we describe the existing adversarial defense methods respectively in three main categories, i.e., modifying data, modifying models and using auxiliary tools.

scholarly journals Evolving Artificial Perception by Integrating Nlp and Cv

2019 ◽  
Vol 8 (4) ◽  
pp. 3656-3659
Keyword(s):  
Artificial Intelligence ◽  
Computer Vision ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Computer Systems ◽  
External Environment ◽  
Two Dimensions ◽  
The Past

This paper discusses the concept of integrating artificial perception of an artificial intelligence by integrating NLP and CV, this should be able to solve 50% of problems where the data is usually in a raw format and not understandable by the machine. This method helps in the automatic labelling and understanding the data so it is easier for the machine to understand and help in our day to day tasks. “Perception is the ability to become aware of something which is internal or in the external environment through the use of the 5 senses” this is a natural capability of humans but has never properly been achieved in a machine. In the past five years massive strides have taken place in both natural language processing and computer vision but none of these advancements have increased the intelligence and perception of computer systems in the dramatic way that was expected. This difference in what was expected and what has finally been delivered is due to the fact that both these fields have evolved separately whereas perception requires these two dimensions of hearing (Natural Language Processing) and vision (Computer Vision) to be integrated.

scholarly journals Artificial Intelligence Surgery: How Do We Get to Autonomous Actions in Surgery?

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5526
Author(s):  
Andrew A. Gumbs ◽  
Isabella Frigerio ◽  
Gaya Spolverato ◽  
Roland Croner ◽  
Alfredo Illanes ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Computer Vision ◽  
Interventional Radiology ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Paradigm Shift ◽  
Timely Manner

Most surgeons are skeptical as to the feasibility of autonomous actions in surgery. Interestingly, many examples of autonomous actions already exist and have been around for years. Since the beginning of this millennium, the field of artificial intelligence (AI) has grown exponentially with the development of machine learning (ML), deep learning (DL), computer vision (CV) and natural language processing (NLP). All of these facets of AI will be fundamental to the development of more autonomous actions in surgery, unfortunately, only a limited number of surgeons have or seek expertise in this rapidly evolving field. As opposed to AI in medicine, AI surgery (AIS) involves autonomous movements. Fortuitously, as the field of robotics in surgery has improved, more surgeons are becoming interested in technology and the potential of autonomous actions in procedures such as interventional radiology, endoscopy and surgery. The lack of haptics, or the sensation of touch, has hindered the wider adoption of robotics by many surgeons; however, now that the true potential of robotics can be comprehended, the embracing of AI by the surgical community is more important than ever before. Although current complete surgical systems are mainly only examples of tele-manipulation, for surgeons to get to more autonomously functioning robots, haptics is perhaps not the most important aspect. If the goal is for robots to ultimately become more and more independent, perhaps research should not focus on the concept of haptics as it is perceived by humans, and the focus should be on haptics as it is perceived by robots/computers. This article will discuss aspects of ML, DL, CV and NLP as they pertain to the modern practice of surgery, with a focus on current AI issues and advances that will enable us to get to more autonomous actions in surgery. Ultimately, there may be a paradigm shift that needs to occur in the surgical community as more surgeons with expertise in AI may be needed to fully unlock the potential of AIS in a safe, efficacious and timely manner.

scholarly journals Does higher education properly prepare graduates for the growing artificial intelligence market? Gaps identification using text mining

2021 ◽  
pp. 1-13
Author(s):  
Lamiae Benhayoun ◽  
Daniel Lang
Keyword(s):  
Artificial Intelligence ◽  
Natural Language Processing ◽  
Text Mining ◽  
Natural Language ◽  
Language Processing ◽  
Academic Training ◽  
Market Requirements ◽  
Job Advertisements ◽  
The Individual

BACKGROUND: The renewed advent of Artificial Intelligence (AI) is inducing profound changes in the classic categories of technology professions and is creating the need for new specific skills. OBJECTIVE: Identify the gaps in terms of skills between academic training on AI in French engineering and Business Schools, and the requirements of the labour market. METHOD: Extraction of AI training contents from the schools’ websites and scraping of a job advertisements’ website. Then, analysis based on a text mining approach with a Python code for Natural Language Processing. RESULTS: Categorization of occupations related to AI. Characterization of three classes of skills for the AI market: Technical, Soft and Interdisciplinary. Skills’ gaps concern some professional certifications and the mastery of specific tools, research abilities, and awareness of ethical and regulatory dimensions of AI. CONCLUSIONS: A deep analysis using algorithms for Natural Language Processing. Results that provide a better understanding of the AI capability components at the individual and the organizational levels. A study that can help shape educational programs to respond to the AI market requirements.

A Hindi Image Caption Generation Framework Using Deep Learning

2021 ◽  
Vol 20 (2) ◽  
pp. 1-19
Author(s):  
Santosh Kumar Mishra ◽  
Rijul Dhir ◽  
Sriparna Saha ◽  
Pushpak Bhattacharyya
Keyword(s):  
Computer Vision ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
English Language ◽  
Image Captioning ◽  
Textual Description ◽  
Proposed Model ◽  
Hindi Language ◽  
The Given

Image captioning is the process of generating a textual description of an image that aims to describe the salient parts of the given image. It is an important problem, as it involves computer vision and natural language processing, where computer vision is used for understanding images, and natural language processing is used for language modeling. A lot of works have been done for image captioning for the English language. In this article, we have developed a model for image captioning in the Hindi language. Hindi is the official language of India, and it is the fourth most spoken language in the world, spoken in India and South Asia. To the best of our knowledge, this is the first attempt to generate image captions in the Hindi language. A dataset is manually created by translating well known MSCOCO dataset from English to Hindi. Finally, different types of attention-based architectures are developed for image captioning in the Hindi language. These attention mechanisms are new for the Hindi language, as those have never been used for the Hindi language. The obtained results of the proposed model are compared with several baselines in terms of BLEU scores, and the results show that our model performs better than others. Manual evaluation of the obtained captions in terms of adequacy and fluency also reveals the effectiveness of our proposed approach. Availability of resources : The codes of the article are available at https://github.com/santosh1821cs03/Image_Captioning_Hindi_Language ; The dataset will be made available: http://www.iitp.ac.in/∼ai-nlp-ml/resources.html .

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