41–50 of 114 posts
Suddenly, a shaded rock appears. https://upload.wikimedia.org/wikipedia/commons/7/77/Martian_... We're doing humans wrong. Maybe not all wrong, and of course, humans are extremely useful things, but think about it: sometimes it almost looks like we're already there. There always going to be an anomaly; lots of them, actually, considering all the things shaded in different patterns. Something have to change. I agree t…
Somehow tangent but this made me think about this quote found on HN last year: Context: Evolutionary algorithms and analog electronic circuits > One thing stands out when you try playing with evolutionary systems. Evolution is _really_ good at gaming the system. Unless you are very careful at specifying all of the constraints that you care about you can end up with a solution that is very clever but not quite what yo…
Given enough time, you'd expect the to develop clever behaviors, but instead they just fuzz-tested the sim and locked in on exploits of bugs or environment settings. They only got a bit more clever when connecting different sims running on different conditions.
Eyes already use different kinds and densities of sensors optimized for either detail and color or movement/edges. I wouldn't expect a single learning method, even after optimizing it to its limits, to be above what two or more layers of different methods could do, especially when trying to avoid exploits like the tank story.
Really wasn't expecting a Terry Davis comment at the bottom.
My son keeps telling me that infants are fine with, say, a truck transforming into a clown (when it emerges from the other side of a visual barrier) but not with it transforming into TWO of something. Apparently, babies subjectively experience this (visual transformation) all the time -- mom moves a plate and what seemed like a big circle is now a flat line or whatever.
So humans apparently get tons and tons of experience with visually mapping 3d reality to mere 2d imagery. I have been thinking somewhat about this of late, in terms of physical attractiveness or "image" -- that pictures of a woman posted on a blog capture a 2d version of her but people interacting with her are interacting with a 3d living, moving creature who also has smell and a voice and her movements may be elegant or may be not elegant. Which is a thought process relevant to a project of mine, something people here surely will have no interest in. But where it is relevant to this article is that we are doing this wrong: Humans have thousands of hours of practice of looking at 3d reality and figuring out how it to interpret 2d images as representative of that 3d reality. Image recognition software is just dealing with 2d images. I don't see how it can hope to compete. Humans don't come preinstalled with the software to make that distinction. We acquire it with enormous repetition.
When do we make a robot and give it some baseline parameters and a learning algorithm (and set it loose in 3d reality and have to learn)? That is when we can get scared about human like AI that can compete on image recognition.
https://www.imageidentify.com/ correctly identified the images as "a small sofa". I think rotating the image is questionable, since there could be an algorithm for first orienting the image correctly based on light and shadow and then the image recognition could be run. https://www.imageidentify.com/result/1ixb9603m9ix1
Giving it an image that we know has all the relevant details of a sofa, but it likely won't have close matches in the dataset, can give us an idea of how clever it is.
But high-level notions like a Jaguar is a cat-like animal aren't necessary to perform well on an N-way classification task like ImageNet.
What's more important to note is everybody knows there's plenty wrong with a pure appearance-based approach like CNNs. Every few years a new approach pops up that is based on ontologies, an approach inspired by Plato, etc, but these systems require a lot of time and effort. More importantly, they don't perform as well on large-scale benchmarks. In the publish-or-perish world, you can jump on the CNN bandwagon or start reading Aristotle's metaphysics and never earn your PhD.
Leopards (or jaguars) are complex 3-dimensional shapes with quite a lot of degrees of freedom (considering all the body parts that can move independently). These shapes can produce a lot of different 2d contours My son keeps telling me that infants are fine with, say, a truck transforming into a clown (when it emerges from the other side of a visual barrier) but not with it transforming into TWO of something. Apparen…
The models have room for improvement, but it's not clear to me that larger datasets won't solve the problem. Larger datasets and more processing power is exactly why neural nets have surged in effectiveness recently. Who knows how much further current models can go with more data and processing power?
Suddenly, a shaded rock appears. https://upload.wikimedia.org/wikipedia/commons/7/77/Martian_... We're doing humans wrong. Maybe not all wrong, and of course, humans are extremely useful things, but think about it: sometimes it almost looks like we're already there. There always going to be an anomaly; lots of them, actually, considering all the things shaded in different patterns. Something have to change. I agree t…
The fact that all classifiers -- including human beings -- fail in some cases is a separate issue. The goal is to create a computer classifier that succeeds and fails in the same cases humans do.