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Artificial Intelligence: A Very Short Introduction Review

Artificial Intelligence: A Very Short Introduction Review

Table of Contents

Have we ever wondered what all the fuss about artificial intelligence is? We always hear about how AI is changing the world, but do we truly understand what it means and how it works? Well, “Artificial Intelligence: A Very Short Introduction Paperback – Illustrated, 23 August 2018” aims to demystify AI for us in a mere 160 pages. So, let’s see what’s packed into this compact, illustrated guide and if it lives up to its promise of simplifying AI.

Artificial Intelligence: A Very Short Introduction Paperback – Illustrated, 23 August 2018

Get your own Artificial Intelligence: A Very Short Introduction Paperback – Illustrated, 23 August 2018 today.

The Compact Size But Big Ambitions

Handy and Portable

This book is not your typical tech tome. Measuring approximately 6.7 x 0.6 x 4.4 inches and weighing just a few ounces, it fits easily into a bag, making it an ideal travel companion. Whether we’re commuting to work or lounging in a cafe, we can easily bring it along. This convenience ensures we can dip into the world of AI whenever we find a spare moment.

Illustrated for Clarity

The addition of illustrations cannot be overstated. Complex topics such as AI benefit greatly from visual aids, and this book includes diagrams, charts, and images that make it easier to grasp. Sometimes, a picture really is worth a thousand words, and these illustrations help in breaking down the intricate concepts into manageable pieces.

Table of Basic Product Information

Feature Details
Title Artificial Intelligence: A Very Short Introduction
Format Paperback – Illustrated
Publication Date 23 August 2018
Dimensions 6.7 x 0.6 x 4.4 inches
Weight A few ounces
Page Count 160 pages
Publisher Oxford University Press
Artificial Intelligence: A Very Short Introduction Paperback – Illustrated, 23 August 2018

Artificial Intelligence: A Very Short Introduction Paperback – Illustrated, 23 August 2018

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Author Expertise

Who’s Behind the Pages?

Written by Professor Margaret A. Boden, a leading expert in the field of cognitive science and AI, this book carries a lot of credibility. Boden has authored numerous influential works and brings decades of research experience to the table. Her linguistic clarity combined with a deep knowledge of AI makes this book both reliable and accessible.

The Author’s Unique Style

Boden’s writing style is refreshingly clear and direct, avoiding the jargon that often plagues technical subjects. She anticipates questions and hesitations we might have, addressing them with the ease of someone who’s explained these concepts countless times. Her tone is conversational yet authoritative, making complex subjects feel approachable.

Get your own Artificial Intelligence: A Very Short Introduction Paperback – Illustrated, 23 August 2018 today.

Content Breakdown

Clear Segmentation

The book is organized into concise chapters, each presenting a different aspect of AI. This segmentation allows us to focus on one topic at a time without feeling overwhelmed. We can read it cover to cover, or skip to the sections that pique our interest the most.

Key Sections

What is AI?

Let’s start with the basics. Boden does an excellent job of defining what artificial intelligence is and isn’t. She simplifies the concept by comparing it to human intelligence and highlights the differences and similarities. The book addresses common misconceptions and sets a solid foundation for the topics to come.

History and Evolution

Understanding the history of AI is crucial to appreciating its advancements. Boden takes us on a journey from the early days of AI, through the ‘AI winters’ when interest and funding waned, to the modern renaissance of machine learning and neural networks. This historical context is essential for understanding the ebb and flow of AI’s popularity and technological breakthroughs.

How AI Works

This chapter is a goldmine for those of us who want to get under the hood of AI. Boden breaks down complex algorithms and the mathematics behind AI into bite-sized, understandable pieces. She explains neural networks, machine learning, and other fundamental technologies driving AI today.

Applications and Impacts

AI is not just theory—it’s everywhere, from Siri on our phones to self-driving cars. Boden explores various applications of AI in medicine, finance, entertainment, and more. She discusses both the positive impacts and the ethical concerns surrounding these advancements, painting a balanced picture.

Ethical and Societal Considerations

No discussion on AI would be complete without addressing its ethical implications. Boden delves into questions of job displacement, privacy, and the potential for AI to be used in harmful ways. This chapter encourages critical thinking about the role of AI in society and our responsibilities as its creators and users.

Table of Chapter Breakdown

Chapter Key Highlights
What is AI? Defines AI, busts myths, and provides a solid foundation.
History and Evolution Traces the timeline of AI development, highlighting key moments and breakthroughs.
How AI Works Explains the underlying technologies, algorithms, and mathematics in an accessible manner.
Applications and Impacts Explores real-world uses of AI and their impacts on various sectors.
Ethical and Societal Considerations Discusses the ethical dilemmas and societal implications of AI.

The Accessibility Factor

Language Simplicity

One of the standout features of this book is its language. Boden avoids tech-speak and instead uses analogies and everyday language to explain complex concepts. This makes it readable for both beginners in AI and those with a bit more background knowledge.

Illustrations and Examples

Visual aids in the form of diagrams and illustrations appear throughout the book. These are incredibly helpful for those of us who might struggle to visualize abstract concepts. Boden also uses real-world examples to explain how AI algorithms function, lending a practical dimension to theoretical explanations.

Length and Readability

At 160 pages, the book is concise without being superficial. We’re treated to a substantial amount of information without feeling like we’re reading a textbook. Each chapter is digestible, and the overall length encourages us to finish it, not just start it.

Artificial Intelligence: A Very Short Introduction Paperback – Illustrated, 23 August 2018

The Visual Appeal

Quality of Illustrations

Illustrations in this book are not merely decorative. They serve a functional purpose by simplifying complex material. Diagrams of neural networks, flowcharts of decision-making processes, and graphical representations of data are among the various illustrations that help demystify AI.

Layout and Design

The book’s design is clean and uncluttered, with sections clearly marked and plenty of white space. This makes it easy to read and reduces the cognitive load, which is essential when dealing with a subject as intricate as AI.

Usefulness and Applications

For Students and Educators

This book is an invaluable resource for both students and educators. Its concise yet comprehensive coverage of AI makes it ideal for introductory courses. Educators can use it as a supplementary text, while students will find it a quick reference guide.

For General Readers

For the average reader who’s curious about AI but doesn’t want to commit to a 500-page textbook, this book is perfect. It makes the field of AI accessible to everyone, regardless of their background in technology or mathematics.

Professional Development

Even those of us working in fields tangentially related to AI will find this book useful. It provides a well-rounded understanding of AI’s current state and future directions, essential for anyone looking to stay relevant in today’s tech-driven world.

Strengths and Weaknesses

Strengths

Simplicity and Clarity

The book excels at breaking down complex topics into easily understandable chunks. Boden’s writing is lucid and engaging, making it approachable for all readers.

Comprehensive Coverage

Despite its short length, the book covers a wide range of topics. From the basics of AI to its societal implications, it gives a well-rounded overview.

Portability

Its small size and light weight make it an easy carry-on, allowing us to read and learn on the go.

Visuals

Illustrations and diagrams significantly enhance our understanding and make abstract concepts more tangible.

Weaknesses

Limited Depth

While the brevity of the book is a strength, it can also be a limitation. Those looking for an in-depth exploration of any one topic may find the coverage too superficial.

Lack of Technical Detail

For advanced readers or professionals already familiar with the basics, the book may not provide enough technical depth. It’s more of an introduction than a comprehensive guide.

Occasional Oversimplification

In an attempt to make complex topics accessible, some aspects are occasionally oversimplified. This might leave some readers wanting more thorough explanations.

Comparison with Other AI Literature

Length and Depth

Compared to other AI books like “Artificial Intelligence: A Modern Approach” by Stuart Russell and Peter Norvig, or “Superintelligence” by Nick Bostrom, this book is significantly shorter and less detailed. While the other books go deep into technical and philosophical discussions, Boden’s work serves as an entry point for beginners.

Readability

Where many AI books are dense and heavy on jargon, “Artificial Intelligence: A Very Short Introduction” is wonderfully readable. Its conversational tone and clear language set it apart, making it accessible to a broader audience.

Visuals

Not all AI books incorporate visuals effectively. Boden’s use of illustrations is a noteworthy feature that aids in comprehension and enhances the learning experience.

Conclusion: Should We Buy It?

So, should we add “Artificial Intelligence: A Very Short Introduction” to our reading list? If we’re new to the world of AI and want a friendly, accessible introduction, the answer is a resounding yes. It offers a thoughtful overview of the field, covering key concepts, historical context, current applications, and ethical considerations, all within a tidy 160 pages. While those of us with a more advanced understanding of AI might find it lacking in depth, it’s an invaluable resource for beginners and a handy quick-reference for everyone else. So, next time we find ourselves wondering about AI, we might just reach for this compact guide, and perhaps, some of the mysteries will start to clear.

Discover more about the Artificial Intelligence: A Very Short Introduction Paperback – Illustrated, 23 August 2018.

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Emmanuel Bazile All Rights Reserved

About Me

With 25 years of experience in healthcare IT implementation, Emmanuel began his career at the University of Pittsburgh Medical Center, working as an assistant manager for a billing system implementation. Over the years, he has explored various aspects of the healthcare IT domain, successfully implementing several laboratory information systems and electronic medical record (EMR) systems, such as Cerner Millennium and Epic EMR.

In 2005, Emmanuel shifted his focus to public health, working on bio-surveillance implementation for the Centers for Disease Control and Prevention (CDC). He contributed to the BioSense Data Provisioning Project and performed extensive analysis of HL7 messages in hospitals and healthcare facilities. Additionally, Emmanuel requirements analysis for the CDC BioSense Analysis, Visualization and Reporting (AVR) project and played a key role in publishing the Situational Awareness updates to the BioSense System Requirements Specification (SRS).

Over the past 11 years, Emmanuel has worked in the Middle East, implementing the Epic EMR system at Cleveland Clinic Abu Dhabi. As a multidisciplinary team member, he has taken on various roles, including SCRUM Master, Project Manager, Integration Engineer, and Platform Engineer. Concurrently working as an adjunct university faculty member, teaching graduate-level courses in Systems Life Cycle and undergraduate courses in Health Information Systems

From a technological standpoint, Emmanuel has designed, installed, and implemented complete hospital integration systems using Rhapsody Integration Engine, MS SQL Server, and Public Health Information Networks Messaging System (PHINMS). He has also developed over 10,000 interfaces some of which coded in Java and JavaScript.

In 2019, Emmanuel expanded his skill set and entered the field of digital marketing, quickly becoming a proficient Digital Marketing Strategist. He has since helped numerous clients develop robust digital marketing strategies for their businesses. His expertise encompasses Social Media Marketing, On-page and Off-page SEO, Google Ads, and Google Analytics. Additionally, he and a team have managed clients’ website development projects, ensuring that each site is optimized for SEO, further enhancing their online presence and performance.

Alongside their digital marketing expertise, Emmanuel has delved into the world of Affiliate Marketing, where Emmanuel and his team successfully managed and executed campaigns for a variety of clients. By identifying the right products and services to promote, Emmanuel and his team helped clients generate passive income streams and increase their overall revenue.

Their approach to Affiliate Marketing involves creating valuable content that educates and engages the target audience, while strategically incorporating affiliate links. Emmanuel and his team have experience working with multiple affiliate networks and platforms, ensuring optimal tracking and reporting of performance metrics. By staying up to date with the latest trends and best practices, Emmanuel and his team have been able to optimize affiliate campaigns for maximum results, fostering long-term partnerships and sustainable growth for their clients.

As an accomplished professional, Emmanuel holds dual Bachelor of Arts degrees in Linguistics and English, a Master of Science in Health Information Systems from the University of Pittsburgh, and a Ph.D. in Information Systems from Nova Southeastern University.

My Teaching History

Professor Bazile is a dedicated technology instructor and Adjunct Faculty professor, who began his teaching career in April 2000 at the Business Career Institute in Las Vegas, Nevada.

In 2001, he expanded his expertise by training nurses in the use of Electronic Medical Records (EMR) systems. His experience in both technology and healthcare led to his appointment as an Adjunct Faculty professor at the University of Phoenix in May 2008, where he has taught several graduate-level information technology and healthcare information systems courses.

Dr. Bazile has also developed an HL7 course, which he has taught at various healthcare facilities, drawing from his own book, “HL7: Introductory and Advanced Concepts,” currently available on Amazon. With a passion for teaching and a commitment to ensuring students get the most out of each course he teaches, Dr. Bazile is a valuable asset to both his students and the institutions he serves.

My Teaching Philosophy

My teaching philosophy as an Information Systems professor in healthcare is built on the concept that education should equip students to be confident and capable problem solvers who are prepared to traverse the complicated and ever-changing landscape of Healthcare IT.

In order to accomplish this, I prioritize the creation of a dynamic and engaging learning environment that encourages students to engage with course material and with one another. This involves employing a range of teaching approaches, such as lectures, seminars, and hands-on activities, to ensure that students learn in the manner that best matches their learning style.

I believe the reason we have Information Systems as a discipline is to allow students to apply technology to solve real world problems. If that is the case, both undergraduate and graduate students have to be challenged to incorporate their core academic courses with their matriculated subjects. As such, it is important that students enter their Junior and Senior years with a strong command of the core courses such as Programming, databases, networks, hardware and software, as they serve as the foundation upon which real-world solutions will be built.

I also believe in the importance of incorporating real-world examples and case studies into my courses, as this helps to connect abstract concepts to practical applications. Additionally, I encourage students to apply what they are learning to their own personal and professional goals, as this helps to make the material more meaningful and relevant to their lives.

I strive to foster a positive and supportive learning environment where all students feel comfortable asking questions and participating in class discussions. I believe that this is key to fostering a sense of community and ensuring that all students have the opportunity to succeed.

I have also taught online courses. I have found in an asynchronous learning environment it can be difficult to apply the Peer Teaching or Experiential Learning Pedagogical Approaches. However, I have found the Discovery Learning approach to works quite well. Along with a boost to students’ self-confidence, Discovery Learning in an online environment allows students to synthesize information, expand on existing concepts on their own, while experiencing a positive outcome through trial and error.

Ultimately, my mission as an educator, and a Healthcare IT Information Systems professor is to provide students with the knowledge, skills, and confidence they need to thrive and succeed in their careers and to be technological leaders. By creating a positive and supportive learning environment, incorporating real-world examples and case studies, and encouraging students to apply what they are learning to their own objectives; my hope is to inspire and empower all students to achieve their full potential.

Population Size:

A total of 310 responses were originally received. Any response containing missing data due to unclicked radio buttons or unchecked checkboxes were first reviewed, and, if justified, were omitted from analysis. For surveys with missing data, a total of 18 responses were removed. In order to address any issues with response-set, the data was downloaded into Microsoft Access and queries ran to identify responses that contained the same values for each question. A total of 16 responses were found to be qualified for removal. Another 18 were identified as outliers and removed leaving a total of 258 responses for the study analysis.

In order to assess multivariate outliers, the Mahalanobis distances were calculated and plotted against their corresponding Chi-Square distribution percentiles (Schmidt & Hunter, 2003). The resulting scatterplot is similar to a univariate normal Q-Q plot, where deviations from a straight line show evidence of non-normality. The data showed indications of moderate deviations from multivariate normality, as indicated by the concavity of the data points. There were no additional multivariate outliers or missing values in the data after the removal of 52 responses.

Descriptive Statistics

Frequencies and percentages were conducted for the demographics indicators, while means and standard deviations were calculated for the continuous indicators. For gender, there were 151 females (59%) and 107 males (41%) in the sample. For ethnicity, most participants were Caucasian (119, 46%), followed by African American (56, 22%). The two most populous education levels were Bachelor’s (90, 35%) and Master’s (62, 22%). The biggest proportion of the sample by age group was the 35-44 age group (101, 39%) followed by the 45-54 age group (59, 23%).

Analysis:

Confirmatory Factor Analysis and Composite Reliability

A CFA was conducted along with a reliability analysis to assess construct validity. Examination of modification indices and factor loadings indicated that CSE1, CSE5, CSE7, PC5, ATE1, ATE6, ATE8, PP5, and PP6 were all causing significant problems with the model parameters. The results of the last iteration of the CFA performed in R showed significantly improved fit, although still poor overall (χ2(545) = 2125.61, p < .001, CFI = 0.82, TLI = 0.81, RMSEA = 0.11). The high degrees of freedom indicate that a very large number of parameters are being estimated in this model.

Composite Reliability

For the full model, each construct had excellent reliability. The ATE latent construct had a composite reliability value of 0.89. The ORC construct had a composite reliability value of 0.94. The CSE latent construct had a composite reliability value of 0.85 and PC had a composite reliability value of 0.95. For PP and RES, the composite reliability scores were 0.80 and 0.96 respectively. These values indicate that the loadings for each construct were all directionally similar, and that the items in each construct show a high degree of consistency.

Cronbach’s Alpha

Cronbach’s alpha values were calculated for the items in each construct. The alphas for PC (α = 0.90), AXY (α = 0.94), and RES (α = 0.94) indicated excellent reliability. The alphas for CSE (α = 0.80), ATE (α = 0.88), and PP (α = 0.83) all showed good reliability. These values confirm the results of the composite reliability tests, and reiterate the high degree of reliability within each latent construct.

Partial Least Squares – Structural Equation Modeling

A partial least squares- structural equation modeling (PLS-SEM) was conducted to determine how well the data fit the proposed model, and discern whether significant relationships existed between the independent and dependent constructs. The full model showed AVE values of 0.53 for ATE, 0.69 for AXY, 0.44 for CSE, .72 for PC, .35 for PP, and 0.81 for RES. The high values for AXY, PC, and RES indicate that the amount of variance accounted for in the manifest variables is sufficiently high. The values for ATE, CSE, and PP indicate that some of the variance in the manifest variables is left unexplained.

Structural Model

Once the measurement model had been tested for model specification, the structural model was tested to determine if ATE, AXY, CSE, PC, and PP had a significant effect on RES. A path weighted model was calculated using 10,000 bootstrap samples in R. The results showed a pseudo R-squared value of 0.78. This indicates that approximately 78% of the variance in RES is explainable by the collective effects of CSE, PC, ATE, PP, and AXY.

Further examination of the effects indicated that AXY had a highly significant effect on RES (B = 0.87, p < .001). This indicates that a standard deviation increase in AXY increases the expected value of RES by 0.87 standard deviations. CSE did not have a significant effect on RES (B = 0.02, p = .423). Additionally, CSE (B = 0.02, p = .423), PC (B = 0.05, p = .334), ATE (B = 0.00, p = .983), and PP (B = 0.03, p = .407) did not have significant effects on RES. Table 11 outlines the results of the path estimates.

Correlation Analyses

Both Pearson and Spearman correlations were calculated on the composite scores. The results of the Pearson correlations indicated that CSE was significantly correlated AXY (r = 0.22, p < .001) and RES (r = 0.21, p < .001). The results also indicated that PC was significantly correlated with ATE (r = -0.79, p < .001), AXY (r = 0.18, p < .001), and RES (r = 0.20, p < .001). ATE was significantly correlated with AXY (r = -0.19, p < .001) and RES (r = -0.19, p < .001). AXY was significantly correlated with RES (r = 0.85, p < .001).

ANCOVA Analyses

An analysis of covariance (ANCOVA) was conducted to determine if a significant relationship existed between the AXY, PP, CSE, PC, ATE scores and RES controlling for Gender, Age, Ethnicity, Education, and Specialty. The overall model was found to be significant (F(63,194) = 53.39, p < .001), with an R2 value of .95, indicating that 95% of the variance in RES was explained by the collective effect of the independent variables and covariates.

Since the overall model was found to be significant, the model’s covariates were assessed. The AXY (F(10,194) = 262.20, p < .001), ATE (F(7,194) = 2.20, p = .036), Years computers (F(1,194) = 5.71, p = .018), and PC (F(12,194) = 2.00, p = .026) scores were found to be significant, indicating that a significant amount of variance in RES is explained by AXY, ATE, and PC.

A path diagram depicting the results of the structural model.

Results

This research investigated Computer Self-Efficacy (CSE), Perceived Complexity (PC), Attitudes toward EMR Systems (ATE), Peer Pressure (PP), and Anxiety (AXY) to determine whether these constructs as individuals, or as a group, or coupled together with some other factors could significantly explain resistance to EMR systems. Quantitative examination of self-reported survey results was performed to understand the strength and significance of the relationships, while these relationships were investigated to test the strength of model fit.

the regression paths of the structural model were examined to test the hypotheses. Significance was determined using an alpha level of .05. The model had an overall R2 value of 0.78. This indicates that approximately 78% of the variability in RES can be accounted for by CSE, PC, ATE, PP, and AXY. Since the overall model was significant, the individual coefficients can be interpreted. Some of the hypotheses were supported by the results of this study, and some were rejected. The construction of a data model of the relationships in this study could not meet thresholds that would be evidence of a good fit of the relationships identified in the study.

The fifth hypotheses tested the influence of AXY on resistance to EMR systems. AXY was expressed to be significantly related to resistance (r=.87, p<.001). This finding supports the hypothesis that anxiety with the EMR system will lead to medical care professionals rejecting use of the system. Unlike the findings of the first four hypotheses, the findings of the current study support previous research. Angst and Agarwal (2009) indicated that AXY is a factor which is significantly related to the problem of EMR system resistance. Based on the empirical findings of previous research, the present research and conceptual propositions and conclusions in previously written scholarly articles, there is a great deal of support for the finding that AXY is significantly influenced by EMR resistance.

The findings of this research do not support all findings by previous researchers, and there are multiple relationships which had been established as being significant that were identified as being insignificant in the current research. Generally, because of the inconsistency of previous findings and the current study there may be elements related to the sample examined or other contextual factors which may contribute to the inconsistency that exists. Ultimately, it is suggested that there be further research done on the problem of resistance to EMR system use.

Ultimately the findings support a new take on the problem of EMR system resistance that may contribute to the ways in which scholars investigate the problem of EMR resistance in general. This may also help with the way practitioners approach EMR systems, and articulate value of the systems to medical professionals investing record-keeping systems in the workplace.