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Artificial Intelligence: Modern Magic? Review

Artificial Intelligence: Modern Magic? Review

Table of Contents

Have you ever wondered if artificial intelligence is the modern equivalent of magic or if it’s leading us toward a dangerous future? The book “Artificial Intelligence: Modern Magic or Dangerous Future? Paperback – Big Book, 6 June 2019” tackles exactly that. This hefty book promises to take us on an exploratory journey through the labyrinth of AI, its incredible potential, and its equally terrifying capabilities.

Find your new Artificial Intelligence: Modern Magic or Dangerous Future? Paperback – Big Book, 6 June 2019 on this page.

What is this Book About?

The Heart of AI

This book unveils the mysteries behind one of the most fascinating yet controversial technologies of our era—artificial intelligence. Unlike most technical manuals, it adopts a narrative style that feels almost like a story being told by a good friend over a cup of coffee. This aspect is part of what makes it an engaging yet insightful read.

Good vs. Evil

We see a balanced discussion about the pros and cons of AI. The authors don’t shy away from presenting the creepy, “Big Brother is watching” angle as well as the miraculous advancements that could potentially make our lives better. It’s almost like they can’t decide whether they love AI or are secretly plotting its demise.

Perspectives and Personalities

One of the book’s strengths is how it incorporates various expert opinions. It brings to the forefront the voices of tech moguls, researchers, and skeptical voices, giving us a multifaceted understanding of AI. Sometimes it’s like being at a dinner party with a diverse group of intellectuals, each chiming in with their take.

Artificial Intelligence: Modern Magic or Dangerous Future? Paperback – Big Book, 6 June 2019

Artificial Intelligence: Modern Magic or Dangerous Future? Paperback – Big Book, 6 June 2019

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Content Breakdown

For the Technically Inclined

Yes, there are some technical sections. But don’t worry; they’re peppered with enough humor and real-world analogies to make even the most arcane topics more digestible. Think of it as a nerdy friend who effortlessly mixes complex topics with casual banter.

Content Table

Chapter Topic Key Takeaway
1 Introduction to AI Sets the stage, explaining what AI is and why it matters.
2 Historical Context How did we get here? Looks at key milestones in AI development.
3 Current Applications How AI is already impacting fields like healthcare, finance, and entertainment.
4 Ethical Considerations Discusses the moral dilemmas and potential for misuse.
5 The Future of Work Will AI replace us? Or will it create new opportunities?
6 The Danger of AI Outlines worst-case scenarios, from rogue AI to global surveillance.
7 Balancing Act—The Road Ahead Recommendations for policymakers, tech companies, and individuals.
8 Conclusion: Modern Magic or Dangerous Future? Wraps up by revisiting the central question, offering no easy answers but a lot to think about.

Case Studies

The authors have included some fascinating case studies. Everything from AI in self-driving cars to its role in diagnosing diseases. These sections could’ve easily turned dry, but the narrative flair keeps things lively. Even our Aunt Mildred, who thinks “AI” is a type of spreadsheet software, found these parts interesting.

Artificial Intelligence: Modern Magic or Dangerous Future? Paperback – Big Book, 6 June 2019

Discover more about the Artificial Intelligence: Modern Magic or Dangerous Future? Paperback – Big Book, 6 June 2019.

Style and Presentation

A Light Touch

The writing style is accessible and often quite funny. Imagine David Sedaris explaining algorithms; that’s essentially what you get. This approachable tone makes for an easy read, even when the subject matter gets complex. It’s like the opposite of that monotonous lecture you dreaded in college.

Balanced Perspective

The book does a commendable job of maintaining neutrality. It’s not an alarmist “we are all doomed” manifesto, nor is it an unabashed celebration of tech. Instead, it’s a balanced discussion that leaves us with a lot to ponder.

Visual Aids and Graphs

While the book is text-heavy, it does include some visuals. Graphs, charts, and even some humorous illustrations lighten up complex explanations. These are particularly useful for people like us who prefer a visual cue to break down a complicated idea.

Pros of Artificial Intelligence

Efficiency and Automation

One of the key advantages discussed is how AI can significantly improve efficiency. From automating mundane tasks to optimizing complex systems, AI has the potential to make processes quicker and more efficient. And who wouldn’t want a little more free time?

Personalization

The book touches on how AI has the power to personalize our experiences. Think of how Netflix recommends shows based on our tastes or how online shopping platforms suggest products we didn’t even know we needed. It’s a bit scary, but undeniably convenient.

Artificial Intelligence: Modern Magic or Dangerous Future? Paperback – Big Book, 6 June 2019

Cons of Artificial Intelligence

Job Loss

On the flip side, the book doesn’t hold back in discussing the potential for job losses. As AI starts to take over routine and even some skilled tasks, the fear of unemployment is a real and pressing issue. So, should we start thinking about a Plan B?

Ethical Quandaries

The ethical concerns surrounding AI are thoroughly examined. Everything from data privacy, surveillance, and the potential for AI to act in ways we can’t control. The book makes it clear that these are issues we can’t afford to ignore.

Expert Opinions

Tech Pioneers

Voices like Elon Musk and Bill Gates are included, offering a mix of enthusiastic optimism and cautious predictions. It turns out, even the tech giants aren’t entirely sold on AI’s benevolent future.

Researchers and Academics

The book also includes the perspectives of researchers who provide a more nuanced view. They often bring a sobering dose of reality to the more grandiose claims. Think of them as the skeptical ones at the party who always ask the tough questions.

The Future: Unrealized Potential or Imminent Threat?

Utopian Dreams

The optimistic chapters present a utopian vision where AI solves world hunger, cures diseases, and even helps us colonize Mars. It’s an appealing narrative that makes us want to believe that technology can be a force for good.

Dystopian Nightmares

Conversely, there are chapters that read like a dystopian sci-fi novel. From rogue AIs taking over the world to mass surveillance, these sections make us want to stock up on canned beans and prepare for the apocalypse.

Conclusion: To Be or Not to Be

No Clear Answers

“Artificial Intelligence: Modern Magic or Dangerous Future?” doesn’t give us a definitive answer, and perhaps that’s a good thing. It leaves us with questions, challenges our preconceptions, and pushes us to think critically.

A Thought-Provoking Read

Overall, this book is a thought-provoking read. It’s ideal for anyone who wants to understand AI beyond the headlines and hype. Just be prepared to have your beliefs challenged and leave with more questions than answers.

So, what do we think? Is AI modern magic, or are we heading toward a dangerous future? We might not know yet, but this book sure makes the journey of discovery an engaging one.

Happy reading, and may your contemplations be as complex and multifaceted as the AI itself.

Find your new Artificial Intelligence: Modern Magic or Dangerous Future? Paperback – Big Book, 6 June 2019 on this page.

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.