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.“
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.