Artificial Intelligence Insights Review

Artificial Intelligence Insights Review

Table of Contents

Let’s journey into the fascinating world of “Artificial intelligence: the insights you need from Harvard Business Review.” This richly illustrated paperback, released on September 17, 2019, promises to shift our understanding of AI from the realm of sci-fi fantasies to practical, actionable intelligence. Picture us, arm in arm with industry leaders and scholars, unraveling how artificial intelligence can transform our businesses and lives—minus the dystopian dread, plus the kind of sophisticated wit that makes deep learning sound almost romantic. Sit back, because we’re about to dive deep into the future, served with a side of brilliance and just the right amount of charm. Can a book really give us everything we need to know about artificial intelligence? That’s the question we asked ourselves when we picked up “Artificial Intelligence: The Insights You Need from Harvard Business Review – Paperback, Illustrated, 17 September 2019.” Let’s dive into the quirks, insights, and revelations nestled within its pages to see what it offers.

Artificial intelligence: the insights you need from harvard business review     Paperback – Illustrated, 17 September 2019

Discover more about the Artificial intelligence: the insights you need from harvard business review     Paperback – Illustrated, 17 September 2019.

A Comprehensive Overview in a Compact Package

The first thing we noticed was the book’s compact size. At just over a hundred pages, it’s deceptively slim for a topic as vast as artificial intelligence (AI). This could be a glimpse into our busy lives—condensing complex learning into manageable chunks.

Authors and Contributors: Harvard’s Finest Minds Unite

This little gem isn’t authored by a single academic but rather draws from multiple pieces penned by various experts. Each chapter reads like a new episode in a Netflix anthology series, alternating between different lenses and experiences. It’s as if Harvard Business Review formed a coalition of the wise and said, “Go forth and put our confusion at ease.”

Easy Digestibility and Utility

Despite the dense subject matter, the language and structure are surprisingly approachable. There’s no need to decode jargon or sift through convoluted sentences. It’s like sipping a fine wine rather than downing a protein shake. The book serves practical advice, not just theoretical fluff, making it an essential tool for managers, entrepreneurs, and anyone mildly curious about AI.

See the Artificial intelligence: the insights you need from harvard business review     Paperback – Illustrated, 17 September 2019 in detail.

Artificial intelligence: the insights you need from harvard business review Paperback – Illustrated, 17 September 2019

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Contents Breakdown

We decided to break down the book in a table to highlight its practical content and unique features:

Section Key Takeaways
Introduction Understanding the basics and setting the stage for AI
The Business of AI Implementation and scaling within organizations
Data is King Data ethics, privacy, and best practices
Algorithm Stories Real-world case studies
The Human Factor Human-AI interaction and its implications
Future Outlook Projections and the evolving landscape of AI

Introduction: Setting the Stage for AI

The introduction sets a comfortable stage for us, like sinking into a well-loved armchair. Here, the book sketches out a basic framework—what AI is, what it isn’t, and why we should care. The clarity here is like putting on glasses for the first time; fuzzy concepts suddenly become crisply defined.

The Business of AI: More Than Just Buzzwords

Moving past the introduction, we confront the real meat—how businesses can actually implement AI. This isn’t just a repackaged TED Talk; it’s grounded advice with steps and actionable strategies. Imagine being served a gourmet meal but in delightful canapé form—each morsel designed to bring you closer to understanding without overwhelming you.

Artificial intelligence: the insights you need from harvard business review     Paperback – Illustrated, 17 September 2019

The Importance of Data

If data were a person, it’d be the life of the party. The book underscores that data is the lifeblood of AI, a truth often glossed over in casual conversations. They explore best practices, ethical considerations, and the monumental task of turning raw data into something a machine can learn from. The authors make us feel like we’re on a treasure hunt, each data point a clue leading us to greater understanding.

Privacy and Ethics: Navigating Murky Waters

In a world increasingly concerned with privacy, the book doesn’t shy away from addressing this anxiety. This chapter feels responsible and timely, like a conscientious friend reminding you to drink water between cocktails. It tackles data ethics with the seriousness it deserves while avoiding a preachy tone.

Artificial intelligence: the insights you need from harvard business review     Paperback – Illustrated, 17 September 2019

Real-World Applications and Case Studies

Now, we’re onto the fun part—stories. Who doesn’t love a good origin story, especially when it involves major corporations and groundbreaking research? These sections read like mini-thrillers, weaving together narratives of success, failure, and surprise plot twists—all thanks to AI.

Algorithm Stories: Real Examples, Real Impact

Where the previous sections dealt with principles and frameworks, this part dives into tangible examples. It’s one thing to discuss AI in abstract terms; it’s another to hear how it transformed a mid-sized retailer or solved logistical nightmares in healthcare. Each case study feels like a parable, providing lessons cloaked in drama and relevance.

The Human Factor: Man vs. Machine

Here’s where things get spicy. We aren’t just learning how machines work; we’re exploring how they coexist with us. This section reads like a juicy gossip column, but with robots and algorithms instead of celebrities. Our relationship with AI—marvelous, complicated, sometimes fraught—is examined with a keen eye.

Societal Impact: Changing the Game

Switching gears, the book delves into the broader societal impact. It’s not just about businesses; it’s about everyone, from your grandma to your barista. The authors argue that AI will shape our world in unpredictable ways, some thrilling, others frankly unsettling. But through it all, their tone remains optimistic, urging us to engage rather than retreat.

Artificial intelligence: the insights you need from harvard business review     Paperback – Illustrated, 17 September 2019

Future Outlook: Navigating the Uncharted

As we move toward the end, the book peers into the crystal ball to speculate on AI’s future. Here, it channels a fortune teller at a farmer’s market, credible yet whimsical. They discuss what’s on the horizon, grounded in current trends but filled with imaginative possibilities. This section is both sobering and exhilarating.

Predictions: What Lies Ahead

Predictive analytics themselves could take a few notes from this chapter. The authors make educated guesses about where AI is heading, but they also invite us to ponder more philosophical questions. What role do we want AI to play in our lives? Are we prepared for the changes it brings?

Evolving Technologies: Beyond the Hype

Wrapping up, the book touches on emerging technologies that could make today’s AI look as quaint as dial-up internet. They manage to excite us about the future while offering a balanced view of the challenges ahead. It’s like getting wrapped in a warm blanket and handed a cup of tea—comforting yet invigorating.

Artificial intelligence: the insights you need from harvard business review     Paperback – Illustrated, 17 September 2019

Summary and Final Thoughts

Walking away from “Artificial Intelligence: The Insights You Need from Harvard Business Review – Paperback, Illustrated, 17 September 2019,” we feel simultaneously enlightened and hungry for more. The book is a paradox in the best way—both an appetizer and a satisfying meal. It serves as an essential primer but also offers nuggets of wisdom that even AI veterans will appreciate.

In conclusion, whether you’re a newbie, a skeptic, or an experienced professional, this book will give you something valuable. It may not answer every question we have about AI, but it reframes our understanding and opens up avenues for further exploration. And for that, we are grateful.

So, can a book really give us everything we need to know about artificial intelligence? Perhaps not. But this one comes pretty close.

See the Artificial intelligence: the insights you need from harvard business review     Paperback – Illustrated, 17 September 2019 in detail.

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University Student Essentials
University Student Essentials

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 (= 0.87, < .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 (= 0.02, = .423). Additionally, CSE (= 0.02, = .423), PC (= 0.05, = .334), ATE (= 0.00, = .983), and PP (= 0.03, = .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 (= 0.22, < .001) and RES (= 0.21, < .001). The results also indicated that PC was significantly correlated with ATE (= -0.79, < .001), AXY (= 0.18, < .001), and RES (= 0.20, < .001). ATE was significantly correlated with AXY (= -0.19, < .001) and RES (= -0.19, < .001). AXY was significantly correlated with RES (= 0.85, < .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, < .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, < .001), ATE (F(7,194) = 2.20, = .036), Years computers (F(1,194) = 5.71, = .018), and PC (F(12,194) = 2.00, = .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.