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by denimalpaca 2900 days ago
I would say it's making a prediction because to me the phrase "drawing conclusions" implies a conscious mental model of information from which a result is established. Making a prediction is not necessarily reliant on these mental models - especially conscious ones - it's about making an inference based on available information.

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.
1 comments
dismantlethesun 2900 days ago
Computers can and do label objects missing most of their features. This is especially important in computer vision work for cars, as inaccurately labeling an arm and a head as "not human" could lead to tragedy.

Now, I am sure you know this, so I don't know why you chose to use an example that's inaccurate in practice.

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denimalpaca 2900 days ago
I chose this example because, in practice, sometimes changing a single feature does ruin the prediction, especially in computer vision. Often, the systems are somewhat resilient to these kinds of errors, but often not also.

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.

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