Once upon a time … maybe four or five years ago … all businesses were supposed to be planning their digital transition. Or maybe it was crafting a digital strategy. Or else it was implementing (or, more accurately, “installing”) a few digital technologies out of a list provided by one of the often-tedious punditocracies our field is plagued by.

Or …

When uncertain, defining terms is often a healthy place to start. With that intention and some Google-enhanced searching I ran across this, a typical example:

A digital strategy is your plan for introducing and using digital technology to meet your business goals. A clear digital strategy can help you make sure that your digital presence is current, future-proof and achieves your intended goals.

I guess starting with defining terms doesn’t guarantee results, as defining “digital” as “being digital” is a few klicks less than helpful.

Nor is the phrase “future proof” any more meaningful than the tiresome and over-used “best practice.” In the case of best practice, “best” only becomes known when the future gets here and a better approach doesn’t. In the case of future proofedness the only business that even barely approximated it is Guinness, whose eponymous founder, Arthur, signed a 9,000-year, 45 £ annual lease. That event happened on December 31, December 1759.

Now that’s future-proofing. Every other example of future-proofing I’ve ever run across is just so much arrogant stupidity, because, speaking of definitions, Ambrose Bierce’s The Devil’s Dictionary defines “future” quite well: it’s “That period of time in which our affairs prosper, our friends are true and our happiness is assured.”

Keep in mind that even the profession with the most expertise in foreseeing the future … science-fiction writers … missed the personal computer entirely.

So let’s put the question I posed at the beginning of this screed to bed: If AI is a digital strategy then “digital” isn’t dead yet. Otherwise?

Back when forward-looking business leaders cared about Digital, whether as a noun-less adjective or an adjective tied to one or more business nouns (“strategy,” “transformation,” and “revolution” come to mind), Digital and its lexicographical derivatives did matter for a fleeting period of time. That was when its proponents briefly recognized that cutting costs was not (and still is not) a perceptive new way of thinking about business success … that for many business contexts, growing profitable revenue wasn’t just more interesting than cutting costs, it was (and still is) more fun, too.

Back in those halcyon days, Digital, according to KJR and its like-minded sources of business insights, meant using newly emerging or under-exploited technologies to build new business capabilities, which, once mastered, could be used to bring new products and services to market quickly, because so much of what’s needed to bring them to market is already in place.

So yes, Digital still lives, because the thought processes now being attached to artificial intelligence are all about using AI to add new capabilities – new capabilities that, for the time being at least, have the potential to create competitive advantage, and especially products and services Real Paying Customers might find interesting and valuable.

Bob’s Last Word: Speaking of future-proofing, competitive advantages, and so on I’m still not convinced Nvidia’s current AI marketplace dominance is sustainable, seeing as how a competitor might ask an Nvidia-based AI to design a superior AI chip.

And, failing that, how one might instead employ genetic algorithms for that purpose.

 

Faced with a discipline that looks too much like hard work, I generally compromise by memorizing a handful of magic buzzwords and their definitions. That lets me acknowledge the discipline’s importance without having to actually learn a trade that looks like it would give me a migraine were I to pursue it.

Which gets us to testing … software quality assurance (SQA) … which I know consists of unit testing, integration testing, regression testing, user acceptance testing, and stress testing.

Although from the developer’s perspective, user acceptance testing and stress testing are one and the same thing – developers tend to find watching end-users try to use their software deeply stressful.

More to the point, I also “know” test automation is a key factor in successful SQA, even though I have no hands-on experience with it at all.

Speaking of no hands-on experience with testing stuff, the headline read, “Bombshell Stanford study finds ChatGPT and Google’s Bard answer medical questions with racist, debunked theories that harm Black patients.” (Garance Burke, Matt O’Brien and the Associated Press, October 20, 2023).

Which gets us to this week’s subject, AI testing. Short version: It’s essential. Longer version: For most IT organizations it’s a new competency, one that’s quite different from what we’re accustomed to. Especially, unlike app dev, where SQA is all about making sure the code does what it’s supposed to do, for the current crop of AI technologies SQA isn’t really SQA at all. It’s “DQA” (Data Quality Assurance) because, as the above-mentioned Stanford study documents, when AI reaches the wrong conclusion it isn’t because of bad code. It’s because the AI is being fed bad data.

In this, AI resembles human intelligence.

If you’re looking for a good place to start putting together an AI testing regime, Wipro has a nice introduction to the subject: “Testing of AI/ML-based systems,” (Sanjay Nambiar and Prashanth Davey, 2023). And no, I’m not affiliated or on commission.

Rather than continuing down the path of AI nuts and bolts, some observations:

Many industry commentators are fond of pointing out that “artificial intelligence” doesn’t really deal with intelligence, because what machines do doesn’t resemble human thinking.

Just my opinion: This is both bad logic and an incorrect statement.

The bad logic part is the contention that what AI does doesn’t resemble human thinking. The fact of the matter is that we don’t have a good enough grasp of how humans think to be so certain it isn’t what machines are doing when it looks like they’re thinking.

It’s an incorrect statement because decades ago, computers were able to do what we humans do when we think we’re thinking.

Revisit Thinking, Fast and Slow, (Daniel Kahneman, 2011). Kahneman identifies two modes of cognition, which he monosyllabically labels “fast” and “slow.”

The fast mode is the one you use when you recognize a friend’s face. You don’t expend much time and effort to think fast, which is why it’s fast. But you can’t rely on its results, something you’d find out if you tried to get your friend into a highly secure facility on the strength of you having recognized their face.

In security circles, identification and authentication are difficult to do reliably, specifically because doing them the fast way isn’t a reliable way to determine what access rights should be granted to the person trying to prove who they are.

Fast thinking, also known as “trusting your gut,” is quick but unreliable, unlike slow thinking, which is what you do when you apply evidence and logic to try to reach a correct conclusion.

One of life’s little ironies is that just about every bit of AI research and development is invested in achieving fast thinking – the kind of thinking whose results we can’t actually trust.

AI researchers aren’t focused on slow thinking – what we do when we say, “I’ve researched and thought about this a lot. Here’s what I concluded and why I reached that conclusion.” They aren’t because we already won that war. Slow thinking is the kind of artificial intelligence we achieved with expert systems in the late 1980s with their rule-based processing architectures.

Bob’s last word: For some reason, we shallow human beings want fast thinking to win out over slow thinking. Whether it’s advising someone faced with a tough decision to “trust your gut,” Obi Wan Kenobi telling Luke to shut off his targeting computer, or some beer-sodden opinionator at your local watering hole sharing what they incorrectly term their “thinking” on a subject. When we aren’t careful we end up promulgating the wit and wisdom of Spiro Agnew. “Ah,” he once rhetorically asked, “What do the experts know?”

Bob’s bragging rights: I just learned that TABPI – the Trade Association Business Publications International – has recognized Jason Snyder, my long-suffering editor at CIO.com and me a Silver Tabbie Award for our monthly feature, the CIO Survival Guide. Regarding the award, they say, “This blog scores highly for the consistent addressing of the readers’ challenges, backed by insightful examples and application to current events.

Gratifying.

Speaking of which, On CIO.com’s CIO Survival Guide:The CIO’s fatal flaw: Too much leadership, not enough management.” Its point: Compared to management, leadership is what has the mystique. But mystique isn’t what gets work out the door.