> But humans are able to reason with orders of magnitude less training data.
Common belief, but false. You start learning from inside the womb. The data flow increases exponentially when you open your eyes and then again when you start manipulating things with your hands and mouth.
> When you ask a model to "think about the problem step by step" to improve its reasoning, you are basically just giving it more opportunities to draw on its huge memory bank and try to put things together.
We do the same with children. At least I did it to my classmates when they asked me for help. I'd give them a hint, and ask them to work it out step by step from there. It helped.
> Common belief, but false. You start learning from inside the womb. The data flow increases exponentially when you open your eyes and then again when you start manipulating things with your hands and mouth.
But you don't get data equal to the entire internet as a child!
> We do the same with children. At least I did it to my classmates when they asked me for help. I'd give them a hint, and ask them to work it out step by step from there. It helped.
And I do it with my students. I still think there's a difference in kind between when I listen to my students (or other adults) reason through a problem, and when I look at the output of an AI's reasoning, but I admittedly couldn't tell you what that is, so point taken. I still think the AI is relying far more heavily on its knowledge base.
There seems to be lots of mixed data points on this, but to some extent there is knowledge encoded into the evolutionary base state of the new human brain. Probably not directly as knowledge, but "primed" to quickly to establish relevant world models and certain types of reasoning with a small number of examples.
Your field of vision is equivalent to something like 500 Megapixels. And assume it’s uncompressed because it’s not like your eyeballs are doing H.264.
Given vision and the other senses, I’d argue that your average toddler has probably trained on more sensory information than the largest LLMs ever built long before they learn to talk.
There's an adaptation in there somewhere, though. Humans have a 'field of view' that constrains input data, and on the data processing side we have a 'center of focus' that generally rests wherever the eye rests (there's an additional layer where people learn to 'search' their vision by moving their mental center of focus without moving the physical focus point of the eye.
Then there's the whole slew of processes that pick up two or three key points of data and then fill in the rest (EX the moonwalking bear experiment [0]).
I guess all I'm saying is that raw input isn't the only piece of the puzzle. Maybe it is at the start before a kiddo _knows_ how to focus and filter info?
Common belief, but false. You start learning from inside the womb. The data flow increases exponentially when you open your eyes and then again when you start manipulating things with your hands and mouth.
> When you ask a model to "think about the problem step by step" to improve its reasoning, you are basically just giving it more opportunities to draw on its huge memory bank and try to put things together.
We do the same with children. At least I did it to my classmates when they asked me for help. I'd give them a hint, and ask them to work it out step by step from there. It helped.