[simianparrot]

Humans can realise they don’t understand something and seek more knowledge to learn to understand it. But also humans can build complex structures out of simple fundamentals: The same logic of counting up beans on a table can be extrapolated to multiplying that table of beans. And then counting horses the same way you count beans but give them a value of multiple beans. And then simplify that by trading in promises of beans in trade of horses.

The fact that so many people can’t see the fundamental differences of an LLM and human intelligence reminds me of back when the very early computer scientists thought they could model the entirety of nature by reducing every “component” to a numeric value and compute it as “transfer of energy”.

Quite literally they did the same thing: They had a new toy (very advanced computation machines) and forced all of nature to “fit” within it. It also ended in failure, obviously. Not because nature or ecosystems (as it was coined) are “magic” but because grossly oversimplifying reality to fit desired models is a fool’s errand.

[throwthrowuknow]

We’ll have to wait and see how far multi modal training takes us. Text only models are extremely limited by the kind of information we can encode as text and the loss of detail e.g. the word “cat” vs an image of a cat vs video of a cat vs direct physical interaction with a cat vs being a mammal that shares a great deal of biology with a cat. You need a table and beans before you can invent a method for counting them