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.
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.
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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.
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.
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.