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Attachment is All You Need emphasizes the need for a ‘Secure Base’ consistent, reliable, trustworthy sources, and creates a mechanism for efficiency through ‘favouritism’. This is a version of the Pareto principle, which is analogous to the efficiency delivered by the fractal geometry of the human brain (Ieva, 2024). 

The currently implemented ECAN model utilises an economised system using Short Term Interest (STI) and Long Term Interest (LTI) currencies, which are allocated to Atoms in the DAS to signify their importance. Building on STI and LTI with a dimension of attachment has the potential for massive social benefit by gently steering awareness to relevant information during a time of information warfare. As a metaphor, modern audiences may know Kim Kardashian because she attracts attention, but they should know Marshall McLuhan because his observations are more relevant now than ever. This is why a model that incorporates concepts of ‘mentorship’ and mimicry is legitimate to assist with helping people lead meaningful lives fostered by mutual understanding.

When data hubs are overwhelmed with demands, the AI may pivot to a less desirable but still tolerable alternative. Alternatively, using the AI to ensure the 20% of favoured data are optimised while leaving the others with more allowable noise, ensures protection of the most valuable data resources while providing possible opportunities for new ‘stars’ from the slush pile.

Utilising Juergen Muller’s Real Time Memory Discovery method of hierarchical packaging, and treating the AI like a data supply chain, provides the mechanism for energy efficiency in an otherwise intensive design. Muller’s design is applicable in scenarios where products are uniquely identified and read events are stored in read event repositories. His proposed discovery service explicitly includes hierarchical packaging relationships, differentiating it from existing approaches. This innovation allows for a new communication concept between the requestor, discovery service, and read event repositories, minimizing the number of messages exchanged.

By using Muller's track and trace mechanism to selectively store key-value pairs related to interactions with the secure base, existing frameworks like SnapKV can help the AI prioritize those interactions, reinforcing the sense of security and trust as a reward in and of itself. This resolves some issues with Markov Decision Processes where rewards are sparse because delayed gratification is possible in this design. In our scenario, using a well-trodden path is rewarding the way a routine is rewarding for the human mind in terms of familiarity, trust, safety, nostalgia, efficiency and predictability. Relentless adaptation and reactive designs are exhausting (energy intensive)–perhaps even for a digital mind. 

To avoid bias and ensure tolerant, productive attitudes, we can use insights from The Optimistic Child to regulate negative ‘beliefs’, their attribution (personal vs external), their pervasiveness (across situations specific vs global), and their persistence (across time temporary vs permanent) should be reviewed. Monte Carlo simulations can introduce testing mechanisms for evaluating ‘strangers’ and early hand-holding in attributing accurate blame to events can assist in early development.

Open Source Licensing

Links and references

https://link.springer.com/book/10.1007/978-3-031-47606-8

https://link.springer.com/book/10.1007/978-3-642-37128-8