Like a lot of the symbolic/embodied people, the issue is they don’t have a deep understanding of how the big models work or are trained, so they come to weird conclusions. Like things that aren’t wrong but make you go ‘ok.. but what you trying to say’.
E.g ‘Instead of pre-supposing structure in individual modalities, we should design a setting in which modality-specific processing emerges naturally.’ Seems to lack the understanding that a vision transformer is completely identical for a standard transformer except for the tokenization which is just embedding a grid of patches and adding positional embeddings. Transformers are so general, what he’s asking us to do is exactly what everyone is already doing. Everything is early fusion now too.
“The overall promise of scale maximalism is that a Frankenstein AGI can be sewed together using general models of narrow domains.” No one is suggesting this.. everyone wants to do it end to end, and also thinks that’s the most likely thing to work. Some suggestions like lecuns jepa’s do suggest to induce some structure in the arch, but still the driving force there is to allow gradients to flow everywhere.
For a lot of the other conclusions, the statements are literally almost equivalent to ‘to build agi, we need to first understand how to build agi’. Zero actionable information content.