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Artificial Intelligence: A Modern Approach Review

Artificial Intelligence: A Modern Approach Review

Table of Contents

Are we ready to understand the future of technology and delve into the intricate world of artificial intelligence? We have been eagerly flipping through the pages of the “Artificial Intelligence: A Modern Approach, Global Edition Paperback – Big Book, 13 May 2021” and we can’t wait to share our thoughts with you.

Learn more about the Artificial Intelligence: A Modern Approach, Global Edition Paperback – Big Book, 13 May 2021 here.

Our Journey Through the Pages

Before we dive into the nitty-gritty, let’s talk about first impressions. The book’s title is, without a doubt, a mouthful. But, it kind of grows on you. The more you repeat it, the more sophisticated you feel. It’s like ordering an extravagant dish at a Michelin-star restaurant. But does it live up to its grand title?

Artificial Intelligence: A Modern Approach, Global Edition Paperback – Big Book, 13 May 2021

Artificial Intelligence: A Modern Approach, Global Edition Paperback – Big Book, 13 May 2021

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A Book for All Seasons

User-Friendly Navigation

Navigating through this book is like taking a leisurely Sunday drive. Well, that is if your leisurely Sunday drive includes debating ethical quandaries of autonomous vehicles. Still, there’s something navigationally satisfying about it. Each section flows naturally into the next, making it difficult to put down.

Proper Formatting

We all remember the textbooks with font sizes so small that deciphering Ancient Egyptian hieroglyphs seemed more pleasant. Thankfully, this book isn’t like that. The formatting is crisp, the text generously spaced, and the graphics plentiful. It’s like walking into a minimalist’s dream apartment—everything is exactly where it should be.

Artificial Intelligence: A Modern Approach, Global Edition Paperback – Big Book, 13 May 2021

Learn more about the Artificial Intelligence: A Modern Approach, Global Edition Paperback – Big Book, 13 May 2021 here.

Content Breakdown

Chapter Highlights

The authors have structured the book in a way that feels like a well-curated Netflix series. Each chapter is like an episode, complete with cliffhangers and oh-so-satisfying twists.

Chapter Main Theme What’s Exciting?
Introduction Fundamentals of AI Sets the tone perfectly for what’s to come
Problem-Solving Search algorithms and optimization A deep dive into what makes machines think
Knowledge & Reasoning Logic and agents Understanding the decision-making process
Learning Machine learning concepts Turning data into knowledge
Communicating & Perceiving Language understanding Can machines understand us fully?
Robotics Integration of AI with robotics Bridging the gap between mind and machine
Philosophical Foundations Ethical and societal implications Deep, thought-provoking discussions

Case Studies and Examples

Throughout the book, the authors sprinkle in numerous real-world applications and case studies that made us pause and think. Each case study feels like a short story, blending facts with engaging narratives.

The Authors’ Expertise

Stuart Russell and Peter Norvig

When you’re writing about artificial intelligence, having reputed authors at the helm makes all the difference. Stuart Russell and Peter Norvig are the Batman and Robin of the AI world, bringing a wealth of knowledge and years of experience to the table. They’re not just academics locked away in ivory towers. These folks are actively engaged in the field, bridging the gap between theory and practice.

Accessible Language

Reading through the book, it’s evident that the authors are passionate educators. They have a knack for breaking down complex concepts into digestible bits without making readers feel like we’re in a remedial math class.

Artificial Intelligence: A Modern Approach, Global Edition Paperback – Big Book, 13 May 2021

Practical Applications

Real-World Impact

We were particularly impressed with how the book makes a concerted effort to relate theoretical concepts to real-world applications. Whether we’re talking about AI in healthcare, transportation, or customer service, the relevance of AI in everyday life is expertly highlighted.

Hands-On Approach

There’s nothing like rolling up our sleeves and getting our hands dirty. Well, metaphorically speaking. The book includes practical exercises that let us implement what we’ve learned. It’s akin to those cooking classes where you actually get to make the soufflé, rather than just watching the chef whisk eggs.

The Ethical Landscape

Thought-Provoking Questions

The ethical discussions in the book are not just an afterthought but thoughtfully interwoven throughout. They tackle some hard-hitting questions that make us ponder the morality of our rapidly evolving technological world. From job displacement to privacy concerns, the book dives deep into the ethical quagmire we find ourselves in today.

Balanced Perspective

The book isn’t a doom-and-gloom prophecy or a utopian fairy tale. Instead, it offers a balanced perspective that presents the pros and cons of AI, leaving us to form our own educated opinions.

For the Layperson and the Expert

Layperson Appeal

One of the most striking aspects of this book is its accessibility. It’s not just for the tech-savvy. Even if AI is as alien to you as a Martian holiday, the book provides enough foundational knowledge to bring you up to speed. It never feels condescending or overly simplistic—just right.

Expert-Level Content

On the flip side, if you’re an AI aficionado, there’s plenty to sink your teeth into. The book is exhaustive, covering a breadth of topics that would make Mars colonization seem like a weekend getaway. We found ourselves constantly challenged, yet satiated.

Room for Improvement

While there isn’t much to nitpick, even the best have room for improvement. There are moments when the text gets dense, particularly in the more mathematical sections. While the authors do a commendable job of simplifying complex theories, some parts do require a bit more mental gymnastics.

Diagrams and Visuals

More diagrams and visual aids could have been beneficial, especially in sections heavy with algorithms and formulae. While we appreciate a good narrative, a picture is worth a thousand words.

Personal Touch

We often read AI books that feel like they were written by a sentient algorithm—not this one. The personal touch from the authors adds a unique warmth, making the material more engaging and less sterile. Their enthusiasm for AI is infectious, paving the way for numerous light-bulb moments.

The Verdict

Final Thoughts

All in all, “Artificial Intelligence: A Modern Approach, Global Edition Paperback – Big Book, 13 May 2021” is a gem. It’s informative, engaging, and accessible—a must-read for anyone with even a passing interest in AI. Whether we’re novices or seasoned professionals, this book offers invaluable insights that are both thought-provoking and applicable.

Would We Recommend It?

In a heartbeat. If you have ever found yourself staring at a self-checkout machine wondering, “How do you work?”, this book is your golden ticket. It pulls back the curtain on the mysterious world of artificial intelligence, making it feel a little less alien and a lot more fascinating.

So, as we place this book back on our bookshelf, slightly dog-eared and well-worn, we can’t help but feel a sense of excitement for the future. And we suspect, after reading it, you will too.

Click to view the Artificial Intelligence: A Modern Approach, Global Edition Paperback – Big Book, 13 May 2021.

Disclosure: As an Amazon Associate, I earn from qualifying purchases.

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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.