Smart Machines Research Articles

ИСПОЛЬЗОВАНИЕ ИСКУССТВЕННОГО ИНТЕЛЛЕКТА ПРИ ПЕРЕВОДЕ ХУДОЖЕСТВЕННОГО ТЕКСТА (НА ПРИМЕРЕ РОМАНА ДЖ. Р. Р. МАРТИНА «ПЛАМЯ И КРОВЬ»)

The article discusses the use of artificial intelligence (AI) tools in the translation of a literary text. The modern era is marked by the transition of mankind to a new stage in the digital technologies development and the emergence of such a terminological designation as “the digital literary translation”. The work of AI has long gone beyond performing simple search requests on various subject areas in the dialog box mode. With the development of neural networks it is possible to mention that “a smart machine” is “an essence that can think, develop and form the rudiments of consciousness, perception of itself and the surrounding reality” [8, pp. 70]. Systems based on neural networks can generate unique texts in different languages, compose poetry and translate an entire book in a matter of seconds. The fragments of the novel “Fire and blood” by George R. R. Martin, translated from the English language into Russian by the expert, ChatGPT, Google and Yandex Translators are the illustrative material of our research. The methods of comparative, oppositional and contextual analysis and deduction were used in the course of the research. The problems connected with the emergence of a new complex system of contradictory relations in the “human – AI” tandem, which the translator encounters in the course of his professional activity, seem to be relevant. The results of the research show that the main obstacle to the full automation of the literary text translation process is the inability to classify and encode the translator’s cognitive abilities and creative activity in the program.

Smart Machine Learning Analysis and Blockchain Healthcare Platform

The Smart Machine Learning Medical Analysis and Blockchain Healthcare Platform (SMLMAP) represents a transformative approach to medical diagnostics and data management by integrating advanced machine learning algorithms with blockchain technology. This platform is designed to accurately diagnose seven critical diseases, achieving impressive diagnostic outcomes across various conditions, including diabetes, breast cancer, heart disease, kidney disease, liver disease, malaria, and pneumonia. SMLMAP employs Random Forest algorithms for text data analysis and Convolutional Neural Networks (CNNs) for image data, ensuring rapid and precise diagnostic results. In addition to enhancing diagnostic capabilities, SMLMAP addresses significant privacy and security concerns inherent in healthcare data management. The integration of blockchain technology facilitates secure, transparent, and tamper-proof storage of patient records, empowering individuals with greater control over their medical information. This dual approach not only improves the accuracy of diagnoses but also fosters trust between patients and healthcare providers by safeguarding sensitive data against unauthorized access. Key functionalities of SMLMAP include the ability to schedule doctor appointments conveniently through the platform, ensuring that patients can receive timely medical attention. Additionally, the application provides users with a weekly healthcare magazine, delivering valuable information on health tips, disease management, and wellness advice to promote informed health decisions. By leveraging the strengths of machine learning and blockchain, SMLMAP paves the way for a more efficient, accurate, and secure healthcare ecosystem. This innovative platform not only enhances the quality of medical diagnostics but also represents a significant step forward in addressing the critical challenges of data privacy and security in the healthcare sector. The findings underscore the potential of SMLMAP to revolutionize patient care and data management in the evolving landscape of healthcare technology.

RISH Assistance: Voice Activated Digital Assistant for Developers

The increasing use of computing devices brings convenience but also challenges. Traditional interfaces can lead to repetitive strain, slow interactions, and accessibility problemsfor software developers, who often find it hard to turn complex ideas into code, especially with unfamiliar APIs. In response, there is a growing interest in automation and smart machines that act like humans. However, a major obstacle remains—the gap in human-machine interaction. Introducing “RISH Assistance,” a voice-activated digital assis- tant designed for developers. RISH enables users to navigate tasks more efficiently, reducing reliance on screens and tra- ditional input methods. Utilizing advanced voice recognition and Natural Language Processing (NLP), RISH transforms the developer experience by providing easy access to resources, seamless code suggestions, and natural troubleshooting—all through voice commands. Additionally, it can manage phone functions, allowing calls and messages directly from the desktop interface, enhancing productivity. Security is ensured with face authentication, and current session chat history tracking allowsusers to revisit previous interactions. RISH boosts productivity through hands-free interaction, en- abling developers to focus on creative problem-solving. It im- proves accessibility by offering an alternative input method and streamlines workflows, reducing cognitive load while fostering a more intuitive development environment. By facilitating natural and efficient engagement with machines, RISH Assistance aims to enhance human-machine collaboration and create a more inclusive software development landscape.

Implementation and Evaluation of a Smart Machine Monitoring System under Industry 4.0 Concept

Production planning and control is essential in industrial manufacturing, ensuring efficient resource allocation and process management. Industry 4.0 introduces advanced technologies like cyber physical systems (CPS), artificial intelligence (AI), and internet of things (IoT) to effectively manage and monitor manufacturing operations. However, integrating these technologies into existing machinery, particularly for small and medium-sized enterprises (SMEs), poses challenges due to complexity and cost. The present study addresses this gap by designing and implementing a Smart Machine Monitoring System (SMMS) compatible with existing machinery such as computer numerical control and special purpose machines. The SMMS integrates IoT-based systems with AI algorithms to enhance machine tool utilization through effective planning, scheduling, and real-time monitoring. Through a nine-month case study in the shackle bolt manufacturing section, it was tested and compared to an Enterprise Resource Planning (ERP)-based system to assess its performance. Results showed significant improvements in production output, machine utilization rates, labor efficiency, and overall manufacturing costs. In conclusion, this study contributes to the body of knowledge on practical Industry 4.0 implementations for SMEs, offering insights into cost-effective solutions for enhancing operational efficiency and resource utilization in manufacturing environments.

The Use of Artificial Intelligence Based Technology in English Language Teaching

Artificial Intelligence (AI) is a technology that enables computers and machines to simulate learning, understanding, problem-solving, decision-making, creativity, and computer-based human intelligence. AI is also one of the driving forces of the Industry 4.0 revolution to facilitate education in the teaching and learning process. This research aims to understand the role of AI in English Language Teaching (ELT) and to investigate AI technologies in ELT. This is a library research study. The results show that AI offers a conducive learning environment for English language learning. It has significant capabilities to create a personalized environment where learners use their senses to simultaneously practice English skills depending on their current English proficiency level, vocational needs, or interests. AI provides a real-life simulation platform for dialogue, such as spoken English, and enhances practical skills like writing. It increases students' practice capacity and optimizes the impact of English teaching in ELT. Learning English has become easier with the development of technology and platforms. AI technology offers opportunities to enhance English language skills. The availability of various learning technologies makes it easier for students to understand English. There are many AI-based ELT applications available for students to use. This technology, as a smart machine that thinks and behaves like humans, can simulate intelligence and make decisions similar to human reasoning through processes on computers or mobile phones, such as Google Translate, Able Platform, Orai, Elsa, Chatbot, Duolingo, Hello English Apk, and many others.

Neurosurrealism in the Service of Revolution: Aesthetic Features and Critical Potential of Neural Network Art

The rapid development of neural network technologies in recent years has given rise to the formation of many interdisciplinary research areas, including neural network aesthetics. The aesthetics of generative creativity today is studied not only in the context of the history of digital art, but also in comparison with the practices of other artistic trends and schools. Thus, the expression “neural surrealism”, which has become entrenched in popular discourse, undoubtedly refers to the aesthetics of classical surrealism, framed and fixed in Andre Breton’s program texts: neural network algorithms allow you to create images that seem both realistic and absurd due to unexpected combinations of objects, distorted proportions and presence of uncanny images. Although “neural surrealism” can be seen as a depoliticized and unbiased game of generated images in contrast to the surrealism of the 1920s and 30s, which was not merely an artistic movement but a radical social project, it appears that the very ability of neural networks to generate extraordinary and unpredictable images holds a certain critical potential. Using the methods of formal-stylistic and ideological-content analysis, the author of the article concludes that “neurosurrealism” in a sense continues the line of classical surrealism to undermine the automatism of perception and patterns of thinking. Generative algorithms become a kind of “exclusion production machines” that allow you to see reality from an unexpected angle. In addition, the very fact of using neural networks in art raises important questions about the nature of creativity, the limits of artificial intelligence and the future of man in the world of smart machines. Neurosurrealism problematizes well-established ideas about the originality and intentionality of artistic expression, which indicates the close connection of generative artistic practices with the aesthetics of classical surrealist creativity. The conducted research will be useful to specialists of a wide profile: media theorists, art historians, philosophers, cultural scientists, as well as developers and representatives of the creative industries.

Collaborative human and computer controls of smart machines – A proposed hybrid control

Human-Machine Interaction (HMI) and Brain-Computer Interface (BCI) are evolving technologies that show the great potentials to extract and utilize humans’ intents in controlling smart machines. However, existing HMI and BCI technologies are limited in terms of (1) the number of Degrees- of-Freedom (DoF) to be controlled and (2) the ways the performance of BCI-enabled control systems are verified and validated. This study aimed to explore the solutions to addree both of above concerns; we proposed a hybrid control system that is capable of training, detecting, and interpreting humans’ intents, and utilizing humans’ intents in real-time controls of smart machines. More specifically, the system acquired brain signals in the form of Electroencephalography (EEG) by an Emotiv Epoc X and processed these signals to detect and extract humans’ intents in real-time machine controls. To cope with the frequency difference of humans’ thinking and machine motion controls, we developed a hybrid control module to fuse humans’ and machine's intelligence so that low-frequency humans’ intents could be used in real-time machine controls. The system was prototyped and verified experimentally. The system was verified to achieve the accuracy of over 90 % in recognizing humans’ intents and controlling a robot by the operator's intents with a satisfactory responding time and accuracy.

Effectiveness of airborne particle monitoring technique for assessment of wear progression of mechanical components

For effective implementation of smart machine technology, there is a great demand for real-time monitoring of the wear progression of machine components. In this work, the effectiveness of assessing wear by monitoring the airborne wear particles was investigated. Sliding wear tests were performed using different combinations of stainless steel (SUS304) and alumina material pairs while monitoring the size distribution and the number of particles using a particle counter. The results showed that particles less than 2 μm in size accounted for more than 83 % of the total number of airborne particles. However, particles greater than 2 μm account for 95 % of the volume of airborne particles. Additionally, in the case of wear volume, the total wear volume, which is the sum of the plate and the ball wear volumes, should be similar to that of the total airborne particle volume. For the SUS304/SUS304 pair, the total wear volume and the total airborne particle volume were very similar. However, in the case of alumina/SUS304 and alumina/alumina, the total wear volume and total airborne particle volume were not similar, and the total wear volume was significantly higher than the total airborne particle volume. The effectiveness of the airborne particle monitoring technique depended strongly on the material pairs. This was due to the fact that particle dispersion behavior varied with respect to the wear mechanisms of the materials.

Role of Digital Supply Chain in Industry 4.0: A Bibliometric Analysis

Digitalization has significantly shifted technology-driven processes, products, services, and business models. This revolution envisions an era in which smart machines can transfer information to automatic manufacturing and production lines, evaluate and grasp specific production difficulties, and solve them with the minimum human intervention. Previously, this revolution was thought to disrupt the manufacturing sector mainly. Therefore, their integration and advancement would influence retailers, distributors, operational organizations, and service providers. This paper assesses how digital supply chains (DSC) are evolving to cater to the needs of Industry 4.0. This literature-based research approaches the broad topic of DSC using bibliometric techniques and network analysis tools using software like Gephi and VosViewer — our study’s data from 2002–2024 from journal databases like Scopus and Google Scholar. Aside from the demands and trends driving supply chain digitalization, digital transformation trends and the changing business environment add challenges and risks. Implementing IT technologies significantly impacts the whole supply chain in order to compete in today’s dynamic and competitive business environment. Increased openness across the whole process, from order dispatch to product end-of-life, depends on collaboration between suppliers, manufacturers, and consumers. Industry 4.0 promotes big data, Internet of Things (IoT) and Artificial Intelligence (AI). Therefore, the urgency and importance of theoretical methods and empirical studies are needed to help businesses establish successful and long-lasting Industry 4.0 supply chains while quickly adapting to continuously changing markets and technology, which can be easily possible through the broad study of the previous study that has been conducted in the past. This study allows for a thorough review of the discipline’s current state and future directions, highlighting significant contributions, emerging trends, and areas requiring additional research that will be helpful for future researchers and policymakers.

THE ARTIFICIAL INTELLIGENCE TECHNOLOGIES APPLICATION IN THE MILITARY SYSTEMS ANALYSIS

This article analyzes the use of artificial intelligence technologies in military systems. Artificial intelligence (AI) is one of the modern areas of information technology. The history of human-like mechanisms development dates back to ancient times and has gone through a complex evolutionary path from myths and legends about the first androids, mechanical chess players and other complex mechanisms with human behaviour to modern autonomous robots. The first research on AI in the modern sense began almost immediately since the appearance of the first computers. Since the late 1950s, AI researchers have been trying to create smart machines that mimic the brain. AI has become a key technology in various industries, including the military, in recent decades. Thanks to its characteristics, AI transforms traditional strategies and tactics into a military art. Various aspects of the artificial intelligence use are discussed, in particular autonomous weapons systems, forecasting and analytics, cybersecurity and strategic planning. The advantages and opportunities provided by the use of artificial intelligence by military formations are highlighted, as well as attention is drawn to the ethical and legal aspects of this issue. Artificial intelligence technologies open up new opportunities for the military in many aspects, from autonomous systems to analytics and strategic planning. However, it is also important to take into account the ethical and legal aspects of these technologies use, as well as the risks associated with their implementation. A balanced approach to the use of artificial intelligence will help ensure its effective application in military systems. The article focuses on the importance of a balanced approach to the artificial intelligence technologies use for military purposes, taking into account their capabilities and risks.

Experimental Evaluation of the Effectiveness of Using Visual Cues for Controlling Unmanned Vehicles

Purpose. The study aims to experimentally evaluate the effectiveness of using visual cues, namely ARToolKitPlus, ArUco markers and two-dimensional QR (quick response code) codes in the tasks of localizing unmanned vehicles (UVs) indoors. Methodology. To enable the implementation of the processes of localization and perception of unmanned vehicles based on visual marks (markers), the structure of the visual marks processing subsystem has been developed. An algorithm for the combined use of three types of visual markers – ARToolKitPlus, ArUco markers and QR codes – for localizing unmanned vehicles and identifying cargo is proposed using the example of an online store warehouse scenario. To conduct experiments on the markers, we chose a hardware and software tool such as the JeVois-A33 smart machine vision camera with the JeVois Markers Combo software module and the JeVois Inventor graphical interface. Findings. An experimental study of the possibility of correct recognition of visual marks for indoor working conditions was carried out. As a result of a series of experiments, the possibilities of correct recognition of visual marks such as ArUco, ARToolKitPlus markers and QR codes during scanning at right angles to the camera at a distance ranging from 0.3 to 2 meters were determined. Originality. The study obtained the probabilities of correct recognition of ArUco, ARToolKitPlus markers and two-dimensional QR codes in the conditions of localization of unmanned vehicles indoors. Practical value. The obtained results of the study can be used to create, simulate, and analyze the effectiveness of algorithms for localizing and perceiving unmanned vehicles indoors using appropriate visual markers. The proposed generalized structure of the visual marker processing subsystem of the localization and perception system can be used in the development of unmanned vehicle control systems.

Correction: Platform Supporting Intelligent Human–Machine Interface (HMI) Applications for Smart Machine Tools

Correction: Platform Supporting Intelligent Human–Machine Interface (HMI) Applications for Smart Machine Tools

Do We Trust Artificially Intelligent Assistants at Work? An Experimental Study

The fourth industrial revolution is bringing artificial intelligence (AI) into various workplaces, and many businesses worldwide are already capitalizing on AI assistants. Trust is essential for the successful integration of AI into organizations. We hypothesized that people have higher trust in human assistants than AI assistants and that people trust AI assistants more if they have more control over their activities. To test our hypotheses, we utilized a survey experiment with 828 participants from Finland. Results showed that participants would rather entrust their schedule to a person than to an AI assistant. Having control increased trust in both human and AI assistants. The results of this study imply that people in Finland still have higher trust in traditional workplaces where people, rather than smart machines, perform assisting work. The findings are of relevance for designing trustworthy AI assistants, and they should be considered when integrating AI technology into organizations.

INTERNATIONAL LEGAL PROTECTION OF CLIMATE CHANGE USING ARTIFICIAL INTELLIGENCE TECHNIQUES

Climate change poses a significant challenge to humanity due to its myriad effects, such as rising temperatures, melting ice caps, extreme weather events, sea level rise, storms, wildfires, droughts, and increased energy demand. The expected global population growth exacerbates these impacts, leading to further deterioration of the climate and other environmental changes that profoundly affect humans. Since ancient times, humans have utilized technology to provide solutions and confront climate challenges, aiming to create a more habitable and prosperous environment. Despite its negative role in increasing pollution rates since the first industrial revolution, which transitioned from coal to steam engines in the 18th century, technological advancements have continuously evolved. This evolution includes the second industrial revolution, introducing electric power in the late 19th century, the third industrial revolution, characterized by the shift to machine-based production and advancements in computing and the internet in the 1960s, and the fourth industrial revolution, which integrates physical, digital, and biological systems through electronically controlled devices and smart machines connected to the internet, such as the Internet of Things, 3D printing, robotics, artificial intelligence, and others. These innovations have permeated all aspects of life and work, significantly impacting the climate both positively and negatively. In jurisprudence, opinions vary regarding the relationship between technology and climate change. Some emphasize the negative impacts of technological advancements on the climate, while others focus on the role of technological innovations in addressing climate change. These innovations aim to make changes in information technology applications that align with

Automatic Testing Technology of BTB Liquid Crystal Display Advanced Fault Detection in Smart Meter for Smart Machine

In smart meter technology, the reliability of Backplane to Bezel (BTB) Liquid Crystal Displays (LCDs) is crucial for efficient functioning within intelligent grid systems. Defects in these LCD screens can significantly impact overall smart meter performance, necessitating effective detection methods for proper management and utilization. Current detection approaches, which combine manual and automatic methods based on machine vision, have shown unsatisfactory performance. This research addresses this challenge by proposing a novel method for advanced fault detection in BTB LCDs of smart meters, Automatic Testing Technology of BTB Liquid Crystal Display Advanced Fault Detection in Smart Meter for smart machine (BTB-LCD-FD- MCDSGAN) is proposed. The study begins by collecting datasets specifically tailored for LCD screen localization and defect detection. To enhance data quality, a Window Adaptive Extended Kalman Filter (WAEKF) is applied during preprocessing for noise removal. Feature extraction follows, utilizing Parameterized Multi Synchrosqueezing Transforms (PMST) with a primary focus on Gray Level Co-occurrence Matrix features. These extracted features serve as input for classification by a Multimodal Contrastive Domain Sharing Generative Adversarial Network (MCDSGAN), categorizing defects into five types likes normal display, no display, abnormal display, liquid crystal rupture, and incomplete display. Furthermore, the proposed method optimizes the MCDSGAN weight parameter using the Red Fox Optimization Algorithm (RFOA) to achieve accurate LCD fault defect prediction. The entire approach is implemented in Python, performance metrics likes accuracy, precision, recall, F-score, computational time are thoroughly analyzed. Performance of proposed BTB-LCD-FD-MCDSGAN approach attains 20.89%, 33.67% and 25.98% high accuracy, and 17.98%, 23.78% and 33.45% higher recall compared with existing methods such as automatic detection of display defects for smart meters depend on deep learning (AD-AM-DL), deep learning-enabled image content-adaptive field sequential color LCDs by mini-LED backlight (DL-AFSC-LCD) and new multi category defect detection method depend on convolutional neural network technique for TFT-LCD panels (MDF-CNN-LCD), methods respectively.

Resilience of sustainability for a smart production system to produce biodiesel from waste animal fat

The smart production system is implemented for more effective labor, less waste, and a reduction of energy consumption. Biofuels are significant substitutes for conventional fuels and decreases air pollution and carbon monoxide, can be made through a smart production system with automated machines. Biodiesel can be produced using an assortment of sources that can be both edible and inedible, but animal fat is a sensible alternative among inedible sources because of its affordable price and ease of availability. The model studies the smart biodiesel production system with energy utilization and less carbon emissions to produce pure biodiesel. The multi-objective mathematical model minimizes the total biodiesel supply chain cost followed by minimizing the total biodiesel supply chain environmental impact and enhancing its social impact. The smart production system with a variable production rate produces the biodiesel with less impurities, but still impurities in the biodiesel, that can be detected by a two-stage autonomated inspection system. The impure biodiesel is remanufactured in the biorefineries at the end of every natural production cycle. For the best utilization of energy, various energy costs, such as lighting and air handling costs are calculated. The improved augmented ϵ-constraint approach with lexicographic optimization is utilized to solve the model. The four mathematical examples are studied, and the repercussions of mathematical experiments reveal that the animal fat purchasing cost is 44.32% of the total cost, and the percentage of impurities in biodiesel can be decreased through minimum energy consumption. The managerial insights of the model, graphical representation, and sensitivity analysis of key parameters are provided in this research. The conclusions can be useful to investors in a sustainable smart production system and biofuel policymakers.

Smart technologies in banking

The article is aimed at the current issues of using smart technologies and innovative approach during evolution and transformation processes in banking. The study identifies the special place of this topic for achieving a high level of efficiency and competitiveness of banks and characterizes the impact of the introduction of technological approaches on the customer base and its perception of banking products. The main functions of banking innovations in this area are analyzed and the justification of their feasibility at the present stage of economic development is provided. A number of the most promising technologies and approaches to banking activities are allocated, namely: contactless payment, digital wallets, biometric identification, person-to-person payments, collective financing, omnichannel banking, interaction with FinTech companies, blockchain, big data, artificial intelligence, smart machines, Internet of Things, behavioral banking, retail bank, application programming interfaces, multi-component bank, open banking, augmented reality, robotic automation, hybrid clouds. The relevance of the identified areas is proved based on their perception by analyzing the popularity of the identified topics in Google search queries using the Google Trends tool. The perception of smart technologies in banking by Internet users in the world and specifically in Ukraine is investigated, which gave grounds to conclude that there is a significant interest in them, and therefore the expediency of further study and implementation in the activities of banks. It is identified that the most perspective technologies are biometric identification, blockchain, Internet of Things, big data analysis, artificial intelligence, etc. Several technologies have been identified, namely, collective financing (crowdfunding), application programming interfaces (APIs) and digital wallets, which are less popular in Ukraine than in the world in general, and therefore require detailed research and study of the relevance of their application in the domestic banking market. Possible directions for further innovative development of banking institutions based on the use of smart technologies are proposed. Based on panel data for 60 banks of Ukraine for the period 2014-2022, the author analyzes the correlations between the indicators of the use of digital technologies and the financial performance of banks and builds regression dependencies of financial indicators of banks on the indicator of the number of electronic means of payment in active circulation. The theoretical value of the study is to identify the most promising smart technologies and innovative approaches to banking business in modern conditions. The practical value lies in studying the level of perception of high-tech innovations in the field of banking services by the active public and identifying further directions for the development of this process. We consider it advisable to direct further research in the context of a detailed study of the possibilities of applying the identified technologies in specific banking products or business processes.

A methodology for laser tool setters calibration and its precise mathematical model

On-machine tool setting is a pivotal approach in achieving intelligent manufacturing, and laser tool setters have become a crucial component of smart machine tools. Laser tool setters play a crucial role in precisely measuring the dimensions of cutting tools during the part machining process, focusing on tool length and diameter. As a measuring instrument, the positions of the laser axis of the laser tool setter need to be accurately calibrated before use. However, in actual calibration scenarios, traditional calibration methods face challenges due to installation errors in the tool setter and geometric errors in the measuring rod. To address this issue, this study proposes a novel calibration method. Initially, the calibration mechanism of the laser beam axis is established. Based on the accurate mathematical model of the laser beam and the measuring rod, and using the polygon clipping algorithm, the mathematical mechanism of the laser tool setter's work is established. Then, a novel method is introduced to calculate the compensation distance between the laser beam reference point and the rod bottom center point at each moment during calibration. Furthermore, by utilizing the kinematic chain of the tool setter calibration system, a new calibration method is developed to accurately calibrate the position of the laser beam axis in the machine tool coordinate system. Finally, the accuracy of the calibration method is verified through simulation experiments and calibration tests. This method improves the calibration accuracy of the tool setter, and the mathematical model of the laser tool setter can be extended to the measurement of tools, thereby improving the precision of tool measurements. This research significantly improves the efficient production performance of smart machine tools.

Fake News Prediction and Analysis in LIAR Dataset Using Advanced Machine Learning Techniques

Fake news detection and prediction is the crucial research issue in now a day because it is very difficult to know the news authenticity on social media. It has a devastating impact on societies and democratic institutions as online life in these days are one of the principal news hotspots for many individuals around the world because of their minimal effort, simple access, and quickly spread of the unauthorized news. However, measurable ways to deal with battling fake news have been drastically restricted by the absence of named benchmark datasets. Smart machine learning classifiers are used to solve the problem of fake news prediction and classification. The proposed research study works on the LIAR dataset, the open-source available dataset for fake news classification with 12.8K decade-long, hand-labelled short statements in various contexts. The proposed research study has used a novel approach to deal with the fake news prediction accurately and this approach outperforms in this scenario for the same dataset. Naïve Bayes classifier for classification is used to reduce the variance values in the dataset to get rid of the overfitting issue. This classifier shows more improved results than other prior classifiers and the accuracy value was 99%. The proposed research study performed experiments and evaluated through different evaluation measures, the results of accuracy for the Naïve Bayes are best as compared to Random Forest, Decision tree, and Neural Networks are computed for each algorithm. The proposed research study could be applied in real-time applications to deal the fake news prediction in social media and digital media platforms.

Steps towards a therapeutic artificial intelligence

Whether artificial intelligence might benefit human well-being in every sense is an open question. I consider it in the following essay, first putting to one side standard accounts of ‘official AI’, then deriving an ‘unofficial’ counterpart from the evidence of newspaper accounts and magazine features from ca. 1945–1965. Unsurprisingly, these demonstrate a bending of artificial intelligence to military and industrial purposes, hence the enormity of the impediment to therapeutic applications, but at the same time the evidence leaves no doubt as to the imaginative power of smart machines. Contemporary commentary is brought to bear to move from intimations of a dark future to possibilities for constructing a healthy practice. I conclude with two quite different 21st century examples of a way towards it.