>as long as we can separate good from bad we can train a neural network to sort out the topology for us.
10-ish years ago, I saw a project training networks to guess biological sex from face photos. They carefully removed makeup, moustache, hair, etc, so the model would be unbiased, yet they only reached 70 to 80% correct guesses. Yet it seemed like a great result, and they were trying to reach 99%.
First thing I did after reading their paper, was seek a paper where people would try and guess the biological sex from similar photos. And people weren't that much better at it. The difference between people and machine guessing was 1 or 2 percent.
I asked the guys that run the project, how they proved that such a division, based only on a photo, was even possible. They didn't understand the question, they just assumed that you can do it.
They couldn't improve their results in the end. Maybe they sucked at teaching neural networks, or maybe a lot of faces just are unisex if you remove gender markers.
I bring this anecdote because this guys, in my eyes, made a reasonable assumption. An assumption that since they in most situations can guess what's in someone pants by seeing someones face, the face has this information.
The assumption that we could somehow separate good from bad, when we rewrite school books every year, when we try to calculate "half-life of knowledge", when philosophy as a science isn't over, and every day there are political and ideological debates about what's best, is a very-very unreasonable assumption.
10-ish years ago, I saw a project training networks to guess biological sex from face photos. They carefully removed makeup, moustache, hair, etc, so the model would be unbiased, yet they only reached 70 to 80% correct guesses. Yet it seemed like a great result, and they were trying to reach 99%.
First thing I did after reading their paper, was seek a paper where people would try and guess the biological sex from similar photos. And people weren't that much better at it. The difference between people and machine guessing was 1 or 2 percent.
I asked the guys that run the project, how they proved that such a division, based only on a photo, was even possible. They didn't understand the question, they just assumed that you can do it.
They couldn't improve their results in the end. Maybe they sucked at teaching neural networks, or maybe a lot of faces just are unisex if you remove gender markers.
I bring this anecdote because this guys, in my eyes, made a reasonable assumption. An assumption that since they in most situations can guess what's in someone pants by seeing someones face, the face has this information.
The assumption that we could somehow separate good from bad, when we rewrite school books every year, when we try to calculate "half-life of knowledge", when philosophy as a science isn't over, and every day there are political and ideological debates about what's best, is a very-very unreasonable assumption.