Intelligence comes down to being able to find correlations in data. Through a combination of the architecture of the system (our genes) and its running behaviour (learning) the intelligent agent is able to find and make use of correlations. Intelligence has nothing to do with survival or maximization.
The input to our vision system is a bunch of very complex data without any higher-level structure visible without a large amount of computation. An intelligent agent would map this data into a 3D model of the world by finding correlations of how this data USUALLY maps to the 3d structure of the world. You'll have the rare optical illusions and weirdness. When we look at common objects we imagine them to be in the location and size that they're usually at, however a random piano we look at could just as well be the size of our moon, far away. What makes us perceive objects as close by is because our vision system has built in assumptions and learned assumptions about correlations in the data stream. Stereoscopic vision certainly helps us detect the actual depth of the scene but it's not needed, we can play 3D games on a 2D monitor.
When I read a text in a certain language my brain begins to find correlations in the data so that you can just barely look at the data stream and already know what it says with a high probability of success. Of course a higher level system then goes over everything and if the sentence you read doesn't make sense, you do a retake and indeed may find that a lower level system guessed wrong on a word or two.
Current artificial intelligence is REALLY bad at this. Consider this: how many image samples of an object do you need to learn to recognize that object later from a different angle? One! Look up a rare animal/subspecies and you'll pick up on its features in 3-dimensional space from just one 2d-picture. When you then go on to look at any other pictures, even if they're from another angle with a different lighting, you'll recognize it as the same turtle. Your brain knows how the 3-dimensional features look from any lighting condition or rotation, though it WILL take a variable amount of time to recognize objects depending on how oddly they are rotated. Our current best neural networks however need a tremendous amount of data of say watches, just to learn what watches look like. They also compute everything in constant time, which IMO is just stupid.