It's frankly amazing that today's systems do as well as they do. But they're nowhere near the perfected-model status of an aircraft-engine simulacrum at GE, Pratt & Whitney, or Rolls-Royce. And they never will be.
The probabilistic path goes beyond marketing, of course, even if that's what we see in most consumer technologies. The same techniques have long been used to optimize delivery routes for UPS and FedEx, for Amazon to figure out what warehouses to ship a product from and by what carrier, to adjust airline schedules and the equipment and crews to be used based on weather and passenger demand, to manage just-in-time manufacturing parts ordering and delivery, and so on. (This used to be called operational business intelligence.)
Those operational BI cases are more exact than the marketing ones, because the idiosyncracies and changing needs of people are less of a factor in their context. Thus, they have more of an engineering feel to them than, say, ad targeting or search results. But they too are situational, so there will never be a perfect model for them, either. However, there can be more perfect data and assurance that product's or vehicle's location and status is known that we'll ever get for the state of mind of a user doing a search or contemplating a purchase. In other words, there's more certainty about the environment and the forces affecting it.
The next time you hear about machine learning, the Internet of things, big data analytics, and other new computing fancies, keep in mind there are two major thrusts for this basket of technologies, and how they work and how to think of them differs greatly based on the specific problem they're being applied to.
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