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Artificial Intelligence Wired Guides Review

Artificial Intelligence Wired Guides Review

Table of Contents

Have you ever wondered how machines might shape our future? It’s a question that has intrigued us for quite some time. In the book, “Artificial Intelligence (wired guides): how machine learning will shape the next decade Paperback – Big Book, 25 March 2021,” we embark on a journey through the world of artificial intelligence and explore its potential to reshape our lives in unimaginable ways. Let’s dive into a detailed review, shall we?

See the Artificial intelligence (wired guides): how machine learning will shape the next decade Paperback – Big Book, 25 March 2021 in detail.

The Topic and Its Relevance

Artificial intelligence, once the stuff of science fiction, is now a palpable reality. This book is essentially our roadmap to understanding how machine learning is set to revolutionize various aspects of life over the next decade. When discussing relevance, we can’t stress enough how important it is for us to understand these advancements. They aren’t just influencing tech-savvy enthusiasts, but they impact all of us, in sectors you might not even associate with advanced technology.

Artificial intelligence (wired guides): how machine learning will shape the next decade Paperback – Big Book, 25 March 2021

Artificial intelligence (wired guides): how machine learning will shape the next decade Paperback – Big Book, 25 March 2021

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The Book’s Structure and Content

The book is laid out in a user-friendly manner, making complex topics accessible even to those of us who might not have a Ph.D. in computer science. By breaking down the content into digestible sections, the author ensures we’re not overwhelmed.

Introduction and Background

The initial chapters serve as an introduction to artificial intelligence. If you’ve always found AI to be an enigmatic subject, this book demystifies it for you. From Alan Turing’s early theories to the inception of machine learning, the history is explored with well-paced storytelling.

Core Concepts

Building on the introduction, the next chapters delve into core concepts like algorithms, neural networks, and data analytics. These sections offer thorough explanations without getting overly technical, perfect for readers who want to grasp foundational knowledge without feeling bogged down.

Practical Applications

Artificial intelligence isn’t just a futuristic concept. It’s already being used in practical applications today. Would you believe that AI is behind the recommendations on your favorite streaming platforms and even the ads you see online? The author covers this with fascinating examples and case studies, showing how these technologies are already integrated into our daily routines.

Future Predictions

One of the most captivating sections of the book deals with future predictions. The author tries to paint a realistic picture of what the next decade might look like, focusing on advancements in healthcare, education, and even entertainment. These predictions are as intriguing as they are plausible, making us ponder the endless possibilities.

Artificial intelligence (wired guides): how machine learning will shape the next decade Paperback – Big Book, 25 March 2021

Click to view the Artificial intelligence (wired guides): how machine learning will shape the next decade Paperback – Big Book, 25 March 2021.

Writing Style and Tone

If you’re familiar with David Sedaris, you’ll appreciate the conversational tone. It feels as if the author is sitting across from us, explaining these complex subjects in a manner that’s both accessible and engaging. It’s often witty, sometimes whimsical, but always inviting, making even the driest portions a pleasure to read.

Humor and Wit

For instance, there’s a delightful bit where the author likens the complexities of neural networks to trying to untangle your grandmother’s Christmas lights. This kind of humor not only keeps us entertained but also serves to simplify intricate subjects.

Real-World Analogies

The use of real-world analogies is another highlight. The use of examples we’re all familiar with helps to ground abstract concepts. Whether it’s equating algorithms to cooking recipes or comparing data sets to wardrobe choices, these analogies make learning about AI almost feel like a casual chat.

Depth of Research

This book stands out for its depth of research. The author has gone to great lengths to offer well-rounded insights backed by solid evidence. With numerous case studies and expert opinions, the book is not just an informative read but also a credible one.

Expert Opinions

The inclusion of expert opinions adds an extra layer of authenticity. We hear from industry leaders and researchers who are at the forefront of AI innovation, making the content not just theoretical but also relevant and timely.

Case Studies

Multiple case studies enrich the reading experience. These real-world examples exemplify how AI is making waves in different sectors, from healthcare advancements like early disease detection to educational tools that personalize learning experiences.

Artificial intelligence (wired guides): how machine learning will shape the next decade Paperback – Big Book, 25 March 2021

Technical Accuracy

Accurate and reliable information is the backbone of any good non-fiction book. This book shines in delivering correct and relevant technical details. While it doesn’t shy away from complexity, the explanations are clear and precise.

Terminology

Fear not if you’re a novice; there’s a handy glossary at the end that simplifies the technical jargon, making it easy for all of us to follow along without constantly having to Google terms.

Algorithms and Neural Networks

When the author gets into the nitty-gritty of algorithms and neural networks, you’ll find clear, concise explanations combined with diagrams to aid our understanding. It’s like getting a free mini-course in AI without the intimidating classroom setting.

Practicality and Usability

For those of us looking to apply these insights practically, the book packs a punch. It gives us not just theoretical knowledge, but also actionable steps to leverage AI in our respective fields.

Everyday Applications

From using AI to enhance business operations to integrating simple machine learning tools into daily tasks, the book serves as a valuable guide. It’s practical and has tangible tips we can implement, whether we’re tech professionals or not.

Future Preparation

In terms of preparing for the future, the book encourages us to stay informed and adaptable. It’s a call to action, urging us to become part of this technological revolution actively.

Artificial intelligence (wired guides): how machine learning will shape the next decade Paperback – Big Book, 25 March 2021

Readability

In terms of readability, the book scores high. The author’s skillful narrative makes even the most complex subjects feel less daunting. It’s a smooth read that’s both informative and enjoyable.

Chapter Length

Chapters are well-paced and broken down into manageable lengths. It’s perfect for those of us who might only have snippets of time to spare but still want to immerse ourselves in the material.

Flow and Transition

The flow between topics is seamless. You’re never left hanging; each chapter transitions gracefully into the next, ensuring that the overarching narrative is cohesive.

Visual Aids

Using visual aids can make or break a book on a technical subject. Thankfully, this book uses a variety of diagrams, tables, and figures that complement the text and facilitate learning.

Diagrams

The diagrams are contemporary and well-designed, effectively illustrating points that might be hard to grasp through words alone. From neural network structures to algorithmic pathways, these visual aids are invaluable.

Tables

Here’s a table that breaks down the main themes covered in the book:

Section Topics Covered
Introduction and Background History of AI, Key Pioneers, Basics
Core Concepts Algorithms, Neural Networks, Data Analytics
Practical Applications Real-world Uses, Industry Impact, Case Studies
Future Predictions Healthcare, Education, Entertainment Advances

Simple and effective, these tables help us to skim through and get an overview of what lies ahead.

Overall Impact

The overall impact of “Artificial Intelligence (wired guides): how machine learning will shape the next decade” is profound. It’s not just a book that sits idly on your shelf; it’s a beacon guiding us through the labyrinth of AI and machine learning. Its comprehensive approach combined with an inviting writing style makes it a must-read.

Societal Implications

One of the most compelling aspects is its discussion of societal implications. How will job markets look? Will AI make our lives significantly easier or pose new challenges we’re currently unprepared for? These are the questions the book encourages us to ponder deeply.

Personal Impact

On a personal level, the book has the potential to inspire us to learn more, to question more, and perhaps to contribute our bit to the evolving world of artificial intelligence. It equips us with the knowledge to not just witness the future but to be a part of it.

Final Thoughts

All in all, “Artificial Intelligence (wired guides): how machine learning will shape the next decade Paperback – Big Book, 25 March 2021” is an enlightening read. It’s engaging, insightful, and surprisingly fun. Perfect for anyone looking to understand how machine learning is poised to change the landscape of our world in the years to come. With a blend of deep research, real-world applicability, and a friendly tone, this book is a treasure trove of knowledge.

Learn more about the Artificial intelligence (wired guides): how machine learning will shape the next decade Paperback – Big Book, 25 March 2021 here.

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.