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Artificial Intelligence: Everything You Need to Know Review

Artificial Intelligence: Everything You Need to Know Review

Table of Contents

Have we ever wondered what the future holds for artificial intelligence? If so, “Artificial Intelligence: Everything you need to know about the coming AI. A Ladybird Expert Book” by Michael Wooldridge offers us a comprehensive, yet accessible, look into this captivating world of AI. Published on 22 March 2018, this hardcover gem distills complex concepts into an enjoyable reading experience.

Artificial Intelligence: Everything you need to know about the coming AI. A Ladybird Expert Book Hardcover – 22 March 2018

Check out the Artificial Intelligence: Everything you need to know about the coming AI. A Ladybird Expert Book Hardcover – 22 March 2018 here.

Overview of “Artificial Intelligence: Everything you need to know about the coming AI”

The book provides a solid foundation for anyone eager to understand the mechanisms, potentials, and pitfalls of artificial intelligence. It’s a clean-cut manual that demystifies AI, providing insights into how it impacts our lives today and what we can anticipate in the near future.

The Author: Michael Wooldridge

A name synonymous with authority in the realm of AI, Michael Wooldridge, offers us his treasure trove of knowledge with a conversational tone that feels more like a friendly chat over coffee than a lecture. His writing not only imparts understanding but does so with a breezy, yet informative style.

Artificial Intelligence: Everything you need to know about the coming AI. A Ladybird Expert Book Hardcover – 22 March 2018

Artificial Intelligence: Everything you need to know about the coming AI. A Ladybird Expert Book Hardcover – 22 March 2018

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Content Overview

The book is perfectly structured to cater to both beginners and those with a casual interest in the field. It employs a clear, chronological framework, moving seamlessly through the timeline and evolution of artificial intelligence.

Historical Context

Understanding AI requires a glance into the past. Wooldridge takes us on a historical journey that traces the origins of AI from its speculative inception in ancient myths to the robust technology shaping our current age. This section is presented in such an engaging manner, we may find ourselves comparing early concepts to modern-day breakthroughs with a sense of wonder.

Key Concepts Explained

Turning to the fundamental principles, the book introduces key concepts such as neural networks, machine learning, and natural language processing. Wooldridge distills these topics into digestible pieces, ensuring we grasp the essentials without feeling overwhelmed.

Practical Applications

Ever wondered how AI is utilized in real-world scenarios? The book delves into various applications, from autonomous vehicles to intelligent personal assistants like Siri and Alexa. It explores the societal impacts, both positive and negative, providing us with a balanced view of AI’s role in our modern lives.

Ethical Considerations

AI ethics is a hot topic, and rightly so. Wooldridge doesn’t shy away from addressing the moral implications associated with AI. From privacy concerns to the potential for job displacement, he provides a nuanced discussion that challenges us to think critically about the choices ahead.

Future of AI

The book culminates with a forward-looking analysis of AI’s future. Wooldridge speculates on advancements and the possibilities they bring, balanced with a healthy dose of caution about potential risks. It’s a well-rounded finale to an enlightening read.

Artificial Intelligence: Everything you need to know about the coming AI. A Ladybird Expert Book Hardcover – 22 March 2018

Discover more about the Artificial Intelligence: Everything you need to know about the coming AI. A Ladybird Expert Book Hardcover – 22 March 2018.

Table for Quick Reference

Chapter Key Insight Importance Level Complexity
Historical Context Origins and Evolution High Low
Key Concepts Machine Learning, Neural Networks, NLP Very High Medium
Practical Applications Uses in Autonomous Vehicles, Personal Assistants High Medium
Ethical Considerations Privacy, Job Displacement, Moral Implications Very High Medium
Future of AI Potential Advancements, Risks High Medium

Standout Features

Let’s delve into the standout features that make this book a must-read for AI enthusiasts.

Accessibility and Simplicity

The charm of this book lies in its simplicity. Wooldridge keeps the language clear and accessible, which means no prior knowledge of AI is required to understand the content. We appreciate the way he uses analogies and everyday examples to elucidate complex topics.

Engaging Writing Style

The conversational tone employed by Wooldridge makes this book an engaging read. His storytelling ability transforms what could easily be dry technical content into an enjoyable narrative. It’s akin to reading a novel—each page turning seamlessly into the next.

Illustrations and Visual Aids

The book is peppered with illustrations that serve to further simplify the content. Flowcharts, diagrams, and pictorial representations help in breaking down sophisticated algorithms and processes into comprehensible visuals. It’s not only informative but visually pleasing, making the learning process more enjoyable.

In-Depth Analysis

For those looking for a deeper dive, there’s a surprising amount of depth within these pages. Wooldridge goes beyond the basics, providing keen insights and critical analysis that provoke thought and encourage deeper investigation into the subject matter.

Critique: What Could Be Improved?

While the book is a fantastic read, no review would be complete without a few notes on areas that might benefit from improvement.

Length and Depth

At times, the brevity of the book might leave certain readers craving more depth, especially on topics like ethical considerations or technical specifics. Expanding these sections could provide a more satisfying experience for advanced readers.

Technical Details

For those of us with some grounding in AI, a bit more detail on the technical mechanism could enhance the understanding. While the book is beginner-friendly, it sometimes glosses over intricacies that could enrich the content for those with a more technical background.

Ideal Audience

Who benefits most from this book? Let’s break it down.

Beginners and Enthusiasts

New to AI? This book serves as an excellent starting point. Its clear language and engaging style make complex theories accessible, ensuring a solid foundation for further learning.

Students and Academia

AI students can benefit from this book as a supplementary text. It provides a broad overview that complements more detailed academic resources, allowing for better comprehension before diving into more technical texts.

General Readers with a Curiosity

Even if we don’t have a particular interest in AI, Wooldridge’s conversational style and engaging narrative make this book a pleasurable read. It opens up a fascinating world that impacts all of us, creating awareness and understanding of the forces shaping our future.

Closing Thoughts

“Artificial Intelligence: Everything you need to know about the coming AI. A Ladybird Expert Book” is an enlightening and enjoyable read, offering valuable insights and a solid knowledge base without intimidating jargon or convoluted theories. Wooldridge’s blend of historical context, key concepts, practical applications, ethical considerations, and future speculations makes it a comprehensive resource. Whether we’re just starting our AI journey or looking to expand our understanding, this book offers a friendly, informative guide that is sure to enrich our appreciation of the fascinating world of artificial intelligence.

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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.