Agreed. Anecdotal (but deep) research led me to postulate that our entire "inner world", for lack of a better word, is an emergent construction based on a fund

Agreed. Anecdotal (but deep) research led me to postulate that our entire "inner world", for lack of a better word, is an emergent construction based on a fundamentally spatiotemporal encoding of the external world. This assumes that feeding and motility, i.e., a geometric interpretation of the external world, is among the first 'functions' of living organisms in the evolutionary order. They subsequently became foundational for neuronal systems when these appeared about 500 million years ago. The hypothesis was informed by language notably, where most things are defined in spatial terms and concepts (temporal too, though more rarely), as if physical experiences of the world were the building blocks of thinking, really. A "high" council, a "sub" culture, a "cover", an "adjacent" concept, a "bigger" love, a "convoluted" or "twisted" idea, etc. Representations in one's inner world are all about shape, position, and movement of things in some abstract space of sorts. This is exactly how I'd use a 4D modeling engine to express a more 'Turing-complete' language, a more comprehensive experience (beyond movement: senses, intuitions, emotions, thoughts, beliefs…): use its base elements as a generator set to express more complex objects through composition in larger and/or higher-dim space. Could nature, Evolution, have done just that? Iteratively as it conferred survival advantages to these genes? What would that look like for each layer of development of neuronal—and later centralized "brain"—systems? Think as in geometric algebra, maybe; e.g., think how the metric of a Clifford algebra may simply express valence or modality, for those neuronal patterns to trigger the proper neurotransmitters. In biological brains, we've already observed neural graphs up to 11 dimensions (with a bimodal distribution peak around ~2.5D and ~3.8D iirc… Interestingly for sure, right within the spatiotemporal ballpark, seeing as we experience the spatial world in 2.5D more than 3, unlike fishes or birds). Jeff Hawkins indeed strongly shaped my curiosity, notably in "A Thousand Brains" and subsequent interviews. The paper here immediately struck me as very salient to that part of my philosophical and ML research—so kinda not too surprised there's history there. And I'm really going off on a tangent here, but I'm pretty sure the "tokenization problem" (as expressed by e.g. Karpathy) may eventually be better solved using a spatiotemporal characterization of the world. Possibly much closer to real-life language in biological brains, for the above reasons. Video pretraining of truly multimodal models may constitute a breakthrough in that regard, perhaps to synthesize or identify the "ideal" text divisions, a better generator set for (any) language.