I think there is a general misconception that we humans without training can learn some features, classes, and semantics based on one or few examples. It seems so in observation and it seems nearly magical, but really it is that way just because we don’t see the whole picture, and we assume that a human is a “carte blanche” at first.
In reality, we are not a blank page when we are born—our brain is an effect of training that took millions of years based on evolution. We have already trained and very similar pattern recognition (aka complex features detection), similar basic senses including basic features in spaces of each sense (like cold or yellow), and image segmentation (aka object clustering) with multilevel capabilities (see the object as a whole and see parts) and pose detection (aka division between object geometry and pose in space).
For example, if you enter a kind of store that you never entered and you see some stuff—you might not know names for the things, maybe don’t know and can’t even guess the purpose for some. Still, you can easily differentiate each object from the background, you are able to take it into hand properly and ask the cashier what is that tool for. Even you can take a small baby, put some things around for which he/she does not know name or purpose, and observe it will rather grab at those things than at random in the background—he/she already can see features and can make that clustering in the same way other humans do (maybe with more errors as brain still develops—but it is very guided development, not training from zero).
What was astonishing to me is that so much about semantics, features, relations, etc. can be learned without having any human-like way to relate words to real objects and features and observations. Transformer-based LLM can grasp it only by reinforced learning on a huge amount of text.
I have some intuition as to why this might be the case. If you take a 2D map with points e.g. cities and measure approximate distances f.ex. by radio signal power loss between points and plug that information into a force-directed graph algorithm with forces based on a measure that approximated distance, then with enough measurements it will recreate something very resembling the original map—even when the original positions are not known and measures are not exact and by proxy. Learning word embeddings is similar to this in an abstract sense. From a lot of text, we measure relations between words in a multidimensional space and the algorithm learns similar structure, even if not anchored to reality. Transformed-based LLMs go one step further—they are learned how to emulate the kind of processing that we do in minds based on “applying additional forces” in that graph (not exactly that, but similar—focus based on weights and transforming positions in embedding space).
I think there is a general misconception that we humans without training can learn some features, classes, and semantics based on one or few examples. It seems so in observation and it seems nearly magical, but really it is that way just because we don’t see the whole picture, and we assume that a human is a “carte blanche” at first.
In reality, we are not a blank page when we are born—our brain is an effect of training that took millions of years based on evolution. We have already trained and very similar pattern recognition (aka complex features detection), similar basic senses including basic features in spaces of each sense (like cold or yellow), and image segmentation (aka object clustering) with multilevel capabilities (see the object as a whole and see parts) and pose detection (aka division between object geometry and pose in space).
For example, if you enter a kind of store that you never entered and you see some stuff—you might not know names for the things, maybe don’t know and can’t even guess the purpose for some. Still, you can easily differentiate each object from the background, you are able to take it into hand properly and ask the cashier what is that tool for. Even you can take a small baby, put some things around for which he/she does not know name or purpose, and observe it will rather grab at those things than at random in the background—he/she already can see features and can make that clustering in the same way other humans do (maybe with more errors as brain still develops—but it is very guided development, not training from zero).
What was astonishing to me is that so much about semantics, features, relations, etc. can be learned without having any human-like way to relate words to real objects and features and observations. Transformer-based LLM can grasp it only by reinforced learning on a huge amount of text.
I have some intuition as to why this might be the case. If you take a 2D map with points e.g. cities and measure approximate distances f.ex. by radio signal power loss between points and plug that information into a force-directed graph algorithm with forces based on a measure that approximated distance, then with enough measurements it will recreate something very resembling the original map—even when the original positions are not known and measures are not exact and by proxy. Learning word embeddings is similar to this in an abstract sense. From a lot of text, we measure relations between words in a multidimensional space and the algorithm learns similar structure, even if not anchored to reality. Transformed-based LLMs go one step further—they are learned how to emulate the kind of processing that we do in minds based on “applying additional forces” in that graph (not exactly that, but similar—focus based on weights and transforming positions in embedding space).