Category: Artificial intelligence

The YouTube video is titled Transport logistic: Artificial Intelligence at Air cargo. It discusses how artificial intelligence (AI) will revolutionize the air cargo industry. It discusses two major developments being implemented. It was published by Transport Logistics on May 23, 2019.

The logistics sector forms an important component of the economy. Due to globalization and the rise of the internet marketplace, the demand for faster air cargo delivery has increased. A recent study published by the German Aerospace Center revealed that the total volume of cargo transported in 2030 will be five times the volume in 2000 (Transport Logistic, 2019). Currently, the process of loading freight is slow and very challenging because it is done manually. An important development being implemented at the Frankfurt Airport is the smart air cargo trailer (Transport Logistic, 2019).

Packages are scanned using a specialized camera system that links the information to the stored shipping information. The data can be retrieved quickly through the cloud at every stage of the process. Autonomous vehicles ensure that the packages are delivered timely at the correct cargo terminals for loading. Another development uses AI to scan the type and composition of packages to generate an optimized loading plan for transportation (Transport Logistic, 2019). The information is disseminated to the on-site operator via augmented reality.

There are several interesting main points regarding the developments. The researchers have incorporated AI into the freight loading process. Moreover, they have streamlined the process through the use of autonomous vehicles and specialized camera systems (Transport Logistic, 2019). The cloud is part of the development as it stores the information for fast retrieval, and connects people involved in the different stages of the process.

I agree this development will benefit the cargo industry. It will increase the effectiveness of loading freight, expand the airports capacities, and shorten the loading and delivery period. Moreover, it will attract more business from forwarders, airline operators, and freight handlers. The technology will also enhance the optimization of the planes for more and faster cargo delivery (Abeyratne, 2018). The researchers are testing the new developments to evaluate their effectiveness.

References

Abeyratne, R. (2018). Law and regulation of air cargo. Springer.

Transport Logistic. (2019). Artificial intelligence at air cargo [Video]. YouTube. Web.

AI has been a part of peoples lives for a long time. Some algorithms adhere the advertisements to people of different ages and backgrounds. There are also systems allowing for ordering food online or suggesting alternative routes from home to work. However, the structure suffers from the lack of diversity and makes assumptions based on false facts. In her TED talk, Kriti Sharma brings an example of fertility clinics being advertised to females because this is what AI has learned from human behavior (2018). Also, searching for the perfect candidate for the workplace in IT, the website AI shows primarily male CVs to the CEO because of this company owner accepts mainly male candidates. Therefore, algorithm bias exists, and better guidelines should be implemented for AI.

AIs algorithm bias is based on assumptions about people of different age groups, races, and gender. People that are chosen to participate in machine learning come from the same background and have benchmark abilities. However, in this case, what has not been considered is the diversified demands and opportunities of different social groups. As Kriti Sharma stated in her TED talk, she received backlash for being female in the IT industry; however, this should be considered a typical case (2018).

Alexa, a personal home assistant with a female voice, has primary commands of ordering food or scheduling a meeting, whereas more serious male-voiced assistants are related to business. Those AI machines are made by people who have the same assumptions inside, imagining a male when talking about a CEO and a woman when talking about personal assistants. In addition, an African American is considered likely by AI to commit a crime rather than a Caucasian, which happens because of the bias people have when programming the machine.

To solve the problem of lack of diversity and assessing human needs correctly, there is a need to implement better guidelines for AI. The policies could solve domestic abuse, which is frequent in some African households, as the AI would know the signals and understand the circumstances of a typical place. People that create AI should teach the system more diverse cases and tell it about different backgrounds people may come from to ensure a better quality of life.

Reference

Sharma, K. (2018). How to keep human bias out of AI [Video]. TED. Web.

Introduction

The advancement and growth of computer technology have increased the intensity of worldwide competitiveness. With Artificial Intelligence (AI), many firms predict that the future of manufacturing operations will alter radically, from planning, scheduling, and optimization. Today, AI has effectively addressed fundamental manufacturing problems such as predicting and avoiding machine-related errors (Litvinova 7). Therefore, this essay explores an operation case, discussing the tools in AI, particularly TensorFlow and Theano, and their implementation issues. Operation technology is also discussed by exploring AI in machines, explicitly focusing on the current state of the technology and its economic impact on companies.

Operations Case

AI has aided in the processing of vast amounts of data and its use in business. With the advancement of AI and Machine Learning (ML), many tools and frameworks accessible to data scientists and engineers have grown. In this case, TensorFlow is utilized as an ML and AI software library. It is used in various applications with a specific focus on deep neural network training and prediction (Géron 33). TensorFlow is useful in environments that handle large amounts of data and require the prediction of the behavior of the systems. Theano is also implemented as a Python module that allows companies to assess arithmetic computations such as multi-dimensional arrays efficiently. It is widely utilized in the development of ML projects.

Operation Related Problem

For instance, Siemens relies on AI to address some of the more complicated quality-related errors that would directly interfere with the operation of the manufacturing process. Siemens has been employing smart boxes to digitalize motors and transmissions for the interconnection of over 65,000 nodes (Blackman 6). According to Blackman (7), sensors and telecommunication connections for data transmission are housed in boxes designed to detect and relay information to actuators. Siemens has employed such techniques to enhance productivity on the factory floor by automating repetitive quality control check operations.

Implementation Issues

TensorFlow and Theano are implemented on Graphics Processing Units (GPUs) for optimum ML performance. The intelligent systems help improve the monitoring process, addressing lead times and quantities consumed at every cycle stage. The AI systems are specifically designed to form judgments about a machines status and spot abnormalities by analyzing data, allowing predictive maintenance. The AI systems ensure a more accurate prediction of the behavior of the systems necessary for timely intervention. Utilizing AI technologies in quality control has since improved the overall efficiencies of operations at Siemens.

Similarly, AI technology is employed in coordinating predictive repair and maintenance of heavy-duty machines such as high-speed trains. For example, Siemens collaborates with Deutsche Bahn on a pilot scheme for high-speed train predictive repairs and maintenance (Kulawiak 8). Data engineers use AI to recognize trends in the operational data of automobiles. In such a setting, the program finds alternatives that meet all requirements, such as those for dependable operation, among the billions of conceivable hardware combinations for a switch tower. With such benefits, the current state of AI in machinery seems to be growing exponentially, with emerging technologies offering several benefits to companies utilizing the technology.

Operations Technology

Artificial Intelligence in Machinery

Siemens and Deutsche Bahn are some of the leading companies that employ AI in machinery. Such companies appreciate that AI systems assist engineers in forecasting when or whether functioning equipment will fail, allowing maintenance and repair to be arranged before the breakdown. Kulawiak (9) notes that manufacturers can enhance productivity while lowering the cost of equipment failure due to AI-powered predictive maintenance. AI technology is significant in addressing machine operation efficiencies because it allows the software to perform human capabilities such as thinking, judgment, and organization more accurately. According to Blackman (7), AI-powered machines can also perform collaboration on real-time adjustments and performance evaluation more effectively, efficiently, and at a lower cost.

The Current State of Artificial Intelligence in Machinery

Currently, AI in machine applications is transitioning from a concept to reality. Many modern applications use AI, and the breakthrough is set to revolutionize the face of automation in the near future. AI has already become a fundamental aspect of manufacturing and automation in most engineering, logistics, and maintenance processes. Many experts believe that AI will revolutionize everything that would otherwise need extensive human attention and interaction (Dolci 21). While many individuals struggle with AI, technical limits are becoming less of an issue overall, with strategic and managerial constraints emerging as the major roadblocks in emerging technologies.

Today, some of the emerging technologies are in operational simulation and optimization. Simulations in machine designs and optimization are significant application areas for AI in machines (Marcus 17). End-users may schedule their equipment usage more efficiently, arrange material flow and supply more dynamically, and predict potential shock events thanks to dynamic modeling and optimization of systems. The desire for end-users to reduce total operating costs and the advent of classical mechanics AI solutions are key driving factors in the category of the AI-powered machine. As additional production lines are connected to the supply chain and operations become more complicated, there will be greater demand for AI solutions that address operational modeling and improvement.

Emerging commercial players include robotics research companies like Tesla, Inc. and Boston Dynamics. The future direction of these companies is the application of AI is optimizing the operation of machines to enable self-sustaining models that can ensure energy efficiencies and high safety standards. Marcus (21) notes that these manufacturing businesses benefit from artificial intelligence because it improves with time. Machine learning models often grow increasingly accurate and can predict errors and abnormalities as they examine data particular to the company and manufacturing process. The efficiencies offer several economic opportunities that improve the economic prospects of the companies.

The Key Economical Aspect of Artificial Intelligence in Machinery

AI facilitates the execution of previously complicated activities without incurring substantial costs. AI also works without interruptions or pauses, and there is no downtime. Reduced downtime and faster processing mean that the economic value of the companys pieces of equipment is realized. Such characteristics of AI give the technology a broad commercial appeal. In comparison, a typical human will work relatively slowly and labor for only six hours every day, excluding breaks (Aoun 17). Humans are designed to take some time off to renew themselves and prepare for the following workday. Human laborers also have weekly breaks to keep up with their work-life and home relationships. But, unlike humans, companies can use AI to make robots work faster without interruptions improving the overall economic gains of the company.

Conclusion

Applying AI-powered industry solutions improves the automation of processes, allowing firms to create intelligent workflows that cut costs and downtime. AI systems employ pattern recognition and combine it with general intelligence to estimate potential flows in machines. Siemens and Deutsche Bahn are leading companies that have successfully used AI in machinery for optimized operations. Other companies like Tesla, Inc. have advanced their research in AI to the extent of augmenting human capabilities in their machines. Such extensive application of AI in machines has several benefits, including improved efficiencies and lower operating costs.

Works Cited

Aoun, Joseph E. Robot-proof: higher education in the age of artificial intelligence. MIT Press, 2017.

Blackman, Greg. The skys the limit: Greg Blackman visits the University of Sheffields Factory 2050, where Rolls-Royce, McLaren, and Siemens, among others, are investing in research on digital manufacturing. Imaging and Machine Vision Europe SI, 2019.

Dolci, Rob. IoT solutions for precision farming and food manufacturing: artificial intelligence applications in digital food. IEEE 41st Annual Computer Software and Applications Conference, 2017.

Géron, Aurélien. Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow: Concepts, tools, and techniques to build intelligent systems. OReilly Media, 2019.

Kulawiak, Karolina. Manufacturing the platform economy. An exploratory case study of MindSphere, the industrial digital platform from Siemens. MS Thesis. 2021.

Litvinova, Tatiana. Managing the development of infrastructural provision of AIC 4.0 on the basis of artificial intelligence: case study in the agricultural machinery market. Institute of Scientific Communications Conference. Springer, Cham, 2019.

Marcus, Gary. Innateness, alphazero, and artificial intelligence. arXiv Preprint, 2018.

For many people, the concept of artificial intelligence is still something related to the area of science-fiction type of dystopia. However, this image seems to be closer to fading into obscurity with each passing day as AI becomes more common in peoples everyday lives. Uzialko (2022) notes that today, not only is AI a household name  it is sometimes even a household presence (think about, for instance, Alexa). Granted, its widespread adoption in society is a fairly new development, but it is not a new notion. AIs modern field came into being in the 1950s; still, decades were spent on making serious progress in the development of an AI system and turning it from a dream into a reality.

There is a high likelihood of you interacting with AI on a daily basis in one way or another without even realizing it. You do not even have to have the afore-mentioned Alexa installed at home: AI is present in all areas of our lives. That includes business, in which artificial intelligence can be applied in a wide variety of ways. Some of these ways might be primitive, however, one thing is true despite it: AI is currently virtually altering almost every industrys every business process. With the spread of AI technologies, they are becoming an essential element to hold a competitive advantage.

Among the uses of artificial intelligence in business, five are the most common. These are cybersecurity, machine learning, Internet searches, management of customer relationship, and personal assistance. When it comes to cybersecurity, AI systems are able to recognize a threat or an attack on a computer-based system, find its source, and prevent similar cases in the future. AI in machine learning is irreplaceable for capturing large amounts of data, and in Internet research it helps better understand search behaviors of users. Moreover, artificial intelligence changes software programs for client communication to ensure they stay accurate and relevant. In addition to that, AI bots are often used in business to facilitate the work of human assistants.

Reference

Uzialko, A. (2022). How artificial intelligence will transform businesses. Business News Daily.

Abstract

The intensive introduction of new and emerging technologies characterizes the current trend in the development of society. The following study analyzes the research results to determine the impact of artificial intelligence (AI) on aviation. As computerized capabilities continue to improve and become more widely available worldwide, their introduction into aviation is inevitable, highlighting the relevance of the work. The article also discusses ways to use AI in the aviation environment, for example, applications in aviation. Qualitative and quantitative analyzes allow a detailed study of the relationship between technological development and changes in aviation.

Proposal Summary

In the last decade, an urgent problem has been the question of the rational use of the achievements of scientific and technological progress in the field of complex systems based on artificial intelligence (AI). It is a field of science and engineering that deals with creating machines and computer programs that solve intellectual problems by modeling intelligent behavior. Solving problems of intelligent control in technical systems is an actively developing interdisciplinary research area. The relevance of intelligent systems lies in the need to reduce the human factors influence on the control object while maintaining a high level of safety and reliability during its technical operation (Asatiani et al., 2021). This problem is considered in the general context of the impact of new technologies on the aviation field: how is the implementation going, and what are the consequences of this?

The new world of air travel undoubtedly requires an innovative approach. Artificial intelligence technologies have changed the way people think about opportunities, and many companies are starting to prepare for an even more modern future today. Some US companies are already using artificial intelligence technologies in training future pilots. It launched a program called Pilot Training in the Future to find new ways to train pilots and use modern biometric systems, artificial intelligence technology, and virtual reality systems in their simulators (Svens, 2019). Such simulators can give an idea of the passage of real tests to provide the students with the correct concept of erroneous actions and teach them how to correct them (Cheung et al., 2022). By implementing artificial intelligence technologies, US companies have created a fast and efficient way for pilots to learn and practice, allowing them to train many pilots each year.

The appearance of the cockpit of modern aircraft has changed significantly. There has been a transition from disparate indicators of physical parameters to integrated multifunctional systems that inform the pilot about various states of the aircraft and the environment (Daniels et al., 2019). At the same time, not only 2D and 3D visual information is used, but also spatial sound and tactile interfaces that are new in aviation, allowing the design and implementation of new adaptive human-machine interface (HMI) concepts (Lim et al., 2018). Adaptive HMIs should increase pilot comfort levels and situational awareness, which should reduce crew members up to and including their total removal without adversely affecting flight safety.

Artificial intelligence (AI) is rapidly penetrating industrial automation, particularly as part of recommender systems and intelligent decision-making systems that advise human operators on managing complex technological systems. That qualitatively enhances human-machine control systems but also creates new challenges for ensuring the safety of workers and production assets (Piller & Nitsch, 2022). At the center of it is the problem of trust/distrust of the human operator in AI-based algorithms.

Incorporating artificial intelligence and autonomy into aviation may not be new, but it has a profound impact. Thus, it is necessary to analyze the problem, assuming previous studies. The work will use qualitative and quantitative research methods to achieve the goals. It is essential to carry out a meaningful interpretation of the results obtained and their discussion. The structure of the proposal will take into account each of the points presented.

Literature Review

In the development of onboard equipment, several stages were observed, each corresponding to the appearance of onboard complexes, determined by the technical capabilities and technological base characteristic of that time. The creation of cybernetic systems capable of performing creative functions traditionally considered mans prerogative has significantly increased productivity in many industries. Aviation is no exception: air travel has not yet become fully automated, but leading airlines and aircraft manufacturers are already investing in artificial intelligence. For example, a deep neural network is currently being developed for an airborne collision avoidance system that will replace its networks (DNNs) without the explicit rule foundation used to implement ACAS Xa (Katz et al., 2021). Instead, it offers a 40% improvement over the most recent iteration of Traffic collision avoidance systems (TCAS) and learns from millions of situations and 180,000 possible conflicts in everyday life (Pavel et al., 2022). Thus, introducing artificial intelligence into aviation will make it even safer.

Human Machine Interfaces

Artificial intelligence applications in aviation and human-machine interfaces include autonomy in air traffic control. Given the current low level of automation in ATM methods, it is unreasonable to assume that performance will be matched by a high degree of automation and increasing autonomy (Sabatini et al., 2020). The National Aeronautics and Astronomy Administration (NASA) asked the National Research Council (NRC) to study freedom of flight in 2013 (Chapman, 2020). Part of the study was establishing operating principles for compatibility between ground stations and aircraft with various automated vehicles. It was supposed to be implemented in addition to predicting the impact of AI systems and aircraft in regulated airspace at the network level.

Applications in Aviation

The Internet of Things is gaining momentum across all industries, but its impact on aviation could be revolutionary. IoT technology has many benefits, from ease of use to fast data collection. There are several exciting applications for IoT technology, from design to baggage claim. In aviation, satellite navigation has long been a complementary means of positioning (Dovis et al., 2020). The development of GNSS has provided an additional positioning service for many phases of flight, both for recreational and commercial air transport (Grote et al., 2022). Improvements and enhancements to satellite navigation through augmentation systems will help pilots through all stages of flight. It will include from taxiing to takeoff, en-route, and landing in all weather conditions, achieving the level of safety needed to handle the continuous flight increase (Zheng et al., 2019). When planning aircraft routes and landing schedules at busy airports, it is important to ensure that aircraft are always at a safe distance from each other. GNSS will provide a guaranteed service with sufficient accuracy so that airlines and pilots can know their location reliably and accurately enough to realize significant efficiency savings.

Implementation of AI in Aviation

A more difficult task is to solve aircraft control issues based on solid intelligence. Scientists and developers are sure that by 2030 a computer will be able to do this, and it will do it better than a person (Schlüter et al., 2022). Experts believe that equipping artificial intelligence with pre-formed rules will ensure the proper safety of passengers and crew of aircraft as well.

It should be noted that the crews presence determines a wide range of features and restrictions imposed on the process of liner control. A person is characterized by a low speed of perception, processing of information, reactions, insufficient reliability when performing monotonous functions, and more. At the same time, when solving flight problems, the crew interacts with onboard equipment in a complex, constantly changing tactical environment (Lim et al., 2018). It should be taken into account when using AI in air traffic participant management systems.

The main problem that hinders the introduction of AI in aviation, especially in civil aviation, is a large number of emergencies that cannot be foreseen and described in the algorithm. The transfer of a person to the status of an operator is dangerous because it is difficult to determine when to intervene (Golan et al., 2019). If artificial intelligence can do routine operations better than a person, then when faced with an emergency, a creative component is needed, which AI is deprived of (Golan et al., 2019). It is necessary to increase the number of sensors of information about the operation of systems in the air and on the ground to give artificial intelligence detailed data for making the right decision. It leads to additional high costs and, as a result, will significantly increase the price of infrastructure (Wamba-Taguimdje et al., 2020). In addition, the question of determining legal liability when a pilot (controller) is replaced by artificial intelligence remains open.

Thus, traditional methods can no longer guarantee an increase in the quality of control of complex objects since they do not consider all the uncertainties in which solutions must be sought. Therefore, the design of new aviation technology requires creating and implementing onboard software using knowledge processing methods using modern intelligent technologies.

Although many factors slow the introduction of AI into the process of controlling an airliner, among them a high degree of uncertainty in control processes, increased demands on flight safety, and economic feasibility, artificial intelligence has enormous potential in the aviation industry. Although introducing artificial intelligence is now at an early stage, some progress has already been made. Due to the high safety requirements in this area, it is necessary to carry out extensive tests and certifications. Many experts say that AI cannot be used on passenger aircraft until neural networks can make informed decisions and learn how to explain and justify their actions (Bird et al., 2020). Thus, one of the main reasons for the slow adoption of AI in aviation is time.

Objectives and Outcomes of the Study

This work aims to explore and thoroughly evaluate AI systems to determine the role of human and machine understanding in flight and its possible ramifications. The paper discusses how aviation safety can be improved through the development and use of technology. It is necessary to analyze AIs early recognizable proof and error detection and its components, including the Internet of Things and aviation applications. Thus, the goals of the paper are:

• to analyze technological development in the field of aviation;
• to assess the degree of implementation of artificial intelligence and its forms in the aviation environment;
• to consider existing AI in aviation, including applications in aviation;
• to study the impact of technological progress on aviation and human-machine interfaces;
• to establish a link between the implementation of artificial intelligence and flight safety.

As a result of the study, the degree of AI implementation in aviation will be analyzed, taking into account the history of its development. Through a quantitative analysis, the level of current use of AI in aviation and how this affects aviation human-machine interfaces will be examined. In addition, the study will determine how safe the use of AI in the air is and whether it is possible to completely abandon human resources.

Interest of the Study and Research Questions

Todays technological environment is characterized by rapid and continuous development. Nanotechnologies, biometrics, computing power and speed, and learning technologies are becoming more popular, accessible, and constantly improving. Based on it, there is interest in conducting this study, which aims to examine the current state of AI in aviation. The commercial sector relies more on automation and technology to increase efficiency, security, and information transfer speed. New technologies open opportunities that require significantly less pilot or controller input. From here, the studys central questions are formed:

• How does the development of technologies in the field of aviation happen?
• Is it possible to completely abandon human participation in aircraft with AI?

Research Methods

The study was organized through a qualitative online survey, during which personal data, opinions, and the level of use of artificial intelligence technologies were determined. The author applied the judgment/target sampling method to obtain participants who can provide important data. In addition, various studies have been conducted to understand this studys implications. The Literature Review section of this dissertation explores the impact that the aviation industry could face if AI expands its capabilities. It also examines the extent to which artificial intelligence applications are applied in various sectors, such as manufacturing or the actual flight process.

The existing trends in the implementation of AI in aviation will be analyzed and compared. Based on a quantitative research approach, an empirical online experiment was conducted to study the impact of the introduction of AI on people, both employees, and passengers in civil aviation. The effect on the public of aviation security measures and their impact on flight intent, customer satisfaction, value for money, and perceived health risks were studied. The study found that airline safety measures are likely to impact expected customer satisfaction positively. At the same time, the direct impact of the safety measures by airlines does not reduce their perceived health risk but also increases their value for money.

The following paragraphs describe the methodology implemented to achieve the goals mentioned above and whose steps are related to the documents structure through successive sections. First, the paper analyzes airline organizational trends, challenges, and AI tools. A comprehensive review of the literature is underway, focusing on these topics, particularly those studies, including developing qualitative and quantitative (not only related to AI) models used for strategic decision-making and related financial objectives. In the case of describing airline trends concerning AI, a general introductory overview of the topic is first presented. It is followed by a deep dive highlighting the most critical aspects of each issue.

The air transport sector, having undergone significant changes in the use of technology, demonstrates both threats and opportunities for airlines. The study includes references to the effect caused by AI, both at the initial stages of implementation and later steps in the near future after establishing the new era. Bibliography and references are selected primarily based on a systematic search of the Google Scholar database, airline websites, and regulatory reports. Specific keywords were used for each topic, and the results were filtered by limiting papers to those within the field of air transport research.

Gantt Chart for a Study

Reference List

Asatiani, A. et al. (2021) Sociotechnical envelopment of Artificial Intelligence: An approach to the organizational deployment of Inscrutable Artificial Intelligence Systems, Journal of the Association for Information Systems, 22(2), pp. 325352.

Bird, E. et al. (2020) The ethics of Artificial Intelligence: Issues and initiatives, European Parliament. Web.

Chapman, B. (2020) Congressional committee resources on space policy during the 115th congress (20172018): Providing context and insight into US government space policy, Space Policy, 51, p. 101359.

Cheung, J. et al. (2022) Using artificial intelligence to clear pilots for take-off, NATS. Web.

Daniels, T.S. et al. (2019) Regarding pilot usage of display technologies for improving awareness of aircraft system states, 2019 IEEE Aerospace Conference [Preprint].

Dovis, F. et al. (2020) Recent advancement on the use of Global Navigation Satellite System-based positioning for intelligent transport systems [guest editorial], IEEE Intelligent Transportation Systems Magazine, 12(3), pp. 69.

Golan, M., Cohen, Y. and Singer, G. (2019) A framework for the operator  workstation interaction in industry 4.0, International Journal of Production Research, 58(8), pp. 24212432.

Grote, M. et al. (2022) Sharing airspace with Uncrewed Aerial Vehicles (UAVS): Views of the general aviation (GA) community, Journal of Air Transport Management, 102, p. 102218.

Katz, G. et al. (2021) Reluplex: A calculus for reasoning about Deep Neural Networks, Formal Methods in System Design [Preprint].

Lim, Y. et al. (2018) Avionics human-machine interfaces and interactions for manned and Unmanned Aircraft, Progress in Aerospace Sciences, 102, pp. 146.

Pavel, M.I., Tan, S.Y. and Abdullah, A. (2022) Vision-based autonomous vehicle systems based on Deep Learning: A Systematic Literature Review, Applied Sciences, 12(14), p. 6831.

Piller, F.T. and Nitsch, V. (2022) How Digital Shadows, new forms of human-machine collaboration, and data-driven business models are driving the future of industry 4.0: A Delphi study, Contributions to Management Science, pp. 131.

Sabatini, R. et al. (2020) Avionics Systems Panel Research and Innovation Perspectives, IEEE Aerospace and Electronic Systems Magazine, 35(12), pp. 5872.

Schlüter, U. et al. (2022) Exposure modelling in Europe: How to pave the road for the future as part of the European Exposure Science Strategy 20202030, Journal of Exposure Science & Environmental Epidemiology, 32(4), pp. 499512.

Svens, E. (2019) OSM Aviation Academy project funded by the European GNSS Agencys aviation grant program, OSM Aviation Academy. Web.

Wamba-Taguimdje, S.-L. et al. (2020) Influence of Artificial Intelligence (AI) on firm performance: The Business Value of AI-based transformation projects, Business Process Management Journal, 26(7), pp. 18931924.

Zheng, Y., Yang, Y. and Chen, W. (2019) New Imaging Algorithm for range resolution improvement in Passive Global Navigation Satellite systembased synthetic aperture radar, IET Radar, Sonar & Navigation, 13(12), pp. 21662173.

Agrawal, A, Gans, J. S. & Goldfarb, A. (2019). Artificial intelligence: The ambiguous labour market impact of automating prediction. The Journal of Economic Perspectives, 33(2), 31-50. Web.

The article in question considers the impact the spread of artificial intelligence technology may have on the labor market. The authors define their goal as to establish and define the way artificial intelligence influences the job market and ratio of work tasks distribution. In addition, they are determined to predict the further course of events concerning the jobs that will be affected the most. The authors state that though machines can substitute workers, they lack one significant characteristic every human being possesses. It is called cognition or, in other words, the ability to make decisions. Decision-making goes hand-in-hand with prediction, which artificial intelligence is already capable of. Throughout the whole article, the authors assess the influence of artificial intelligence on both these aspects. The authors explicitly state that the artificial intelligence technology may substitute people who work on prediction tasks such as weather forecasts or human resources area since the process of documents-related work can be automized.

Throughout the whole article, the authors see their task as to highlight the necessity of thinking regarding prediction and decision-related tasks where prediction has no value without decision. That is why it is necessary to assess if the process related to a particular work activity involves both prediction and decision. The authors stress that if people who perform prediction-related tasks are substituted by artificial intelligence, the decision-related sphere will be affected as well. The influence may be positive or negative since the changes may cause either the work tasks downstream or upstream. The authors repeatedly address the ambiguity of the issue throughout the article because artificial intelligence technology is constantly developing, so it is difficult to assess its abilities adequately.

This article is devoted to the theme of the development and implementation of elements of artificial intelligence (AI) in the context of various countries. While this technology can significantly simplify many peoples lives, according to the authors, those with the greatest economic power will benefit most from it (Chen et al., 2021). Thus, improving the life of society with the help of this technology is complicated by the presence of global inequality, which Western institutions are actively trying to eliminate.

However, the measures taken often consist only in forming more multinational groups, which makes it possible to tackle the problem of inclusion only in the context of direct research. Since the overwhelming majority of experimentation is still focused on the West, especially in America, unfair distribution of resources will hinder the future implementation of this technology. As the main existing barriers, the authors highlight the need for data collection, which requires high-quality technology and a reliable Internet connection and is much more expensive in the southern hemisphere (Chen et al., 2021). The second limitation is insufficient research laboratories, which cannot be created due to the attendant risks and unstable political and economic situations. However, these problems can be addressed by developing existing opportunities in three directions: the creation of affinity groups, the inclusion of residents in research, and programs to reduce the development gap (Chen et al., 2021). Without the introduction of such applications, the development of AI will remain non-inclusive.

This article was written by four authors, each of whom is from a different university. Each of them is a Ph.D. student whose research area includes various technical disciplines, including artificial intelligence. However, at the moment, they do not have enough background to assess their authority fully. Accordingly, it is advisable to approach the existing text with caution. Its content is intended for a broad audience and provides an overview and theoretical analysis of possible ways to resolve AI development inequalities. The text contains a small number of specific terms, making it easier for people from the topic under study to understand the material.

Reference

Chan, A., Okolo, C. T., Terner, Z., & Wang, A. (2021). The limits of global inclusion in AI development. arXiv preprint arXiv:2102.01265.

Introduction

The business model canvas is the most commonly used tool in developing a business strategy. Its application ranges from schooling to entrepreneurial simulation to real-world company planning. Furthermore, the business model canvas is a means of describing, assessing, and constructing business models, with nine building elements that demonstrate the logic of how an organization plans to create money. The nine blocks represent customers, offer, infrastructure, and financial viability. The business model canvas can assist entrepreneurs in identifying their resources and capabilities and matching them to market needs. The nine blocks in the business model canvas, which include vital partners, cost structure, and others in relation to AI, can be summarized to explain their significance in a business.

Business Model Canvas Summary in Relation to AI

Key partners explains the network of suppliers and partners required by a firm. Some activities, for example, are outsourced, and some resources are obtained outside the organization (Bretones et al., 2021). Key activities are the most crucial things a firm must do to have its business model succeed. The business model can include everything from software development to supply chain management to consulting (Bretones et al., 2021). In addition, key resources are the essential assets required by a company model. Depending on the company model, it can take several forms, such as physical, intellectual, human, or financial. A value proposition is the core value of a product or service that may be communicated to clients (Bretones et al., 2021). Usually, it validates in which width one product or service can stand out among nobles.

Furthermore, customer relationship refers to the kind of ties that a firm has with its customers. How a company interacts with its clients significantly impacts the user experience. On the other hand, Channels explain how a corporation communicates with its customers (Fatima et al., 2022). It acts as a link between the company and its customers and is crucial to the user experience. Customer segments are specific groups of people that a company wishes to reach. The segmentation could be based on shared requirements, habits, or other characteristics. For the organization to create value, a precise categorization is required (Keane et al., 2018). The cost structure describes the essential costs spent when running a business model. Some company models are cost-driven, focusing on minimizing and optimizing overall expenses, while others are value-driven, aiming to provide a premium user experience (Keane et al., 2018). Revenue Streams describe the primary revenue streams generated by the business model. It can come from a one-time transaction or foreseeable future revenue.

The Business Model Canvas has long been considered a valuable tool in the corporate innovation process, assisting teams of individuals to convey their new (or current) service or product value proposition. With every company leadership team worldwide discussing or at least thinking about their Artificial Intelligence strategy, it is critical to update the original Business Model Canvas (Carter M. & Carter C., 2020). That is due to be more specific in capturing future Artificial Intelligence goods and services considerations. As a result, Senior Leadership Teams and Innovation teams will be able to define the opportunities in their organization better (Fatima et al., 2022). Most organizations have a considerable gap between aspiration and execution, and three-quarters of leaders believe AI will help their companies to enter new markets (Ojasalo J. & Ojasalo K., 2018). A significant proportion believes AI will help companies gain or maintain a competitive advantage (Ojasalo J. & Ojasalo K., 2018). On the other hand, around one out of every five businesses have implemented AI in some of their offerings or operations.

Conclusion

AI and machine technologies transform how organizations interact with customers and provide more in less time. AI technology has numerous applications in various industries, including marketing and technology. The potential for a company is limitless; yet, to incorporate AI and machine learning technology into businesses, individuals must have a capable workforce managing the technologies. Furthermore, revenue streams, channels, customer interactions, critical collaborations, and cost structure are some of the pieces that comprise the Business Model Canvas.

References

Bretones C., B., Hoffmann, F., & Metternich, J. (2021). Comparison of AI-Based Business Models in Manufacturing: Case Studies on Predictive Maintenance. ESSN: 2701-6277. Web.

Carter, M., & Carter, C. (2020). The creative business model canvas. Social Enterprise Journal. Web.

Fatima, S., Desouza, K. C., Buck, C., & Fielt, E. (2022). Public AI canvas for AI-enabled public value: A design science approach. Government Information Quarterly. Web.

Keane, S. F., Cormican, K. T., & Sheahan, J. N. (2018). Comparing how entrepreneurs and managers represent the elements of the business model canvas. Journal of Business Venturing Insights, 9, 65-74. Web.

Ojasalo, J., & Ojasalo, K. (2018). Service logic business model canvas. Journal of research in marketing and entrepreneurship, 20(1), 70-98. Web.

Overview of the Issue

It is possible to develop a particular insight into the perspectives of Artificial Intelligence (AI) integration in the private health sector. The range of options through which AI-driven robots can enhance healthcare delivery is broad. An instance is AI machine learning that serves replacement for traditional machine learning in precision medicine. This opportunity to predict the chances for particular treatment procedures to succeed is a breakthrough. AI also can help nurses with structuring clinical notes on patients due to natural language processing features. Moreover, AI can fulfill an administrative function, significantly improving efficiency in hospital administrative areas. In contrast, integration of Artificial Intelligence can be a substantial issue and challenge for the private health sector, and in particular, for Ramsay Health Care. On the one hand, rapid development and gradual integration of AI in the private health care sector potentially minimize the number of workplaces and reduce work hours. Attempting to gain maximal profit from this intervention, business owners may even renounce some job types currently done by employees. On the other hand, AI integration into the health care system may impact human behavior. For instance, AI-driven bots may become better at modeling conversations, and most interactions of patients would be done with robots instead of actual medical specialists. It would negatively impact social experience resulting in deterioration of communication skills of the entire population.

Stakeholder Groups and Their Perspectives

With respect to types of stakeholders, the perspectives of the three most essential groups can be examined. Investors are the ones most concerned with minimizing costs and increasing profit obtained from a private health care sector. It is not in their interest that growth of shares value, net profit, or another form of financial return is limited by the necessity to fulfill social responsibilities. That is why it is possible to expect investors to argue for the maximal integration of AI. Then it is customers, as another vital group of stakeholders. They are concerned with service quality, safety, and cost. As mentioned above, AI-driven bots may deteriorate the social communication experience. However, the moderate and gradual integration of AI is cost-effective and improves services quality from the perspective of customers. Employees are ones who can be both benefited and be negatively affected by AI integration in the private health care sector. They may expect robot-driven applications to facilitate them in their responsibilities fulfillment. It may enhance the quality of service and reduce work-related stress. However, it is also possible that the complete integration of AI would increase the unemployment rate. Some workers may lose their jobs, or work hours would be reduced. It is likely that without having strong social policies in the private health care system and established ethical guidance, employees would become the most vulnerable and unsatisfied group of stakeholders.

The Challenges and Opportunities

Challenges to the private health care sector presented by the integration of AI are substantial. From the infographics, it is possible to see that the healthcare and social assistance spheres are most endangered. 11% of jobs would become automatable in the next 15 years. It would affect all the people employed in this sector. Other industries are less influenced, but changes in their operation would cause indirect influence on the functioning of the private health care system as well, which is detailed in the next slides. The other issue presented by the integration of AI in the health care system is the change in social behavior. The prevalence of chatbots and reduction in interaction between specialists and patients results in the gradual deterioration of communication skills. Infographics show that patients have favorable experience using applications for coping with severe events. It may indicate a gradual increase in the popularity of AI-driven digital applications. Such tendency leads to lowering the number of everyday social interactions overall. It may negatively impact the behavior of humans because of inevitable adapting to communication with bots. On the other hand, the number of opportunities presented by the integration of AI in the private health care system is impressive. It includes but is not limited to improving treatment outcomes through machine learning and rule-based systems intended for diagnosing and treating disease. A variety of administrative applications may reduce the scope of duties of healthcare workers, while natural language processing may assist nurses. AI integration would also optimize expenses, making the private health sector more profitable. Other industries may also become more cost-effective, resulting in more affordable medical equipment available.

Ethical Responsibilities

With respect to ethical responsibilities in relation to the AI integration in the private health care system, there are some implications and respective measures to be discussed. As it was outlined in previous slides, the possibility of reduction in the number of workplaces and even extinction of some jobs is a significant challenge. The respective ethical responsibility of employers, with the support of investors and customers, is to ensure that workers are protected from job loss. It is also vital to replace types of positions that would become obsolete with new ones, fully compatible with AI-driven opportunities. The other crucial concern to discuss is the ethical responsibility of employers and investors to maintain humanity or prevent potential changes in peoples behavior because of the spread of machines in most spheres. Replacement of administration and customer services roles leads to making humans addicted to technologies. Employers should ensure that patients always have access to conversations with a respective specialist in case AI-driven applications are not a solution. Employers also should limit the daily interacting of people with bots.

Recommendations for a Strategic Response

It is possible to suggest a set of recommendations for a strategic response to the adverse consequences of integrating AI in the private health care sector. First, there should be a focus on capital-intensive strategies of care. It would enable to prevent the reduction in the number of jobs while not depriving AI of its usefulness as the complementary tool for some processes facilitation. Moreover, continue enabling people to provide some services AI can also do not only limits the negative influence on human behavior but may be beneficial for companies that intend to show their superior care to customers. The other potentially useful strategic response is a direct limitation of influence AI has on peoples behavior and unemployment rates. It can be done by banning the complete automatization of processes in the private healthcare sector. Also, should be ensured equal access to AI-based and labor-based services. The additional recommendation is to consider replacing positions that would become obsolete with ones compatible with AI-driven solutions. It would ensure an unchanged number of types of jobs and reduce unemployment rates. Finally, it is possible to suggest a vital solution to address ethical implications. It is the rise of private clinics social responsibility. It should be done by the promotion of stakeholders to establish ethical guidelines to ensure the protection of workers from unemployment regardless of the level of AIs development. This intervention is less likely to influence the deterioration of behavior that AI might cause directly, but it is rather beneficial for the sense of humanity overall. Business ethics is a crucial part of the functioning of both society and corporations. The simultaneous effect of all three strategic responses would address existing threats and challenges.

References

Davenport T, Kalakota R. (2019) The potential for artificial intelligence in healthcare, Future Healthc, 6(2), pp. 94-98.

Davidson, J. (2020) The robots are coming: 2.7 million Aussie jobs to disappear. Web.

Inkster, B., Sarda, S. and Subramanian, V. (2018) An empathy-driven, conversational artificial intelligence agent (Wysa) for digital mental well-being: real-world data evaluation mixed-methods study, JMIR mHealth and uHealth, 6(11). Web.

Korinek, A. and Stiglitz, J. (2021) Artificial Intelligence, Globalization, and Strategies for Economic Development, National Bureau of Economic Research, w28453. Web.

Matjaz, P., Mahmut, O. and Janja, H. (2019) Social and juristic challenges of artificial intelligence, Palgrave Commun, 5(61).

University of Bern and Inselspital Opens the Center for artificial Intelligence in Medicine (2020) Web.

Innovations in health care play an essential role for people and all of humanity. TThe development and implementation of the latest technologies in medicine can significantly increase the duration and improve the quality of human life. Innovative technologies in medicine are now emerging at an unprecedented rate. For the successful development of medicine, it is necessary to pay attention to new technologies that can be used to improve equipment and tools. This paper will discuss two medical technological innovations that are significant for the future of a medical organization and how different stakeholders could benefit from them.

The first medical innovation that can be proposed is 3D bioprinting that is continuously being improved. It brought humanity as close as possible to the real synthesis of living tissues that do not differ from those created by nature. The first tissue recreated on such a printer was a fragment of the liver (Alexandrea, 2019). Now developments are underway in terms of creating neurons, internal mucous surfaces, bones, and tendons. Tests for the transplantation of a part of the liver created by using 3D bioprintinghaves already passed, and its results were successful (Alexandrea, 2019). Bioprinting has become a perfect novelty in transplantation and was initially conceived as a method of cell regeneration after severe burns (Alexandrea, 2019). But then the path opened up to more exciting possibilities. 3D printers appeared to play a vital role in regenerative medicine, helping specialists create blood vessel tissue, bones, heart valves, cartilage, artificial skin, and even organs (Chameettachal et al., 2019). The ability to create artificial organs that are not rejected by the bodys immune system could be a revolutionary find that will save millions of lives.

3D printed prostheses are becoming more popular as they are entirely custom-made. Besides, digital functionality allows them to match individual measurements with millimeter accuracy (Yan et al., 2018). This provides an unprecedented level of comfort and mobility for patients and ease-to-use devices for doctors. Significant progress has been made in the use of 3D printing for cancer treatment, stomatology, and other spheres. For instance, scientists have created a fast and inexpensive approach to make facial prostheses for people who had surgery for eye cancer (Hendricks, 2016). Furthermore, using printers can create durable and water-soluble items (Lamichhane et al., 2019). For example, 3D printing can be used to print tablets that contain many chemical elements aimed at a comprehensive cure of a particular disease.

Heart issues that people experience worldwide can also be resolved or at least mitigated slightly with the help of 3-D models. Only in US heart diseases are the leading causes of death for people of different races and ethnicity (Heart Disease Facts, 2020). Invasive catheterization that is needed to diagnose blocked or narrowed arteries and complicated methods of improving blood flow, such as stenting, can be avoided using 3-D printing. Charles Taylor, a professor who launchethe a company HeartFlow, helps patients evade invasive diagnostic methods and enhance treatment results (Time staff, 2019). HeartFlow developed creates personalized 3-D models that can move and zoom scenes to imitate different ways to identify a disease on a screen (Time staff, 2019). By adding the HeartFlow & to our available resources for diagnosing stable coronary disease, we can provide patients with better care as we evaluate risk, stated a cardiologist at the American College of Cardiologys annual meeting (Time staff, 2019, para. 11).

The second innovation that can boost medicine and help medical professionals to approach various illnesses is artificial intelligence (AI). Artificial intelligence (AI) is one of the most promising technologies in Medtech. AI services can improve diagnostics accuracy, automate the doctors work, choose the best treatment method, create new medicines, etc. AI is now one of the fastest-growing segments of the global healthcare market: Frost&Sullivan forecasts it will reach $6.6 billion by 2021 (2016).

Due to extensive historical medical data, artificial intelligence can be useful in making a diagnosis and choosing the appropriate treatment, giving the doctor a third opinion. With all the available medical information about a particular disease, the AI can analyze it and determine which treatments and medications have been most effective in the history of medical practice. A promising area of application of AI is the analysis of medical images (Ahuja, 2019). The AI system is trained to identify various diseases and pathologies. In this direction, technologies have achieved evident success and, therefore, are already being gradually introduced into clinical practice (Ahuja, 2019). Also, there is great potential for using AI in the development and testing of new drugs. According to some studies, large pharmaceutical companies spend up to $2.6 billion to develop and market a single drug (Sullivan, 2019). Some of the largest pharmaceutical companies, Novartis (2019), and Microsoft opened an AI lab to use smart algorithms in the creation of medicines.

Another example of an AI application is the use of an IBM Watson supercomputer in Tokyo to clarify the diagnosis of a 60-year-old patient with leukemia and prescribe successful treatment by comparing the genetic data of millions of research papers (Sanchez, 2019). There are more and more such cases: for example, the Google Health team also reported that the cancer-diagnosing scans based on AI identified 5% more cancer cases and delivered 11% fewer false positives than a control group of six human radiologists when researching lung cancer (Time staff, 2019).

Practice and experience of the doctor may not be enough for the correct diagnosis oThe practicesease. With access to scientific literature and millions of case histories, a neural network developed with AI can quickly classify a case, correlate it with similar situations, and formulate suggestions for a treatment plan (Shahid, 2019). Nevertheless, at the current stage of technology development, AI cannot solve complex tasks, such as creating devices that independently scan a person and prescribe effective treatment. Artificial intelligence has yet to earn its credibility-both from patients and practitioners (Engler, 2020). The majority of people are still skeptical about predictions made by algorithms (Engler, 2020). To overcome this barrier, it is necessary to create a large number of successful cases in the field of computer diagnostics for various medical areas, as well as a lot of work on the formation and observance of ethical principles for the use of AI for the industry.

To make a conclusion, one can say that new technologies in the field of medicine are in constant development, giving humanity hope for longer life, in which there will be no diseases and unpleasant sensations. Unexpected approaches make it possible to give birth to children who previously did not have such an opportunity, cure diseases that were recently considered incurable, prolong youth, and provide humanity confidence in the future.

References

Ahuja A. S. (2019). The impact of artificial intelligence in medicine on the future role of the physician. Peer Journal, 7, e7702.

Alexandrea P. (2019). Is 3D bioprinting the future of tailor-made medicine? 3Dnatives. Web.

Chameettachal, S., Yeleswarapu, S., Sasikumar, S. et al. (2019). 3D bioprinting: Recent trends and challenges. Journal of Indian Institution Science, 99, 375403.

Engler, A. (2020). A guide to a healthy skepticism of artificial intelligence and coronavirus. Brookings. Web. 

Frost&Sullivan. (2016). From $600 m to $6 billion, artificial intelligence systems are poised for dramatic market expansion in healthcare.

Heart Disease Facts. (2020). Web.

Hendricks, D. (2016). 3D printing is already changing health care. Harvard Business Review. Web.

Lamichhane, S., Bashyal, S., Keum, T., Noh, G., Jo, S., Rakesh, E., Choi, B., Sohn, J., Hwan, D., Sangkil, L. (2019). Complex formulations, simple techniques: Can 3D printing technology be the Midas ttouch in the pharmaceutical industry? Asian Journal of Pharmaceutical Sciences, 14(5), 465-479.

Novartis. (2019). Novartis and Microsoft announce a collaboration to transform medicine with artificial intelligence. Web.

Sanchez, G. (2019). AI: Almost immortal. Medium: Towards data science. Web.

Shahid, N., Rappon, T., & Berta, W. (2019). Applications of artificial neural networks in health care organizational decision-making: A scoping review. PloS one, 14(2), e0212356.

Sullivan, T. (2019). A tough roadThe costs to develop one new drug is $2.6 billion; the approval rate for drugs entering clinical development is less than 12%. Policy & Medicine. Web.

Time staff. (2019). 12 innovations that will change health care and medicine in the 2020s. Time. Web.

Yan, Q., Dong, H., Su, J., Han, J., Song, B., Wei, Q., & Shi, Y. (2018). A review of 3d printing technology for medical applications. Engineering, 4(5), 729-742. Web.