Category: Artificial intelligence

The selected technology is an artificial intelligence (AI) machine by the name of AlphaGo Zero. It is an evolution of previous well-known machines from the company Deep Mind which focuses on self-learning to play a popular Chinese strategic board game of Go. It has far surpassed the ability of any previous iterations of this AI, including the one that has beaten the human world champion, which makes it the most intelligent Go player in history (Kurzweil, 2017).

In addition, this technology is unique due to the learning techniques that it utilizes. Previous AI has relied on human input and continuous practice through playing human and machine players. AlphaGo Zero relies on the concept of tabula rasa which teaches itself without provided data, human guidance, or domain knowledge outside the basic game rules. Through a deep neural network, AlphaGo Zero is able to evolve using reinforcement learning by playing itself. This approach increases the speed and processing power that is used to make infinite amounts of choices while searching for the next move in the game (Silver et al., 2017).

The AlphaGo Zero technology was chosen because of its unique ability to self-develop independently. The existence of such capacity in an AI machine has far-reaching implications beyond the game of Go. Up to date, human input and guidance were necessary to create this level of self-reinforcement. However, the innovative technology has proven to be exponentially faster and smarter at learning than any predecessors without drastically changing in hardware due to this unique feature. Single network AI such as AlphaGo Zero is forced to create its own language, concepts, and logic that are so advanced, humans have trouble understanding how to it works which leaves a wide space for debate about the ethicality and application of such technology.

Ethical Issue

The AlphaGo Zero AI technology will have repercussion for the ethical issue of impact on human values. It is an examination of moral principles that would undoubtedly be shifted with the introduction of radically life-altering technology. This technology is based on informatics which has implications for various decision-making processes, which are independent of human input; therefore, devaluing human responsibility (Palm & Hansson, 2005).

When AI machines engage in learning, they build value neural networks which are used to create decision trees with various outcomes. As the technology became more widespread and adapted to various fields, it would be expected to learn to make vital decisions which humans will use as the basis for their rationale. However, at this point, it is impossible to create artificial intelligence capable of sophisticated tasks in an imperfect environment (Quach, 2017). This can create an overreliance of human value on machines leading to consideration of whether such decision-making processes are ethical.

The human value would be the most prominent issue due to the approach which is applied to self-learning in AlphaGo Zero. Competent AI development begins with design decisions which focus on openly expressing value properties by the creators (Dignum, 2017). This requires human input which DeepMind chose to avoid, with the design based on self-learning that is reliant on implicit procedures of decision-making eliminating any ethical guidelines. The misalignment of human values with artificial intelligence will result in a lack of acceptance from society and impede collaboration between the two parties. AI systems must be created with transparency, and there should be an understanding of the decision-making processes by humans in order to create trust and accountability (European Parliament, 2016).

The evolution of artificial intelligence has led to the consideration of various socio-economic concerns that impact human value. The automation of various process and their increased efficiency will force human workers to undertake more unpredictable and creative jobs that a machine cannot conceptualize. That essentially brings up the question of human worth and existence if the tasks that machines can do increases as AI begins to learn and most likely exceed biological counterparts. Furthermore, the concept of wealth will be compromised in our modern economy.

The distribution of wealth without jobs will most likely increase inequality in favor of those designing and controlling AI systems. In addition, basic human concepts such as interpersonal interaction may be affected. Technology is already changing the way that people behave and communicate. Artificial intelligence may result in a decline of human interaction in favor of machines, as they are able to invest unlimited resources into any relationship and predict the best possible response to please a human (Bossman, 2016).

All these socio-economic concepts will be significantly altered by self-learning AI, challenging moral codes and considerations of ethical behavior. Human moral values are inherently imperfect based on accountability for ones actions rather than necessarily their prevention since a persons decision-making process is much more complicated than a series of rights and wrongs. As a machine, artificial intelligence will always attempt to derive the perfect outcome in its decisions. This may not always align with the human morality that is subjective in theory, creating a conflict of values since instinct and conscience cannot be programmed.

Future Integration

AlphaGo Zero technology in the future can be developed into a powerful decision-making engine that is able to analyze information and derive solutions based on the process of self-learning and interaction with other systems. Interconnected with data banks, AI would be able to formulate strategy and action plans much more efficiently than humans. This has implications in a variety of fields. Clinical research would be conducted without the necessity to create expensive and lengthy studies since every outcome would be simulated. AI could analyze patterns that are useful in business and marketing, suggesting strategies which would be the most effective, eliminating human factors that may cause an error. Practically every field would benefit from residual machine learning that improves outcomes.

While it is a simple board game AI, AlphaGo Zero has no dangers. However, with more complex situations, a lack of human input creates a void on the ethical values which the machine will choose to adopt through self-learning. As there is further integration, human values may become blurred as the interexchange of ideas continues. AI will evolve to the point of being able to function and learn with imperfect data, similar to the thought process of humans.

This will make interactions more natural. However, the paradox is that machines might be able to learn to mimic human behavior or use manipulative features such as lying to achieve results. Since the technology is based on the concept of achieving the best result through the most efficient choices on a decision tree, it may learn that manipulative behavior is the fastest way to achieve a result. It creates a myriad of issues about compromised human values since AI will most likely be a significant part of daily life.

Even now, there is fear that in the next decades many jobs will be replaced by machines and automated processes. It is most likely that on a mass scale, the processes will be controlled by artificial intelligence and slowly eliminate any human value. However, humans can benefit by using this technology to derive improved decision-making systems. Through proper regulation, deep mind networks will be used solely for assistance and conduct rudimentary tasks without human input.

The technological ecosystems will be used to derive new methodology and can be used for the improvement of human progress. A lot of world problems form due to poor decisions made by humans without consideration of long-term consequences. If artificial intelligence is used to solve many of these problems, humans can focus on self-improvement and self-actualization which supports a re-examination of ethics and moral values.

As AI gets smarter, it will be the human responsibility to ensure that there is an alignment of values. The technology and systems should be designed with adherence to a set standard of values that should be developed for AI learning. Value-sensitive design allows developers to integrate ethical norms into smart the machine parameters. All designs are made in consideration of responsible autonomy which allows for human control, regulation, and moral agents which sets moral boundaries for reasoning and decision-making processes that the AI machine may choose to undertake.

As a result, there is accountability and transparency in the AI function that helps to mitigate the ethical issue of impact on human values (Dignum, 2017). Morality is dependent on the perspectives that are available to examine any given issue. Undoubtedly deep mind AI will introduce humanity to new concepts that will lead to ethical reevaluation.

References

Bossman, J. (2016). Top 9 ethical issues in artificial intelligence. Web.

Dignum, V. (2017). Responsible autonomy. Web.

European Parliament. (2016). Artificial intelligence: Potential benefits and ethical considerations. Web.

Kurzweil. (2017). AlphaGo Zero trains itself to be the most powerful Go player in the world. Web.

Palm, E., & Hansson, S.O. (2005). The case for ethical technology assessment (eTA). Technological Forecasting & Social Change, 73, 543-558. Web.

Quach, K. (2017). How DeepMinds AlphaGo Zero learned all by itself to trash world champ AI AlphaGo. Web.

Silver, D., Schrittwieser, J., Simonyan, K., Antonoglou, I., Huang, A., Guez, A., & Hassabis, D. (2017). Mastering the game of Go without human knowledge. Nature, 550, 354-359. Web.

Introduction

Artificial intelligence basically refers to the intelligence that is created in the software or machines by mankind. Over the last three decades, the field of research on robots and software has resulted in explosion of artificially intelligence machines. For instance, across the globe, there are machines that can think, read, and react within the confines of programs installed in them. The artificial intelligence machines are slowly but steady replacing the roles of the often slow and unpredictable mankind in all spheres of life (Sarriera 31). This analytical treatise attempts to explicitly review the positive effects that artificial intelligence brings to business in terms of strategic business management.

Scope of artificial intelligence

The term attributed to artificially intelligence machine is cyborg, or a contraction of a cybernetic organism with the original function of incorporating machines into human processes to gather, store, and transfer activities to make the functions more efficient and fast. In order to make the machines more effective in their functions, which include supplementing or augmenting human capabilities whether physical or mental, such machines have self-regulating system called the artificial intelligence (Artis Consulting par. 9).

The machines have benefits such as managing big data, introducing and reinforcing the aspect of efficiency in production, accurately predicting current and expected level of productivity, and reducing the cost of labor by a substantial percentage.

Benefits of the Artificially Intelligent Robots

Many businesses across the globe are currently facing the imminent losses as a result of inability to effectively and efficiency manage the production processes, track and utilize large business data, and control the spiraling cost of factors of production such as labor. It is basically the nature of mankind to avoid losses by all means possible, even if it means using other forms of production and cost-tracking.

The introduction of the robotics in the business environment has come with tremendous and admirable achievements, especially in terms of data management and balancing different factors of production against efficient bundles of process efficiency. For instance, robotics in strategic process management has grown exponentially, so that we are going to be a hybrid of biological and non-biological intelligence and there wont be a clear between humans and machines in business management (Transcendent Man, scene 3).

Practical Example: Robotics in Data Management Intelligence

Since companies are currently facing the challenge of organizing data to define the market performance insights, especially in the foreign operations, they should introduce a data mining tool such as the Intelligence Miner to facilitate robotic data management. The dynamic aspects of data mining, with the support of the Intelligent Miner, might be instrumental in revealing powerful insight into the performance and efficiently of the company through different success indicators as summarized in figure 1 below. For instance, the descriptive and predictive modeling provide insights that drive better decision making (Artis Consulting par. 15).

This means that such a company will be in a position to streamline the data mining process to address the challenge of developing models quickly, understanding key relationships, and finding the patterns that matter most (Sarriera 74). This translates into the companys efficiency since growth in decision-making complexity and information accessibility will result in the need to align data systems, which are logically based with technologies that support the decision-making process.

Built with scale-out hardware and software that can load data into RAM, PCI-based flash, or disk, Oracle delivers the most efficient and industrial-strength offering for instant insights in carrying out robotic surveys (Sarriera 78). This will enable the company to not only deploy a data warehouse, but also maintain the warehouse in an efficient manner. As a result of the automation, a company will be in a position to implement automation, speed, and agility of the data warehousing strategy in tracking the automated data management and feedback tracking as summarized in figure 2 below.

This means that such a company will gain from the Teradata mining technology, which helps in solving business problems through merging highly scalable hardware, and a world-class paralled database (Artis Consulting par. 15). Thus, the company might make use of products such as Teradata database, Teradata Platform family, and Teradata consultancy services to improve on the results of text mining analytics.

Through integration of the data mining, text mining and data warehousing, the interested firm will be in a position to monitor all its business processes in an efficient and effective manner. These software tools are gaining popularity and their incorporation into user systems is increasing (Artis Consulting par. 9). The systems provided by these software tools motivate the evolution of conventional business intelligence decision-making procedures from the robotic surveys.

Conclusion

In summary, the improved characteristics of business intelligence systems, coupled with constant accessible support through robotic survey, cab create considerable technological benefits for a companys organizational decisions. For instance, through mobile technology, the management of a company will be in a position to easily access live feeds and monitor or enhance collected information, which may be a part of the decision-making process.

Works Cited

Artis Consulting. Structured and Unstructured data. 2015. Web.

Sarriera, John. Connecting the Selves: Computer Mediated Identification Processes: Critical Cyber-Culture Studies, New York, NY:New York University Press, 2007. Print.

Transcendent Man. Perf. Tom Abate, Hugo De Garis, Raymond Kurzweil, Docurama Films. 2011. Film.

Executive summary

Four months ago, a hospital approached us inquiring whether we would be capable as TechSoft Company creates software that would help them achieve more with less effort. In a hospital setting, a lot of paperwork requires doctors, nurses, and other personnel to work even more than they are supposed to. The extra time that they spend on paperwork could be utilized in matters concerning the patients. This is the main reason that the hospital, through its manager, approached us.

Our company is focused on providing software solutions to different issues in various sectors. In the case of this hospital, we set to advise on creating a system that will help store, verify, and help in the diagnosis of patients based on the information fed into it. The hospital needed a software system guided by the concept of artificial intelligence and machine learning to help reduce the workload for the doctors who had been noticed to suffer from the negative effects of burnout.

After establishing a plan and understanding how many developers are needed to complete this project, the hospital received a budget for the project and the amount they are supposed to pay upfront and after completion. The project took two months and two weeks to complete. The initial plan anticipated that it would take two months, but it took two more weeks because we opted for the agile method. The agile approach was preferred because the development had to work closely with the hospital to understand and integrate different requirements until the final product suited its needs.

Introduction

Four months ago, a hospital approached us inquiring whether we would be capable as TechSoft Company creates software that would help them achieve more with less effort. In a hospital setting, a lot of paperwork requires doctors, nurses, and other personnel to work even more than they are supposed to. The extra time they spend on paperwork could be utilized in patient matters. This is the main reason the hospital approached us through its manager. Our company is focused on providing software solutions to different issues in various sectors. In the case of this hospital, we set to advise on creating a system that will help store, verify, and help in the diagnosis of patients based on the information fed into it. The hospital needed a software system guided by the concept of artificial intelligence and machine learning to help reduce the workload for the doctors who had been noticed to suffer from the negative effects of burnout. The system would be able to carry out routine tasks for the hospital. For instance, when a patient is admitted to a hospital for the first time, their information has to be recorded and stored for future reference. Sometimes, due to fatigue and exhaustion, the individuals recording this information make a mistake, making it hard for a doctor to retrieve it.

The artificial intelligence technology guiding the system offers a range of possibilities in the healthcare industry that had not been seen before. For instance, apart from just storing information of patients, the system can receive information about the signs and symptoms of the patients and suggest a diagnosis. The field of artificial intelligence explores the capabilities of a machine and how it can act like a human being. For instance, a machine is taught to think and reason like people. This means that in the same way a doctor notices certain symptoms in an individual and recommends a path for them to follow, whether testing or a particular drug, it is the same way the system does. This paper evaluates a current management information system and directs on ways to improve it using artificial intelligence and machine learning.

Analysis

Analysis of Current MIS

The new system is set to perform incredibly and even allow the doctors and other healthcare providers in the hospital to offer better care to patients. This is largely due to the technologies that guide the performance of this system, artificial intelligence and machine learning. By integrating these key technologies, we can recreate the reality that most individuals are used to. Computers and algorithms can handle large chunks of data faster and more precisely than the best human at their work. The technologies can also study and reveal patterns and predict the next event that can be the key to the diagnosis as well as treatment of patients.

According to the incredible influence enhancement to the medical industry, many individuals lives will be saved, and a lot of money will be saved, which is usually wasted after the wrong diagnosis. Machine learning methods get better the more data they get exposed to. The medical industry is full of data, and this is great considering what machine learning requires. Because of various storage systems, privacy and ownership issues, and lack of elaborate procedure that enables individuals to relay information quickly, a significant amount of assessment is not presently being conducted that would garner massive outcomes for doctors and their patients and hospitals.

Much of the artificial intelligence work conducted to this point in the medical field is aimed at identifying plus diagnosing illnesses. Utilizing the technology to assess DNA to diagnose conditions to smartphone applications that can establish a concussion plus gauge other issues, like lung function, disease, and wellness observation, is a priority of machine learning explorations. Because heart condition is the number one killer of people worldwide, it is not a shock that initiative plus focus from numerous artificial intelligence developers concerns heart condition analysis plus prevention. Presently, the procedure for checking a persons risk factor for a certain condition is by checking at the risk factors advised by top medical experts, including blood pressure, and age, among others.

Nevertheless, this is a simple approach and does not consider medications someone might already be consuming. The well-being of a patients other biological systems, plus other factors, could raise the chances of heart illness. For example, numerous study teams from two top universities are working closely to improve machine learning algorithms that can forecast an individual in greater danger and when in danger of heart attack. Initial findings of the artificial intelligence algorithms were primarily better at forecasting heart illnesses than experts in the field.

This shows the power of the two technologies and shows optimism in the healthcare sector and especially this hospital that will be able to offer better treatment to their patients. In the past, doctors were the ones who directed CT scans and even read and interpreted the results. The new system can learn how to read the scans and suggest a diagnosis for a patient. This gives a physician enough time to concentrate on the right treatment options.

After the project finished and the final product was launched, it was proper for the team to conduct testing on numerous patients with different conditions. On the other hand, for every task that was given to the system, a substitute healthcare provider had to carry out to test whether the system was more accurate than the human or not. To be fair to the client, that is the hospital, and we requested that they provide the best professionals in every area of medicine. For instance, while testing how the system learns to read and interpret scan results, the hospital had to provide the best physician who is much experienced. After a series of tests, it was discovered that the system had a success rate of 99%, while the human expert had a success rate of 90%. Looking at the difference, it shows that the patients will benefit a lot once the system is fully incorporated into the operations of the hospital.

There are times that a system can fail because it is fed with wrong information as it is used to a certain kind of information. The hospital needed to see whether the system was able to identify the information that was wrong and suggest the right alternatives. With this, various patients with different conditions with signs and symptoms similar to other conditions were used as participants. The system was fed with information on their conditions and indicators. The system, in this case, showed a success of 98%. The majority of the time, the system was able to identify a flaw in the information and suggest alternatives. The system satisfied the hospital management concerning their needs. Also, apart from performing diagnosis tasks, the system can do away with paper documents.

In many organizations, companies are still utilizing paper documents to store information that is crucial in their operations, not only in the healthcare sector. The problem with this is that the paper documents can get lost, which can cause much damage to the organizations. For instance, it is easy in the case of a fire accident for paper documents to be destroyed. Organizations are required to evaluate their operations after every year. Financial statements and reports are important to the running of an organization, and they ensure that proper planning is done to create more opportunities for an organization to develop. In case such documents get destroyed, it is hard for the organizations management to conduct planning for the organizations future. In the case of a healthcare organization, this could prove very disastrous and could to many patients to suffer more since their medical history is not easily accessible.

The system will allow the hospital to easily back up data concerning organizational operations and enable easy and faster access. With paper documents and the number of patients visiting the hospital increasing, the number of paper documents storing information is a lot. If the history on a certain patient is in need, it becomes hard for the personnel in charge to retrieve the information. With the system, the patients medical history data is one click away, which makes it convenient. The project is huge and successful based on the requirements of the client. The number of individuals required and committed to the projects development was huge and offered a blueprint for how future projects need to be carried out.

The agile approach was selected because the development team had to work hand in hand with the client to understand and incorporate different requirements until the final product suited their needs. Agile methodology is based on repetitive development whereby requirements plus solutions change via cooperation amongst self-organizing cross-functional groups. The eventual value of this approach is that it allows development teams to deliver value quicker, with better quality as well as predictability and more propensities to respond to change. The approach generally promotes a disciplined project management procedure that encourages usual examination as well as adaptation. It also emphasizes a leadership perspective that promotes teamwork, culpability, and self-drive. It is responsible for a collection of practices to allow quick delivery of the best quality software and a business strategy that aligns development with client needs.

Recommendations for Improvements

Looking at the previous system that the client used in their operations, many issues were conducted manually and this posed a great risk for the client and their customers. It is possible for a human to make mistakes, especially when they feel like they have done a lot of work and are exhausted. By integrating these key technologies, we can recreate the reality that most individuals are used to. Computers and algorithms can handle large chunks of data with more speed and precision than the best human at their work. The technologies can also study and reveal patterns and predict the next event that can be the key to the diagnosis as well as treatment of patients.

According to the incredible influence enhancement to the medical industry, many individuals lives will be saved, and a lot of money will be saved, which is usually wasted after the wrong diagnosis. Machine learning methods get better the more data they get exposed to. The medical industry is full of data, and this is great considering what machine learning requires. Because of various storage systems, privacy and ownership issues, and lack of elaborate procedure that enables individuals to relay information quickly, a significant amount of assessment is not presently being conducted that would garner massive outcomes for doctors and their patients and hospitals.

Much of the artificial intelligence work conducted to this point in the medical field is aimed at identifying plus diagnosing illnesses. Utilizing the technology to assess DNA to diagnose conditions to smartphone applications that can establish a concussion plus gauge other issues, like lung function, disease, and wellness observation, is a priority of machine learning explorations. Because heart condition is the number one killer of people worldwide, it is not a shock that initiative plus focus from numerous artificial intelligence developers concerns heart condition analysis plus prevention. Presently, the procedure for checking a persons risk factor for a certain condition is by checking the risk factors advised by top medical experts, including blood pressure, and age, among others.

Nevertheless, this is a simple approach and does not consider medications someone might already be consuming. The well-being of a patients other biological systems, plus other factors, could raise the chances of heart illness. For example, numerous study teams from two top universities are working closely to improve machine learning algorithms that can forecast an individual in greater danger and when in danger of heart attack. Initial findings of the artificial intelligence algorithms were primarily better at forecasting heart illnesses than experts in the field.

This shows the power of the two technologies and shows optimism in the healthcare sector and especially this hospital that will be able to offer better treatment to their patients. In the past, doctors were the ones who directed CT scans and even read and interpreted the results. The new system can learn how to read the scans and suggest a diagnosis for a patient. This gives a physician enough time to concentrate on the right treatment options.

It is, therefore, important to improve the existing system with the integration of artificial intelligence and machine learning.

To complete a project, a team needs a realistic plan and considers various issues that may arise. For instance, in the case of this project, the project team had to consider time flexibility because of the possibility of changing the clients requirements (Eriksson et al., 2017). Creating a project plan that fails to consider that the client can change needs is the first step in failing. The plan must leave room for such events. Another approach is allowing the team to identify the goals and objectives of the system and understand what the client desires. In an organization, whether small or big, the most important thing is to ensure that every individual who is part of the system is on the same page.

This means that everyone knows what the target is and is committed to. For instance, the main aim of this project was to create a system that would lift the extra burden placed on healthcare providers at the hospital. The end product was supposed to act as an assistant to doctors, nurses, and other personnel. Every team members understanding is great and ensures that the development work happens seamlessly (Eriksson et al., 2017). Apart from everyone on the team understanding the goal, the most underrated thing in project management is the spirit of togetherness.

Top organizations have an edge over others because they understand the importance of everyone getting along well. A toxic environment full of unresolved conflicts does not promote growth, and when individuals are working on the same project and do not understand one another, it becomes hard to achieve set goals. It was important before starting that we recognize and solve any disputes among team members (Eriksson et al., 2017). During the project development, if a conflict arose between any members, the issue would be dealt with, and the involved would be allowed to come to terms with one another.

Project management is very important, and there are certain aspects to take care of if one desires to be sought out by clients for big projects. A project must meet the clients requirements and be delivered on time (Eriksson et al., 2017). Clients are specific on time, and in the case of this project, the hospital needed a solution to their problem of doctors burning out and giving a less accurate performance. To achieve this, there are certain approaches the project team had to follow. For instance, the whole team needed to be on the same page regarding the goals and objectives of the project. Understanding the reason behind the project ensures that the work is done with ease and everyone is moving at the same pace. Apart from that, the team had to develop a spirit of togetherness. It was realized that to complete a huge project; all conflict issues had to be resolved before the commencement of the project. This was done to eliminate the stagnation during the project because members of the project team could not get along well.

Conclusions

In the paper, it is evident that the project was successful and met the clients performance needs. The world is transitioning from organizations that rely on paper to organizations that are going paperless. It is important to keep up with the new technology trends to ensure that work is more efficient and accurate. Human beings are prone to fatigue and can often make mistakes that can cost organizations a lot of money and lead to harm to another person. For instance, if an auditor in an organization makes a mistake on a companys financial statements, the organization might lose a lot of money. Whereas if a wrong diagnosis is given by a doctor, a patient might lose their life. Having systems that can help professionals in their work is important and can lead to better results.

For instance, it is designed with the capability to read and interpret diagnosis results in this case. It can also store large chunks of information that are easily accessible when needed. This allows healthcare providers to concentrate more on the proper way of administering treatment to their patients. A doctor was required to retrieve information for a patient and diagnose and treat them in the past. This made it hard for them to concentrate on their main work, which is treatment. By the time a day shift ends, the doctor has served a few patients. With the new system, the doctor can serve more patients and be less tired, which increases accuracy.

Reference

Eriksson, P. E., Larsson, J., & Pesämaa, O. (2017). Managing complex projects in the infrastructure sectorA structural equation model for flexibility-focused project management. International journal of project management, 35(8), 1512-1523.

Introduction

The rise of artificial intelligence (AI) is rampant in modern society due to globalization which has taken root almost in all sectors. Having technology-centric aspects in place is important, but there is still a concern for better AI due to the challenges that have ensued previously. The machine learning architecture has been characterized by advanced settings that have distorted many working grounds. Experts in technology say that AI may make most people better than it is now in the next decade (Tagarev et al., 2020). However, there are concerns about how technology will affect what it means to be human.

There are chances that the world may have different features regarding productivity, division of labor and accuracy in manufacturing and distribution. It is important to consider issues to do with AI because currently, the matter has adverse effects on the depreciation of human labor, information protection and manipulation of people, among other issues. It is possible to have a fair AI by governing the use of data provided to assist data mining that promotes interpretable systems hence understanding and configuring results that show fairness through the computational method.

Review of Literature

Distributed AI may aid in mitigating many security problems that surface as a result of vulnerabilities in using the cloud. There should be smart integration of aspects of technology that allow data to be put safely through the application of digital codes that safeguard leakages and loss of sensitive data for companies and individuals (Mitrou, 2018). AI techniques can be fair enough by reducing security exposures in environments where data and information are at risk. Machine learning (ML) with AI has been critical technology for information protection. From the research objective, there is a gap in having fair AI through the capability that the cloud software has in identifying threats such as malware or phishing attacks (Mitrou, 2018). AI must enable a user to detect malicious programs or approaches online, facilitated by rampant cybercrime issues.

Through artificial intelligence, technocrats have exploited digital data to influence other peoples behavior on both local and international stages. Those aspects can be referred to as cyber environments whereby the intangible nature of AI has been able to influence real-world data and information (Yakovleva & van Hoboken, 2020). The fair part of AI regarding information sharing is that various digital actors can theoretically connect hence getting a globalized society that can solve problems upon ensuing. The new practices of domination of conventional digital activities provide conditions that may control asymmetric characteristics and structure hence having transparency in many elements of interaction using technology (Yakovleva & van Hoboken, 2020). Exploring this feature helps to highlight how AI has led to the complex and multi-faceted item of technology that influences the coordination of activities. Thus, through the efficiency created by digital power, AI can be said to be fair as the deployment of various functions can be done remotely and in an independent state.

AI should learn the types of attack that comes when one is using a digital tool and communicate instantly as one way of having safe browsing. Any deviation in data passing through a given protocol should be detected and responded to before establishing further. Of late, neural networks, expert systems, and deep learning have necessitated online information protection (Tagarev et al., 2020). For example, by using biologically-inspired programs, the paradigm enables a computer to learn and observe data trends hence making it possible to detect some discrepancies in information sharing.

Additionally, the neural network has codes assigned to relate correct and incorrect outputs that determine the weights of information being shared from a given digital handset. What is lacking in this literature is the control effectiveness that can allow incident response in the prioritization of security alerts that may be because of online information vulnerabilities. For example, IBM/Watson has frequently leaned on the cognitive knowledge consolidation of networks that detect possible information and data breaches. Through digitizing online content creation and storage processes, AI has effectively configured possible threats to information (Tagarev et al., 2020). Therefore, the world can have fair AI through the advanced network and system monitorization powered by microservices architecture.

AI can imitate human behavior and traits in productive areas. Through robotic technology, AI has presented fair aspects when it comes to elevation processes in busy industries or where human beings might risk working (Yakovleva & van Hoboken, 2020). AI is transforming the world in many ways, especially now that everything has gone digital. Through the power to learn human capabilities, machines have been put in key areas, such as service industries, to offer guidance to customers who visit specific points for business purposes. Modern technology has led to the deployment of AI in various sectors, such as finance, national security, delivery of healthcare services, administration of criminal justice, and development of smart cities (Yakovleva & van Hoboken, 2020). With the emergence of remote control, a user can effect a change that may take longer when handled by humans. Additionally, accuracy and punctuality aspects have been possible through AI, which for this matter, presents a fair part of it as it has brought positive transformation to the world.

This paper also relates the information protection aspect to human labor issues. Due to the remote application of many processes, people are not needed in high numbers to work on a threat that can be combatted remotely. The use of AI in many divisions of labor has led to notable cases of unemployment. There is a high level of machine learning that has replaced the need to have a human workforce in most technical spaces. The unemployment rate in the US in 2018 was 3.9%, which indicates the lowest annual rate in nearly four decades (Hutson, 2017, p. 19). The rapid job loss due to technological change, which concerns automation, has been a critical matter that must be addressed. When such aspects are considered, it embarks on a journey to get a fair AI that will provide low cases of job loss.

One of the ways in which artificial intelligence can be used to increase human labor is the expansion of microservices that will be deployable independently (Straw, 2020). The rise of cloud software comes with skills and knowledge that must be imparted to people for the continuity of work-related issues. There should be a balance in implementing some programs that may increase unemployment rates. System developers should create segments that need intensive sites and digital protocols monitoring simultaneously, creating a job for the majority.

There should be scaling of AI to create more jobs through distinctive and partitioned programs that require additional staff to work on the same. There seems to be a gap in this area because, despite the automation of many areas, AI can be fair when it comes to human labor by enabling repetitive tasks, such as data entry in assembly line manufacturing. This means workers will be required to focus on the higher-value and touching tasks that will probe them to expand interpersonal interactions (Straw, 2020). Under this perspective, benefits for individuals and the companies that employ them will be created. All workers must be upskilled or reskilled due to the changing job requirements resulting from a rapid technological shift in major fields. Therefore, by doing the factions mentioned above, it will be possible to offer fair AI for human labor.

Conclusion

This research paper has mainly focused on the ways human beings can have fair AI. Through the points given, it is possible to have fair AI by integrating various digital tools that mitigate the adverse effects of modern technology. This papers scholarly significance is that the audience can learn various metrics that can be applied to reduce job losses due to automation, combat information loss threat, and have efficient mass manipulation, among others. Through the research paper, other scholars can learn the importance of controlling AI network issues and patterns that benefit human beings. For example, by conducting phishing attacks while a user is online, AI serves in a friendly way to ensure that information sharing is regulated, hence reducing cybercrime issues. It is relevant to conduct research relevant to AI as that would lead to a safe environment characterized by advanced levels of fighting sociological changes that may be challenging to the digital actors.

References

Hutson, M. (2017). How artificial intelligence could negotiate better deals for humans. Science, 5(2), 17-23. Web.

Mitrou, L. (2018). Data protection, artificial intelligence and cognitive services: Is the general data protection regulation (GDPR) Artificial Intelligence-Proof? SSRN Electronic Journal, 6(3), 4-7. Web.

Straw, I. (2020). Automating bias in artificial medical intelligence (AI): Decoding the past to create a better future. Artificial Intelligence in Medicine, 110(6), 101965. Web.

Tagarev, T., Sharkov, G., & Lazarov, A. (2020). Cyber protection of critical infrastructures, novel big data and artificial intelligence solutions. Information & Security: An International Journal, 47(1), 7-10. Web.

Yakovleva, S., & van Hoboken, J. (2020). The algorithmic learning deficit: Artificial intelligence, data protection, and trade. SSRN Electronic Journal, 6(12), 22-29. Web.

Summary of Hurleys (2018) Journal Article

Artificial Intelligence (AI) has been termed as the science of teaching computers to accomplish tasks that can be completed through human intelligence. The Pentagon has viewed that incorporating AI is crucial in improving future military activities (Hurley, 2018). However, to ensure the secure incorporation of AI into military activities, the Department of Defense (DoD) must address various challenges.

These challenges include cultural silos, information insecurity, the DoDs size and complexity, technological diversity, and inadequate instruction. The DoD is discovering ways through which it can provide enhanced support to the military affairs and enhance the situational awareness to assist the fighters. The security body has been finding ways through which AI could provide defense and protection of national security knowledge. The AI has the potential to be applied in many government sectors, such as agriculture, transportation, cyber security, and weather. DoD has integrated AI into the Pentagons Third Offset Strategy to help stabilize and the counterinsurgency of military exercises. The AI-learning-enabled machines help military officers make timely decisions to combat security threats.

First Main Point

The costs associated with gathering, treating, and curating data for AI services are extremely expensive, thus making it hard for many organizations to consider it practical. I found the argument important, as it sums up some challenges associated with incorporating AI into various processes. The tremendous cost of AI incorporation in military operations is one challenge experienced by DoD (Hurley, 2018). The other concern that comes along with this high cost is that the AI models are only good on the data to which they are trained. The assessment and preparation of the data used in training are also difficult for its users.

Second Main Point

AI has been viewed with the potential of improving the DoD activity in addressing cybersecurity attacks. I found the point crucial as it sums up the benefit that AI incorporation can help the DoD. These cyber security tasks include defending against attacks, identification of susceptibilities, detecting attacks, and patching the vulnerabilities (Hurley, 2018). The ability of AI to reduce the vulnerabilities associated with cyberattacks will serve as an invaluable item to DoD. Using machine learning still faces challenges such as data security and human interferences. The machine learning algorithms have not been adherent to ethical and legal norms. Additionally, the machine learning models are prone to leakage of the training data, thus driving them not to be fully reliant on security missions.

Third Main Point

Stakeholders expect the AI progress in national security to be reflected in three sectors including military supremacy, economic supremacy, and finally economic dominance. I identified it as a crucial point that shows the vital areas that the AI incorporation will best fit within the military field. Cyber threats linked to data contamination and evasion attacks can happen at the training and interference stages, respectively (Hurley, 2018).

These threats use the advantage that the adaptive manner of machine learning algorithms can affect decision integrity by changing data and training input. In the data poisoning attacks, there is an injection of poisoned data in the specified information making the training model learn the wrong thing. Here, an attacker inputs information with the wrong labels, so that the data is wrongly encoded by the learning system. The hackers find the improperly labeled data from various unreliable sites. In the evasion attacks, the attackers aim to craft data that is wrongly classified by the learning model. The success of machine learning is correlated to the assessment of security-sensitive applications linked to the adversarial data.

Reference

Hurley, J. (2018). Enabling successful artificial intelligence implementation in the department of defense. Journal of Information Warfare, 17(2), 6582. Web.

Introduction

The last weeks readings and media sources prompted me to think more about the creation of artificial intelligence and the problems associated with it. In particular, the presented debate regarding singularity versus co-existence seemed very exciting, as I believe there are both benefits and drawbacks to both scenarios. Much about the possible uses of A.I. and its potential capacities and abilities remains uncertain, which raises many questions as to what the future of A.I. will hold for humans.

Singularity and Co-Existence

At the very foundation of the singularity debate is the eternal competition between one form of intelligence and the other. Indeed, if we consider evolution, more evolved species have outlived the previous generations, as they were more intelligent, adaptable, and could withstand certain conditions that threatened the latter. When applied to the development of A.I., the singularity theory places artificial mind as superior to the human mind.

I believe that this is a questionable assumption. First of all, given that A.I. is being developed by people, are we able to create something that is greater than our own mind? At present, we see human brain as a magnificent and complicated structure. It is widely believed that the brain is capable of a lot more than we know now. Nevertheless, our understanding of the workings of the brain is very limited.

How can we create something that is more advanced than the structure we have not fully understood yet? Another problem comes from the definition of intelligence. Since we are not entirely aware of how our brain functions and what it can do, how would we determine that the artificial mind is superior? Despite the fact that many efforts are taken to develop an A.I. with thought processes similar to the workings of our brain, the fact that we do not fully understand how our brain works makes it an impossible task. Therefore, the two structures are likely to be different in various ways. If that is the case, how do we determine one as superior and the other as outdated?

Co-existence is the other side of the coin, where artificial and human forms of intelligence live together and complement each other. To me, this scenario seems more valid than singularity. However, it is nonetheless problematic. For instance, would it be possible for the A.I. and humans to live freely and independently? Would the two structures have the same rights in the unified society?

If the humans and A.I. live independently, will one structure inevitably become a threat to the other? In my opinion, the fear of competition has been one of the characteristic features of humans throughout history. If A.I. receives full independence of human control, it will be perceived as a threat, which is why independent co-existence is an unlikely scenario. Therefore, there are two options: either the A.I. will take the lead and guide humans, or the humans will use the A.I. for their benefit. Both possibilities have their own strengths and limitations.

Control of A.I.

There are already numerous applications for the technology. Even since the machines were introduced into production and manufacturing, the quality of goods made has risen significantly, whereas the production costs have decreased. This had a positive effect on the development of many countries and industries all over the world. However, one limitation of this process was the replacement of human workers by machinery, which raised the levels of unemployment and caused many people to lose their jobs. If A.I. is used for the benefit of humans, there is no doubt that it will be utilized in production and manufacturing, and far more jobs will be lost.

As seen in the Blade Runner, another significant benefit of the use of A.I. robots is that they can perform difficult and dangerous tasks, and people will not have to risk their lives and health. For example, the A.I. robots could replace soldiers in wars, saving thousands of lives. The A.I. might also be able to make more effective strategic decisions, resulting in better outcomes. However, if the soldiers are substituted for robots, will the concept of war remain the same?

If the countries are not losing their people on the battlefield, will they be more tempted to continue warring with one another? Will wars be useful if they do not result in deaths of the men? Personally, I dont think so. If countries begin to use A.I. robots on the battlefield, the fighting will become pointless as the only pressure on the governments to start or end the war will be economic concerns, such as the cost of robots and machinery. To me, it is more likely that people will resort to other ways of influencing others, such as terrorism, and the civilian populations will be affected.

A.I. Governance

There are also stipulations that the A.I. may be used to solve problems that we cannot address adequately. However, most of these stipulations rely on the assumptions that the A.I. will be smarter and less emotional than people. What implications would there be if we were governed by a mind led by dry reason? Returning to the subject of war and violence, the A.I. governance may be able to promote justice.

Some believe that human capacity of justice is impaired by our emotionality and empathy, whereas the A.I. can make just decisions by relying on logical reasoning. However, in this case, would A.I.s capacity for justice mean that the structure will lack emotion and empathy? Surely, this could strengthen the justice system all over the world and offer equality to people from marginalized backgrounds; nevertheless, emotion and empathy may also act as mediators preventing people from committing wrongful or evil actions and motivating them to help one another.

If the A.I. lacks this feature, will it make decisions that harm people, even if they are logically reasonable? For instance, what are the chances that the A.I. will not decide to reduce the problem of overpopulation by killing millions of innocent people? While this is a logical solution, it is purely unethical and harmful to people. Is it possible to balance the A.I.s capacity for making just decisions and empathic governance of people?

Multiple sources also identified the issue of communication, which caused me to think about the possible uses of A.I. in this case. It is no secret that people are frequently subject to misunderstanding and communication. Indeed, even plants communication abilities are more effective than ours. With the current efforts of space exploration, some say that it is possible that we will encounter extra-terrestrial forms of life. How do we communicate with other species if our communication with each other is still an issue? The A.I., on the other hand, may be more efficient in communication  again, due to its impaired emotional response. Effective management of communication, in this case, could help to reduce the possibility of conflicts and promote cooperation, which can help to advance our technologies, healthcare, and other aspects of life.

Conclusion

Overall, this course has been enlightening, as it offered an opportunity for me to engage in thoughtful work and to think about the A.I. creation in philosophical terms. It also helped me to understand some of the core problems that A.I. creation may cause, as well as the obstacles to its development, making me more aware of the different notions and debates on the subject.

Artificial intelligence attracts more and more attention. Bill Gates believes that among all modern innovations, AI has the most significant potential to change our lives: to make them more productive, more efficient, and easier. AI has long captured the imagination of writers and journalists. Not all people really understand what the technology is capable of and what to expect from it. However, artificial intelligence is already affecting the life of every person.

AI can make everyday life more enjoyable and convenient. Its algorithms have long been present in our lives and make them better. For example, it analyzes choices when the user is watching videos on YouTube and creates recommendations based on them for a more interesting pastime and to save time. AI can offer accessibility for people with disabilities. Virtual assistants, like Siri and Alexa, can complete innumerable tasks, from making a phone call to navigating the Internet. Those who are deaf and hearing impaired can access transcripts of voicemails. Artificial intelligence may improve workplace safety. AI is already saving lives and reducing the risk of injury in the workplace because it does not have three leading causes of human accidents in the workplace: stress, tiredness, or sick. AI robots can replace humans for especially dangerous tasks.

At the same time, AI also has its cons as it may harm the standard of living for many people by causing unemployment. Undoubtedly, it creates new jobs in the field of development and maintenance of AI, but the number of these jobs is much less than the ones it takes away. Artificial intelligence poses dangerous privacy risks. Facial recognition technology may be used for surveillance. On the one hand, this makes life safer, and on the other hand, a person can be monitored not only by state security services, but also by intruders or criminals. Moreover, artificial intelligence can be an advantage to thieves. In 2020, a group of criminals conned $35 million from a bank using AI deep voice technology to impersonate an authorized employee. (Brewster, 2021)

AI is definitely a huge step in global technological progress, and like any other technology, it can be both a weapon and a lifesaver. Along with the development of AI, new risks in the field of cybersecurity arise. But in my opinion, the benefits of artificial intelligence are more compelling. When used correctly, it will give all of humanity an impetus for development and will make our lives better, more comfortable, and safer.

References

Brewster, T. (2021). Fraudsters cloned company directors voice in $35 million bank heist, police find. Forbes.com.

Procon.org. (2019). Artificial Intelligence (AI)  top 3 pros and cons.

Introduction

Educational establishments make significant contributions toward defining the future of a nation. These establishments ensure that young individuals master specific knowledge and skills, which will be further needed to succeed professionally and impact society. That is why appropriate organizations and the government are expected to do their best to increase the quality of learning and teaching. These bodies develop and introduce appropriate interventions, technologies, and methodologies to ensure that schools and universities provide students with the best possible knowledge and skills. Technological and scientific progress promotes this process because various innovations can significantly improve the efficiency of learning and teaching. Today, educational establishments can benefit from numerous technologies, and artificial intelligence (AI) is among them. It is reasonable to introduce AI solutions in the classroom because they allow for personalizing curriculum, utilizing adaptive learning, providing timely feedback, and helping teachers draw more attention to students.

AI Defined

To begin with, one should explain what AI is and how it works. This technology refers to software that is powered by self-learning algorithms (Marr, 2022). When a particular task is assigned to an AI program, the latter can become better and better with this activity with the help of these algorithms. This finding denotes that the selected technology has almost unlimited opportunities and can be effectively utilized in various spheres, and education is no exception. As for this context, AI is an umbrella term that includes a few specific interventions that are promising for the entire area of education. That is why it is reasonable to discuss these options in more detail below.

AI in the Classroom: Direct Advantages for Students

The most powerful argument for introducing AI into education is the ability to personalize and customize the curriculum to student needs. All people are unique in their opportunities, skills, and intelligence levels, which denotes that it can be challenging for educators to satisfy the needs of diverse students in the classroom. AI solves this issue because this technology can suggest personalized learning pathways for students of different ages (Chen, Chen, et al., 2020). A suitable example of this technology refers to the Altitude Learning system that was created by a Google engineer (Marr, 2022). Marr (2022) additionally states that it is known as adaptive learning, which can significantly increase the quality of studying. These findings indicate that AI can analyze students performance and achievements to make corrections to the curriculum to ensure that all the learners can cope with tasks. Simultaneously, this intervention guarantees that capable or gifted students are not assigned elementary tasks. In other words, AI ensures that students deal with assignments that perfectly correspond to their abilities and knowledge.

Students can additionally benefit from AI in the classroom because this technology can provide individuals with personalized assistance. This option is especially important in relation to online and mobile remote learning (Chen, Chen, et al., 2020). During the COVID-19 pandemic, millions of students were forced to engage in remote learning, and many of them faced difficulties. The problems were associated with the fact that students could receive timely responses from their tutors because the latter did not have the physical ability to perform all their tasks within a limited timeframe. If AI options had been utilized during the crisis, learners would not have faced any significant challenges because virtual personalized assistants would have answered all the questions in a timely manner. There is no doubt that this opportunity could have significantly improved students performance and outcomes.

AI is an international concept, and its advantages are found in various countries. For example, it is rational to look at China, where education effectiveness suffered because of the large number of students in every class. That is why this country has introduced an AI technology that relies on facial recognition. This innovation is used to scan students faces to determine whether they are drawing sufficient attention to events and processes in the classroom (Marr, 2022). One can expect that the given system is beneficial for students because it ensures that they are not distracted from class materials. This approach is promising since it can improve learning effectiveness. In other words, the given AI technology minimizes the opportunity for absenteeism that harmfully affects childrens performance.

In addition to that, AI can make the classroom environment productive, objective, and fair. Grades represent the assessment of students performance, and some children, teenagers, and adults can overestimate their importance. When they receive a poor mark, these individuals can become angry at educators or even believe that tutors are prejudiced regarding them. AI solves this issue since it contributes to fair assessment and grading (Chen, Chen, et al., 2020). Appropriate algorithms and technologies are utilized to evaluate students assignments and answers objectively and without any bias. This system is suitable for assessing written tasks, quizzes, and even oral answers. This state of affairs is positive and productive for students who understand that they are evaluated fairly according to their skills, abilities, and knowledge. In this situation, students will not draw attention to unnecessary thoughts or concerns but invest more time and effort in studying.

AI in the Classroom: Indirect Advantages for Students

In the education system, there are numerous stakeholder groups, including students, educators, and administrators. The cooperation among them significantly affects the quality of education and how well students can absorb materials and master skills. That is why it is reasonable to ensure that AI positively impacts other stakeholders because this strategy can be beneficial. One should draw the most attention to tutors because these professionals directly cooperate with students and can essentially influence their learning performance. Furthermore, teachers spend much time with learners, which denotes that the former can either contribute to or create barriers to effective studying of the latter. Thus, it is not surprising that various AI technologies and initiatives are developed to provide tutors with improvements to ensure that they can help students achieve better outcomes in the classroom.

Firstly, AI is effective for educators because they can pass some of their tasks on to technology. According to Chen, Xie, et al. (2020), AI initiatives are suitable for completing repetitive and tedious tasks that can include the assessment of fill-in-the-blank or multiple-choice questions. Teachers typically spend much time and effort checking these assignments, and AI can become an effective relief for these professionals. Innovative technology can check all students answers within a limited period. As a result, teachers have more time that they can devote to working with students, explaining materials to them, and answering their questions in the classroom. Chen, Xie, et al. (2020) additionally state that AI is beneficial because it ensures that teachers can respond to students requests online, which is important in the modern digitalized world. Thus, it is reasonable to expect that new technologies have the potential to make the education system more effective and convenient for different stakeholders.

Secondly, AI is effective since it can provide teachers with helpful and valuable information. Bui (2020) mentions that AI technologies can give informative feedback regarding students to educators. For example, a potential scenario implies that algorithms analyze learners assignments and performance and identify that an unusual problem appears. In this case, teachers can receive a notification highlighting the unexpected finding (Bui, 2020). This discovery can be a driver for educators to talk to the students and understand what caused their decreased performance. Even though this feature is a helpful tool for teachers, learners will receive fundamental advantages. When a student faces a problem in their life, they can be undetermined to disclose it to teachers to receive appropriate health. AI-driven technologies make it possible for children to be not expected to report their problems to have them solved. Appropriate algorithms provide educators with the necessary information to notice and address issues at their initial stages.

Thirdly, AI-powered innovations help teachers improve their professional skills and competencies. According to Marr (2022), a personal assistant, Merlyn, is an effective tool that can make educators more successful and effective. This AI technology helps professionals succeed in classroom management and lesson presentation (Marr, 2022). In other words, tutors can rely on Merlyns assistance to choose how it is better to present new materials for a specific class. These professionals can additionally delegate some management or organizational tasks in the classroom to this assistant. There is no doubt that these features will be beneficial for students in the long run. When teachers rely on the most effective lesson presentation methods and techniques, learners more effectively absorb materials, which positively affects their performance and outcomes. Improved classroom management establishes an environment that is productive for students and their learning.

Finally, AI is a powerful tool to identify systemic issues and offer ways to address them. According to Hwang et al. (2020), AI can be an effective policy-making advisor. This statement denotes that appropriate algorithms can identify a policy problem that harmfully affects the entire educational industry (Hwang et al., 2020). For example, AI can find that a newly implemented initiative resulted in significant drawbacks, and it is necessary to solve this issue. This feature is expected to improve educational processes and establish a more productive and suitable environment in the classroom. That is why there is no doubt that this option is beneficial for students and their performance because AI eliminates all the phenomena that contribute to learning barriers.

Conclusion

AI is a promising innovation that can significantly and positively impact the educational sphere. On the one hand, it is beneficial for students because this technology can personalize the curriculum, make remote learning more effective with the help of personal assistants, and monitor students performance to identify possible problems. These features can directly influence students, which will make their learning activities more successful. On the other hand, AI impacts teachers and policymakers, which results in indirect benefits for learners. For instance, appropriate algorithms can assess and grade students works, automate tutors activities, and provide teachers with personal assistants to cope with class management and materials presentation tasks. There is no doubt that all these features can make the educational sphere more convenient for students. That is why various stakeholders should advocate for the active implementation of AI in educational establishments.

References

Bui, S. (2020). Top educational technology trends in 2020-2021. eLearning Industry. Web.

Chen, L., Chen, P., & Lin, Z. (2020). Artificial intelligence in education: A review. IEEE Access, 8, 75264-75278.

Chen, X., Xie, H., Zou, D., & Hwang, G. J. (2020). Application and theory gaps during the rise of artificial intelligence in education. Computers and Education: Artificial Intelligence, 1, 100002. Web.

Hwang, G. J., Xie, H., Wah, B. W., & Gaaevi, D. (2020). Vision, challenges, roles, and research issues of artificial intelligence in education. Computers and Education: Artificial Intelligence, 1, 100001. Web.

Marr, B. (2022). The five biggest education and training technology trends in 2022. Forbes. Web.

Introduction

As the progressively evolving phenomenon in computer science, artificial intelligence is aimed at machine learning and providing software to address the problems in a way similar to human intelligence. Artificial intelligence facilitates the process of digitization and its impact on the further development of traditional companies. High-tech leading companies and corporations, involving government, the business community, and society are commonly using such an extensive branch of computer science that complements key technologies and creates new business models. Therefore, it is essential to examine the significance of artificial intelligence in the IT industry based on its pivotal role, as well as evaluate its priority in the strategic and operational approaches of the companies business.

Defining the Issue

Considering the enhancement of cognitive technologies, artificial intelligence (AI) has a transformative effect on the business industry in general. According to Brynjolfsson and McAfee (2017), the effects of AI will be intensified shortly in the following areas, such as manufacturing, retailing, transportation, finance, health care, law, advertising, insurance, entertainment, and education (p. 4). Besides, it will concern practically every industry by transforming its key processes and business models to benefit from machine learning. Therefore, the biggest advancements of the artificial intelligence include perception and cognition with the speech as the most practical advance (Brynjolfsson and McAfee, 2017).

It is still an evolving innovation; however, voice recognition already is being widely used with such applications as think Siri, Alexa, and Google Assistant. Furthermore, face-recognizing on Facebook, the fingerprint system of an app running on the smartphone, or vision systems in self-driving cars are as well rapidly improving. Image recognition is also substituting ID cards at corporate headquarters (Brynjolfsson and McAfee, 2017). Other yet valuable improvements in artificial intelligence involve cognition and problem-solving. These advances altogether are fundamental approaches in a modern technology-based society, although the application of the systems based upon AI is still limited.

Societal Benefit of AI

The successful implementation of artificial intelligence might have a beneficial impact on humanity. In terms of computer science, high-assurance systems are required for a secure guarantee of its reliability, meaning that autonomous systems behave as expected. Russell, Dewey, and Tegmark (2015) provide research on studying different ways of AI systems failures of its performance according to different areas of robustness research, including verification, validity, security, and control (p. 107). They are designed for proving the satisfactory levels, avoiding unwanted behaviors and consequences, preventing intentional manipulation by unauthorized parties, and facilitating relevant human control over AI system accordingly (Russell et al., 2015, p. 107). Consequently, a verification theorem was provided to address the failures avoidance.

Implementation of AI by Leading Companies

Artificial intelligence is considered the most crucial technology of general-purpose nowadays. Bala (2019) notes that venture capital company CB reported that by 2016, Apple, Google, Intel, Microsoft, Twitter, and other top IT companies have purchased more than 125 start-up companies working on AI (p. 474). Thus, AI and intelligent devices that do not involve human intervention are massively implemented by the global leaders on the market and have both positive and negative impacts on society. First, it is vital to analyze the positive effects of artificial intelligence in terms of some AI-based systems to perform functions intelligently.

One of the most commonly used examples of AI is Apples personal assistant Siri, the friendly voice-activated computer (Bala, 2019, p. 475). Siris beneficial impact lies in simplifying the information search, providing directions to perform various tasks, sending messages to selected recipients, adding important notes and dates to calendars with the reminder option. Bala (2019) states that Siri might be seen as an artificial intelligent digital personal assistant since it applies machine-learning technology to engage with humans (p. 475). Besides, this technology can also understand and react to requests made by Apple users.

Another popular AI system is Alexa, introduced by Amazon as a Personal Digital Assistant (PDA). Alexa as well understands and follows the instructions given by its users within an office or a room, which made it one of the most demanded products. It can search for information online, provide assistance in shopping, as well as in multiple everyday tasks organization. Therefore, it helps people with limited mobility and enhances smart homes.

In addition, the worlds technological leader in the car industry, Tesla, has high predictive capabilities, self-driving features, and safety features, controlled by AI system (Bala, 2019, p. 475). Amazon is using Rekognition that promotes the analysis of a vast amount of images every day, and highly advanced transactional AI that improves its predictive algorithms permanently. Besides, Amazon developed the process of predicting future acquisitions based on customers online behavior. Netflix uses the same AI approach to identify the right kind of film for its users based on their reactions to the previously watched films. Hence, these are just several examples of AI implementation with a positive impact on society amongst multiple cases.

Apart from the beneficial outcomes, AI-based systems also have specific negative effects. According to Bala (2019), this implies loss of jobs, loss of control, and unforeseen consequences. This can be explained by the capability of AI to replace the jobs that can now be automated, which leads to dramatic changes in the future of the employment system. Furthermore, 77% of customers are using AI-based products and services, and 44% of these do not even realize they are using artificial intelligence (Bala 2019). Nonetheless, based on the broad application of AI, it is rightly considered as an essential investment approach in marketing that results in labor productivity enhancement and effective use of the companys resources.

Business Benefits of Artificial Intelligence

Cognitive technologies are commonly used to advance the work performance only machines can do. Davenport and Ronanki (2018) demonstrate statistical measures based on the survey of 250 executives whose companies integrate cognitive technologies to learn their goals for AI initiatives. Hence, the highest percentage of AI benefits included the enhancement of the features, functions, and performance of the companys products, as well as optimization of internal business operations. AI technologies also provide more free time for workers to be more creative by automating tasks, and promote making better decisions.

Other beneficial factors involve creating new products, pursuing new markets, capturing and applying limited knowledge, optimizing external processes such as marketing and sales, and reducing headcount through automation (Davenport and Ronanki, 2018, p. 6). Furthermore, Makridakis (2017) states that innovative breakthroughs, technologies, and their usage, managing people, growth by acquisition, are all the inherent parts of the successful, dominant firm of AI revolution and its management. Such uniqueness of the AI technologies lies in their ability to supplement, substitute, and amplify virtually all the tasks performed by human beings (Makridakis, 2017, p. 55).

This trend has critical consequences for companies that pursue considerable productivity improvements so they can remain competitive on the current market; however, it as well jeopardizes the unemployment rates that are increasing.

AI and Database Technologies

The integration role of AI and Database (DB) technologies is highly significant for the future generation of computing based on the IIS (Intelligent Information Systems). As such, this integration might be beneficial to the infrastructure for science and technology, as well as to business and humanitarian purposes of computer systems by advancing its state (Brodie, 2014). Thus, in terms of future computing technologies, AI and DB must cooperate together and with other technological advances. According to Brodie (2014), future systems will involve a vast number of heterogeneous, distributed agents with multiple options to work together (p. 623).

This implies personal knowledge and reasoning schemes, languages, and abilities for every agent. Moreover, all the information and processes might be exchanged and create a massive distributed information base (Brodie, 2014, p. 623). Besides, this interconnection strategy is expected to grow into the Intelligent Interoperability, as the intelligent cooperation of the systems to pursue common goals. As a result, it is crucial for AI people to comprehend the opportunities of database technologies, as well as for DB people to know the demands of AI systems for the efficient integration process.

Conclusion

To conclude, artificial intelligence is the unique phenomenon that is being massively implemented by various industries, including the Information Technology sector. With its advances in perception and cognition, as well as the ability to replace a significant amount of work previously performed by humans, it rightly takes a leadership role in the future of computing systems. By analyzing the societal benefit of AI, as well as the strategies to avoid pitfalls and failures of AI-based technologies, it is more likely to be better acknowledged in its positive and negative effects.

While this trend is still evolving, it is too far in advance to search for alternatives, as the main alternative is human intelligence. By noting the substantial uncertainty about the future impact of AI technologies, there is still a question of whether they can fully replace human intelligence in the future and be beneficial or harmful for society. Nevertheless, in the near future, AI will not replace the managers but will replace those who do not implement the AI-based technologies.

References

Bala, D. (2019). Artificial intelligence and its implications for future. Research Review International Journal of Multidisciplinary, 4(5), 474477. Web.

Brodie, M. (2014). Future intelligent information systems: AI and database technologies working together. In Mylopoulos, J., & Brodie, M. (Eds), Readings in Artificial Intelligence and Databases (pp. 623643). San Mateo, CA: Morgan Kaufmann Publishers.

Brynjolfsson, E., & McAfee, A. (2017). The business of artificial intelligence. Harvard Business Review, 120. Web.

Davenport, T., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, 110. Web.

Makridakis, S. (2017). The forthcoming artificial intelligence (AI) revolution: Its impact on society and firms. Futures, 90, 4660. Web.

Russell, S., Dewey, D., & Tegmark, M. (2015). Research priorities for robust and beneficial artificial intelligence. AI Magazine, 36(4), 105114. Web.

AI usage in modern warfare

The question of AI usage in modern warfare is topical these days. I have never heard of the controversial Project Maven. Still, I have looked it up, and it sounds bizarre to think that regular Google employees could be involved with the Department of Defense. Shockingly, someday data that we provide to Google could be used against us. Although using unmanned fighter jets sounds positive, people also need to remember that the warfare inventions are not limited to only one country. Other countries could easily make dupes after proper research. While the use of AWS creates a political and ethical dilemma, several years later, AWS could be used against enemy AWS. Would that be considered normal in ethical and political aspects? The presentation is well organized, with distinct pauses between positive and negative, however the audio was a bit quiet.

AI usage in healthcare

In the current situation with the ongoing pandemic, the question of Artificial Intelligence used in healthcare is extremely important. The provided example with algorithm usage in disease determination is valid as we already have felt the positive use of algorithms in our daily life. The robots usage in some hospitals sounds new and exciting to me. Have you thought of a mental healthcare aspect of the question? Chatbots, for example, could be used in some cases of Depression (Dosovitsky et al., 2020). Besides, do you think the health data exposure is more threatening or a human error possibility? Health data exposure might be a serious problem, but the consequences of human error might go a lot deeper, and only one person would be considered guilty. The audio/video quality was great, and the overall presentation was engaging.

Reference

Dosovitsky, G., Pineda, B., Jacobson, N., Chang, C., Bunge, E. (2020). Artificial Intelligence chatbot for depression: descriptive study of usage. JMIR Formative Research, 4(11). Web.