Built-in Artificial Intelligence Research Articles

Using artificial intelligence teaching assistants to guide students in solar energy engineering design

Engineering projects, such as designing a solar farm that converts solar radiation shined on the Earth into electricity, engage students in addressing real-world challenges by learning and applying geoscience knowledge. To improve their designs, students benefit from frequent and informative feedback as they iterate. However, teacher attention may be limited or inadequate, both during COVID-19 and beyond. We present Aladdin, a web-based computer-aided design (CAD) platform for engineering design with a built-in artificial intelligence teaching assistant (AITA). We also present two curriculum units (Solar Energy Science and Solar Farm Design), where students explore the Sun-Earth relationship and optimize the energy output and yearly profit of a solar farm with the help of the AITA. We tested the software and curriculum units with over 100 students in two Midwestern high schools. Pre- and post-survey data showed improvements in understanding of science concepts and self-efficacy in engineering design. Pre-post analysis of design performance gains reveals that AI helped lower achievers more than higher achievers. Interviews revealed students’ values and preferences when receiving feedback. Our findings suggest that AITAs may be helpful as an additional feedback mechanism for geoscience and engineering education. Future efforts should focus on improving the usability of the software and providing multiple types of feedback to promote inclusive and equitable use of AI in education.

Artificial intelligence analysis of over a million Chinese men and women reveals level of dark circle in the facial skin aging process.

To better compare the progression of dark circles and the aging process in Chinese skin. A total of 100589 Chinese males and 1838997 Chinese females aged 18 to 85, without facial skin conditions, and who had access to a smartphone with a high-resolution camera all took selfies. Using a smartphone application with a built-in artificial intelligence algorithm, facial skin diagnostic evaluated the selfies and score the severity of the dark circles with four other facial indicators (including skin type, Pores, Acne vulgaris, and Blackheads). Basic information was collected with online questionnaire, including their age, gender, skin sensitivity, and dietary habits. In users between the age of 18 and 59, the prevalence of comprehensive, pigmented, and structural type of dark circles all rose with age. However, between the age of 60 and 85, the intensity of all types of dark circles diminished. Besides, vascular dark circles progressively worsen from the age of 18 to their peak at 39, and then gradually decline with age. Females typically have more pronounced black circles under their eyes than males in China. Bad eating habits, urbanization, regular cosmetics use, and sensitive skin positively correlate with severe dark circles. Vascular, comprehensive dark circles were worse in spring. Both pigmented and structural dark circles were worse in the summer. The results indicated that the intensity of dark circles was influenced by oily skin, wide pores, severe blackheads, and severe acne. Chinese men and women differed noticeably in the prevalence of each face aging indicator and the appearance of aging dark circles. Selfies could be automatically graded and examined by artificial intelligence, which is a quick and private method for quantifying signs of facial aging and identifying major problems for different populations. Artificial intelligence would assist in the development of individualized preventive and therapeutic interventions.

Digital Transformation in Entrepreneurship Education: The Use of a Digital Tool KABADA and Entrepreneurial Intention of Generation Z

This paper aims to investigate the impact of using the business planning digital tool KABADA in a study process on entrepreneurial intentions in Generation Z, based on the study conducted in selected Central and Eastern European countries and Southern European countries. The authors developed a literature review on digital transformation and digital tools in entrepreneurship education and their role in increasing entrepreneurial intentions in the context of sustainability. In the empirical part, the authors conducted a quasi-experiment, tested the digital entrepreneurship education tool KABADA among students with the analysis of sequentially obtained statistical data, using descriptive statistics, statistical tests of eight hypotheses, as well as the effect size test. The study reveals a mostly positive effect of the digital tool KABADA on the entrepreneurial intention of Generation Z, thus confirming the effectiveness of digitalization-based entrepreneurial education in promoting entrepreneurial intention. However, this effect is not statistically significant for some components forming intention, as proven by hypothesis testing. The study contributes to the role of the digital transformation of entrepreneurial education and the use of digital tools with built-in artificial intelligence algorithms in improving the effectiveness of education in terms of raising entrepreneurial intentions.

Аналіз методів і засобів забезпечення кібербезпеки веб-сервісів з використанням штучного інтелекту

A thorough analysis of literary sources on the application of artificial intelligence (AI) in cyber security was carried out. During the examination, the most significant attention was paid to sources describing the use of AI-based applications to analyze and evaluate existing systems for vulnerabilities, as well as to sources that consider the features of using built-in AI mechanisms for searching, detecting, classifying, and combating attacks on the system during her works. The types, impacts, and features of attacks on web services are defined. The features of the application of AI for the classification of web services under test are considered, with the aim of further justifying the selection of the best tools for ensuring their cyber security. The methods of using AI in the cyber security of web services during the introduction of built-in mechanisms and models for searching, detecting, classifying, and countering threats are analyzed. The accuracy of machine learning methods used to detect intrusions was compared. Directions for further research can cover: the development of methods, models, and applications based on the use of AI for analyzing the source code for possible vulnerabilities of a web service with support for various programming languages and the development of mechanisms for search and classification of threats based on the use of AI built into the web service.

Способ исследования устойчивости систем со встроенным искусственным интеллектом, использующихся на промышленных объектах, к состязательным атакам

The paper presents a method for evaluating the industrial systems with built-in artificial intelligence (AI) robustness to adversarial attacks. The influence of adversarial attacks on the systems performance has been studied. The scheme and the scenarios to implement attacks on industrial systems with built-in AI were presented. A comprehensive set of metrics used to study the robustness of ML models has been proposed, including test data set quality metrics (MDQ), ML model quality metrics (MMQ), and model robustness to adversarial attacks metrics (MSQ). The method is based on the use of this metrics set and includes the following steps: generating a set of test data containing clean samples; assessing the quality of a test data set using MMQ metrics; identification of relevant adversarial attacks methods; generating adversarial examples and a test data set, containing the adversarial samples, to evaluate the robustness of the ML model; assessing the quality of the generated adversarial test data set using MDQ indicators; evaluating the quality of a ML model using MMQ indicators; evaluating model robustness using MSQ scores.

RESEARCH OF THE PROBLEM OF SPEECH RECOGNITION FOR SOLUTION OF SPECIAL TASKS

In the article, the authors conducted a study of the actual problem of machine translation of information from audio or video files into text form (transcription). This is necessary for people with limited physical capabilities, or diseases or for those who need to process information in the form of a text file. The process of transcription is relevant at present (in the conditions of hostilities). Today in Ukraine, transcription is necessary to solve complex special tasks. Namely, solving the task of searching and identifying certain content that is transmitted by various means of communication in conversations in the form of audio files. Such tasks are currently quite relevant and quite time-consuming and take same time. To solve this problem, the authors conducted a study and identified the strengths and weaknesses of the programs that are often used for these purposes. The types of transcription and the software currently used are presented in separate tables with all their features. Existing automatic language transcription algorithms still make significant errors, but their main advantage is time (or synchronicity). When it comes to solving special tasks, time is the most decisive factor. Terabytes of clearly annotated data are needed to increase the accuracy of the text received by the transcriber program. Programs with artificial intelligence, in addition to extracting essences to understand the meaning of language, allow us to recognize and understand the form: combinations of sounds, letters, and syllables that are built into words and sentences. Only in this way will the machine be able to decode human speech correctly and correctly. An extremely important task is to determine the location of the speaker - geolocation, even with the determination of the specific location of the real estate object. This can be used for data collection and subsequent analysis of public sentiment and rapid response with subsequent localization of illegal activities. In the article, the authors concluded that for decoding audio files and automatically converting them into text format, a promising direction is the use of not just ready-made services, but the use of services with a built-in artificial intelligence function, so-called self-learning systems. Keywords: scription, time code, language decoding, geolocation, database, geographic information system.

Acceptability of artificial intelligence-based retina screening in general population.

Purpose:A deep learning system (DLS) using artificial intelligence (AI) is emerging as a very promising technology in the future of healthcare diagnostics. While the concept of telehealth is emerging in every field of medicine, AI assistance in diagnosis can become a great tool for successful screening in telemedicine and teleophthalmology. The aim of our study was to assess the acceptability of AI-based retina screening.Methods:This was a prospective non-randomized study performed in the outpatient department of a tertiary eye care hospital. Patients older than 18 years who came for a regular eye check-up or a routine retina screening were recruited in the study. Fundus images of the posterior pole were captured on fundus on a phone camera (REMIDIO™, India) with a built-in AI software (Netra.AI) that can identify normal versus abnormal retina. The patients were then given an 8-point questionnaire to assess their acceptance and willingness toward AI-based screening. We recruited 104 participants.Results:We found that 90.4% were willing for an AI-based fundus screening; 96.2% were satisfied with AI-based screening. Patients with diabetes (P = 0.03) and the male population (P = 0.029) were more satisfied with the AI-based screening. The majority (i.e., 97.1%) felt that AI-based screening gave them a better understanding of their eye condition and 37.5% felt that AI-based retina screening prior to a doctor’s visit can help in routine screeningConclusion:Considering the current COVID-19 pandemic situation across the globe, this study highlights the importance of AI-based telescreening and positive patient approach toward this technology.

Internet of Things in the Bathroom: Smart Health-Monitoring Bidet System

The Internet of things (IoT) helps our everyday lives such as by monitoring objects and tracking behaviors in various settings, but studies on enhancing the bathroom experience are rare. This article describes full details about development and implementation of a smart health-monitoring bidet based on our study published previously in the conference. A smart bidet system is designed to monitor the users’ health through several contact-type sensors, such as pressure, oxygen, and thermometer. The system is equipped with a built-in artificial intelligence software platform and is designed to detect anal and spinal diseases. The attached sensors normally operate under waterproof conditions: we tested their performances under X6 international protection marking conditions. These devices were designed to operate properly even in extremely waterproof conditions. The temperature, pressure, and oxygen sensors of the bidet system had error rates of about 4.1, 0.6, and 1.1 percent, respectively.

From Automation to Autonomy: Legal and Ethical Responsibility Gaps in Artificial Intelligence Innovation

The increasing prominence of artificial intelligence (AI) systems in daily life and the evolving capacity of these systems to process data and act without human input raise important legal and ethical concerns. This article identifies three primary AI actors in the value chain (innovators, providers, and users) and three primary types of AI (automation, augmentation, and autonomy). It then considers responsibility in AI innovation from two perspectives: (i) strict liability claims arising out of the development, commercialization, and use of products with built-in AI capabilities (designated herein as “AI artifacts”); and (ii) an original research study on the ethical practices of developers and managers creating AI systems and AI artifacts. The ethical perspective is important because, at the moment, the law is poised to fall behind technological reality—if it hasn’t already. Consideration of the liability issues in tandem with ethical perspectives yields a more nuanced assessment of the likely consequences and adverse impacts of AI innovation. Companies should consider both legal and ethical strategies thinking about their own liability and ways to limit it, as well as policymakers considering AI regulation ex ante.

Actor–critic-based decision-making method for the artificial intelligence commander in tactical wargames

In a tactical wargame, the decisions of the artificial intelligence (AI) commander are critical to the final combat result. Due to the existence of fog-of-war, AI commanders are faced with unknown and invisible information on the battlefield and lack of understanding of the situation, and it is difficult to make appropriate tactical strategies. The traditional knowledge rule-based decision-making method lacks flexibility and autonomy. How to make flexible and autonomous decision-making when facing complex battlefield situations is a difficult problem. This paper aims to solve the decision-making problem of the AI commander by using the deep reinforcement learning (DRL) method. We develop a tactical wargame as the research environment, which contains built-in script AI and supports the machine–machine combat mode. On this basis, an end-to-end actor–critic framework for commander decision making based on the convolutional neural network is designed to represent the battlefield situation and the reinforcement learning method is used to try different tactical strategies. Finally, we carry out a combat experiment between a DRL-based agent and a rule-based agent in a jungle terrain scenario. The result shows that the AI commander who adopts the actor–critic method successfully learns how to get a higher score in the tactical wargame, and the DRL-based agent has a higher winning ratio than the rule-based agent.

Attentive Relational State Representation in Decentralized Multiagent Reinforcement Learning.

In multiagent reinforcement learning (MARL), it is crucial for each agent to model the relation with its neighbors. Existing approaches usually resort to concatenate the features of multiple neighbors, fixing the size and the identity of the inputs. But these settings are inflexible and unscalable. In this article, we propose an attentive relational encoder (ARE), which is a novel scalable feedforward neural module, to attentionally aggregate an arbitrary-sized neighboring feature set for state representation in the decentralized MARL. The ARE actively selects the relevant information from the neighboring agents and is permutation invariant, computationally efficient, and flexible to interactive multiagent systems. Our method consistently outperforms the latest competing decentralized MARL methods in several multiagent tasks. In particular, it shows strong cooperative performance in challenging StarCraft micromanagement tasks and achieves over a 96% winning rate against the most difficult noncheating built-in artificial intelligence bots.

From Automation to Autonomy: Legal and Ethical Responsibility Gaps in Artificial Intelligence Innovation

The increasing prominence of artificial intelligence (AI) systems in daily life and the evolving capacity of these systems to process data and act without human input raise important legal and ethical concerns. This article identifies three primary AI actors in the value chain (innovators, providers, and users) and three primary types of AI (automation, augmentation, and autonomy). It then considers responsibility in AI innovation from two perspectives: (i) strict liability claims arising out of the development, commercialization, and use of products with built-in AI capabilities (designated herein as “AI artifacts”); and (ii) an original research study on the ethical practices of developers and managers creating AI systems and AI artifacts. The ethical perspective is important because, at the moment, the law is poised to fall behind technological reality—if it hasn’t already. Consideration of the liability issues in tandem with ethical perspectives yields a more nuanced assessment of the likely consequences and adverse impacts of AI innovation. Companies should consider both legal and ethical strategies thinking about their own liability and ways to limit it, as well as policymakers considering AI regulation ex ante.

Securing Deep Learning Based Edge Finger Vein Biometrics With Binary Decision Diagram

With built-in artificial intelligence (AI), edge devices, e.g., smart cameras, can perform tasks like detecting and tracking individuals, which is referred to as edge biometrics. As a driving force for AI, machine/deep learning plays a critical role in edge biometrics. Machine/deep learning based edge biometric systems outperform their nonmachine learning counterpart. However, research shows that artificial neural networks, e.g., convolutional neural networks, are invertible such that adversaries can obtain a certain amount of information about the original inputs/templates. This information leakage is not tolerable for biometric systems because biometric data in the original (raw) templates cannot be reset or replaced. Once compromised, they are lost forever. Therefore, how to prevent original biometric templates from being attacked through inverting deep neural networks is a pressing, but unsolved issue, for deep learning based biometric recognition. To address the issue, in this paper, we develop a novel biometric template protection algorithm using the binary decision diagram (BDD) for deep learning based finger-vein biometric systems. The proposed algorithm is capable of creating a new noninvertible version of the original finger-vein template, which is stacked with an artificial neural network–the multilayer extreme learning machine (ML-ELM) to generate a privacy-preserving finger-vein recognition system, named BDD-ML-ELM. The proposed BDD-ML-ELM ensures the safety of the original finger-vein template even if its transformed version is compromised. The transformed template, if compromised, can be revoked and replaced with another new version by simply changing the user-specific keys. Therefore, the BDD-ML-ELM has a clear advantage over the existing machine/deep learning based biometric systems, whose raw biometric templates are vulnerable when the artificial neural network suffers an inversion attack.

Comparative study of cloud platforms to develop a Chatbot

Before chatbots there were simply bots: The invention of a chatbot brought us to the new era of technology, the era of conversation service. A chatbot is a virtual person that can effectively talk to any human being with the help of interactive conversion textual skill. Now a days there are many cloud-based platforms available for developing and deploying the chatbot such as Microsoft bot framework, IBM Watson, Kore, AWS lambda, Microsoft Azure bot service, Chatfuel, Heroku and many more but all those techniques has some drawbacks such as built-in Artificial Intelligence, NLP, conversion service, programming etc. This paper represents the comparison between all cloud-based chatbot technologies with some constraint such as built-in AI, setup time, completion time, complexity etc. Finally, by the comparison, we will get to know that which cloud platform is efficient and suitable for developing chatbot.