Have you actually sat down and used these models quite a bit?
I pay for GPT-4 and it is worth its weight in gold, I can engage in a conversation about technical topics.
Sure, they do "hallucinate" sometimes (so do my coworkers FWIW), but you can engage in a conversation about why it is wrong and they are quick to correct themselves (...this part can be less like certain coworkers).
Here's an example of a random question (relevant to my job) that I asked gpt-4
> In linear/logistic models, it is common to take the multiplicative interaction between two features which might be one hot encoded. For instance, A is a vector that one hot encodes your college and B is a vector that one hot encodes your employer, you would take the product len(A)*len(B) as the size of the new interaction vector. How do you make such cross/interaction terms in tensorflow?
GPT-4 is less full of shit than most people I know and I don't think it is even close.
I have always found the knock that it confidently answers things it really doesn't know even if wrong quite hysterical. That exactly describes some of the smartest people I have met in my life.
I pay for GPT-4 and it is worth its weight in gold, I can engage in a conversation about technical topics.
Sure, they do "hallucinate" sometimes (so do my coworkers FWIW), but you can engage in a conversation about why it is wrong and they are quick to correct themselves (...this part can be less like certain coworkers).
Here's an example of a random question (relevant to my job) that I asked gpt-4
> In linear/logistic models, it is common to take the multiplicative interaction between two features which might be one hot encoded. For instance, A is a vector that one hot encodes your college and B is a vector that one hot encodes your employer, you would take the product len(A)*len(B) as the size of the new interaction vector. How do you make such cross/interaction terms in tensorflow?