Book review: GenAI for Dummies by Pam Baker

Pam Baker has written a very useful resource for AI beginners and experts alike. Don’t let the “Dummies” title fool you into thinking otherwise. This is also a book that is hard to get your hands around – in that respect it mirrors what GenAI itself is like. Think of it as a practical tutorial into how to incorporate GenAI into your working life to make you a more productive and potent human. It is also not a book that you can read in some linear front-to-back sense: there are far too many tips, tricks, strategies and things to think about as you move through your AI journey. But it is a book that is absolutely essential, especially if you have been frustrated at learning how to better use AI.

Underlying it all is Baker’s understanding on what the winning formula for using GenAI is – to understand that the output from the computer sounds like a human. But to be really effective, the human must think like a machine and tell GenAI what you want with better prompt engineering. (She spends an entire chapter on that subject with lots of practical suggestions that combine the right mix of clarity, context and creativity.  And so you will find out there is a lot more depth to this than you think.) “You must provide the vision, the passion, and the impetus in your prompts,” Baker writes. Part of that exploration is understanding how to best collaborate with GenAI. To that end, she recommends starting with a human team to work together as moderators in crafting prompts and refining the results from the GenAI tool.” The more information the AI has, the more tailored and sophisticated the outputs will be,” she writes.

To that end, Baker used this strategy to create this very book and was the first such effort for its publisher Wiley. She says it took about half the time to write this, when compared to other books that she has written on technology. This gives the book a certain verisimilitude and street cred. This doesn’t mean ripping the output and setting it in type: that would have been a disaster. Instead, she used AI to hone her research and find sources, then go to those citations and find out if they really exist, adding to her own knowledge along the way. “It really sped up the research I needed to do in the early drafts,” she told me. “I still used it to polish the text in my voice. And you still need to draft in chunks and be strategic about what you share with the models that have a public internet connection, because I didn’t want my book to be incorporated into some model.” All of this is to say that you should use AI to figure out what you do best and that will narrow down the most appropriate tools to use to eliminate the more tedious parts.

Baker makes the point that instead of wasting effort on trying to use GenAI to automate jobs and cut costs, we should use it to create rather than eliminate. It is good advice, and her book is chock full of other suggestions on how you can find the sweet spot for your own creative expressions. She has an intriguing section on how to lie to the model to help improve your own productivity, what she calls a “programming correction.” The flip side of this is also important, and she has several suggestions on how to prevent models from generating false information.

She catalogs the various GenAI vendors and their GenAI tools into how they craft different text, audio and visual outputs, and then summarizes several popular uses, such as in generating photorealistic artworks from text descriptions, some of which she has included in this book. She also explodes several AI myths, such as AI will take over the world or lead towards massive unemployment. She has several recommendations on how to stay on top of AI developments, including continuously upskill your knowledge and tools, become more agile to embrace changes in the field, have the right infrastructure in place to run the models, and keep on top of ethical considerations for its use.

By way of context, I have known Baker for decades. We were trying to figure out when we first began working together, and both our memories failed us. Over time, one of us has worked for the other in numerous projects, websites and publications. She is an instructor for LinkedIn Learning, and has written numerous books, including another “Dummies” book on ChatGPT.

Book review: Mapping St. Louis

Andrew Hahn’s delightful compendium of 40 rare maps of the St. Louis area is informative and an amazing record of the growth — and decline– of the region. He has put together maps from 1767 to the present, including some “fantasy maps” of how contemporary geographers envision the future infrastructure of the city. The maps show how the city developed around the confluence of the Missouri and Mississippi Rivers, and how events such as the Cyclone of 1896 and the fire of 1846 damaged various neighborhoods.

There are many different styles of maps featured, including maps for exploration and navigation, pocket and atlas maps, development and planning maps and pictorial maps.

Two places in the history of the city are chronicled with maps:

There are maps which show the massive population movements of the city — reaching a peak population of some 850,000 in 1950, only to decline to about 280,000 residents today.

Han is a seventh generation St. Louis native, and since 2003 he has worked as director of the Campbell House Museum, an 1851 townhouse in downtown St. Louis.

Andrew Hahn’s delightful compendium of 40 rare maps of the St. Louis area is informative and an amazing record of the growth — and decline– of the region. He has put together maps from 1767 to the present, including some “fantasy maps” of how contemporary geographers envision the future infrastructure of the city. The maps show how the city developed around the confluence of the Missouri and Mississippi Rivers, and how events such as the Cyclone of 1896 and the fire of 1846 damaged various neighborhoods.

There are many different styles of maps featured, including maps for exploration and navigation, pocket and atlas maps, development and planning maps and pictorial maps.

Two places in the history of the city are chronicled with maps:

There are maps which show the massive population movements of the city — reaching a peak population of some 850,000 in 1950, only to decline to about 280,000 residents today.

Hahn is a seventh generation St. Louis native, and since 2003 he has worked as director of the Campbell House Museum, an 1851 townhouse in downtown St. Louis.

Book review: Casket Case by Lauren Evans

Normally, I try to write reviews without any spoilers, but the main spoiler has already been revealed in the blurb about this very inventive and realistic novel about a very modern relationship. She has inherited her family business, and falls in love with a handsome gentlemen. What saves this from being another romance is that the business is a casket showroom, and he is actually a representative of Death. His job is to visit someone who is about to die and comfort them in their final moments. It is an interesting conceit, and his business doesn’t get revealed to her until halfway through the novel, at which point their love affair has fully blossomed. The book nicely deals with mutual trust, sharing one’s feelings, and one’s place in the family in a way that is fresh and interesting. This novel could border on the trite or the macabre, but doesn’t. And the topic of death for me personally is a tough one, having lost my adult son a few years ago to cancer. But Evans treats the topic with a great deal of sensitivity and verve, and I won’t give away the ending but this is a book that is interesting and well worth your time as well as well-written. Highly recommended.

Book Review: Bad Tourists, an AI-themed plot line

Three women nearing 50 share a vacation to celebrate one of them getting divorced. The three share a common tragedy 20-plus years ago involving a grisly mass murder scene in a guesthouse and have since bonded over the experience. This isn’t the most unique plots for a thriller until the bodies start dropping when the vacation turns sour, relationships strain, and the trio meets a mysterious couple of newlyweds. Then things get interesting, and we learn more about the backgrounds of all the parties and try to solve both the original mystery that brought the women together as well as what is happening in the current timeline. One of them puts it quite eloquently when she says she has been listening to the soundtrack of life and she is caught up in her grief over the original grisly murder scene — which somehow she escaped. The characters are finely drawn, and this is the first murder mystery that hinges on an artificial intelligence plot twist which was cleverly conceived. Highly recommended.

Book review: Long Island Compromise

I am of two minds with this novel, which chronicles a fictional Jewish family on the north shore of Long Island and how they devolve after the father is kidnapped for a week. The three children are tracked as they grow up into dysfunctional adults with addiction problems, with marital problems, and with various other issues in trying to cope with their father’s ordeal. The Long Island Compromise is really a devil’s bargain — having lived in one of the wealthiest suburbs in America, after escaping the Holocaust, after dealing with numerous anti-semitic people, places, and circumstances. Having grown up on Long Island’s south shore and raised my daughter on the North Shore in a community that mirrors what is described in fictional terms in the novel, this story resonated with me. The excesses experiences with the family’s wealth, and with trying to out-Jew their neighbors is all too real.

So is their reaction to the father’s kidnapping, which manifests itself in different ways to each family member. Some choose avoidance : “any reference to a thing that could later be a trigger to discuss The Thing” — the kidnapping — is a very apt way to describe grief and the fragility of those who are grieving.

So what is there not to like about this book? It isn’t that it cuts too close to home. It isn’t that its scenes of BDSM or drug abuse or numerous hooker and mystic encounters are (as I imagine) too realistic. The descriptions are sometimes just so filled with irony and accuracy that I would often pause while reading to let them sink in. But they could be hard to take for some readers. And for those of you who grew up in suburbia, or who are Jewish, this could be entertaining, poignant, or both. Certainly, its treatment of how families confront their destinies and future potential is laid bare in a way that I haven’t seen very often, and is quite genuine.

The novel is based on this actual kidnapping that happened in the 1970s. Read it here.

A voyage of personal discovery set in the high Sierra town of Cerro Gordo

 

One of my guilty pleasures has been watching the videos of Brent Underwood, a 30-something dreamer who for the past four years has been living in the high Sierra ghost town of Cerro Gordo and filming a series of videos for his YouTube Channel. There is now a book that he wrote about his experiences.

I am a big fan of what he is doing, not that I would want to uproot my very comfortable life in St. Louis and move to a place where there is hardly any running water, where you are at the mercy of massive weather systems that can flood or block a torturous eight-mile dirt road for days at a time. A place that is a study in contrasts: at one point, the town’s mines were responsible for creating great wealth in extracting silver, zinc and lead deposits, yet like Ozymadius, very little remains of the town apart from numerous abandoned buildings and lots of memories of the thousands of its former inhabitants.

What resonates with me about Underwood’s personal journey is that he is very honest and articulate about his experiences. The book captures more of his philosophies and musings about human nature. These don’t really come across in the video episodes, which usually center on various construction challenges or averting near-disasters as he is snowed in, flooded out, or at the mercy of contractors that decide to not show up for a promised work session.

Some of these events have been heart-breaking. The old American Hotel, once a centerpiece of the town, burned down at the height of the Covid pandemic. Rebuilding it has required immense quantities of concrete, steel, lumber and water that needed to be trucked up that dirt mountain road and put in place with dozens of volunteers who came to help out the effort. The floods that hit Death Valley also took out the town’s access road not once but twice in quick succession, as the road follows what is normally a dry wash through the mountains and was transformed into a raging river. And the town’s main water source is a creaky Rube Goldberg collection of antique spare parts that is connected 700 feet below ground inside one of the mine’s tunnels. Any one of these things would have sent a normal person heading back down the mountain to seek some less challenging life, but Underwood persists in his quest to bring the town into the modern era.

Underwood often gives himself various challenges: how to operate a backhoe, how to refine silver from the raw ore-bearing rocks he digs out of his mine, how to build a deck from scrap 140-year old wood that has been exposed to the elements, learning how to create a successful video series. “Mastery comes from learning a variety of skill sets and combining them in a way nobody else can,” he writes in his book, which is a theme that I realize I also live my own, somewhat less-frantic life. “I was learning what I loved and learning how to make a living doing that.”

One of the main characters in the book is an elder named Tip who has a great deal of knowledge of local lore and takes Underwood under his wing and share his perspective. Along the way, Tip helps him unlock many of the secrets of the town and its environs, and helps him learn more about himself in the process. Their relationship is astounding, given that many of the  lessons learned happen on the steep cliff sides of the Sierras and hundreds of feet underground as they try to navigate the century-old caverns and tunnels.

Tip is taciturn and dying of cancer but his Jedi wisdom seems to be delivered to Underwood at just the right moments that can be appreciated and where he can learn some important lessons. But unlike the plot lines of numerous movies, this is real life, wrought large at 8000 feet.

The looming AI bias in hiring and staffing decision-making

Remember when people worked at jobs for most of their lives? It was general practice back in the 1950s and 1960s. My dad worked for the same employer for 30 or so years. I recall his concern when I changed jobs after two years out of grad school, warning me that it wouldn’t bode well for my future prospects.

So here I am, ironically now 30-plus years into working for my own business. But this high-frequency job hopping has also accelerated the number of resumes that flood a hiring manager, which in turn has motivated many vendors to jump on board various automated tools to screen them. You might not have heard of companies in this space such as HireVue, APTMetrics, Curious Thing, Gloat, Visier, Eightfold and Pymetrics.

Add two things to this trend. First is the rise in quiet quitting, or employees who just put in the minimum to their jobs. The concept is old, but the increase is significant. Second and the bigger problem is another irony: now we have a very active HR market segment that is fueled by AI-based algorithms. The combination is both frustrating and toxic, as I learned from reading a new book entitled The Algorithm, How AI Decides Who Gets Hired, Monitored, Promoted, and Fired and Why We Need to Fight Back Now.Hilke Schellmann It should be on your reading list. It is by Hilke Schellmann, a journalism professor at NYU, and it examines the trouble with using AI to make hiring and other staffing decisions. Schellmann takes a deep dive into understanding the four core technologies that are now being deployed by HR departments around the world to screen and recommend potential new job candidates, along with other AI-based tools that come into play to evaluate employees performance and try to inform other judgments as to raises, promotions, or firing. It is a fascinating look at this industry, fascinating and scary too.

Thanks to digital tools such as LinkedIn, Glassdoor and the like, sending in your resume to apply for an opening has never been easier. Just a few clicks and your resume is sent electronically to a hiring manager. Or so you thought. Nowadays, AI is used to automate the process: These are automated resume screeners, automated social media content analyzers, gamified qualification assessments, and one-way video recordings that are analyzed by facial and tone-of-voice AIs. All of them have issues, aren’t completely understood by both employers and prospects alike, have spurious assumptions and can’t always quantify the important aspects of a potential recruit that would ensure success at a future job.

What drew me into this book was that Schellmann does plenty of hands-on testing of the various AI services, using herself as a potential job seeker or staffer. For example, in one video interview, she replies to her set questions in German rather than English, and receives a high score from the AI.

She covers all sorts of tools, not just ones used to evaluate new hires, but others that fit into the entire HR lifecycle. And the “human” part of HR is becoming less evident as the bots take over. By take over, I don’t mean the Skynet path, but relying on automated solutions does present problems.

She raises this question: “Why are we automating a badly functioning system? In human hiring, almost 50 percent of new employees fail within the first year and a half. If humans have not figured out how to make good hires, why do we think automating this process will magically fix it?” She adds, “An AI skills-matching tool that is based on analyzing résumés won’t understand whether someone is really good at their job.” What about tools that flag teams that have had high turnover? It could be two polar opposite causes: a toxic manager or a tremendous manager that is good at developing talent and encouraging them to leave for greener pastures.

Having my own freelance writing and speaking business for more than 35 years, I have a somewhat different view of the hiring decision than many people. You could say that I either had infrequent times that I was hired for full-time employment, or that I face that decision multiple times a year whenever I get an inquiry from a new client, or a previous client that is now working for a new company. Some editors I have worked for decades as they have moved from pub to pub, for example. They hire me because they are familiar with my work and value my perspective and analysis that I bring to the party. No AI is going to figure that out anytime soon.

One of the tools that I have come across in the before-AI times is the DISC assessment that is part of the Myers-Briggs, which is a psychological tool that has been around for decades. I wrote about my test when I was attending a conference at Ford Motor Co. back in 2013. They were demonstrating how they use this tool to figure out the type of person who is most likely to buy any particular car model. Back in 2000, I wrote a somewhat tongue-in-cheek piece about how you can use Myer-Briggs to match up our personality with that of our computing infrastructure.

But deciding if someone is an introvert or an extrovert is a well-trod path, with plenty of testing experience over the decades. These AI-powered tools don’t have much of this history, are based on data sets that are shaky with all sorts of assumptions. For example HireVue’s facial analysis algorithm is trained on video interviews with people already employed by the company. That sounds like a good first step, but having done one of those one-sided video interviews — basically where you are just talking to the camera and not interacting with an actual human asking the question — means you aren’t getting any feedback from your interviewer, either with subtle facial or audio clues that are part of normal human discourse. Eventually, in 2021, the company stopped using both tone-of-voice and facial-based algorithms entirely, claiming that natural language processing had surpassed both of them.

Another example is capturing when you use your first person pronouns during the interview — I vs. we for example. Is this a proxy for what kind of team player you might be? HireVue says they base their analysis on thousands of questions such as this, which doesn’t make me feel any better about their algorithms. Just because a model has multiple parameters doesn’t necessarily make it better or more useful.

Then there is the whole dust-up on overcoming built-in AI bias, something that has been written about over the years going back to when Amazon first unleashed their AI hiring tool and found it selected white men more often. I am not going there in this post, but her treatment runs deep and shows the limitations of using AI, no matter how many variables they try to correlate with their models. What is important, something Mark Cuban touches on frequently with his posts, is that diverse groups of people produce better business results. And that diversity can be defined in various ways, not just race and gender, but by people with disabilities both mental and physical. The AI modelers have to figure out — as all modelers do — what is the connection between playing a game, or making a video recording, and how that relates to job performance? You need large and diverse training samples to pull this off, and even then you have to be careful about your own biases in constructing the models. She quotes one source who says, “Technology, in many cases, has enabled the removal of direct accountability, putting distance between human decision-makers and the outcomes of these hiring processes and other HR processes.”

Another dimension of the AI personnel assessment problem is the tremendous lack of transparency. Potential prospects don’t know what the AI-fueled tests entail, don’t know how they were scored or whether they were rejected from a job because of a faulty algorithm or bad training data or some other computational oddity.

When you step back and consider the sheer quantity of data that can be collected by an employer: keystrokes on your desktop, website cookies that record the timestamp of your visits, emails, Slack and Teams message traffic, even Fitbit tracking stats — it is very depressing. Do these captured signals reveal anything about your working habits, job performance, or anything really? HR folks are relying more and more on AI-assistance, and now can monitor just about every digital move that an employee makes in the workplace, even when that workplace is the dining room table and the computer is shared by the employee’s family. (There are several chapters on this subject in her book.)

This book will make you think about the intersection of AI and HR, and while there is a great deal of innovation happening, there is still much work to be done. As she says, context often gets lost. Her book will provide plenty of context for you to think about.

Book review: Micah Lee’s Hacks Leaks and Revelations

There has been a lot written about data leaks and the information contained therein, but few books that tell you how to do it yourself. That is the subject of Hacks, Leaks and Revelations that was recently published.

This is a very unique and interesting and informative book, written by Micah Lee, who is the director of information security for The Intercept and has written numerous stories about leaked data over the years, including a dozen articles on some of the contents of the Snowden NSA files. What is unique is that Lee will teach you the skills and techniques that he used to investigate these datasets, and readers can follow along and do their own analysis with this data and others such as emails from the far-right group Oath Keepers. There is also materials leaked from the Heritage Foundation, and chat logs from the Russian ransomware group Conti. This is a book for budding data journalists, as well as for infosec specialists who are trying to harden their data infrastructure and prevent future leaks from happening.

Many of these databases can be found on DDoSecrets, the organization that arose from the ashes of WikiLeaks and where Lee is an adviser.

Lee’s book is also unique in that he starts off his journey with ways that readers can protect their own privacy, and that of potential data sources, as well as ways to verify that the data is authentic, something that even many experienced journalists might want to brush up on. “Because so much of source protection is beyond your control, it’s important to focus on the handful of things that aren’t.” This includes deleting records of interviews, any cloud-based data or local browsing history for example. “You don’t want to end up being a pawn in someone else’s information warfare,” he cautions. He spends time explaining what not to publish or how to redact the data, using his own experience with some very sensitive sources.

One of the interesting facts that I never spent much time thinking about before reading Lee’s book is that while it is illegal to break into a website and steal data, it is perfectly legal for anyone to make a copy of that data once it has been made public and do your own investigation.

Another reason to read Lee’s book is that there is so much practical how-to information, explained in simple step-by-step terms that even computer neophytes can quickly implement them. Each chapter has a series of exercises, split out by operating system, with directions. A good part of the book dives into the command line interface of Windows, Mac and Linux, and how to harness the power of these built-in tools.

Along the way you’ll learn Python scripting to automate the various analytical tasks and use some of his own custom tools that he and his colleagues have made freely available. Automation — and the resulting data visualization — are both key, because the alternative is very tedious examination line by line of the data. He uses the example of searching the BlueLeaks data for “antifa” as an example (this is a collection of data from various law enforcement websites that document misconduct), making things very real. There are other tools such as Signal, an encrypted messaging app, and using BitTorrent. There is also advice on using disk encryption tools and password managers. Lee explains how they work and how he used them in his own data explorations.

One chapter goes into details about how to read other people’s email, which is a popular activity with stolen data.

The book ends with a series of case studies taken from his own reporting, showing how he conducted his investigations, what code he wrote and what he discovered. The cases include leaks from neo-Nazi chat logs, the anti-vax misinformation group America’s Frontline Doctors and videos leaked from the social media site Parler that were used during one of Trump’s impeachment trials. Do you detect a common thread here? These case studies show how hard data analysis is, but they also walk you through Lee’s processes and tools to illustrate its power as well.

Lee’s book is really the syllabus for a graduate-level course in data journalism, and should be a handy reference for beginners and more experienced readers. If you are a software developer, most of his advice and examples will be familiar. But if you are an ordinary computer user, you can quickly gain a lot of knowledge and see how one tool works with another to build an investigation. As Lee says, “I hope you’ll use your skills to discover and publish secret revelations, and to make a positive impact on the world while you’re at it.”

Book Review: Your Face Belongs to Us by Kashmir Hill

Author Logo“Instantaneous photographs and newspaper enterprise have invaded the sacred precincts of private and domestic life.” You might be surprised to find out that this quote is more than 130 years old, from a law review article co-authored by Louis Brandeis, and inspired by the invention of Kodak film. It appears in a new book “Your Face Belongs to Us,” by Kashmir Hill, a tech reporter for the NY Times. She chronicles the journey of digital facial recognition software, focusing on Clearview AI Inc. from scrappy startup to a powerful player in the field, and exposes their many missteps, failures, and successful inroads into becoming a potent law enforcement tool.

Clearview wasn’t the only tech firm to develop facial recognition software: Google, Facebook, Microsoft, IBM, Apple and Amazon all had various projects that they either developed internally or purchased (Google with Pittsburgh Pattern Recognition and Apple with Polar Rose for example). In either case, these projects were eventually stopped because they were afraid to deploy them, as Hill writes. Facebook, for example, had face recognition projects as early as 2010 “but could afford to bide its time until some other company broke through.” But Facebook didn’t delete the code but merely turned it off, leaving the door open for some future time when perhaps the technology would be more accepted.

She documents one of the biggest challenges: being able to identify people in various candid poses, with dim lighting, with poor resolution street surveillance cameras, and looking away from the ever-seeing lens. Another challenge is legal, with lawsuits coming at Clearview from literally all corners of the globe. Leading the charge is ACLU lawyer James Ferg-Cadima and the state of Illinois, which was an early adopter of biometric privacy.

Clearview has also brought many activists to protest and lobby for restrictions. One shared his opinion that “face recognition should be thought about in the same way we do about nuclear or biological weapons.” Clearview soon “became a punching bag for global privacy regulators,” she writes, and describes several efforts in Europe during the early 2020’s that resulted in various fines and restrictions placed on the company.

Police departments were early adopters of Clearview, thanks to today’s smartphone users that post everything about their lives. That has led to one series of legal challenges which was self-inflicted. Hill documents many cases where the wrong person was identified and then arrested, such as Robert Williams. “It wasn’t a simple matter of an algorithm making a mistake,” she writes. “It was a series of human beings making bad decisions, aided by fallible technology.” She wrote that one for a NY Times article entitled, “Wrongly Accused by an Algorithm.” In many of these wrongful arrest cases, the accused were black men, which could be tracked back to inadequate training data of non-white images. (Facebook had this problem for many years with its image recognition algorithm.)

Some of Clearview’s story is inextricably bound to Hill’s own investigations, where early on she tipped off the company about her interests and was initially blocked from learning more about their technology. Eventually, she would interview Clearview’s CEO Hoan Ton-That numerous times to connect the dots. “It was astonishing that Ton-That had gone from building banal Facebook apps to creating world-changing software,” she sums up his career.

The company was determined to “scrape” the web for personal photos, and today various sources claim they have accumulated more than 30 billion images. All of these images, as she points out, were collected without anyone’s explicit permission. This collection would become infamous and exemplify a world “in which people are prejudged based on choices they’ve made in the past, not their behavior in the present,” she wrote. You could say that on the internet, everyone knows you once were a dog.

She finds that Clearview created a “red list” which would remove certain VIPs from being tracked by its software by government edict. “Being unseen is a privilege.” Unfortunately, it is getting harder and harder to be unseen, because even if you petition Clearview to remote your images from their searches and from public web sources, they still have a copy buried deep within their database. Her book is an essential document about how this technology has evolved, and what we as citizens have to do to protect ourselves.

Book review: The Traitor by Ava Glass

The Traitor: A NovelAccidental superspy Emma is back in this second volume, which can be read independently of the author’s first book chronicling her adventures eluding her Russian counterparts. This time she is put on a Russian’s oligarch’s yacht to try to figure out the cause of one of her fellow secret agent’s death in London. Emma is a delightful character and this book adds to her allure as someone who can kick ass when she needs to but still figure out the subtle tells of the spies around her. The yacht is sailing between Monaco and Barcelona and is the site of numerous near-mishaps and espionage moments that are just a joy to read. The supporting cast from the first book is back making the plot points even more compelling. Highly recommended.