alt Hacker News> never be able to learn basic common-sense physics
And has it at this stage, within in-depth take of said "learning", foundationally?
I have not been able to properly check the studies for a long time now, but I remain unaware of achieved solutions on the problem of reliably referencing a world model out of a language model - that "counting the 'r's in 'raspberry'" be not guessing, not memory, but actually counting.
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LeCun's argument wasn't about the definition of learning though. He stated that they would never get these common sense things correct because they weren't sufficiently part of the training data. A statement that we can hopefully all agree has been thoroughly refuted.
To determine this, it would first need to be able to spell "raspberry" as letters rather than as tokens.
Given you also don't want it to memorise [for all tokens, count([for all letters]), this would probably be more like "here's two images, count all things in the big image that look like the thing in the small image", which can then be r's in a photo of a raspberry jam jar in a supermarket, or dragons in a photo of a furry convention, or whatever.
That said, they are competent enough at coding that I keep seeing them write code to do even simple tasks.
On a related note: why did I see Claude editing a file by using cat to write a python script to do a grep search and replace?
counting 'r' in 'raspberry' to the LLM is similar to 4-dimension space to human. Their world's unit is token, not character, although they could use indirect method such as "run code" to find out. It will stay that way until they change the fundamental of the token that the LLM can perceive characters.
Can you tell me what is the exact frequency of light hitting your eye as you read this comment? Not by guessing, not from knowledge, but from actually counting? No? Then you are not generally intelligent :)
My perspective is that the addition of thinking loops to models allows sufficiently advanced ones to approximate world models.
Incredibly inefficiently because of the recursive loops ("Wait, the object is on the table. I should think about this more deeply..."), and likely instantly surpassed by large world models if/when those are shipped, but effectively enough vs non-thinking models.