I think things like computer vision would be more accurately labeled as "drawing conclusions" rather than "making predictions". And if you can get your prediction success rate close to 100% that seems more like drawing a conclusion as well.
Another way to put it is a person would look at a person and notice fur, eyes, paws, ears, and the specific shapes and colors of these things and conclude it's a cat. Take away a leg, or an ear, or have it half out of frame, and most likely a person would still recognize it as a cat. The idea of "cat" exists in the mind of the agent in this case, but a computer may predict cat only if the animal is fully in frame and not missing any parts. The machine is entirely reliant on features whereas a person is reliant on a mental model that has more elasticity in what it defines.
Now, I am sure you know this, so I don't know why you chose to use an example that's inaccurate in practice.
The fact that a computer can label an object missing many features does not imply that it cannot also make a mistake doing so. Like the Tesla that couldn't recognize a truck right in front of it.
Then there's Google's Deep Dream, which did silly things like think that all hammers had arms attached to them.
Then there's also this: http://www.evolvingai.org/fooling
and many other examples like it. I chose a simple example that would be maximally relatable and still accurate even with respect to state of the art algorithms and datasets with billions of samples.
Does a tree that falls alone in the forest make a sound? Potato, potatoe. shrug
Say you have a photo, and your image tagger says it's a photo of an apple. What that really means is "if I were to go to the place and time where this photo was taken, there would be an apple there in front of the camera".
So all predictions are conclusions but not all conclusions are predictions. I.e, a deductive argument comes to a conclusion, but, as we know, inferential methods aren't deductive.
I was commenting from the perspective of an analogy from personal experience of my own conscious.
This is a pop-sci article, so take it with a grain of salt, but there is a growing body of research that attempts to frame many cognitive processes as inherently predictive (inferential?). I think this view arises pretty naturally when you start thinking about us having a 'model' of reality that we is updated based on sensory information, but I digress.
I think I see where you're coming from.
A human might not be 'predicting', since the term has some kind of temporal element connotation. But we could say that it's 'inferring' what the photo represents?
All of our sensory systems work this way - we use an enormous amount of context to bubble up predictive categories and the collect marginal amounts of information until one prediction dominates over all others.
When one doesn’t, we get gestalt like illusions; like the cubes that are projected both forwards or backwards - or dresses that are blue and black and white and gold - or laurel and yanni in perfect harmony, etc.