Artificial Intelligence: A Guide for Thinking Humans Review

Artificial Intelligence: A Guide for Thinking Humans Review

Table of Contents

In our latest literary odyssey, we delve into “Artificial Intelligence: A Guide for Thinking Humans,” a paperback gem that hit international bookshelves on September 24, 2020. This isn’t just a dry, tech-heavy tome; it’s a delightful journey into the world of AI, sprinkled with that rare combination of wit and wisdom. We’ll laugh, we’ll learn, and most importantly, we’ll think deeply about the intersection of technology and humanity, guided by an author who makes even the most complex concepts accessible and engaging. Let’s dive in and explore the fascinating narrative that promises to enlighten and entertain. Have you ever wondered what artificial intelligence (AI) actually is and how it affects us as thinking humans? We certainly have! That’s why we dove into “Artificial Intelligence: A Guide for Thinking Humans Paperback – International Edition, 24 September 2020” by the brilliant author, Melanie Mitchell. This review is our take on the insightful journey Mitchell guides us through, and trust us, it’s as enlightening as discovering an unexpected fifty-dollar bill in an old winter coat.

Artificial Intelligence: A Guide for Thinking Humans     Paperback – International Edition, 24 September 2020

See the Artificial Intelligence: A Guide for Thinking Humans     Paperback – International Edition, 24 September 2020 in detail.

Unboxing the Basics of AI

What Is AI Anyway?

AI often feels like that elusive dream job that’s just out of reach. We know it’s out there, but what does it actually entail? Mitchell breaks it down for us in a refreshingly simple manner. Imagine AI as the brainy cousin at family gatherings. It excels at specific tasks—be it cooking the perfect souffle or winning chess games—but is still bafflingly clueless about basic social norms, like not asking Aunt Susan why she’s still single.

Why Should We Care About AI?

Remember the first time we heard about the internet? We were skeptical, amused, but also a bit in awe. AI feels like that internet moment, only magnified. Mitchell gives a compelling argument as to why AI is not just for nerds with thick glasses but for all of us. It’s shaping everything from our Spotify recommendations to the diagnostics our doctors use.

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The Good, The Bad, and The Ugly of AI

The Good: AI as Our Helpful Assistant

Imagine having a personal butler who knows your quirks, like always putting an extra scoop of ice cream in your bowl or reminding you to water the plants. AI has the potential to become that indispensable helper, aiding us in ways we never imagined. Mitchell walks us through captivating examples, like how AI algorithms can now compose original music and diagnose diseases with incredible accuracy.

The Bad: When AI Gets It Wrong

Picture giving your golden retriever the keys to your car. It’s bound to be a disaster, right? While Mitchell extols AI’s virtues, she is equally candid about its limitations. From self-driving cars mistaking a tree for a person to AI bots spreading misinformation, learning about AI’s errors feels like watching hilarious bloopers—entertaining but a little frightening.

The Ugly: The Ethical Dilemmas

Ah, ethics—the broccoli of discussions. Necessary but often avoided. One of the most provocative parts of Mitchell’s book delves into the ethical quandaries of AI. Can we trust a machine to make decisions during a crisis? What about privacy? Imagine finding out your diary has been published online by your least favorite sibling! Mitchell makes us ponder these heavy questions, leaving our minds whirling like a suspenseful cliffhanger.

Learn more about the Artificial Intelligence: A Guide for Thinking Humans     Paperback – International Edition, 24 September 2020 here.

A Journey Through AI History

The Backstory: Humble Beginnings

Like all epic tales, AI has its own origin story. Mitchell takes us back to the days when AI was nothing more than the fever dream of a few eccentric scientists. It’s like reading about your favorite rock band’s garage days before they hit it big. The author’s engaging storytelling helps us appreciate AI’s humble beginnings, from Alan Turing’s groundbreaking ideas to the advent of neural networks.

The Evolution: From Dream to Reality

Remember the first time we got a smartphone? It felt like holding a piece of the future in our hands. That’s exactly how Mitchell describes the evolution of AI—gradual, groundbreaking, and deeply transformative. She connects all the dots, showing us how advancements in technology and human curiosity have shaped AI into what it is today.

Breaking Down AI’s Mechanisms

Neural Networks: The Brain Mimics

Neural networks can sound as intimidating as calculus, but Mitchell explains them in a way that’s akin to learning multiplication tables—with flashcards and patient, step-by-step illustrations. It turns out, that these “brains” of AI are designed to mimic our own, albeit imperfectly. It’s like watching a toddler try to walk—a bit wobbly but undeniably fascinating.

Machine Learning: Getting Smarter Every Day

Machine learning is the Monty Hall Problem of AI. At first glance, it seems perplexing, but once you get the hang of it, it’s oddly satisfying. Mitchell elaborates on how machine learning models digest data and make predictions, akin to how we deduce which door hides the car in a game show. Each example she gives feels like an “ah-ha!” moment, demystifying the complex.

Deep Learning: Going Deeper and Deeper

Deep learning is like the AI equivalent of that surprising, rich cheesecake you came across in a quaint coffee shop. Deliciously complex. Mitchell does a fabulous job of explaining layered algorithms without leaving us bewildered. She likens it to peeling an onion—each layer gives us a deeper understanding of how AI systems can recognize patterns and make decisions.

Concept Description Real-World Example
Neural Networks Algorithms designed to mimic the human brain Image and speech recognition
Machine Learning AI systems that learn from data to make predictions Predictive text, recommendation systems
Deep Learning Advanced algorithms with multiple layers for pattern recognition Autonomous vehicles, advanced data analysis

Artificial Intelligence: A Guide for Thinking Humans     Paperback – International Edition, 24 September 2020

The Societal Impact of AI

On Employment: Friend or Foe?

Imagine a future where robots flip our burgers and fold our laundry. While this sounds whimsical, Mitchell’s analysis is sobering. She delves into how AI could potentially displace jobs, causing us to rethink our skills and adapt. Yet, there’s hope. She presents a balanced view, suggesting that new types of jobs will emerge—ones we can’t yet imagine.

On Healthcare: Promises and Pitfalls

If our stethoscope becomes intelligent, diagnosing illnesses with pinpoint accuracy, do we say goodbye to physicians? Not quite. Mitchell illuminates the incredible potential of AI in healthcare, like early disease detection and personalized medicine. However, she is quick to caution about over-reliance, pointing out the importance of the human touch and ethical considerations.

On Relationships: Man vs. Machine

Will we fall in love with robots? Will our friendships be replaced by digital companions? Mitchell addresses these futuristic, almost sci-fi scenarios with thought-provoking insights. AI might enhance our social interactions, but it could also create a detachment from genuine human connections. It’s a quandary that makes us reevaluate our relationships both online and offline.

Navigating AI’s Future

Collaboration or Competition?

Is AI going to be our collaborative partner or a fierce competitor? This theme runs like a lively debate throughout Mitchell’s book. She isn’t giving us any straight answers but rather shows us the different pathways AI could take. It’s like standing at a crossroad, pondering which direction leads to prosperity and which to peril. We feel equipped but also aware of the uncertainties.

The Role of Policy and Regulation

New technologies often run like untamed horses, and AI is no different. Mitchell emphasizes the critical role of policy and regulation in harnessing AI’s potential while mitigating risks. She paints a vivid picture of what might happen if we turn a blind eye to regulation; imagine Wild West, but with robots instead of bandits—exciting but chaotic. Clear guidelines and ethical standards, she argues, are the lasso we need.

AI and Human Creativity

One of the book’s highlights is exploring AI’s role in creativity. Can machines create art? Write poetry? Compose symphonies? Mitchell’s examples—from AI-generated paintings to algorithmic music—are nothing short of captivating. It’s like discovering that your monotone chemistry teacher has a secret life as an opera singer. AI’s involvement in creative processes challenges us to think about originality and the essence of human creativity.

Artificial Intelligence: A Guide for Thinking Humans     Paperback – International Edition, 24 September 2020

Putting it All Together

Reflecting on AI’s Complexities

After traversing through the intricacies of AI, its history, mechanisms, and societal impact, we find ourselves at a reflective juncture. Mitchell excels at simplifying these complexities without diluting their essence. Her compassionate yet frank narrative makes us ponder deeply about our role in this AI-driven world. It’s like reading a love letter to humanity disguised as a tech manual.

Our Takeaway: A Balanced Perspective

What makes “Artificial Intelligence: A Guide for Thinking Humans” so compelling is Mitchell’s balanced perspective. She’s neither an alarmist predicting doomsday nor a starry-eyed idealist. Instead, she provides a grounded understanding, highlighting both the potential and pitfalls of AI. This balanced view encourages us to educate ourselves and engage in the critical dialogues shaping our future.

Final Thoughts

In conclusion, “Artificial Intelligence: A Guide for Thinking Humans” isn’t just another book on AI; it’s a thoughtful guide and a call to action. Melanie Mitchell’s approachable writing style makes a complex subject accessible and engaging. It’s like having a long, enlightening conversation with a particularly wise friend over a cup of coffee. As we close the final page, we feel better equipped to step into the future—curious, cautious, but undoubtedly more informed.

Click to view the Artificial Intelligence: A Guide for Thinking Humans     Paperback – International Edition, 24 September 2020.

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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.