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.

The future of the automobile industry seems pretty clear, and relevant to anyone involved in things Digital.

For those living in the suburbs, exurbs, and beyond, it will look a lot like it does right now: People will own or lease cars and use them to take themselves from place to place.

Having moved to a downtown condo a couple of years ago, I’m pretty confident a different model will become popular among us urbanites:

When I need a car, my NeedACar app will dispatch one. It will drive itself to wherever I am, take me wherever I want to go, and drive itself back to its home garage when I don’t need it anymore.

Imagine you (1) agree; and (2) are involved in strategic planning for a company in the industry. Plus, you’re digitally literate and consume science fiction at least occasionally — two essential qualifications.

And yes, while we’re exploring the automobile industry, the same thought processes apply to players in any other industry, too.

So … how do you size up your situation?

Start with the field of logical competitors, and how readily each of them can adapt to this new world of transportation.

Other automobile manufacturer/dealer consortia: These all have one certain and one potential advantage. The certain advantage is that no matter what the future looks like, someone will have to manufacture the cars. The potential one is brand loyalty: Even in an era of autonomous cars it seem likely that many drivers …well, in this scenario passengers … might have a preference for some makes and models over others.

But dealers will probably lack the scale needed to just house the fleets in question let alone manage them, and manufacturers by themselves have no ability to sell directly to consumers.

Uber: Using an app to get personal transportation to someone who needs it sounds a lot like Uber, except for the autonomous car part. Uber pretty much owns the mindshare of people who want on-demand transportation.

What Uber doesn’t have is a fleet of autonomous cars and the infrastructure that goes with one. Quite the opposite: Uber’s model works in large part because it’s made car ownership and maintenance Someone Else’s Problem.

Traditional taxi services do own and manage car fleets. But they haven’t even figured out how to compete with Uber. It’s unlikely they’ll figure out how to deal with on-demand autonomous self-service transportation.

Also, traditional taxis are pretty ugly.

Car rental companies would seem to be well-positioned for the scenario we’re exploring. They’re accustomed to managing large fleets and they’re in the business of providing vehicles on demand.

What they don’t have is the ability to cater to make-and-model preferences. Also, their fleets are concentrated in single large locations proximate to the local airport. In most cities the airport … and rental car centers … are located well away from the metro core, which means long delivery delays for the urbanites who are the core market for the service we’re talking about.

Turo and its brethren are Airbnb for car owners. If you won’t be using your car for a while you could make it available to people who need one.

With the addition of autonomous vehicles this model would seem to have a lot going for it — no need to own a fleet; a wide variety of makes and models on the street; and because the cars are self-driving the major inconvenience barrier — having to arrange meeting places to deliver and return the vehicle — goes away. It would, however, depend on car owners leaving their garage doors open.

Amazon: I have no idea what Amazon would bring to this party, beyond its obvious broad reach among consumers. Except for this: It didn’t occur to Best Buy that Amazon was even a competitor until far too late in the game, just as it didn’t occur to mainstream publishers that Amazon was a competitor until far too late in that game, and didn’t occur to traditional data-center outsourcers that Amazon might become a competitor until the game was nearly over.

Which gets us to the point of this little exercise: As companies dip their toes in the Digital waters, they’re (and by “they’re” I mean “you’re”) going to have to do far more than use Digital capabilities to gain an edge over the competitors they have right now.

They’re going to have to anticipate who their competitors will be once the innovation gears have gone through a few rotations.

Or, even better, they’ll choose some better competitors to beat than the ones they’re competing with right now.