It's an activation pattern, but it's not "just an activation pattern". The face activation relies on previous layers, the detection of each component. We can co

It's an activation pattern, but it's not "just an activation pattern". The face activation relies on previous layers, the detection of each component. We can conjecture probably fair to say human brain object recognition (and other subconscious processes) use similar principles. All the components required to efficiently "understand", say a face, are there (not the shown visualization and architecture is for AlexNet, by now a very old and primitive model). I don't think we can ask for much more. What transformers do differently from CNNs is attention/recurrence. They have modifiable internal state, while feedforward models just have the feedforward state that can't temporally be reused (which is what we mean by 'algorithm'). This is a feature of logical thinking (and our own logical thinking), but I suspect most of what is meant by understanding the world is already contained in the internal structure captured by those models. Most of I understanding, as far as I can tell, comes from both this structural, intuitive inference (that CNNs and language models do), allied with our ability to think -- that is, talk to ourselves -- and thus build explanations and models on the fly, still reliant on the structural, intuitive understanding that comes from just very large networks generating abstract representations, classifications, etc.