Come and get it!!

My pre-launch book sale campaign is underway!! Here’s a taste of what’s up for sale!

What’s Your Problem

Solving world hunger or fending off COVID-19 are a different problem space than what’s for dinner. It’s pretty easy to follow that distinction; however, it’s important now because we do have much more information and many more tools for tackling the former. Twenty years ago, solving world hunger seemed to be a matter of getting the best minds together with a sufficient checkbook. The matrix of issues and identities though is much more complicated than just ideas and the finances to back them. Although it does take funds to make things happen, the funds don’t solve the problem of itself.

Better understanding the dimensions of problems determines solution space as well as tools and methods for addressing its aspects and effects – because it may or may not be solvable.

“We are doomed to choose” – Isaiah Berlin

The world and its problems are becoming more complex. 

The world and its problems are becoming much more intricately entwined.

We all need to make better data decisions because subsequently the world we live in is subject to greater reactions and effects.

We gotta figure this out. We can’t just keep pushing more and more words and numbers into documents without providing a way to comprehend them better.

I may vomit. This part of writing a book is the hardest – actually trying to get people to buy it. For those who’ve been following my book posts on LinkedIn and Facebook, this is the pre-sale campaign I’ve been hinting at for the past few months. Now that I’m in the revision phases of my manuscript and have New Degree Press as my publisher, I now get to worry about funding the actual publication process!

If you’d like to help me on my publishing journey, please pre-order a signed copy by clicking here!


#privacy  #personalization

Wouldn’t it be nice if what you were just thinking of something you needed to get . . . and it came right to you. I imagine that’s the futuristic goal of #amazon. Haven’t they made our lives sssoooo much more convenient? On the one hand, it’s so easy to “shop around” at home. It’s so nice when a solution isn’t that hard to find. Right? But it’s a fine line to cross into where you’re not driving the train for what you need (or think you need.) Who is looking into your #personalinformation?

#theguardian reported last week on Amazon in “The Data Game: what Amazon knows about you and how to stop it.”. Some things you may know already, but others maybe not. Their source document, though, came from a #wired magazine article “Amazon’s Dark Secret: It Has Failed to Protect Your Data” (link in second comment) printed in November 2021 – that packs a punch:

“According to internal documents reviewed by Reveal from the Center for Investigative Reporting and WIRED, Amazon’s vast empire of customer data—its metastasizing record of what you search for, what you buy, what shows you watch, what pills you take, what you say to Alexa, and who’s at your front door—had become so sprawling, fragmented, and promiscuously shared within the company that the security division couldn’t even map all of it, much less adequately defend its borders.

In the name of speedy customer service, unbridled growth, and rapid-fire “invention on behalf of customers”—in the name of delighting you—Amazon had given broad swathes of its global workforce extraordinary latitude to tap into customer data at will. It was, as former Amazon chief information security officer Gary Gagnon calls it, a “free-for-all” of internal access to customer information. And as information security leaders warned, that free-for-all left the company wide open to “internal threat actors” while simultaneously making it inordinately difficult to track where all of Amazon’s data was flowing.”

Both articles are well worth your time to understand the power and vulnerability of #personalinformation. This is my take on #bigdata pricing and what the future of hyper-niche looks like. It’s in my upcoming book #thefallacyoflayingflat.

The future – the Big Data pricing – is that the price will shift according to your ability as much as willingness to pay.  That means the (online) webinar product that I didn’t want to pay $497 would have been further discounted to the $247 that would have made me uncomfortable but ready to use my credit card.

How is this possible?  For one, it is a product (online webinar) with almost zero production cost.  Unlike the widget which requires materials, labor, manufacturing, storage, and distribution, the digital warehouse doesn’t need a water supply or a janitor.  The one stored copy (and backup) replicates instantly.  

The next component is YOU. Big Data knows YOU along the same lines I mentioned that I use to make a purchase.  Big Data figures out if I have enough cash or credit for the sale.  Big Data knows if I spend money on these types of products already.  Big Data knows whether this fits my spending lifestyle or if it is a reach.  Big Data tells me I can or cannot deduct it as a business expense.   Even more so, Big Data knows whether I need deductions at this point, against how many deductions I have already accrued in my fiscal year.

The more uncomfortable aspect? Big Data Pricing will charge me a different amount than the next sale to a different customer.  An entity with a bigger bankroll may get charged more, or they may be offered a morphed package of sales for services I cannot afford:  X downloads, unlimited downloads, additional webinars or custom services.  In the lesser bankrolls, perhaps we will get a deeper discount or an extended opportunity to buy or a few more promptings in our email inbox.

“So pricing in the virtual world has not gone into our personal pocket books yet (that we know.)  The online market does use digital information such as browsing history and location to triangulate your willingness to pay a certain price.  This is still within the Small Data genre of capability, utilizing mean and median sources. Big Data Pricing though – and I believe it will – knows YOUR personal bottom line.  This is not a random variable calculated through the local and not so location population supply and demand.  Big Data Pricing knows exactly what price to set for you from all your transaction history in stores and online, your taxes, your job, your household status, and much more.

Is that scary?  Perhaps.  But it is already very close to possible.  

The world out there is waiting to sell you the next Best Thing and Big Data or not, marketing will continue to morph to find the magic price you are willing to pay.  Big Data Pricing though will be oh-so intimately familiar with you and your money.  In the end though, Big Data Pricing can only posture the question:  will you buy?  

The answer is still up to you.

Great News!

I have been “greenlit” by #newdegreepress. They’ve reviewed my work so far on my book “The Fallacy of Laying Flat”. I’ve got the words, the format and the concept in control and on track.  My first draft is due late February for publishing by the fall of #2022. Wish me luck! This is tough stuff!! I couldn’t have done it without the wisdom and support of #bookcreators and #georgetownuniversity#publishing #authorlife #bigdata #decisionmaking #decisionintelligence #data4good #data

Life before Facebook Newsfeed

Almost 15 years ago, finding out what your friends were up to meant going to individual FB pages to check on them. Click. Read. Click. Read. In 2006, one of Zuckerberg’s famous notebook sketches came to life – news feed. Hence the scroll was born and we’ve been scrolling ever since. And Mark Zuckerberg has been answering time and again over its effect.

Now the norm, news feed’s arrival was wildly unpopular. Actually “This SUCKS” was the collective comment; complaints of creepy and stalker-like were the reaction. Ten percent of FB users joined groups (ironically on Facebook) demanding its removal. FB itself simmered internally with controversy over keeping it versus trashing it.  The solution came as a compromise: opt-in privacy settings that allow control over who sees the news feed. Makes sense . . . because that’s what we’ve lived for years now. Scroll. Scroll. Scroll.

This isn’t a recall lesson for FB devops; this is for us to remember. 1) We consumers don’t like change for the most part. Unless there’s a huge visible upside, we fight the upsets to our expectations of how life should go. 2) Great ideas – even game changers – need tweaking. Don’t be afraid or too proud to see the faults in your work. Take the edits; listen to the critics. There’s going to be unintended consequences to any magical kingdom. 

Mark Zuckerberg may always have the ego to conquer the world with his creation. But he’s also admitted to failure. He has also adapted to the responsibility. His famous “Move fast and break things” became people’s lives – sometimes literally. And I doubt he’s seen his last appearance before Congress.

How well can we predict the future of #coronavirus?

Did COVID-19 start in a lab or naturally progress from animals to humans?

While there’s no shortage of speculation on #COVID-19 – especially how it got started – the answer(s) have yet to become clear. It’s doubtful we will ever learn all the specifics that seem simple enough to determine and necessary to prevent the next iteration. COVID-19 and the next pandemic are subject to the butterfly effect and likely will remain a cloudy opinion. 

The butterfly effect is casually referred in pop culture but usually incorrectly. Edward Lorenz developed the concept while working meticulous calculations for predictions in the weather.  He observed that the smallest deviations in a system can lead to dramatically different results.  Hence a butterfly flapping its wings in Brazil causes tornadoes in Texas. This sensitivity to original conditions is why we can’t well predict the weather beyond 2 weeks.

His work led to the founding of chaotic study – which is not randomness. Chaotic systems are ordered but they vary – within limits. The solution to predicting the next pandemic is like hurricane watching. We can learn the pertinent path with variance but only know its exact movements as it unfolds.

“You’re touching too much”

#coronavirus #techhacks

Touching is tangency. We didn’t realize how much we touched stuff until health and life demanded you pay attention. Realizing how much you touch stuff other people touched is really creepy.

There can be an app for that – touching too much. Your phone’s sensitivity can pick up how much you touch things – door handles, drinking & eating, your wallet, private/public transportation/buildings. It knows your meeting sked.  It can remind you to wash hands or wipe down surfaces. At given intervals too, it politely says, “You need to wipe me down too; I’m getting kinda funky.”

There’s also already an app for one of the dominant touch activities – paying for stuff. ApplePay and GooglePay (did you know you can add money as an attachment with gmail??) already provide touchless options for transactions from your phone. Even before the outbreak, I cringed whenever I pulled out a credit card for payment. Either the cashier is touching it (who touched a hundred before) or I’m touching a kiosk innumerable people before me have.  Contactless is exactly that – no touching! Although quite prolific in Europe, many kiosks in the US have the symbol but it isn’t active. Having universal contactless transactions would hack a major TOUCH vulnerability.

Up next: Your health record: you can take it with you

what is google pay googlepay5
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Artificial Intelligence Rule #7: Close Enough


Rule #7 of artificialintelligence: close enough.

Number 7 is pretty far down the list, but “close enough” is an equally important concept for AI. Have you ever queried Google or Bing and gotten a single entry? Aka “the answer” to your question? No. I know I’ve gotten a single page of items in return (I ask some weird questions) but it always provides a menu of options.

The page ranking algorithms of Google are legendary and as closely guarded as military secrets. They aren’t carved in stone. Indeed the algorithms are manipulated in order to adjust for specific hacks as well as smoothing trends.

But artificial intelligence doesn’t provide solutions like an algebraic math problem. It’s stoic in its reply, showing no emotion and yet posing a voluminous suite of possibilities to be considered by the inquirer. Indifferent to the vicissitudes of fortune, the ai sweeps the oceans of the internet to provide you what is . . . close enough.

Humans calculate those algorithms and only you decide what is “the answer.”

#machinelearning #aibots #algorithms #aibot #deeplearning #ml


Rule #4 of Artificial Intelligence: no context.  

#AIbots have been trained to do many mundane, repetitive jobs. Training involves utilizing data sets with often millions of data points. Without enthusiasm or angst, the AI ingests these volumes of data and returns outcomes by direction of its creator.  Given x-ray images of healthy and diseased lungs, we have to tell the AI which is good and bad. AIs are “rewarded” for correctly identifying the difference.

But an AI doesn’t know what a lung is, how it works, that it actually exists inside a person or what a person is. 

#Google was the first major developer in #recognition when it looped together 16,000 computer processors with one billion connections in order for it to watch #YouTube and find . . . cats.  This was a tremendous breakthrough for 2012, but isn’t this what a 5 year old understands without seeing millions of videos?

And a toddler knows colors and textures and if the cat is missing an eye or leg or tail. He or she knows cats are pets and most are found in or around homes. Cats walk on four legs; they don’t swim. Not everyone likes cats but they are loveable. 

This is context and the richness of a five year old’s perception outweighs a million data points.



Pet peeve – use of “literally.”

If you don’t understand literally vice figuratively, #artificialintelligence can set you straight. Rule#2 of #AI is everything is literal. AI does exactly as you tell it.  That can be annoying from a child or spouse or customer service chatbot. AI doesn’t have the contextual preferences of humans – which emotes angst and joy in the uncovering. Given a problem, AI is going to take the tasking literally. For example:

I hooked a neural network up to my Roomba. I wanted it to learn to navigate without bumping into things, so I set up a reward scheme to encourage speed and discourage hitting the bumper sensors. It learnt to drive backwards, because there are no bumpers on the back. – @smingleigh

This is an interesting concept because the “bugs” that you deal with your computer, your phone, your network, your business are likely a synergy of literal translation. Code knows 0 or 1, and coders get that.  The rest of us are swimming in “why the hell is this broken” when the answer is a literal question return.

#saywhatyoumean #meanwhatyousay

AI Bots on Billie Eilish

#Imabiscuit #billieeilish

In my last post, i talked about how #artificialintelligence is NOT the super borg/being that could take over the world. So why not?  #AI is #machinelearning and we are the teachers. 

One of the earliest and prolific examples is Google translate.  Instead of using rules based learning: vocabulary + grammar = new language, AI consumes the Internet’s volumes of translations online, eating everything, idioms and nuances.  The human level equivalent for a single language would be blind, total immersion. Go to a country knowing nothing of the language and simply listen, read and repeat, making mistakes along the way. 

Humans have to train the data too, teaching it right from wrong.  #AIbots don’t know – and don’t care – what the output is – as your text auto completion can attest. What comes out as a result is sometimes odd and sometimes beautiful – kinda like its human creators.

If you’re thinking wow, how cool, and wouldn’t something that can learn language better than we can take over the world? Not so much. The #trialanderror is as large as the training data set (100 billion translations as of 2016).  The results of a single translation are a lottery ticket sample size. #youtube abounds with examples of how #googletranslate doesn’t quite figure it out.

Check out “I’m a biscuit.” Better known as #badguy