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|In: Cognitive Enterprise|Last Updated:

Irony fans rejoice. AI has entered the fray.

More specifically, the branch of artificial intelligence known as self-learning AI, also known as machine learning, sub-branch neural networks, is taking us into truly delicious territory.

Before getting to the punchline, a bit of background.

“Artificial Intelligence” isn’t a thing. It’s a collection of techniques mostly dedicated to making computers good at tasks humans accomplish without very much effort — tasks like: recognizing cats; identifying patterns; understanding the meaning of text (what you’re doing right now); turning speech into text, after which see previous entry (what you’d be doing if you were listening to this as a podcast, which would be surprising because I no longer do podcasts); and applying a set of rules or guidelines to a situation so as to recommend a decision or course of action, like, for example, determining the best next move in a game of chess or go.

Where machine learning comes in is making use of feedback loops to improve the accuracy or efficacy of the algorithms used to recognize cats and so on.

Along the way we seem to be teaching computers to commit sins of logic, like, for example, the well-known fallacy of mistaking correlation for causation.

Take, for example, a fascinating piece of research from the Pew Research Center that compared the frequencies of men and women in Google image searches of various job categories to the equivalent U.S. Department of Labor percentages (“Searching for images of CEOs or managers? The results almost always show men,” Andrew Van Dam, The Washington Post’s Wonkblog, 1/3/2019.

It isn’t only CEOs and managers, either. The research showed that, “…In 57 percent of occupations, image searches indicate the jobs are more male-dominated than they actually are.”

While we don’t know exactly how Google image searches work, somewhere behind all of this the Google image search AI must have discovered some sort of correlation between images of people working and the job categories those images are typical of. The correlation led to the inference that male-ness causes CEO-ness; also, strangely, bartender-ness and claims-adjuster-ness, to name a few other misfires.

Skewed Google occupation image search results are, if not benign, probably quite low on the list of social ills that need correcting.

But it isn’t much of a stretch to imagine law-enforcement agencies adopting similar AI techniques, resulting in correlation-implies-causation driven racial, ethnic, and gender-based profiling.

Or, closer to home, to imagine your marketing department relying on equivalent demographic or psychographic correlations, leading to marketing misfires when targeting messages to specific customer segments.

I said the Google image results must have been the result of some sort of correlation technique, but that isn’t entirely true. It’s just as possible Google is making use of neural network technology, so called because it roughly emulates how AI researchers imagine the human brain learns.

I say “roughly emulates” as a shorthand for seriously esoteric discussions as to exactly how it all actually works. I’ll leave it at that on the grounds that (1) for our purposes it doesn’t matter; (2) neural network technology is what it is whether or not it emulates the human brain; and (3) I don’t understand the specifics well enough to go into them here.

What does matter about this is that when a neural network … the technical variety, not the organic version … learns something or recommends a course of action, there doesn’t seem to be any way of getting a read-out as to how it reached its conclusion.

Put simply, if a neural network says, “That’s a photo of a cat,” there’s no way to ask it “Why do you think so?”

Okay, okay, if you want to be precise, it’s quite easy to ask it the question. What you won’t get is an answer, just as you won’t get an answer if it recommends, say, a chess move or an algorithmic trade.

Which gets us to AI’s entry into the 2019 irony sweepstakes.

Start with big data and advanced analytics. Their purpose is supposed to be moving an organization’s decision-making beyond someone in authority “trusting their gut,” to relying on evidence and logic instead.

We’re now on the cusp of hooking machine-learning neural networks up to our big data repositories so they can discover patterns and recommend courses of action through more sophisticated means than even the smartest data scientists can achieve.

Only we can’t know why the AI will be making its recommendations.

Apparently, we’ll just have to trust its guts.

I’m not entirely sure that counts as progress.

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|In: Architecture|Last Updated:

We consultants live and die on methodologies. Just as double-blind therapeutic trials are what make modern doctors are more reliable than shamans for preventing and curing diseases, the methodologies we consultants use are what make our analyses and recommendations more reliable than an executive’s gut feel.

Take, for example, the methodology I use for application, application portfolio, and application integration rationalization (AR/APR/AIR).

It starts with collecting data about more than twenty indicators of application health, redundancy, and integration for each application in the portfolio. It’s by analyzing this health data that my colleagues and I are in a position to reliably and provably recommend programs and strategies for improving the enterprise technical architecture’s application layer, along with the information and platform layers the applications rely on.

For large application portfolios the process is intimidating, not to mention invasive and expensive. Fortunately for you and unfortunately for me when I’m trying to persuade clients to engage our services, there is a more frugal alternative. In most situations it’s amply reliable for guiding AR/APR/AIR priorities as our sophisticated methodology, while costing quite a lot less.

Call it the TYE methodology, TYE standing for “Trust Your Experts.”

But first, before we get to TYE, take the time to clean up your integration architecture.

Maybe the techniques you use to keep redundant data synchronized and present it for business use through systematic APIs are clean and elegant. If so, you can skip this step on the grounds that you’ve already taken it. Also, congratulate everyone involved. As near as I can tell you’re in the minority, and good for you.

Otherwise, you need to do this first for two big reasons: (1) it’s probably the single biggest architecture-related opportunity you have for immediate business and IT benefit; and (2) it creates a “transition architecture” that will let you bring new application replacements in without hugely disrupting the business areas that currently rely on the old ones.

And now … here’s how TYE works: Ask your experts which applications are the biggest messes. Who are your experts? Everyone — your IT staff who maintain and enhance the applications used by the rest of the business, and the business users who know what using the applications is like.

And a bit often missed, no matter the methodology: Make sure to include the applications used by IT to support the work it does. IT is just as much a business department as any other part of the enterprise. Its supporting applications deserve just as much attention.

What do you ask your experts? Ask them two questions. #1: List the five worst applications you use personally or know about, in descending order of awfulness. #2: What’s the worst characteristic of each application on your list?

Question #1 is for tabulation. Whichever applications rank worst get the earliest attention.

Question #2 is for qualification. Not all question #1 votes are created equal, and you’re allowed to toss out ballots cast by those who can produce no good reason for their opinions.

Once you’ve tabulated the results, pick the three worst applications and figure out what you want to do about them — the term of art is to determine their “dispositions.”

Charter projects to implement their dispositions and you’re off and running. Once you’ve disposed of one of the bottom three, determine the disposition of what had been the fourth worst application; repeat for the fifth.

After five it will probably be a good idea to re-survey your experts, as enough of the world will have changed that the old survey’s results might no longer apply.

You can use the basic TYE framework for much more than improving the company’s technical architecture. In fact, you can use it just about any time you need to figure out where the organization is less effective than it ought to be, and what to do about it.

It’s been the foundation of most of my consulting work, not to mention being a key ingredient in Undercover Boss.

TYE does rely on an assumption that’s of overwhelming importance: That you’ve hired people worth listening to. If you have, they’re closer to the action than anyone else, and know what needs fixing better than anyone else.

And if the assumption is false … if you haven’t hired people worth listening to, what on earth were you thinking?

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