Welcome to the world of artificial intelligence (AI) and machine learning (ML), where the struggle is real.

At some point in your career, you have experienced a failed project. If not, don’t worry, you will. Projects fail for all sorts of reasons. Unclear objectives. Unrealistic expectations. Poor planning. Lack of resources. Scope creep. Just to name a few of the more common reasons.

When it comes to projects with AI/ML at the core, all those same reasons apply, plus a few new ones. AI/ML is perhaps the most important piece of general-purpose technology today, which means we are bombarded with AI/ML solutions to solve random or ill-defined problems in much the same way we are bombarded by blockchain solutions for tracking fruit trucks or visiting the dentist.

The overhype of AI/ML has left people skeptical regarding the promises made through project proposals. Even if you manage to get a project funded, the initial results produced by your model may be difficult to explain, leading to apprehension about deploying solutions which cannot be understood. Nobody wants to blindly follow the decisions and predictions produced by machine learning models no one understands.

It is clear the business world needs a way to build, deploy, and maintain AI/ML models in a consistent manner, with a higher rate of success than failure, and completed on time and within budget.

bizML

Thankfully, there exists a modern approach to AI/ML projects. It is called bizML, and it is the core subject inside the new book by Dr. Eric Siegel – The AI Playbook.

For any project, not just AI/ML projects, to succeed there must be a rigorous and systematic approach for real-world deployments. Every successful project has similar characteristics – measurable goals, stakeholder involvement, risk management, resource allocation, fighting scope creep, effective communication, and monitoring project progress before, during, and after deployment.

The AI Playbook breaks this down into digestible sections for anyone with business experience to understand. It outlines bizML as a six-step process for guiding AI/ML projects from conception to deployment: define, measure, act, learn, iterate, and deploy. Using stories from familiar companies such as UPS, FICO, and various dot-coms, Dr. Siegel leans on his experience to help the reader understand how and why even the best ideas often fail.

I don’t want to give away the surprise ending, so I will just say the real secret behind bizML is starting with the end state in mind. Many projects fail due to stakeholders not aligned with the reality of deployment versus expectations. bizML attempts to remove this roadblock by getting everyone aligned with what the end state will look like, and then build towards the agreed upon state.

I read through the book in less than a couple of days, absorbing the material as fast as possible. The use of personal stories was easier to read as opposed to a purely technical book focusing on code and examples. I cannot emphasize enough how this book is not a technical manual, but a business guide for business professionals, executives, managers, consultants, and anyone else wanting to learn how to capitalize on AI/ML tech and collaborate with data professionals.

Summary

As AI/ML solutions continue to gain traction in the market, this book provides the right framework (bizML) for successful AI/ML deployments at the right time. Anyone, or any company, looking to deploy (or has deployed) AI/ML projects should buy copies of this book for all stakeholders.

I’m putting this onto my bookshelf and 15/10 would recommend.

The post Book Review: The AI Playbook appeared first on Thomas LaRock.

Written by Carl Bergstrom and Jevin West, Calling Bullshit is their effort at helping everyone develop the necessary skills for critical thinking. The book reads as if you were following a college lecture. And this makes sense, since the authors are professors at the University of Washington in Seattle. You can see the course syllabus here. The book is organized close to what you see in the syllabus.

The authors start strong with “The world is awash with bullshit, and we’re drowning in it.” They point out how creating bullshit is easier and often simpler than speaking the truth. You have likely heard the phrase “the amount of energy required to refute bullshit is an order of magnitude bigger than [that needed] to produce it” from Italian software engineer Alberto Brandolini.

Everyone knows this, everyone wishes the bullshit would go away, and yet we seem to accept nothing can be done.

Well, one of the main purposes of education is to teach students to think critically. And the authors want to help people separate fact from fiction. Therefore, the need for the course, and this book.

I thoroughly enjoyed this book. My favorite chapter was ‘calling bullshit on big data’. I want to personally thank the authors for those 25 wonderful pages. This section alone is worth the price of the book. Anyone currently using, or considering, data science projects at their company will want to read this chapter.

The chapters on numbers and nonsense, selection bias, and data visualization all struck a chord for me. The authors do a wonderful job of detailing their thoughts and using practical examples. And they don’t just tell you how to call bullshit, they remind you to do so in a respectful way, again with examples. Part of the problem when trying to refute bullshit tossed at you from your crazy uncle at Thanksgiving involves confirmation bias, a similar topic discussed in the social dilemma. You must find a way to separate identity from the topic being debunked.

I’ve added this book to my bookshelf. With the holidays coming up, you may want to consider buying a few copies for your friends and relatives. Might make holiday dinners a bit more palatable.

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(Wait. Did you know I have a bookshelf? Well, now you do. It’s filled with lots of good database-centric reference material that I’ve collected over the years. Have a look and bookmark it for future use. I add to the bookshelf often as I try to keep it current. In fact, you can probably expect I expand it a bit to include some books on data science. You know, in case you are into that sort of thing.)

If you are responsible for the administration of database servers running inside of VMware then you will want a copy of this book. The book is aimed at the VMware admin audience, not a database audience. But anyone that administers databases would find this information valuable. If you fancy yourself a database tuning expert or have databases in VMware, then you will want this book.

Topics inside the book that would be of interest to DBAs include:

vNUMA; advanced balancing, optimization, memory speeds
CPU core counts versus clock speed
vSphere balanced power management
Queues and where they live inside the end-to-end storage data paths

If you are reading this then you are likely on a device that has the ability to order this book from Amazon right now. So, you should do that, because it’s that good.

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The book is one part math (cryptography), one part spy novel, and one part world history. The book is a history of the NSA, exploring the reasons why the NSA existed: the Russian Problem.

Starting with a backdrop of Arlington Hall in 1943, the book lays out the early days of cryptography as computer processing was evolving. Many folks know about the efforts of Bletchley Park during the second world war. The first few chapters detail the American efforts at breaking codes during the war. These attempts include breaking codes against other allied nations (something Britain had agreed to not do, but America saw it necessary). The author provides details on why spying on our friends was considered justified, both then and now.

One main narrative of the book is describing how America failed at breaking Russian codes. The American cryptography strategy centered around building bigger and faster computers. The Russian strategy for breaking foreign codes was simpler. The Russians didn’t use computers, they used people. Their network of agents was extensive. Their methods for eavesdropping was an art form. They were decades ahead of America when it came to playing the spy game. The book provides details on Russian spying and why it was successful. It also details the success in NSA efforts in Korea and their failures in Vietnam.

A side note that the book didn’t discuss is how the computers built to crack codes allowed for America to create the technology necessary send men to the moon. We failed in one area but applied the technology to succeed in another. Warrants mentioning.

If you have an interest in cryptography or modern world history, I’d recommend this book. I’ve added it to my bookshelf, you can find it at the bottom under “Some favorite non-SQL books”.

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Frequent readers of this blog know a few things about me. You know about my hatred of NULLs and distrust of Wikipedia. You may also know I wrote a book review for The Cult of the Amateur, which helps to explain how websites such as Wikipedia are making many people dumber, not smarter. In The Death of Expertise (DoE from now on, because I’m lazy), Nichols hammers home this point by sharing personal stories as well as data to back up his assertions.

In my career as a data professional, I witnessed examples where a person with less knowledge and experience than another has dismissed the expert as being wrong. At times it would appear the reason for the dismissal is only the result of the person being an expert. It is “hip” to bash someone for the simple reason that they have knowledge and you do not. There were also times I saw an expert opinion dismissed because they “weren’t well known”. This is a common tact by which one person tries to elevate their status by demeaning the status of another.

[Look, if someone wrote code for the SQL Server Engine you might want to take their word for how things work. Their word is better than some random blog post or Wikipedia article. But hey, that’s me. You should do you.]

DoE helps to break down why the human race is getting dumber with each passing year. I’m not going to ruin the book for you, but I do want to share my take on some of the points made in the book.

Access to the internet has made everyone believe they can be an expert in anything. You can’t read a book about databases, or business, or consulting and think you are an expert. And yet, that’s what we are seeing daily. There are many blogs, podcasts, and newsletters from people claiming to be an expert by…well…because they said they were an expert, I guess.

Much of the information searched for online is a result of confirmation bias. That is, we search for or accept information that confirms what we already believe to be true. When facts do surface, they become evidence to support the claim. But anything to the contrary is often dismissed as not reputable, or “fake news”.

The rise of the Internet, along with confirmation bias, also leads to cognitive bias. The most famous cognitive bias is the Dunning-Kruger effect. The simple view of Dunning-Kruger is this: the people who are most certain about being right are also the ones with the least reason for such self confidence. From time to time we all overestimate our abilities. But those of us with the least amount of competence do it the most often. Every profession has over-achievers that aren’t that good at their jobs. Thanks to the Internet, those people are able to GO ALL CAPS AS NECESSARY.

In addition to all of the above, I want to mention that a college education is no longer about knowledge. For over sixty years colleges have been more about being the business of higher education rather than providing an education. Schools are there to make a profit. They do that by having customers (i.e., students). And the customer is always right. This is why issues such as grade inflation exist, along with the idea that everyone wins a trophy.

The end result is that students believe that it is their right to earn an A, regardless of effort, or if they actually know the material. I once witnessed a woman discussing with her professor a failing grade in a class on Real Analysis. She was upset about failing, as can be expected. When she pressed about the grade the professor asked a simple question:

“If I asked you to tell me right now what it meant for an infinite series to converge what would you say?”

Her response?

“I don’t see what that has to do with my grade!”

Colleges are in the business of creating alumni, not scholars. If a college happens to find a person good enough to be both, that’s just a bonus.

Summary

Too many people want shortcuts in life. The internet provides shortcuts for everyone.

The internet also provides a platform for people to profess their expertise in subjects for which they are not experts. It is also great at sharing pictures of cats.

All of the above reasons (and more) are why we are witnessing the Death of Expertise.

But, you should buy the book to read for yourself, and draw your own conclusions. Don’t just take my word for it.

I’m not the expert on such things.

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Maybe it does, maybe not. The only way to know for certain is to collect the data. But collecting such data may prove to be more expensive than it is worth. After all, you can’t control the weather.

How about happiness and stock prices? What if you could show that the stock market goes up when people are happy, and nosedives when we are miserable. You don’t control happiness, but you can control the timing of your purcahse and sales of stocks. How much would *that* data be worth to you?

Done right, it could be the same amount that Rockefeller made off of the Pennsylvania oil fields in the mid-nineteenth century. That’s how much.

And that’s why data is the new oil.

The title of this post is a direct quote from the book Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die by Eric Siegel. I found the book to be a well written overview for the field of predictive analytics (PA). You can find the book in my bookshelf but I also wanted to share some thoughts on the book itself.

First, it’s an overview of PA as a whole, so if you are looking for a deep technical book with samples and examples, this isn’t the one for you. And yet, if you read this book, you do come away with a feeling that you could go off and start doing PA for yourself. All you need is some data, some math, and some logical reasoning. I enjoy books that are engaging, and this one certainly was for me.

The book does a wonderful job of weaving in stories about PA use cases that are familiar to many. Examples included were when retail giant Target decided they wanted to predict which customers are likely pregnant, the creation of the Netflix Prize, and the creation of the Watson supercomputer by IBM. Each story helped to reinforce a specific point or challenge about PA.

With Target, the issue was one of ethics and privacy. With all of this data available it can be possible to determine which customer may be pregnant, but does that mean you should send them coupons for baby related products? There is also work being done with PA in regards to police trying to predict crime. Even Hewlett Packard is using PA to determine if employees are likely to quit, but is that data that should be made known to everyone or just a select few, or simply not known at all? PA is not supposed to invade privacy. In fact much of the data is supposed to remain free of personal identification. The idea is to build a predictive model that works for any resulting data set.

The creation of the Netflix Prize serves as an excellent example in how having multiple predictive models working together can boost accuracy over a single model, something Siegel refers to as the Ensemble Effect. This also leads to a discussion on crowdsourcing in general, and the book shows examples of where crowdsourcing works only to a point, and after that point you get diminishing returns (*COUGH* Wikipedia *COUGH*).

IBM’s Watson computer is a triumph of predictive analytics. All of the work that went into Watson is astounding. The end result is a machine (essentially an Ensemble of Ensembles) that is able to come back with a textual reply that is most likely the answer to a written question. Well, being Jeopardy, it’s really the question to a written answer (which makes it even more difficult). Watson even has it’s own Twitter account, where you can find out that it is answering questions you didn’t even think to ask.

Lastly, I’ve talked before about how PA can’t predict the future. I think PA has a poorly chosen name because business falsely believe that they can use PA to predict future events. Siegel does a great job in this book explaining the same idea; PA doesn’t predict the future, PA gives you data-driven insight into trends that may (or may not) be useful for you to act upon.

I highly recommend this book for any data geek that is eager to learn more about PA, predictive models, machine learning, and how to effectively use data to make better decisions.

The post Data Is The New Oil appeared first on Thomas LaRock.

As with any great discovery the solution was apparent and not unfamiliar to anyone that has time-shared an airplane journey with leisure reading. An underlying complexity to the solution would be the selecting of quality reading materials.

Wasting time on an unsuitable choice would be frustrating, as would a selection that did not last for the exact duration of the flight.

But that amount of agony is below that of having paid for Wi-Fi service that could not be delivered in a dependable manner on behalf of a major airline. Rather than continue to pay a service provider that could not provide the service they had promised I decided to sever all ties with them without delay.

The Book Project

I therefore found myself in the position of requiring reading materials and the Book Project was born. I decided that as part of the Book Project I would make a list of the desired attributes found in a good book:

1. Characters – A good story must have complex characters that draw you in and change over time. If the characters are forgotten when the book is finished, then the book will be forgotten as well.
2. Drama – There must be some element of drama or conflict in the story. Even better if the drama also makes a statement about society in some way.
3. Mystery – A good story allows one to spend time guessing as to what might happen next. Suspense is critical to keeping one wanting to turn the next page, even suspension of belief.
4. Quest – There must be a common goal for all of the characters coming together and there should be an emotional attachment between the characters.
5. Romance – Because everyone not named “Shakespeare” enjoys a happy ending.
6. Length – The story must be consumed in its entirety during the length of the flight. Anything too short would be an inefficient use of time. Anything too long and I would need to rearrange my schedule to finish the book at a later time, leading to future inefficiencies on other areas. I would use a ratio of one page per minute as a standard and adjust for books with unconventional page sizes.

The entire Book Project selection process was initiated by Karen (blog | @datachick), who had suggested a book (The Rosie Project) written by her friend Graeme (blog | @GraemeSimsion) as one that I would find suitable. I did not want to risk offending Karen so I promised to include the book in my list of potentials. I later found Rosie to be listed on the New York Times Best seller list which helped mitigate the anxiety surrounding the uncertainty of selecting this book.

The Wife Project

I purchased the Kindle version (at 300 pages, perfect for my upcoming five hour flight) and waited until the day of my excursion to begin reading. I’ve decided to share my thoughts about The Rosie Project with the following book review bullet points:

• At the center of Rosie is Professor Don Tillman (@ProfDonTillman), someone we all know. Not in a literal sense, since Don is a character in a book. Everyone knows someone who exhibits the same characteristics as Don. Perhaps it is a colleague, a teacher, yourself…or even your own son.
• Don is an unexpected hero exhibiting signs of autism, specifically Asperger’s Syndrome. As such it is fascinating to be inside the mind of the character as he processes data throughout his day. What one takes to be routine, such as meal preparation, Don calls his “Standardized Meal System”.
• Don is on a quest to find a wife (called the Wife Project). He does so by putting together a questionnaire to collect more data. He quickly calculates the BMI of each candidate, records their eating habits, and their punctuality as well.
• Throughout the story we follow Don as he applies logic and reasoning to everything only to arrive at the conclusion that logic and reasoning can only go so far.

The Reading Project

Having enjoyed this book throughly I have decided to encourage others to read it also. It is currently for sale at $1.99 USD, marked down from $24.00 USD. At 8.2% of the Kindle retail price that means you are paying only $0.00652 USD per page. A remarkable bargain.

Since Americans spend less than 3 hours a week reading for leisure I prepared a list of suggestions where leisure reading may be time-shared with other activities:

1. Coffee shop – Many coffee shops encourage patrons to spend time in leisure activities such as reading this book.
2. Breakfast table – You may find it suitable to time-share your breakfast consumption with reading this book.
3. Chair – Many of us have a favorite comfy chair in our domiciles that would be suitable for reading this book.
4. Hammock – Reading this book outdoors on a warm day would be a wonderful experience for many.
5. Bed – Reading this book before bedtime is a great way to relax.

I am fascinated at all of the events that led me to reading this book. If I had to sum up the experience in one word it would be this:

Incredible.

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