by SubiculumCode | 2025-06-04 17:35:52+00:00
Embodiment can mean a physical body, but I'd argue that embodiment, as a construct/concept, is not so much about physicality, but as being situated in an environment that you can perceive then act upon during learning. Car simulations for driver-less AI training is embodied, where it learns by perceiving and acting on the environment. However, I'd argue that allowing an AI to interact in an entirely digital office environment is also "embodied" as long as it can receive information from the digital workplace and act on the information in the digital workplace (do office work). So to me, it is less about embodiment as a principal, but on the richness of the environment of that embodiment.

We have long known that experimental animals raised in impoverished, unchanging, bare, environments (say in a cage) leads to animals with inferior problem solving capacity than those with enriched environments (things to climb on), even outside of social manipulations (alone versus multi-animal stalls). This is also true in humans, although I won't review the literature on the subject. I've also heard people saying similar things about the difference between house plants and outdoor plants, lol [1].

So, for me, the argument for (physical) embodiment being key to cognition and AI can I think, be misconstrued. As a developmental psychologist whose pHD work focused on memory development, I tend to think of all this as encompassing:

1. Environmental richness: complexity of information and interactions. 2. Capacity to perceive and to effect change[2] and to observe and integrate consequences. 3. Scaffolding [3]..i.e. temporary support structure provided by a more knowledgeable person (like a teacher or parent) who adjusts their assistance based on the learner's current abilities, gradually reducing help as competence grows.(think curriculum learning, shaped rewards in ML maybe).

So the question is not about physicality for me, but whether these training environment(s) meet and learning capacities meet these criteria.

Relatedly, the model must have these capacities:

1. Semantic Memory. i.e. knowledge. Learning leads to changes in weights to that knowledge can be recalled, but doe snot necessarily encode where that knowledge was learned (implicit). 2. Autobiographical Episodic Memory. i.e. One-shot learning that encodes a conception of self (a spacial "I" token?), along with events (snapshots of the multimodal contents of experience (thoughts, perceptions, invoked schemas, evoked semantic information), into a set of flexibly linked representations). 3. Central Executive: A circuit that guides learning and recall via strategic, goal-directed means, and to make attributions about what is recalled (yeah that memory is vivid, its probably true, or ooh, that memory is really vague, it could be wrong, or reality monitoring: "Am I remembering taking out the trash, or remembering thinking about taking out the trash."

Semantic memory allows someone to say, "All birds have feathers", while the latter allows them to recollect, "I remember the first time I plucked a chicken in Kentucky, just outside that musty coal mine of grand-dad's." The Central-Executive can guide future learning or current understanding.

In terms of AI development:

1. Semantic Memory is solves: LLMs have extraordinary semantic memory, in my opinion. 2. Autobiographical Episodic Memory: There are some models that do one-shot learning, but I've never seen them paired with [1] in a dual system approach. ...but I am not an expert in AI, I could easily be wrong. 3. Central Executive kind of component (I predict) would be less important in the early half of model training, but more important in later training. I suppose we already kind of see this with RL tuning on reasoning on a base LLM (semantic model).

[1] https://www.theparisreview.org/blog/2019/09/26/the-intellige... [2] https://xkcd.com/326/ [3] https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=scaf...