This is "category" in the sense of Gilbert Ryle's category error.
A logical type or a specific conceptual classification dictated by the rules of language and logic.
This is exactly getting hung up on the precise semantic meaning of the words being used.
The lack of precision is going to have huge consequences with this large of bets on the idea that we have "intelligent" machines that "think" or have "cognition" when in reality we have probabilistic language models and all kinds of category errors in the language surrounding these models.
Probably a better example here is that category in this sense is lifted from Bertrand Russell’s Theory of Types.
It is the loose equivalent of asking why are you getting hung up on the type of a variable in a programming language? A float or a string? Who cares if it works?
>It is the loose equivalent of asking why are you getting hung up on the type of a variable in a programming language? A float or a string? Who cares if it works?
No, it's not. This is like me saying "string and float are two types of variables" and you going "what is a 'type' even??? Bertrand Russell said some bullshit and that means I'm right and you suck!"
When you are talking about machine cognition you are talking philosophy, and are building up on what other philosophers have done in this area. One of which is Bertrand Russell which funded type theory, and Gilbert Ryle which described category mistake as “a property is ascribed to a thing that could not possibly have that property”.
Cognition is a term from psychology, not statistics, if we are applying type theory, cognition would be a (none-pure) function term which take the atom term stimulus and maps them to another atom term behavior and involves states of types including knowledge, memory, attention, emotions, etc. In cognitive this is notated with S → R where S stands for stimulus, and R stands for response.
Attributing cognition to machine learning algorithms superficially takes this S → R function and replaces all state variables of cognition with weight matrices, at that point you are no longer talking about cognition. The S → R mapping of machine learning algorithms are most glaringly (apart from randomness) pure functions, during the S → R mapping of prompt to output nothing is stored in the long term memory of the algorithm, the attention is not shifted, the perception is not altered, no new knowledge is added, etc. Machine learning algorithms are simply just computing, and not learning.
A logical type or a specific conceptual classification dictated by the rules of language and logic.
This is exactly getting hung up on the precise semantic meaning of the words being used.
The lack of precision is going to have huge consequences with this large of bets on the idea that we have "intelligent" machines that "think" or have "cognition" when in reality we have probabilistic language models and all kinds of category errors in the language surrounding these models.
Probably a better example here is that category in this sense is lifted from Bertrand Russell’s Theory of Types.
It is the loose equivalent of asking why are you getting hung up on the type of a variable in a programming language? A float or a string? Who cares if it works?
The problem is in introducing non-obvious bugs.