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And what does tech support do if you ask them to help you with a truly complex problem?Chances are that they send you documentation and ask you to call back if you get stuck.Not only can the chat bot not show you anything, it can’t see you do anything either. An expert can look at your work and tell you what is wrong.A coach can watch you execute a maneuver and critique your form. Even if it could, the ability of the AI to make sense of what it is seeing and put it in the context of the user’s task just isn’t there yet. It requires you to understand and articulate the problem, and by the time you can understand a problem well enough to articulate it, your are well on your way to solving it. And you can’t tell it what the problem is because you cannot see the problem, only the result of the problem.John Carroll’s work that led to the publication of The Nurnberg Funnel showed that different people traverse texts in different ways driven by the particulars of their individual task and background.No simple, comprehensive, logical treatment of the paradox of sense-making is possible.Here’s why: No, I don’t mean that they are a stupid idea. ) Human do this so easily we rarely even notice that the ambiguity exists. As Brian Bergstein points out in The Great AI Paradox (also AI and computer science at MIT, says it would be more helpful to describe the developments of the past few years as having occurred in “computational statistics” rather than in AI.
In fact, tales of tech support are predominantly tales of frustration on both sides of the phone.
And what to they do when they come across a common problem: they write a knowledge base article about it.
Some of that, of course, is about saving money, as tech support people have to be paid.
And suddenly every tech comm and content strategy conference seems to be about getting your content ready for chatbots. Chatbots are sexy and sex sells, even if the definition of sexy is a grey box with a speaker sitting on the counter.
But chatbots are not the future of technical communication. As Will Knight writes in Tougher Turing Test Exposes Chatbots’ Stupidity in the , current AI does barely better than chance in deciphering the ambiguity in a sentence like: “The city councilmen refused the demonstrators a permit because they feared violence.” (Who feared the violence?So you have to describe your problem to the chatbot. The primary virtue of an expert is that they can look at what you are doing and spot the flaw that you cannot see. The expert spots the one thing in the hundreds of parts and pieces you have assembled that is on backwards. But even if your chatbot grew up and became a robot with the vision and the common sense and the specialized knowledge and experience to watch you, to examine your work, and to spot the flaw in it the way a human expert would, it still would not be the future of technical communication. Setting aside the fact that if you had a robot with those capabilities you really would not need to learn to do the task yourself, the real problem is that there is only so much that experts can do for us.In the end, learning is about rearranging our own mental furniture, finding our way through the thickets of our own minds.There are, to be sure, ancillary media that can play a valuable role in our foraging: maps, graphics, animations, etc.