The Coming Wave: Technology, Power, and the Twenty-First Century's Greatest Dilemma Mustafa Suleyman with Michael Bhaskar New: Crown 2023

The author begins his analysis by identifying the two technological opportunities and threats: Artificial Intelligence (AI) and Biotechnology (BT). Regarding AI, the author reiterates the fear that the application can go rogue, operate beyond human control. With BT, the concern is tampering with the fundamental elements of life. With both, Suleyman ponders the ability to contain the consequences of their results. He posed the following question, "What if we could distill the essence of what makes us humans so productive and capable into software, into an algorithm?" (pp. 7-8). The author believes that we are proceeding to an affirmative to his question. The risks include an upheaval and replacement to manual and intellectual labor and, biologically, the ability to purchase DNA synthesizers that will produce and modify strands of DNA. Suleyman wrote this book to warn readers of the potential dangers and propose safeguards. He divided the book into four parts: Part 1, the history of technology, Part 2, the current state with AI and BT, Part 3, the politics of current technology, and Part 4, the interventions that humans should take to have the technologies work toward the common good. Defining containment as "a set of interlinked and mutually reinforcing technical, cultural, legal, and political mechanisms for maintaining societal control of technology during a time of exponential change" (pp. 37-38), the term implies a multi-dimensional approach to human control over technology, extending to various social institutions and political layers, national and international. The failure of nuclear containment shows the difficulty of the task.

How Big Things Get Done. 2023 Bent Flybjerg and Dan Gardner New York: Currency

Meticulous planning, as was done with the construction of the Empire State Building, that resulted in a project that came 17 percent under budget and ahead of schedule, caused the authors of How Big Things Get Done to conclude that the secret to success results in "think slow, act fast" (p. xvi). Think slow implies adequate planning and analyzing the benefits of the project. Planning, according to the authors, "explores, imagines, analyzes, tests, and iterates" (p. 46); planning explores questions and an understanding of the goal and the reason for it. With all important challenges addressed, the project team can "act fast". The authors distill these two concepts into the team dynamics of power and psychology. Power, the politics of the group, controls the decision-making process. Psychology gets at the emotional temper of the group, optimism, pessimism, and something in between. Flybjerg and Gardner consider optimism the human default, we defer to the "best case" scenario. They also acknowledge the human preference for action over thought. Looking at project development linearly, the authors supported this progression, "think from right to left," the title of the third chapter. Multiple thinkers have characterized this form of analysis. John B. Robinson, a professor of urban and environmental planning, coined the term, "backcasting", envisioning a future state and explaining the steps to attain it. Jeff Bezos, at Amazon, took a different approach. To initiate a project, employees would write a Press Release and Frequently Asked Questions that they would present to execs, in multiple iterations, refining with each version. However, even this process failed with the introduction of the Fire Phone, because of the insistence of the chief executive officer. One fundamental ingredient to successful project completion, experience, includes not only explicit knowledge, understanding the processes, structure, and details of the task, but also tacit knowledge, the unwritten, intuitive understanding. Because no two projects are exactly the same, testing, simulating, and experimenting allow for the better methods to manifest and move the project forward. Eliminating or minimizing bias also clarified the end goals. Uniqueness bias prevents applying tested actions from facilitating the process. The authors applied the concept of Daniel Kahneman and Amos Tversky, "reference class" (p. 105) that derives from your perspective, "inside" or "outside" (p. 106-107). To obtain a realistic cost, the authors recommend that project managers calculate all possible cost, time, benefits and compare your project to the mean of all projects, then decide on cost, time, and resources. The robustness of the data determines the accuracy of the estimate, so collect lots of data. With this approach, you have to factor in the "regression to the tail", the unknown risks. The authors advise to skew the estimate to the fat tail. For extra assurance, add a contingency of 10 to 20 percent. Even with all these safeguards, a "black swan" event might occur, such as the pandemic, rare occurrences that completely alter the context in which your project takes place. By amassing the data, discovering the fat tail, identifying potential black swans, and adding contingencies, can a project team remove their uniqueness bias. A portfolio of projects does not reduce the overall losses. With a portfolio, managers factor in opportunity costs, money spent that could have produced greater returns. After undergoing extensive planning, the authors explain "acting fast" with Chapter 8 entitled, "A single, determined organism" (p. 143), a cohesive, committed team, with each member operating according to her or his strengths. They go through the planning stage, for a construction project, they simulate the project and the assembly--the analysis of the site and the parts for construction, and, third, team optimization and devotion to the project. For the team element, identity with the team and understanding the team purpose and standards underpin team success. The book concludes by describing projects that usually do not go terribly wrong--"solar power, wind power, fossil thermal power (power plants that generate electricity by burning fossil fuels), electricity transmission, and roads" (p. 155). Modularity, having blocks from which to build structures reduces risks. Nuclear power plants do not fit this list, as the author explained with the construction of the Monju nuclear plant in Japan. Why, because the structure does not permit simulation and testing. Secondly, so few have been built and each setting is unique. Additionally, the project only starts to generate revenue after complete commissioning. What makes the projects listed above less risky? They are smaller, modular, with few variables, and often duplicated, and scale-free--expandable without a change of plan. The authors define "'scale-free scalability', meaning you can scale up or down following the same principles independently of where you are scalewise, which is exactly what you want in order to build something huge with ease" (pp. 165-166). Real world examples include projects as diverse as Norway's hydroelectric projects, Elon Musk's factories, Planet's satellites, Madrid's subway, and shipping containers. The authors end the book with the "eleven heuristics for better project leadership" (p. 185). They include: "hire a masterbuilder. . . get your team right. . .ask 'why'. . . build with lego. . .think slow, act fast. . .take the outside view. . . watch your downside. . . say no and walk away. . . make friends and keep them friendly. . . build climate mitigation into your project. . . know that your biggest risk is you" (pp. 185-190).

The Cloud Revolution: How the Convergence of New Technologies will Unleash the Next Economic Boom and a Roaring 2020s

Focusing on three "technological domains" (p. xiii), Mark P. Mills reflected the optimism that the title exudes--"information, materials, and machines" (p. xiii) that he prefaced in his book. He cited the "cloud" as the infrastructure that enables the domains. Mills tempered his enthusiasm by admitting to the decline in drug discovery, "50 percent per year per billion dollars spent since 1950" (pp. xx-xxi) with equal declines in productivity and in "manufacturing, services, and education" (p. xxi). You might think of other industries in that category. With those qualifiers, Mills continued to reference the advances in the three domains. The consequence of the cloud backbone facilitating the three domains results in economic growth for entire populations, healthier lives, and greater conveniences, according to the author. Divided into four parts, the book covered, first, the author's technology forecasting methods, second, the cloud infrastructure, third, the cloud's impact on the three domains, and, part four, the business implications of parts two and three.

Data Detective: Ten Easy Rules to Make Sense of Statistics Tim Harford 2021 Riverhead Books

For those intimidated by math and anything mathematical, this book presents guidelines for readers to overcome mental phobias when confronted with statistical data. Filled with examples from literature of all types, Harford instructs the reader on how to approach statistics. Harford begins by attacking head-on the idea of lying with statistics, the antithesis of his book. Next with his first rule, he defined the basic mindset of any reviewer of statistics--one free of personal bias. The attempt to be objective is the first step for anyone who wants to evaluate the validity of statistical or any kind of data. Second, he advised that individuals understand the perspectives from which they view the numbers in front of them, from a close-up and personal vantage point or a distant and wider angle. As we have seen with the Covid-19 data worldwide, different countries record infection rates and death rates differently. Understanding the differences in methodology helps to avoid what Harford calls "premature enumeration" (p. 65). He used the example of the difference between miscarriages and premature deaths. How each gets defined depends on the country's health criteria. In the chapter, Step Back and Enjoy the View, Harford encouraged us to view statistical data in terms of the broader trends rather than accepting information in isolation. He made the comparison of "rolling business coverage of Bloomberg TV, the daily rhythm of the newspaper the Financial Times. . . and the weekly take of The Economist--each operates on unique information cycles. The fifth rule, "Get the Backstory" highlights the bias of what gets published in scientific journals and what gets omitted, the peer-reviewed "publication bias" . . .[i]nteresting findings are published; non-findings, or failures to replicate previous findings, face a higher publication hurdle" (p.113). The reader has the task of discerning the population sample and sample bias, "Ask Who Is Missing" constitutes the sixth rule. With the focus currently on the disaggregation of data, readers can target to which group the study applied to and to which group the data does not apply. Even the aspiration of N=All, an attempt for an all-inclusive population, will contain only those who conform to the criteria of inclusion. Harford concludes with one certainty. If algorithms are shown in a skewed sample of the world, they will reach a skewed conclusion" (p. 151). Continuing with the discussion of algorithms, Harford instructed the reader to ask the following questions when confronted with conclusions from big data: "Are the underlying data accessible? Has the performance of the algorithm been assessed rigorously--for example, by running a randomized trial to see if people make better decisions with or without algorithmic advice? Have independent experts been given a change to evaluate the algorithm? What have they concluded?" (p. 183). Within the chapter, he explained the difference between causation and correlation. "Figuring out what causes what is near-impossible, some say. Figuring out what is correlated with what is much cheaper and easier" (p. 156). Rule Eight, "Don't Take Statistical Bedrock for Granted", Harford warned that the data from such agencies as the Bureau of Economic Analysis, the Bureau of Labor Statistics, the Census Bureau, the Federal Reserve, the Department of Agriculture, and the Energy Information Administration provide the "nation's statistical bedrock" (p. 190). Here Harford defends the products of his profession. The last two chapters admonish us to delve carefully into the data and "Remember that Misinformation Can Be Beautiful Too" (p. 213) and that through all our investigation, we should keep an opened mind.

The Gray Rhino : How to Recognize and Act on the Obvious Dangers We Ignore by Michele Wucker

 After hearing Michele Wucker's TedTalk and long after I read The Black Swan: The Impact of the Highly Improbable, I decided to read her book on gray rhinos. She contends that many social phenomenon that receive the label of black swan actually come under the designation of gray rhinos.  As the title, The Black Swan suggests, events occur unexpectedly and without warning. Gray rhinos, in contrast, result in "highly obvious but ignored threats" (p. x) or respond insufficiently, weakly, and ineffectively. Among past "clear dangers that were recognized but weren't being addressed" (p. x), Wucker listed: climate change, financial crises, digital technologies, infrastructure failures, wildfires, water shortages, and others. 

To distinguish the types of events that individuals, organizations, agencies, governments and others face, Wucker categorized them according to four characteristics: low probability and high probability and low impact and high impact. Of the three types, white swans, black swans, and gray rhinos, she placed each into one of the four slots. White swans have a high probability of occurrence and low impact. In contrast, black swans and gray rhinos have high impact. However, black swans have low probability and gray rhinos have high probability. With any risky situation, the faster the response, the lower the cost.  

Leaders, who procrastinate when confronted with major challenges, ignore the opportunity or the avoidance of danger that the challenges offer. Wucker views this as the counterpart to danger. Assessing risk is inherent in the avoidance of danger or calamity. Each year at the World Economic Forum, assembled leaders in government, business, media, and Non-governmental Organizations prioritize what they consider the greatest risks in the Global Risks report. The United Nations conducts a similar survey. "In 2013, only 32 percent of those CEOs believed the economy was on track to meet the demands of a growing population within environmental and resource constraints, and just 33 percent believed that business was doing enough to meet those challenges" (p. 12). 

Behaviors that prevent action include many reactions: freezing and a lack of any response or denial and ignoring the threat. These reactions describe the first stage of the gray rhino response. The other four responses, according to Wucker, include" muddle along. . .come to an alert. . .play the blame game as we search for solutions. . .and, finally, we do something--occasionally before the trampling, but all too often after the fact" (p.27). These five responses constitute the five phases that most go through. To create a culture vigilant about gray rhinos or any of the other threats, "change perverse incentives in order to encourage leaders to act sooner, and uses out understanding of the weaknesses of human nature to make us more likely to do the right things" (p.27). In short, leaders thwart groupthink and encourage diverse and independent thinking. 

Project directors of the Good Judgment Project identified traits that separated a good forecaster from others: "First were psychological factors: 'inductive reasoning, pattern detection, open-mindedness and the tendency to look for information that goes against one's favored views, especially combined with political knowledge.' Second was the forecasting environment, including training in probabilistic reasoning and team discussion of rationales. Finally, not surprisingly, effort made a difference; the more time forecasters spent deliberating their predictions, the better they did" (p. 50). 

Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy Cathy O'Neil

The head of the computer department at Regis University, where I teach, recommended this book to all faculty. The author, a math nerd, who started her career as an academician teaching at Columbia and the switching to finance, worked as a quant for D. E. Shaw, a hedge fund company. The Great Recession caused her epiphany, when she realized the damage that numbers can inflict. Although numbers present themselves a objective and unbiased, she realized that numbers and the models, the assumptions behind the numbers, "were based on choices made by fallible human beings . . many of these models encoded human prejudice, misunderstanding, and bias into the software systems that increasingly managed your lives." (p. 3)

Through out the book, O'Neil gives examples of how numbers, models, algorithms, and bid data offer false conclusions. Because the numbers and the assumptions behind them usually can be understood only by the individuals who construct them, people who apply them to actual people, do not understand the consequences of the logic. These "weapons of math destruction (WMDs)" have permeated our society.  In education these numbers and assumptions evaluate students and teachers. In the judicial system, they determine convictions and prison sentences. In finance systems, they caused the crash of 2007, stop and frisk searches in large urban centers, U.S. News and World Report college rankings. President Obama unsuccessfully attempted to correct the U.S. News and World Report college rankings by basing rankings on "graduation rates, class size, alumni employment and income, and other metrics" (p. 67). With computerized health and credit data, zip code information, and Facebook friend information, the numbers, algorithms can determine employ ability,  innocence or guilt in the justice system, and by scanning online data, the amount of ideas a person generates, which determines employment desirability. What website a person accesses might affect their FICO or credit score. Call center personnel data found that people who engaged in job-related chatter performed better at their jobs than those who did not.

With all the scores and assessments of individuals, based on data, a dossier of who we are, what we have done, and what we have can contain correct or incorrect information. Will our job, health, credit, education, insurance, safety, news, and social prospects result from who we really are or what incorrect online information attributes to us.

Workplace Poker: Are You Playing the Game or Just Getting Played, by Dan Rust

Workplace Poker : Are You Playing the Game or Just Getting Played, a 2016 book by Dan Rust, suggests practical tips for navigating the corporate maze. The book offers solutions that range from increasing your understanding of office politics, responding to failures, managing work/life balance, handling difficult colleagues, preparing for performance appraisals, maximizing your professional appeal, to using multiple lenses to analyze complex situations. Put this book on your reading list!