[teekert]

I don’t get this: “What that means is not only that they learn what it means to be a dog or a cat, …“

We don’t have any dataset of dog or cat experience right? OP probably means that he models learns wat a dog or cat is, right?

I find the whole piece somewhat vague btw. No real insights if you ask me. Sure if all you put in is a dataset, that should be all you get out. What’s surprising (worth HN) here?

[empath-nirvana]

> “What that means is not only that they learn what it means to be a dog or a cat, …“

I think he's referring to the famous paper: "What is it like to be a bat"

https://en.wikipedia.org/wiki/What_Is_It_Like_to_Be_a_Bat%3F

[omnicognate]

> OP probably means that he models learns wat a dog or cat is, right?

Yes, "What it means to be" does appear to be meant that way and it didn't occur to me to interpret it the other way.

> Sure if all you put in is a dataset, that should be all you get out. What's surprising (worth HN) here?

You put in a particular choice of nn architecture as well as the dataset. The insight (to the extent that it is insightful, and true) is that the architecture doesn't affect the results you get much compared to the dataset.

[teekert]

Ok the first thing must be just my non-native speaker mind then.

The second: still fills like Duh. It’s what these models are meant to do right? Form an internal representation of the relations hidden in the data. It’s what complex systems are, they hold models of reality and use those to predict. That is in fact what Claude Shannon meant with his definition of information. Idk maybe I’m getting it wrong.