Neural networks were known as being "always second best" for many decades not that long ago... Its very possible (highly likely even) that elements of gofai en

Neural networks were known as being "always second best" for many decades not that long ago... Its very possible (highly likely even) that elements of gofai end up being implemented into some of the upcoming RL/GNN combo based architectures. I highly doubt that the transformer will be the end-all-be-all for generating representations. At the very least, many of those 'in-the-know' around these GNNs realize that sheaf-NNs are much more expressive and can yield far better general results if improved properly for long range dependences - perhaps with a performer or longformer -like addition. Ultimately, some of the best researchers in the field (Velockovic is one of the best, and heavily focused on dynamic programming for example) are not just focused on the transformer or any of the hype around it right now. In order to improve, you have to look elsewhere. Understanding old methods is typically a great resource to draw that inspiration / algorithm from.