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Why the optimism? If it hasn't picked up on the gist of the meaning of 1-10 after 10 billion examples, why expect it to work after 20 billion examples? These mo

Why the optimism? If it hasn't picked up on the gist of the meaning of 1-10 after 10 billion examples, why expect it to work after 20 billion examples? These models have worked unreasonably well so far, but there has to be a limit to how much we can substitute more data for our lack of conceptual understanding of cognitive processes.

pwinnski 4y ago
Because I'm not sure counting is something that has been explicitly tagged or trained for. Clearly always-accurate counts are not an emergent feature, but if you tagged a large set of images with tags we think are too obvious to tag as humans, like "ten fingers" and so on, if that was an actual goal to be rewarded, I think it could improve results. I'm overall in the skeptic camp, but it seems like these models can generally deliver what they're trained for. It doesn't appear any have been trained with counting as a primary goal.
- IIAOPSW 4y ago
  Ten fingers surely wouldn't be tagged, but the thing about numbers is that they work as well for 10 apples as they do for 10 coins and 10 cups and 10 whatever. It never made the leap of abstraction to learn in the latent space the meaning of numbers 1-10 independent of the particular object. This lack of extrapolation lends it to a very rote learned style. I already have a way around the specific numerical vocabulary/training problem. When I ask for "a picture in a frame", "a picture of a picture in a frame", "a picture of a picture of a picture in a frame" etc, I'm trying to use linguistic recursion to make numeracy emerge. But even that form of counting without predefined numbers fails. There's no reliable way to make a prompt the produces k of something. That's a deeper issue than it not deducing the specific meaning of characters 0-9 by example. It can't learn that by example as there really isn't training data with more than 2 nested pictures of pictures, and by itself it will never realize it can just fill in the nested painting by prompting itself with the nested statement. It lacks thought loops.

pwinnski 4y ago

Because I'm not sure counting is something that has been explicitly tagged or trained for. Clearly always-accurate counts are not an emergent feature, but if you tagged a large set of images with tags we think are too obvious to tag as humans, like "ten fingers" and so on, if that was an actual goal to be rewarded, I think it could improve results. I'm overall in the skeptic camp, but it seems like these models can generally deliver what they're trained for. It doesn't appear any have been trained with counting as a primary goal.

IIAOPSW 4y ago
Ten fingers surely wouldn't be tagged, but the thing about numbers is that they work as well for 10 apples as they do for 10 coins and 10 cups and 10 whatever. It never made the leap of abstraction to learn in the latent space the meaning of numbers 1-10 independent of the particular object. This lack of extrapolation lends it to a very rote learned style. I already have a way around the specific numerical vocabulary/training problem. When I ask for "a picture in a frame", "a picture of a picture in a frame", "a picture of a picture of a picture in a frame" etc, I'm trying to use linguistic recursion to make numeracy emerge. But even that form of counting without predefined numbers fails. There's no reliable way to make a prompt the produces k of something. That's a deeper issue than it not deducing the specific meaning of characters 0-9 by example. It can't learn that by example as there really isn't training data with more than 2 nested pictures of pictures, and by itself it will never realize it can just fill in the nested painting by prompting itself with the nested statement. It lacks thought loops.