> Technically, they aren't trained that way, their opposite recognition AI is trained that way. No, they are trained that way. These aren't GANs or anything re

> Technically, they aren't trained that way, their opposite recognition AI is trained that way. No, they are trained that way. These aren't GANs or anything resembling that. There is a reinforcement model that is build from expert human guidance which is then used to fine-tune the LM. From the HuggingFace explainer page I linked above: [there are] three core steps: 1. Pretraining a language model (LM), 2. gathering data and training a reward model, and 3. fine-tuning the LM with reinforcement learning. Read https://arxiv.org/abs/2203.02155 https://arxiv.org/abs/2203.02155 if you want all the details (although the HF explainer is easier to follow). From the abstract of that paper: > Starting with a set of labeler-written prompts and prompts submitted through the OpenAI API, we collect a dataset of labeler demonstrations of the desired model behavior, which we use to fine-tune GPT-3 using supervised learning. We then collect a dataset of rankings of model outputs, which we use to further fine-tune this supervised model using reinforcement learning from human feedback. There is no "opposite recognition AI".