Earlier quoted context omitted.
I used to agree with you but overtime I’ve changed my mind. For reference I created predictive linguistics at Google in the first products and this is a many order scale up of that, with new complexities of course. The best analogy I can give you is that it is a really advanced synthesis machine, which looks like human thought but is more of a hyper advance “replay” of human thought in various contexts. Where you beg…
I feel like that's more saying they can't train on the fly, and also that serializing spatial data and world models is something we haven't really done fully. For me all neural networks synthetic or otherwise are replay machines or stream prediction machines. Nerve signals in, and nerve signals out. If I create output signals to the muscle nerves like this when my eyes see signals like that, good things happen, i get…
That messier part is the complexity that is the difference.
What we have is a model. It’s still very distant from the original in meaningful ways.