Good that you say “AI with ChatGPT” as this extremely blurs what the public understands. ChatGPT is an LLM (an autoregressive generative transformer model scaled to billions of parameters). LLMs are part of of AI. But they are not the entire field of AI. AI has so incredibly many more methods, models and algorithms than just LLMs. In fact, LLMs represent just a tiny fraction of the entire field. It’s infuriating how many people confuse those. It’s like saying a specific book is all of the literature that exists.
ChatGPT itself is also many text-generation models in a coat, since they will automatically switch between models depending on what options you choose, and whether you’ve passed your quota.
To be fair, LLM technology is really making other fields obsolete. Nobody is going to bother making yet another shitty CNN, GRU, LSTM or something when we have transformer architecture, and LLMs that do not work with text (like large vision models) are looking like the future
Nah, I wouldn’t give up on these so easily. They still have applications and advantages over transformers, e.g., efficiency, where the quality might suffice for the reduced time/space conplexity (Vanilla transformer still has O(n^2), and I have yet to find an efficient and qualitatively similar causal transformer.)
But regarding sequence modeling / reasoning about sequences ability, attention models are the hot shit and currently transformers excel on that.
Good that you say “AI with ChatGPT” as this extremely blurs what the public understands. ChatGPT is an LLM (an autoregressive generative transformer model scaled to billions of parameters). LLMs are part of of AI. But they are not the entire field of AI. AI has so incredibly many more methods, models and algorithms than just LLMs. In fact, LLMs represent just a tiny fraction of the entire field. It’s infuriating how many people confuse those. It’s like saying a specific book is all of the literature that exists.
ChatGPT itself is also many text-generation models in a coat, since they will automatically switch between models depending on what options you choose, and whether you’ve passed your quota.
To be fair, LLM technology is really making other fields obsolete. Nobody is going to bother making yet another shitty CNN, GRU, LSTM or something when we have transformer architecture, and LLMs that do not work with text (like large vision models) are looking like the future
Nah, I wouldn’t give up on these so easily. They still have applications and advantages over transformers, e.g., efficiency, where the quality might suffice for the reduced time/space conplexity (Vanilla transformer still has O(n^2), and I have yet to find an efficient and qualitatively similar causal transformer.)
But regarding sequence modeling / reasoning about sequences ability, attention models are the hot shit and currently transformers excel on that.