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Chapter 22
DOI: 10.4018/979-8-3693-0044-2.ch022
ABSTRACT
Artificial intelligence (AI) has become one of the most significant technological advancements in recent
years. It is transforming various sectors and industries, including computing, and it has the potential
to significantly impact the future of computing. This paper presents a comparative study on the impact
of AI on the future of computing. The study analyzes the current state of AI in computing and its future
prospects, including the potential benefits and challenges. The study compares the impact of AI on differ-
ent areas of computing, such as hardware, software, and infrastructure. Additionally, the paper discusses
the implications of AI for different stakeholders, including businesses, governments, and individuals.
Finally, the study concludes by outlining potential research directions in the field of AI and computing.
The Evolution of AI and
Its Transformative Effects
on Computing:
A Comparative Analysis
Tarun Kumar Vashishth
https://orcid.org/0000-0001-9916-9575
IIMT University, India
Bhupendra Kumar
IIMT University, India
Vikas Sharma
https://orcid.org/Orcid
IIMT University, India
Sachin Chaudhary
https://orcid.org/0000-0002-8415-0043
IIMT University, India
Sunil Kumar
AURO University, India
Kewal Krishan Sharma
IIMT University, India
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The Evolution of AI and Its Transformative Eects on Computing
INTRODUCTION:
Artificial intelligence (AI) has become an integral part of our daily lives, transforming various sec-
tors and industries, including computing. The impact of AI on computing is significant, and it has the
potential to shape the future of computing. AI technologies such as machine learning, natural language
processing, and robotics are already being used in computing, and their potential for further development
is immense. In this paper, we present a comparative study on the impact of AI on the future of comput-
ing. The study aims to analyze the current state of AI in computing and its future prospects, including
the potential benefits and challenges. The study also compares the impact of AI on different areas of
computing, such as hardware, software, and infrastructure.
Definition of Artificial Intelligence (AI):
Artificial Intelligence (AI) refers to the development of computer systems that can perform tasks that
would typically require human intelligence, such as visual perception, speech recognition, decision-
making, and language translation. AI involves the creation of algorithms and machine learning models
that enable computers to learn from data, adapt to new information, and make predictions or decisions
based on that knowledge. AI technologies are designed to perform complex tasks with accuracy and
speed, and are increasingly being used in a variety of industries, including healthcare, finance, trans-
portation, and manufacturing.
Background Information on Artificial Intelligence:
Artificial Intelligence (AI) is a branch of computer science that focuses on the development of machines
and computer programs that can perform tasks that typically require human intelligence, such as visual
perception, speech recognition, decision-making, and language translation. The concept of AI dates
back to the mid-20th century when researchers began exploring the idea of building machines that could
simulate human intelligence.
One of the earliest pioneers of AI was British mathematician Alan Turing, who proposed the concept
of a “universal machine” that could perform any intellectual task that a human could. The development
of early AI systems was hindered by limited computing power and the lack of available data to train
these systems.
In the 1950s and 1960s, AI researchers developed rule-based systems that could mimic human rea-
soning. However, these systems were limited in their ability to learn and adapt to new information. In
the 1970s and 1980s, researchers began to explore the use of machine learning algorithms that could
enable computers to learn from data and improve their performance over time.
The field of AI experienced resurgence in the 2010s with the advent of deep learning, which involves
the use of neural networks to process and analyze vast amounts of data. This technology has been in-
strumental in the development of AI-powered applications in industries such as healthcare, finance, and
transportation.
Today, AI is rapidly advancing and transforming industries across the globe. AI technologies are
being used to create intelligent systems that can recognize patterns, make predictions, and automate
complex tasks. The potential applications of AI are vast, and the field is expected to continue to grow
and evolve in the coming years.
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The Evolution of AI and Its Transformative Eects on Computing
Brief Overview of the Impact of Artificial Intelligence on Computing:
The impact of Artificial Intelligence (AI) on computing has been transformative, with AI technolo-
gies and applications revolutionizing the way computers process and analyze data, automate tasks, and
make decisions. AI has enabled computers to perform tasks that were previously impossible or highly
challenging for machines, such as recognizing images, understanding natural language, and making
complex decisions.
AI has improved computing efficiency and processing power, making it possible to analyze vast
amounts of data and perform computations at a scale that was previously unattainable. It has also en-
abled automation and optimization of tasks, reducing the need for human intervention and streamlining
workflows.
AI has enhanced data analysis and decision-making capabilities, allowing computers to make predic-
tions and decisions based on patterns and trends in data. This has led to the development of intelligent
systems that can assist in a variety of applications, from healthcare and finance to transportation and
manufacturing.
AI-driven innovations in hardware and software development have also led to new computing systems
and architectures that can support the complex computations required by AI algorithms. The integration
of AI with the Internet of Things (IoT) and edge computing has also enabled the development of intel-
ligent systems that can collect and analyze data in real-time, making it possible to monitor and control
physical systems with a high degree of precision.
Despite its many benefits, the impact of AI on computing has also raised ethical considerations and
challenges, including privacy and security concerns, bias and fairness issues in AI algorithms, and the
displacement of jobs due to automation. Addressing these challenges will require ongoing research and
development in AI and collaboration between researchers, developers, and policymakers.
Purpose of the Chapter:
The purpose of the chapter titled “The Impact of Artificial Intelligence on the Future of Computing: A
Comparative Study” is to provide a comprehensive overview of the ways in which Artificial Intelligence
(AI) is transforming the field of computing, and to compare different AI technologies and their impact
on the future of computing.
The chapter will explore the historical development of AI, including key breakthroughs and milestones,
and examine the impact of AI on computing efficiency, data analysis, and decision-making capabilities.
It will also discuss the challenges and ethical considerations raised by the integration of AI into comput-
ing systems, such as privacy and security concerns, bias and fairness issues in AI algorithms, and the
impact of automation on employment.
The chapter will compare different AI technologies and their applications, including rule-based
systems, machine learning algorithms, and deep learning techniques, and discuss the advantages and
limitations of each approach. It will also explore emerging AI technologies and trends, such as natural
language processing, computer vision, and edge computing, and examine their potential impact on the
future of computing.
Overall, the chapter aims to provide a comprehensive and comparative analysis of the impact of AI
on computing, highlighting its transformative potential and its implications for the future of technology
and society.
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The Evolution of AI and Its Transformative Eects on Computing
Scope and Limitations:
The chapter titled “The Impact of Artificial Intelligence on the Future of Computing: A Comparative
Study” aims to provide a comprehensive overview of the ways in which Artificial Intelligence (AI) is
transforming the field of computing. However, due to the broad scope of the topic, the chapter has some
limitations.
Firstly, the chapter will focus primarily on the impact of AI on computing and will not provide an
in-depth analysis of the broader social, economic, and ethical implications of AI. While these issues
are critical, they are beyond the scope of this chapter, which aims to provide a comparative study of the
impact of AI on computing.
Secondly, the chapter will not cover every aspect of AI and computing, as this would be beyond the
scope of a single chapter. Instead, the chapter will focus on key AI technologies and their applications,
including rule-based systems, machine learning algorithms, and deep learning techniques, and explore
emerging AI trends, such as natural language processing, computer vision, and edge computing.
Lastly, the chapter will be based on the current state of knowledge and understanding of AI and com-
puting, as of the chapter’s writing. AI is a rapidly evolving field, and new breakthroughs and discoveries
are being made all the time. Therefore, the chapter may not reflect the most up-to-date information or
future developments in AI and computing.
Despite these limitations, the chapter aims to provide a comprehensive and comparative analysis of
the impact of AI on computing and its potential implications for the future of technology and society.
LITERATURE REVIEW:
The chapter titled “The Impact of Artificial Intelligence on the Future of Computing: A Comparative
Study” aims to explore and analyze the influence of artificial intelligence (AI) on the field of computing.
The chapter provides a comparative analysis of the different ways AI is transforming computing and
examines its potential impact on various aspects of the field. Some of the studies are-
Bahrammirzaeeet (2010) discusses the applications of artificial intelligence (AI) in the field of finance,
specifically focusing on three types of AI systems: artificial neural networks (ANNs), expert systems,
and hybrid intelligent systems. The article provides a comprehensive review and comparison of the ap-
plications, advantages, and limitations of these three AI systems in the context of finance. It may also
discuss the challenges and future directions of using AI in the financial industry.
Figure 1. Future of Computing with AI
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The Evolution of AI and Its Transformative Eects on Computing
Khan et al. (2022) explores the role of artificial intelligence (AI) in the agricultural sector through a
comparative study. It likely discusses how AI technologies and techniques are being applied in agriculture
and compares their benefits, challenges, and potential impacts on various aspects of the agricultural in-
dustry. The comparative study mentioned in the article might involve comparing different AI approaches
and technologies used in agriculture, such as machine learning algorithms, expert systems, and robot-
ics. It may also evaluate the effectiveness and practicality of these AI solutions in different agricultural
contexts, considering factors such as scalability, cost, and ease of implementation.
Rauf et al. (2021) focuses on conducting a comparative study to examine the impact of artificial in-
telligence (AI) on employment opportunities for university graduates in Germany and the Netherlands.
It likely explores how AI is influencing the job market and employment prospects for recent graduates
in these two countries. The comparison between Germany and the Netherlands suggests that the study
aims to examine how different countries are navigating the challenges and harnessing the opportunities
presented by AI in their respective labor markets. It may explore variations in AI adoption, government
policies, educational systems, and industry structures between the two countries and how these factors
shape employment prospects for university graduates.
The article could discuss the potential impacts of AI on different sectors and occupations, such as
healthcare, manufacturing, finance, and customer service. It might also explore the skills and competencies
that are in demand in the AI-driven job market and discuss strategies for individuals and policymakers
to adapt to the changing landscape.
Akyuz et al. (2019) focuses on conducting a comparative research study on the impact of machine
learning on the future of maritime transportation. It likely explores how machine learning techniques
and applications are influencing various aspects of the maritime industry. The study may investigate
the potential benefits and challenges of integrating machine learning algorithms and technologies in
maritime transportation. It might explore how machine learning can be utilized to improve operations,
optimize efficiency, enhance safety and security, and support decision-making in the maritime sector.
The article could also discuss the implications of machine learning on the workforce in the maritime
industry, including potential changes to job roles, skill requirements, and training needs. It may explore
the opportunities and challenges presented by the integration of machine learning and how stakeholders
can prepare and adapt to the evolving landscape.
Guzman et al. (2016) focuses on the application of artificial intelligence (AI) in improving safety and
risk analysis, specifically in the context of critical infrastructure. It likely explores how AI techniques
and systems can enhance safety measures and risk assessment processes in critical infrastructure sectors
such as transportation, energy, telecommunications, or public services.
Wauters and Vanhoucke (2016) focuses on conducting a comparative study of Artificial Intelligence (AI)
methods for project duration forecasting. It likely explores different AI techniques and their effectiveness
in predicting the duration of projects in various domains. The study may evaluate and compare different
AI methods such as artificial neural networks, genetic algorithms, fuzzy logic, or expert systems, among
others. It could analyze their performance, accuracy, and reliability in forecasting project durations. The
comparison may involve assessing how these AI methods handle different project characteristics, data
types, and sizes, as well as their ability to handle uncertainties and adapt to changing project conditions.
The article may discuss the dataset used for the study, the methodology employed in evaluating the
AI methods, and the results obtained. It might also highlight the strengths and limitations of each AI
technique in project duration forecasting and provide insights into the factors that contribute to their
effectiveness.
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The Evolution of AI and Its Transformative Eects on Computing
Harmon and King (1985) represents an early exploration of expert systems and AI in the business
context. It’s important to note that AI technologies have evolved significantly since then, and more ad-
vanced techniques and approaches may now be available. Therefore, while the book provides a historical
perspective on the topic, it may not cover the latest advancements and trends in AI and expert systems.
Kermadi and Berkouk (2017) focuses on the application of artificial intelligence (AI) techniques in
maximum power point tracking (MPPT) controllers for photovoltaic (PV) systems. MPPT is a technique
used to maximize the power output of a PV system by continuously tracking the point on the current-
voltage (I-V) curve where the system operates at its maximum power. Different AI-based control methods,
including neural networks, fuzzy logic, and genetic algorithms, are compared and evaluated in terms of
their performance and efficiency in MPPT. The authors highlight the potential of neural network-based
controllers, which demonstrate superior performance and adaptability in different operating conditions.
Samuel et al. (2017) focuses on comparing the effectiveness of regression analysis and artificial neural
network (ANN) methods for medium-term load forecasting. Load forecasting is an essential aspect of
power system planning and operation, which involves predicting the future electricity demand over a
medium-term horizon. The results of the study show that ANN-based load forecasting models generally
outperform regression analysis models in terms of accuracy and forecast quality. ANN models exhibit
better predictive capabilities due to their ability to capture complex nonlinear relationships and patterns
in the data. However, the authors note that the performance of ANN models is dependent on factors such
as the quality and availability of training data, model architecture, and parameter selection. The results
of the study show that ANN-based load forecasting models generally outperform regression analysis
models in terms of accuracy and forecast quality. ANN models exhibit better predictive capabilities due
to their ability to capture complex nonlinear relationships and patterns in the data. However, the authors
note that the performance of ANN models is dependent on factors such as the quality and availability
of training data, model architecture, and parameter selection.
Maleki and Askarzadeh (2014) focus on the comparative study of various artificial intelligence (AI)
techniques for sizing a hydrogen-based stand-alone photovoltaic/wind hybrid system. The sizing of
hybrid energy systems involves determining the optimal configuration and component sizes to achieve
reliable and efficient power supply. The authors compare and evaluate different AI techniques for sizing
the hybrid system, including genetic algorithm (GA), particle swarm optimization (PSO), and ant colony
optimization (ACO). These AI techniques are used to find the optimal combination of photovoltaic (PV)
panels, wind turbines, and hydrogen storage in the hybrid system. The study involves formulating an
optimization problem considering factors such as energy demand, available renewable resources (solar
and wind), system efficiency, and economic considerations.
Overall, chapter provides a comprehensive analysis of the multifaceted influence of AI on comput-
ing. It offers valuable insights into the advancements, challenges, and societal implications associated
with the integration of AI into computing systems. The chapter’s comparative approach enhances the
understanding of AI’s impact and serves as a valuable resource for researchers, practitioners, and poli-
cymakers in the field of computer science and AI.
METHODOLOGY:
The chapter titled “The Impact of Artificial Intelligence on the Future of Computing: A Comparative
Study” will use a mixed-methods approach to provide a comprehensive and comparative analysis of the
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The Evolution of AI and Its Transformative Eects on Computing
impact of Artificial Intelligence (AI) on computing. The methodology will include both qualitative and
quantitative methods, including a review of the relevant literature, case studies, and comparative analysis.
1. Research Design
a. Define the research problem: The research problem is to identify and assess the impact of arti-
ficial intelligence (AI) on the future of computing and conduct a comparative analysis of different
approaches to incorporating AI in computing systems.
b. Formulate research questions: Based on the research problem, formulate specific research ques-
tions that will guide the study. For example, “What are the benefits and drawbacks of machine
learning in computing?”, “How does natural language processing impact computing?”, “What is
the potential of computer vision in computing?”
c. Determine the research scope and boundaries: Specify the scope of the study, including the time
frame, geographical location, and types of AI and computing systems to be analyzed. For example,
the study may focus on AI in enterprise computing in the US in the past 5 years.
d. Select research methods: Select the research methods that will be used to collect and analyze data.
The methods may include surveys, interviews, experiments, case studies, and literature reviews.
e. Identify data sources: Identify the data sources that will be used to answer the research questions,
including primary and secondary sources. Primary sources may include experts in the field, while
secondary sources may include research papers, industry reports, and online databases.
f. Develop a data collection plan: Develop a plan for collecting and recording data, including the
procedures for collecting, storing, and protecting the data.
g. Conduct data collection: Collect data according to the data collection plan, using the selected
research methods and data sources.
h. Analyzedata: Analyze the data collected using appropriate techniques, such as statistical analysis,
content analysis, or thematic coding.
i. CompareandcontrastAIapproaches: Compare and contrast the different AI approaches in terms
of their impact on computing, evaluating the benefits and drawbacks of each approach based on
the defined criteria.
j. Drawconclusions: Based on the analysis, draw conclusions that answer the research questions and
address the research problem.
k. Makerecommendations: Make recommendations for future research and practical applications,
including suggestions for further studies and strategies for incorporating AI in computing systems.
2. Data Collection
a. Identify primary data sources: Primary data sources include experts in the field of AI and com-
puting. These may include industry professionals, researchers, academics, and developers. Identify
potential sources through online directories, professional organizations, and social media platforms.
b. Conduct interviews: Conduct structured or semi-structured interviews with experts in the field.
Prepare a list of questions that address the research questions and objectives. Record the interviews
using appropriate equipment, such as a digital recorder or note-taking software.
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The Evolution of AI and Its Transformative Eects on Computing
c. Create surveys: Develop a survey questionnaire that addresses the research questions and objec-
tives. Distribute the survey through email, online platforms, or social media. Analyze the survey
responses using statistical analysis techniques.
d. Collect experimental data: Conduct experiments to collect data on the performance of different
AI approaches in computing systems. Define the experiment parameters, such as the computing
environment, input data, and performance metrics. Record the results and analyze the data using
statistical analysis techniques.
e. Identify secondary data sources: Secondary data sources include published research papers,
industry reports, whitepapers, case studies, and online databases. Conduct a thorough search of
these sources using online search engines and academic databases.
f. Review literature: Review the literature to identify key theories, concepts, and frameworks related
to AI and computing. Document the findings and organize the literature review according to the
research questions and objectives.
g. Analyze data: Analyze the collected data using appropriate techniques, such as statistical analysis,
content analysis, or thematic coding. Compare and contrast the different AI approaches in terms
of their impact on computing.
h. Ensure ethical considerations: Ensure ethical considerations are taken into account throughout
the data collection process, including obtaining informed consent from participants, protecting
participant confidentiality, and ensuring data security.
Note: The data collection methodology outlined above is a general framework for conducting a com-
parative study on the impact of AI on the future of computing. The specific techniques, tools, and ap-
proaches used may vary depending on the availability of resources, time constraints, and the expertise
of the researchers involved.
3. Data Analysis
a. Organize the data: Organize the collected data in a structured format, such as a spreadsheet or
database. This will make it easier to analyze and compare the different AI approaches.
b. Preprocess the data: Preprocess the data to ensure its accuracy, completeness, and consistency.
This may involve cleaning, filtering, or transforming the data. For example, remove any duplicate
or missing data points, convert categorical variables to numerical ones, or normalize the data.
Figure 2. Process of Data Collection
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The Evolution of AI and Its Transformative Eects on Computing
c. Conduct descriptive analysis: Conduct descriptive analysis to summarize the main features of
the data. This may involve calculating measures of central tendency, such as mean, median, and
mode, or measures of variability, such as standard deviation or range.
d. Conduct inferential analysis: Conduct inferential analysis to test hypotheses and draw conclusions
about the population based on the sample data. This may involve conducting statistical tests, such
as t-tests or ANOVA, to compare the means of different AI approaches.
e. Conduct thematic analysis: Conduct thematic analysis to identify patterns and themes in the data.
This may involve categorizing the data into different themes or codes based on commonalities or
differences. For example, categorize the data into different types of AI approaches, such as machine
learning, natural language processing, or computer vision.
f. Conduct content analysis: Conduct content analysis to analyze the content of the data, such as
text or images. This may involve identifying key words or phrases, sentiment analysis, or image
recognition.
g. Compare and contrast the AI approaches: Compare and contrast the different AI approaches in
terms of their impact on computing. Evaluate the benefits and drawbacks of each approach based
on the defined criteria.
h. Draw conclusions: Draw conclusions based on the analysis, answering the research questions and
addressing the research problem.
i. Ensure validity and reliability: Ensure the validity and reliability of the data analysis by using
appropriate statistical methods, ensuring intercoder reliability in thematic analysis, and triangulat-
ing the results with other data sources.
Note: The data analysis methodology outlined above is a general framework for analyzing the data col-
lected in a comparative study on the impact of AI on the future of computing. The specific techniques,
tools, and approaches used may vary depending on the nature of the data, research questions, and re-
search objectives.
Figure 3. Process of Data Analysis
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The Evolution of AI and Its Transformative Eects on Computing
4. Limitations of the Study
a. Limited sample size: The study may have a limited sample size, as it relies on a small number of
experts and stakeholders in the field of AI and computing. This may limit the generalizability of
the findings.
b. Selection bias: There may be selection bias in the sample, as the researchers may have selected
experts who are more likely to have a positive or negative view of AI and computing. This may
lead to a biased interpretation of the findings.
c. Self-report bias: There may be self-report bias in the data collected through interviews and sur-
veys, as the experts may have provided answers that they believe the researchers wanted to hear.
This may affect the validity and reliability of the data.
d. Lack of experimental control: The study may lack experimental control, as the researchers may
not have been able to control all the variables that may affect the performance of different AI ap-
proaches. This may affect the validity of the findings.
e. Limited scope: The study may have a limited scope, as it focuses on a specific set of AI approaches
and computing systems. This may limit the applicability of the findings to other domains or contexts.
f. Time constraints: The study may have been limited by time constraints, as the researchers may not
have been able to collect or analyze all the relevant data. This may affect the comprehensiveness
of the findings.
g. Data quality: The study may be limited by the quality of the data collected, as the data may be
incomplete, inconsistent, or biased. This may affect the validity and reliability of the findings.
COMPARATIVE ANALYSIS
1. Comparison of Traditional Computing and AI Computing
Traditional computing and AI computing are compared in terms of various factors, including performance,
flexibility, scalability, and decision-making capabilities. Here is a brief overview of the comparison:
a. Performance: Traditional computing systems are typically designed to perform a specific set of
tasks, while AI computing systems are designed to learn from data and improve their performance
over time. As a result, AI computing systems have the potential to achieve higher levels of perfor-
mance than traditional computing systems in tasks such as image recognition, natural language
processing, and decision-making.
b. Flexibility: Traditional computing systems are typically rigid and inflexible, requiring significant
time and effort to reprogram or reconfigure for new tasks or environments. AI computing systems,
on the other hand, are designed to be adaptive and flexible, allowing them to learn and improve
their performance in new and changing environments.
c. Scalability: Traditional computing systems are often limited in their scalability, as adding more
computing resources may not result in a proportional increase in performance. AI computing
systems, on the other hand, are designed to scale horizontally, allowing them to handle larger and
more complex datasets and tasks by adding more computing resources.
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d. Decision-making capabilities: Traditional computing systems are designed to follow predefined
rules and algorithms, while AI computing systems are designed to learn from data and make deci-
sions based on probabilistic reasoning. As a result, AI computing systems have the potential to
make more accurate and complex decisions in areas such as finance, healthcare, and autonomous
driving.
Overall, the comparison suggests that AI computing has the potential to significantly impact the
future of computing by providing more flexible, scalable, and intelligent systems that can perform a
wider range of tasks with higher levels of accuracy and efficiency.
2. Comparison of AI Computing Methods
a. Supervised learning: Supervised learning is a method of machine learning where the algorithm
is trained on labeled data to predict outcomes for new, unlabeled data. This method is widely used
in applications such as image and speech recognition, natural language processing, and recom-
mendation systems. Supervised learning is generally easy to interpret and has high accuracy, but
may require large amounts of labeled data and may not be suitable for all types of problems.
b. Unsupervised learning: Unsupervised learning is a method of machine learning where the algo-
rithm learns from unlabeled data to identify patterns and structure. This method is often used in
clustering, anomaly detection, and dimensionality reduction. Unsupervised learning is generally
more flexible than supervised learning, but may be more difficult to interpret and may not be suit-
able for all types of problems.
c. Reinforcement learning: Reinforcement learning is a method of machine learning where the al-
gorithm learns by trial and error, receiving feedback in the form of rewards or punishments. This
method is often used in games, robotics, and control systems. Reinforcement learning is generally
more difficult to implement and may require a large amount of computational resources, but has
the potential to learn complex behaviors and make optimal decisions in dynamic environments.
d. Deep learning: Deep learning is a subset of machine learning that uses neural networks to learn
from data. This method is widely used in image and speech recognition, natural language processing,
and recommendation systems. Deep learning is generally more powerful than traditional machine
learning methods, but may require large amounts of computational resources and may be difficult
to interpret.
3. Comparison of AI Computing Techniques
a. Neural networks: Neural networks are a set of algorithms that mimic the structure and function of
the human brain. They are composed of interconnected nodes or neurons that process information
and learn from data. Neural networks are used in a variety of applications, including image and
speech recognition, natural language processing, and autonomous vehicles. They are powerful and
can learn complex patterns, but require large amounts of data and computing resources to train.
b. Deep learning: Deep learning is a subset of machine learning that uses neural networks with many
layers to learn from data. It is used in applications such as image and speech recognition, language
translation, and game playing. Deep learning is highly effective in handling large and complex
datasets, but requires significant computing power and is often resource-intensive.
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The Evolution of AI and Its Transformative Eects on Computing
c. Machine learning: Machine learning is a subset of AI that uses algorithms to learn from data
and improve performance over time. It is used in a variety of applications, including fraud detec-
tion, recommendation systems, and predictive maintenance. Machine learning algorithms can be
supervised, unsupervised, or semi-supervised and can handle large datasets. However, they require
significant human intervention for feature selection and model optimization.
d. Natural language processing (NLP): NLP is a field of AI that focuses on the interaction between
humans and machines using natural language. It is used in applications such as language transla-
tion, speech recognition, and sentiment analysis. NLP algorithms can be rule-based or machine
learning-based and require large datasets for training. However, they face challenges in understand-
ing context and sarcasm and require significant human intervention for feature engineering.
Overall, the comparison suggests that these AI computing techniques have unique strengths and limi-
tations, and may be more or less suitable for different types of applications. However, advancements in
hardware and algorithms have made it possible to apply these techniques to a wide range of real-world
problems, from healthcare and finance to education and entertainment.
4. Comparison of AI Computing Applications
a. Healthcare: AI is being used in healthcare for a variety of applications, including medical image
analysis, drug discovery, personalized medicine, and patient monitoring. Medical image analysis
uses computer vision algorithms to analyze medical images and assist in diagnosis and treatment
planning. Drug discovery uses machine learning algorithms to identify potential drug candidates
and optimize drug development processes. Personalized medicine uses AI to analyze patient data
and tailor treatment plans to individual patients. Patient monitoring uses sensors and AI algorithms
to monitor patients remotely and detect changes in health status.
b. Finance: AI is being used in finance for a variety of applications, including fraud detection, credit
scoring, investment management, and risk assessment. Fraud detection uses machine learning
algorithms to identify fraudulent transactions and prevent financial loss. Credit scoring uses AI to
analyze credit history and other factors to determine creditworthiness and assess risk. Investment
management uses AI to analyze financial data and make investment decisions. Risk assessment
uses AI to identify potential risks and develop mitigation strategies.
c. Transportation: AI is being used in transportation for a variety of applications, including autono-
mous vehicles, traffic management, and logistics optimization. Autonomous vehicles use computer
vision and machine learning algorithms to navigate roads and avoid obstacles. Traffic management
uses AI to analyze traffic patterns and optimize traffic flow. Logistics optimization uses AI to
optimize delivery routes, reduce costs, and improve customer satisfaction.
d. Education: AI is being used in education for a variety of applications, including personalized learn-
ing, educational content development, and student assessment. Personalized learning uses AI to
analyze student data and tailor instruction to individual students. Educational content development
uses AI to generate educational content and improve the efficiency of content creation processes.
Student assessment uses AI to evaluate student performance and provide feedback to teachers and
students.
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Overall, the comparison suggests that AI computing applications have the potential to revolutionize vari-
ous industries and improve efficiency, accuracy, and decision-making. However, ethical and regulatory
considerations must be taken into account to ensure the responsible and equitable use of AI. Addition-
ally, the success of AI applications depends on the quality and availability of data, the scalability of
algorithms, and the integration with existing systems.
CASE STUDIES
Case Study 1: Impact of AI on Healthcare
AI is being used in healthcare for medical image analysis, and one notable case study is the use of AI
in breast cancer detection. In 2020, Google Health announced that it had developed an AI system that
could detect breast cancer with greater accuracy than radiologists. The system uses a combination of
deep learning and mammography images to identify potential cancerous lesions, and in clinical trials,
it achieved a reduction in false negatives by 9.4% and false positives by 5.7%.
Case Study 2: Impact of AI on Finance
AI is being used in finance for investment management, and one notable case study is the use of AI in
quantitative finance by the hedge fund Renaissance Technologies. Renaissance Technologies uses AI
algorithms to analyze vast amounts of financial data and make high-frequency trades based on market
patterns. The hedge fund has been highly successful, with an average annual return of 39% over the past
three decades.
Case Study 3: Impact of AI on Transportation
AI is being used in transportation for autonomous vehicles, and one notable case study is the use of AI by
Waymo, a subsidiary of Alphabet Inc. Waymo’s autonomous vehicles uses a combination of lidar sensors,
radar, and cameras to perceive the environment and navigate roads. The AI algorithms used by Waymo
are designed to adapt to a variety of driving scenarios, including complex intersections, roundabouts,
and construction zones. In 2020, Waymo’s vehicles had driven over 20 million miles on public roads.
Case Study 4: Impact of AI on Education
AI is being used in education for personalized learning, and one notable case study is the use of AI by
Duolingo, a language learning app. Duolingo uses machine learning algorithms to analyze user data and
tailor language instruction to individual learners. The app also uses gamification techniques to engage
learners and provide immediate feedback. In a study conducted in 2018, Duolingo users who completed
34 hours of study achieved the equivalent of a semester of college-level language instruction.
These case studies demonstrate the potential impact of AI across various industries and highlight the
potential for increased efficiency, accuracy, and innovation. However, they also underscore the impor-
tance of responsible and ethical use of AI to ensure that these benefits are distributed equitably and that
potential risks and drawbacks are addressed.
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RESULTS AND DISCUSSION:
The findings from the literature review and comparative analysis will be presented and discussed in this
section. The results will include an overview of the current state of AI applications in computing and the
key trends, challenges, and opportunities that AI presents for the future of computing. The results will
also include a comparative analysis of different AI approaches, algorithms, and frameworks, along with
their implications for computing systems. The discussion will provide insights into the potential benefits
and limitations of AI, informing the development and deployment of AI technologies in the future.
1. Findings of the Study
The findings of the study suggest that AI has the potential to transform computing and revolutionize
various industries. Compared to traditional computing, AI computing offers several advantages, includ-
ing increased efficiency, accuracy, and adaptability. AI computing techniques such as neural networks,
deep learning, machine learning, and natural language processing have shown promise in addressing
complex problems and achieving better results than traditional computing methods. Furthermore, the
study found that AI has applications in various industries, including healthcare, finance, transportation,
and education. Case studies in these industries have shown that AI can improve outcomes, reduce costs,
and enhance the overall quality of services.
2. Implications of the Study
The implications of this study are significant. As AI continues to advance and become more sophisti-
cated, it is likely that its impact on computing and various industries will only grow. Companies and
organizations that invest in AI and integrate it into their operations may gain a competitive advantage
and stay ahead of the curve. However, the study also highlights the potential risks and drawbacks of AI,
such as bias, job displacement, and privacy concerns. It is crucial to address these issues and ensure that
the development and use of AI are responsible, ethical, and aligned with societal values.
CONCLUSION
1. Summary of the Chapter
The chapter titled “The Impact of Artificial Intelligence on the Future of Computing: A Comparative
Study” explores the influence of artificial intelligence (AI) on computing and compares different aspects
of AI computing methods, applications, and their implications across various industries.
The chapter begins with an introduction to AI and its significance in shaping the future of comput-
ing. It highlights the advancements in AI technologies and their potential to revolutionize traditional
computing approaches.
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The Evolution of AI and Its Transformative Eects on Computing
The methodology section outlines the research design, data collection, and data analysis methods
employed in the study. This ensures the reliability and validity of the findings presented in the chapter.
Next, the chapter compares traditional computing with AI computing methods, such as supervised
learning, unsupervised learning, reinforcement learning, and deep learning. It discusses their respective
strengths, weaknesses, interpretability, and scalability.
The study then delves into the comparison of AI computing techniques, including neural networks,
genetic algorithms, decision trees, and Bayesian networks. Each technique’s advantages and limitations
are analyzed, considering their applicability to different problem domains.
To understand the practical impact of AI, the chapter presents case studies from various industries,
including healthcare, finance, transportation, and education. These case studies highlight how AI is be-
ing used to improve diagnosis accuracy in breast cancer detection, enhance investment management in
finance, enable autonomous vehicles in transportation, and personalize language learning in education.
Furthermore, the discussion section presents the findings of the study, emphasizing the transformative
potential of AI in computing and various industries. It emphasizes the need for responsible AI develop-
ment, addressing ethical concerns, and ensuring equitable distribution of AI benefits.
The chapter concludes by summarizing the implications of the study. It underscores the importance
of continued research, investment, and responsible use of AI to harness its full potential while addressing
challenges and risks associated with its deployment.
Overall, the chapter provides valuable insights into the impact of AI on the future of computing. It
offers a comparative analysis of AI computing methods, techniques, applications, and their implications,
paving the way for further exploration and advancements in this rapidly evolving field.
2. Future Prospects of AI in Computing
The potential for further development of AI in computing is immense. AI technologies have the potential
to significantly improve the performance and efficiency of computing systems, such as faster processing
times and reduced energy consumption. The development of AI algorithms and software is also expected
to drive innovation in various areas of computing, such as cybersecurity, data analysis, and automation.
However, there are also potential challenges, such as the impact of AI on employment and the need for
ethical guidelines for the development and deployment of AI technologies.
a. Impact of AI on Different Areas of Computing
The impact of AI on different areas of computing is significant and varied. In hardware, AI technologies
are driving the development of specialized chips and systems that are optimized for AI workloads. In
software, AI algorithms are being used in various applications, such as recommendation systems and
predictive analytics. In infrastructure, AI technologies are being used to optimize and automate vari-
ous tasks, such as network management and resource allocation. The impact of AI on different areas of
computing also varies depending on the specific application and industry.
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The Evolution of AI and Its Transformative Eects on Computing
b. Implications of AI for Different Stakeholders
The implications of AI for different stakeholders, such as businesses, governments, and individuals, are
also significant. AI technologies have the potential to improve productivity and efficiency in businesses,
while also creating new business models and opportunities. Governments are also exploring the use of
AI technologies for various applications, such as smart cities and healthcare. However, there are also
potential risks and challenges, such as the impact of AI on privacy and security, and the need for ethical
guidelines and regulations.
3. Final Thoughts
The chapter titled “The Impact of Artificial Intelligence on the Future of Computing: A Comparative
Study” provides a comprehensive overview of the role of AI in computing and its impact across various
industries. It highlights the potential of AI to transform traditional computing methods and presents a
comparative analysis of AI computing methods, techniques, and applications.
The chapter emphasizes the importance of responsible AI development and deployment to address
ethical concerns and ensure equitable distribution of benefits. It underscores the need for continued
research and investment in AI to harness its full potential while addressing challenges and risks.
Overall, the chapter provides valuable insights into the impact of AI on the future of computing.
It offers a thought-provoking discussion on the transformative potential of AI and its implications for
society, economy, and technology. It encourages further exploration and advancements in this rapidly
evolving field while addressing ethical considerations to ensure AI’s benefits are realized by all.
REFERENCES
Akyuz, E., Cicek, K., & Celik, M. (2019). A Comparative research of machine learning impact to future
of maritime transportation. Procedia Computer Science, 158, 275–280. doi:10.1016/j.procs.2019.09.052
Bahrammirzaee, A. (2010). A comparative survey of artificial intelligence applications in finance: Arti-
ficial neural networks, expert system and hybrid intelligent systems. Neural Computing & Applications,
19(8), 1165–1195. doi:10.100700521-010-0362-z
Guzman, A., Ishida, S., Choi, E., & Aoyama, A. (2016, December). Artificial intelligence improving
safety and risk analysis: A comparative analysis for critical infrastructure. In 2016 IEEE International
Conference on Industrial Engineering and Engineering Management (IEEM) (pp. 471-475). IEEE.
10.1109/IEEM.2016.7797920
Harmon, P., & King, D. (1985). Expert systems: Artificial intelligence in business. John Wiley & Sons, Inc.
Kermadi, M., & Berkouk, E. M. (2017). Artificial intelligence-based maximum power point tracking
controllers for Photovoltaic systems: Comparative study. Renewable & Sustainable Energy Reviews, 69,
369–386. doi:10.1016/j.rser.2016.11.125
441
The Evolution of AI and Its Transformative Eects on Computing
Khan, R., Dhingra, N., & Bhati, N. (2022). Role of artificial intelligence in agriculture: A comparative
study. In Transforming Management with AI, Big-Data, and IoT (pp. 73–83). Springer International
Publishing. doi:10.1007/978-3-030-86749-2_4
Maleki, A., &Askarzadeh, A. (2014). Comparative study of artificial intelligence techniques for sizing
of a hydrogen-based stand-alone photovoltaic/wind hybrid system. international journal of hydrogen
energy, 39(19), 9973-9984.
Rauf, M. A., Ashfaq, M., Hasan, R., & Manju, M. A. (2021). A comparative study on the impact of
artificial intelligence on employment opportunities for university graduates in Germany and the Neth-
erlands: AI opportunities and risks. International Journal of Environment. Workplace and Employment,
6(3), 185–204. doi:10.1504/IJEWE.2021.119679
Samuel, I. A., Adetiba, E., Odigwe, I. A., & Felly-Njoku, F. C. (2017). A comparative study of regres-
sion analysis and artificial neural network methods for medium-term load forecasting. Indian Journal
of Science and Technology, 10(10), 1–7. doi:10.17485/ijst/2017/v10i10/86243
Wauters, M., & Vanhoucke, M. (2016). A comparative study of Artificial Intelligence methods for project
duration forecasting. Expert Systems with Applications, 46, 249–261. doi:10.1016/j.eswa.2015.10.008
KEY TERMS AND DEFINITIONS
Artificial Intelligence (AI): Artificial Intelligence (AI) is a technology that enables computers
and machines to think and act like humans. It involves creating smart systems that can learn from data,
solve problems, and make decisions. AI helps computers recognize images, understand speech, translate
languages, and even play games. It is used in many areas, such as self-driving cars, voice assistants like
Siri or Alexa, and personalized recommendations on websites. AI is constantly improving and has the
potential to revolutionize how we live and work.
Internet of Things (IoT): The Internet of Things (IoT) is a concept that refers to the connection
of everyday objects to the internet, allowing them to send and receive data. These objects can include
devices like smartphones, thermostats, wearables, home appliances, and even vehicles. The idea behind
IoT is to create a network where these objects can communicate with each other, collect and share data,
and perform tasks more efficiently.
Supervised Learning: Supervised learning is a type of machine learning where a computer system
is trained to make predictions or take actions based on labeled examples provided by humans. In super-
vised learning, the computer is given a dataset consisting of input data and corresponding output labels
or desired outcomes. The goal is for the computer to learn the relationship between the input and output
so that it can accurately predict the output for new, unseen inputs.
Unsupervised Learning: Unsupervised learning is a type of machine learning where a computer
system learns patterns and structures in data without any explicit guidance or labeled examples. Unlike
supervised learning, unsupervised learning algorithms work with unlabeled data, meaning there are no
predefined output labels or desired outcomes provided.
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Deep Learning: Deep learning is a subfield of machine learning that focuses on teaching computers
to learn and make decisions in a way inspired by the human brain. It uses artificial neural networks, which
are computational models composed of interconnected nodes called “neurons.” These neural networks
are structured in multiple layers, hence the term “deep” learning.
Stakeholders: Stakeholders are individuals, groups, or organizations that have an interest or “stake”
in a particular project, decision, or organization. They can be affected by or have an impact on the out-
come or success of a project or initiative.
Natural Language Processing (NLP): Natural Language Processing (NLP) is a branch of artificial
intelligence that focuses on enabling computers to understand, interpret, and interact with human lan-
guage in a natural and meaningful way. NLP involves the development of algorithms and models that
allow computers to process, analyze, and generate human language.
