Compositional Grounded Language for Agent Communication  in Reinforcement Learning Environment

In a context of constant evolution of technologies for scientific, economic and social purposes, Artificial Intelligence (AI) and Internet of Things (IoT) have seen significant progress over the past few years. As much as Human-Machine interactions are needed and tasks automation is undeniable, it is important that electronic devices (computers, cars, sensors…) could also communicate with humans just as well as they communicate together. The emergence of automated training and neural networks marked the beginning of a new conversational capability for the machines, illustrated with chat-bots. Nonetheless, using this technology is not sufficient, as they often give inappropriate or unrelated answers, usually when the subject changes. To improve this technology, the problem of defining a communication language constructed from scratch is addressed, in the intention to give machines the possibility to create a new and adapted exchange channel between them. Equipping each machine with a sound emitting system which accompany each individual or collective goal accomplishment, the convergence toward a common ‘’language’’ is analyzed, exactly as it is supposed to have happened for humans in the past. By constraining the language to satisfy the two main human language properties of being ground-based and of compositionality, rapidly converging evolution of syntactic communication is obtained, opening the way of a meaningful language between machines.

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Using intelligent technologies for tourism

Problems of Innovation and Investment Development ◽

10.33813/2224-1213.24.2021.9 ◽

2021 ◽

pp. 96-105

Author(s):

Nataliia Lytvyn ◽

Svitlana Panchenko

Keyword(s):

Neural Network ◽

Artificial Intelligence ◽

Neural Networks ◽

Knowledge Management ◽

Research Methodology ◽

Network Technology ◽

Inaccurate Information ◽

Tourism Product ◽

The Subject ◽

Neural Network Technology

The purpose of the article is to explore the essence and features of using intelligent technologies in tourism and to develop proposals for their implementation. The subject of research – intelligent technologies in tourism, the technology of forming the “profile” of the tourist. The research methodology consists in the application of methods of analysis, synthesis, comparison, generalization, forecasting, as well as in the use of systematic, activity approaches. The article presents the technology of forming the “profile” of the tourist. It is established that it is necessary to create a world of tourist models, the “profile” of the tourist, as it is a matter of formalizing such poorly structured concepts as “impressions”, “intentions”, etc., it is necessary to use artificial intelligence technologies, in particular neural networks. The scientific novelty is that this article proves the effectiveness of the use of intelligent technologies to create a model of the tourist, his “profile” using neural networks. Conclusions. Effective using of information from various sources in the field of tourism is an important and difficult task. Managers are often forced to make decisions based on partial, incomplete and inaccurate information. The article considers knowledge management in a rapidly changing environment for the task of promoting a tourism product. Neural network technology allows for the effective formation of the “tourist profile” and use all the information in available databases. Key words: tourism, intelligent technologies for tourism, neural networks, tourist profile, tourist product.

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Advances in Intelligent Information Technology: Re-Branding or Progress towards Conscious Machines?

Journal of Information Technology ◽

10.1057/palgrave.jit.2000001 ◽

2004 ◽

Vol 19 (1) ◽

pp. 21-27 ◽

Cited By ~ 1

Author(s):

Igor Aleksander

Keyword(s):

Artificial Intelligence ◽

Information Technology ◽

Human Brain ◽

Paradigm Shift ◽

Personal View ◽

Current Information ◽

The Past ◽

The Future ◽

The Subject ◽

Intelligent Information

Is artificial intelligence (AI) just something that is done in laboratories disconnected from the development of the pragmatic computing, which constitutes current information technology or does it contribute to progress in computing and information technology? It has even been suggested that advances in AI are merely a re-branding exercise for promises that are rarely kept. This paper is a personal view of the forces that have driven the development of AI in the past and what might be a serious paradigm shift in the future. The latter points to what appears to be the most abstruse corner of the subject: the modelling of the human brain and the possibility of designing systems with the brain's ability to create conscious thought. There have been accusations that AI is always ahead on promise and behind on delivery. This is an inaccurate view. In broad terms, the argument presented here suggests that as AI developed, progress was achieved by overcoming unforeseen difficulties in the pursuit of very ambitious targets, not just a re-branding of promises. This process not only advanced AI but also fed into the mainstream of computing that underpins the information technology of the present time. While the outcome of the paradigm shift towards conscious machines, which is examined at the end of this paper is still unclear, it is possible to speculate how information technology might be affected in the future.

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ANALYSIS OF THE BRAIN COMPUTER TOMOGRAPHY RESULTS USING THE CONVENTIONAL NEURAL NETWORK

SYNCHROINFO JOURNAL ◽

10.36724/2664-066x-2020-6-5-6-11 ◽

2020 ◽

Vol 6 (5) ◽

pp. 6-11

Author(s):

Ji Wang ◽

◽

V.I. Voronov ◽

Keyword(s):

Neural Network ◽

Artificial Intelligence ◽

Computed Tomography ◽

Neural Networks ◽

Optimal Algorithms ◽

Brain Computed Tomography ◽

The Subject ◽

Medical Examinations ◽

The Brain ◽

Research Analysis

Advances in technology are making health research increasingly complex. Artificial intelligence is widely used in this research. Convolutional neural networks are one of the most common and optimal algorithms for working with images. Image recognition results are used to analyze the results of medical examinations of patients. The subject of the research – analysis of the human brain computed tomography results using a convolutional neural network based on the Keras library.

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Chapter 7. Neuro-Symbolic AI = Neural + Logical + Probabilistic AI

10.3233/faia210354 ◽

2021 ◽

Author(s):

Robin Manhaeve ◽

Giuseppe Marra ◽

Thomas Demeester ◽

Sebastijan Dumančić ◽

Angelika Kimmig ◽

...

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Neural Networks ◽

Probability Theory ◽

Research Agenda ◽

Probabilistic Logic ◽

Future Directions ◽

The Past ◽

Symbolic Ai ◽

Broad Consensus

There is a broad consensus that both learning and reasoning are essential to achieve true artificial intelligence. This has put the quest for neural-symbolic artificial intelligence (NeSy) high on the research agenda. In the past decade, neural networks have caused great advances in the field of machine learning. Conversely, the two most prominent frameworks for reasoning are logic and probability. While in the past they were studied by separate communities, a significant number of researchers has been working towards their integration, cf. the area of statistical relational artificial intelligence (StarAI). Generally, NeSy systems integrate logic with neural networks. However, probability theory has already been integrated with both logic (cf. StarAI) and neural networks. It therefore makes sense to consider the integration of logic, neural networks and probabilities. In this chapter, we first consider these three base paradigms separately. Then, we look at the well established integrations, NeSy and StarAI. Next, we consider the integration of all three paradigms as Neural Probabilistic Logic Programming, and exemplify it with the DeepProbLog framework. Finally, we discuss the limitations of the state of the art, and consider future directions based on the parallels between StarAI and NeSy.

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Artificial Intelligence-Based Polyp Detection in Colonoscopy: Where Have We Been, Where Do We Stand, and Where Are We Headed?

Visceral Medicine ◽

10.1159/000512438 ◽

2020 ◽

Vol 36 (6) ◽

pp. 428-438

Author(s):

Thomas Wittenberg ◽

Martin Raithel

Keyword(s):

Artificial Intelligence ◽

Neural Networks ◽

Big Data ◽

Deep Learning ◽

Convolutional Neural Networks ◽

Lesion Detection ◽

Screening Colonoscopy ◽

Computer Assisted ◽

Polyp Detection ◽

The Past

Background: In the past, image-based computer-assisted diagnosis and detection systems have been driven mainly from the field of radiology, and more specifically mammography. Nevertheless, with the availability of large image data collections (known as the “Big Data” phenomenon) in correlation with developments from the domain of artificial intelligence (AI) and particularly so-called deep convolutional neural networks, computer-assisted detection of adenomas and polyps in real-time during screening colonoscopy has become feasible. Summary: With respect to these developments, the scope of this contribution is to provide a brief overview about the evolution of AI-based detection of adenomas and polyps during colonoscopy of the past 35 years, starting with the age of “handcrafted geometrical features” together with simple classification schemes, over the development and use of “texture-based features” and machine learning approaches, and ending with current developments in the field of deep learning using convolutional neural networks. In parallel, the need and necessity of large-scale clinical data will be discussed in order to develop such methods, up to commercially available AI products for automated detection of polyps (adenoma and benign neoplastic lesions). Finally, a short view into the future is made regarding further possibilities of AI methods within colonoscopy. Key Messages: Researchofimage-based lesion detection in colonoscopy data has a 35-year-old history. Milestones such as the Paris nomenclature, texture features, big data, and deep learning were essential for the development and availability of commercial AI-based systems for polyp detection.

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Literature Analysis of Artificial Intelligence in Biomedicine

10.20944/preprints202105.0056.v2 ◽

2021 ◽

Author(s):

Tim Hulsen

Keyword(s):

Artificial Intelligence ◽

Neural Networks ◽

Deep Learning ◽

The United States ◽

Biomedical Literature ◽

Computing Power ◽

The Past ◽

Privacy Laws ◽

Trends Over Time ◽

Treatment Surgery

Artificial Intelligence (AI) refers to the simulation of human intelligence in machines, using machine learning, deep learning and neural networks. AI enables machines to learn from experience and perform human-like tasks. The field of AI research has been developing fast over the past five to ten years, due to the rise of ‘big data’ and increasing computing power. In the medical area, AI can be used to improve diagnosis, prognosis, treatment, surgery, drug discovery, or for other applications. Therefore, both academia and industry are investing a lot in AI. This review investigates the biomedical literature (in the PubMed and Embase databases) by looking at bibliographical data, observing trends over time and occurrences of keywords. Some observations are made: AI has been growing exponentially over the past few years; it is used mostly for diagnosis; COVID-19 is already in the top-5 of diseases studied using AI; the United States, China, United Kingdom, South Korea and Canada are publishing the most articles in AI research; MIT is the world’s leading university in AI research; and convolutional neural networks are by far the most popular deep learning algorithms at this moment. These trends could be studied in more detail, by studying more literature databases or by including patent databases. More advanced analyses could be used to predict in which direction AI will develop over the coming years. The expectation is that AI will keep on growing, in spite of stricter privacy laws, more need for standardization, bias in the data, and the need for building trust.

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Automation and Machine Learning in Transforming the Financial Industry

Asian Business Review ◽

10.18034/abr.v9i3.494 ◽

2019 ◽

Vol 9 (3) ◽

pp. 129-138 ◽

Cited By ~ 5

Author(s):

Praveen Kumar Donepudi ◽

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Banking Sector ◽

Financial Industry ◽

Major Purpose ◽

Financial Valuation ◽

The Past ◽

Job Openings ◽

The Subject ◽

Start Ups

The major purpose of this study was to analyze the influence of machine learning on the digital age, particularly in the field of finance. This study involves the application of machine learning, its challenges, opportunities and effect on job openings and operations. This paper is based on the findings of a qualitative study of the text on the subject of machine learning in finance. The theoretical portion of this paper explores the universal framework, such as the past, existing and the next level of the machine learning, with emphasis on its advantages and drawbacks. The study also examines the global recognition of machine learning in the review of artificially intelligent development and start-ups in European countries. The research methodology used in this study was the evaluation of the qualitative methods in the paper. The study also reviewed twenty electronic records and articles on machine learning in finance. During the research on how computer technology transforms the banking sector, the implementation and impact of artificial intelligence in financing was discussed. Research shows that several financial institutions have significantly benefited from the introduction of a variety of machine learning and artificial intelligence. This paper demonstrates that there is a lack of experience in the field of machine learning, even as many unskilled or semi-qualified tasks carried out by individuals are carried out by machines. This study has shown that, through banking and financial valuation, whether it is manufacturing, data analysis or continuing to invest, there will be many more developments that can get the job done.

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Quenched: Five Fires for Thinking Extinction

Oxford Literary Review ◽

10.3366/olr.2019.0262 ◽

2019 ◽

Vol 41 (1) ◽

pp. 1-17

Author(s):

Jemma Deer

Keyword(s):

Human Language ◽

The Past ◽

Ipcc Report ◽

The Future

By the light or remains of five fires, this paper considers how the current extinction crisis might be thought in relation to the future and the past, to speed and acceleration, to ir/reversibility, and to the evolution of human language and consciousness. The thought of extinction as the extinction of thought is elaborated through engagement with J.G. Ballard's The Drowned World, Jacques Derrida's ‘No Apocalypse, Not Now’ and ‘White Mythology’, and the October 2018 IPCC report. The paper concludes by speculating upon an answer to the following questions: if we know that there will be an end to thought, what will have been the end of thought? To what end do we think at all?

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Improving Students’ Achievement in Reading Comprehension Through Advance Organizer Strategy

LINGUISTIK TERAPAN ◽

10.24114/lt.v12i1.2912 ◽

2015 ◽

Vol 12 (1) ◽

Author(s):

Tuti Andriani Siregar ◽

Didik Santoso ◽

Anni Holila Pulungan

Keyword(s):

Reading Comprehension ◽

Key Words ◽

Learning Process ◽

Advance Organizer ◽

Teaching Reading ◽

Significant Progress ◽

The Subject ◽

Passing Grade

This study deals with the improvement of the students’ achievement in reading comprehension through advance organizer strategy. The objectives of this research were to improve the students’ achievement in reading comprehension as well as the process of learning reading comprehension by using advance organizer strategy. The subject of the study was grade XI IPA 2 Madrasah Aliyah Negeri Binjai consisting of 33 students. The data of this study were obtained by using test, observation sheets and interview. This research was conducted in two cycles because in the pre-test (without treatment), the average of the students’ score was 59,24. In the first cycle test, it was 74,30. It is lower than the minimum passing grade, and if is viewed from the observation sheets, the situation of the learning process hasn’t met the criteria of the success. So the writer continued to the second cycle. There was a significant progress on the students’ achievement in reading comprehension in the second cycle, and the average of the students was 82,94. The students were more active and enthusiastic in following the lesson. Therefore, the teaching reading comprehension through advance organizer strategy can improve the students’ achievement in reading comprehension quantitatively and qualitatively. Key words: improving, reading comprehension, advance organizer strategy.

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Zur Pragmatik und Grammatik der Wissenschaftskommunikation. Ein Modellierungsvorschlag

Fachsprache ◽

10.24989/fs.v33i1-2.1380 ◽

2017 ◽

Vol 33 (1-2) ◽

pp. 36-60

Author(s):

Mathilde Hennig ◽

Dániel Czicza

Keyword(s):

Language Variation ◽

Common Language ◽

Passive Voice ◽

The Subject ◽

The Relationship ◽

Communication In Science

The article aims to examine grammatical features and pragmatic concerns of communicating in the sciences. In the research of certain languages, it became common to explaingrammatical features such as the usage of passive voice and nominal structures by communication requirements such as objectivity and precision. With the assumption that communication in science is designed to help gain and spread new insight, the authors tried to integrate several approaches to pragmatic and grammatical features of communication. By discussing the relationship between the grammar of certain languages and of the corresponding common language, the article also places the subject of communication in the sciences in the discipline of language variation.

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