My whole point is that the way in which AI progress can intersect itself can lead to compounding effects in ways technology has not. One way to improve upon GP

My whole point is that the way in which AI progress can intersect itself can lead to compounding effects in ways technology has not. One way to improve upon GPT-3 is almost certainly going to be adding discriminators into the mix: "GeDi: A Powerful New Method for Controlling Language Models" https://blog.salesforceairesearch.com/gedi/ https://blog.salesforceairesearch.com/gedi/ And it may be that rather than it being a CNN discriminator such as in that case, it will be a second transformer: "[2102.07074] TransGAN: Two Pure Transformers Can Make One Strong GAN, and That Can Scale Up" https://arxiv.org/abs/2102.07074 https://arxiv.org/abs/2102.07074 My point is we may not need to reinvent some new model separate from transformers/GANs and the work so far as long as novel ways of intersecting trained models produces results that can in turn feed intersect with other models. NVIDIA in particular has had some interesting focus on using AI to generate training data for AI. "NVIDIA Omniverse Replicator Generates Synthetic Training Data for Robots | NVIDIA Technical Blog" https://developer.nvidia.com/blog/generating-synthetic-datasets-isaac-sim-data-replicator/ https://developer.nvidia.com/blog/generating-synthetic-datas... I think it's very unlikely we need to wait 25 years for a new paradigm. In fact, I'm willing to wager a bet that whatever new paradigm will arrive will itself be the output of AI simulating different models.