you could train GPT-1 all you want and it would never become GPT-3
True. One algorithm is going to be different than another. The metrics you measure them on (e.g. precision recall) ultimately determines how you are going to use that algorithm. General learners can be modified to become domain specific, but usually narrowing your data down to specific domain and build an ensemble learner would give you better results. GPT parses on general string data, and then applies classification on them. When you become domain specific, you can usually find better algorithms, or different ways of processing the data that would give you better results.
The question is what do you want to learn from the data? For general learners, you can ask for all kinds of answers. General learners are designed to do different things than domain specific ML. My experience with GPT is that the interface is rather general, like you can ask for any kind of data.
True. One algorithm is going to be different than another. The metrics you measure them on (e.g. precision recall) ultimately determines how you are going to use that algorithm. General learners can be modified to become domain specific, but usually narrowing your data down to specific domain and build an ensemble learner would give you better results. GPT parses on general string data, and then applies classification on them. When you become domain specific, you can usually find better algorithms, or different ways of processing the data that would give you better results.
The question is what do you want to learn from the data? For general learners, you can ask for all kinds of answers. General learners are designed to do different things than domain specific ML. My experience with GPT is that the interface is rather general, like you can ask for any kind of data.