"This 1990 paper demonstrated how neural networks could learn to represent and reason about part-whole hierarchical relationships, using family trees as the example ___domain.
By training on examples of family relations like parent-child and grandparent-grandchild, the neural network was able to capture the underlying logical patterns and reason about new family tree instances not seen during training.
This seminal work highlighted that neural networks can go beyond just memorizing training examples, and instead learn abstract representations that enable reasoning and generalization"
Comparing a 175 Billion parameter model with a ~2 Trillion parameter model. The difference is real. GPT 3.5 is obsolete, not state of the art.
> its answers will be patterned as excellent English variations of the common knowledge it was trained with
That's not how deep learning works.
https://www.cs.toronto.edu/~hinton/absps/AIJmapping.pdf
"This 1990 paper demonstrated how neural networks could learn to represent and reason about part-whole hierarchical relationships, using family trees as the example ___domain.
By training on examples of family relations like parent-child and grandparent-grandchild, the neural network was able to capture the underlying logical patterns and reason about new family tree instances not seen during training.
This seminal work highlighted that neural networks can go beyond just memorizing training examples, and instead learn abstract representations that enable reasoning and generalization"