Synergistic Data and Model Integration

Synergistic Data and Model Integration

One of the most powerful aspects of Rejuve's AI approach is its ability to effectively integrate patterns found in multiple types of relevant data.

The amount of data and computational power required to solve complex health problems like aging has only recently become available to researchers. Much of AI's recent impressive gains remain mostly in silos, such as healthcare claims and biomarker, genomic, and imaging studies. Despite this progress, these successful siloed applications only have a weak relationship to the broader issue of aging, and when analyzing a complex system like the human body, siloed artificial intelligence is insufficient.

Rejuve.AI aims to improve this situation by bringing together these siloed views of the human body in order to better understand the underlying complex system. Rejuve's analysis tools combine multiple data channels, making it easier to interpret sparsely overlapping observational data. When a dataset contains patients with measurable variables A and B, as well as patients with measurable variables B and C, Rejuve's AI can interpret variables 'A' and 'C' by using the overlapping variable 'B'. Rejuve's AI combines specific data and insights to develop a more comprehensive understanding of the human body. Rejuve.AI provides a platform that enables highly synergistic data integration so that various silos can be combined. Synergistic Model Integration with The Generative Cooperative Network (GCN)

Rejuve incorporates models from contributing scientists into a holistic simulation based on the principles of complex adaptive systems. In a broad sense, generative modeling is a form of mimicry, but it is important to distinguish between mimicry that replicates underlying processes and mimicry that simply reflects surface level patterns. If AI is to solve medical problems as complex as human aging, we believe it must mimic not only the outer appearances of living systems, but also their inner processes, mechanistic explanations, and emergent structures.

The Generative Adversarial Network (GAN) is an example of surface level AI mimicry, as it uses coevolutionary principles to direct and scaffold learning to generate fake pictures that are indistinguishable from real.³ In the GAN architecture, one neural net attempts to generate fake data that is indistinguishable from real data, while another model attempts to distinguish between the two. These two networks are trained in tandem, so as one improves, so does the other, with each serving as "scaffolding" for the other's learning.

The GCN extends scaffolding to multiple agents. To create a causal simulation, GCN is a framework of multiple intelligent agents that scaffold each other's learning through coevolution, but in a cooperative manner capable of reproducing inner dynamics as well as directly mimicking surface appearances. GCN incorporates causation and emergence principles into a framework of continuous improvement that is based not only on the ingestion of new observations and experience, but also on an endogenous process of symbol emergence and interpretation that promotes innovation.

By emphasizing interpretation, GCN's sign formation process exhibits the two principles of decentralization and autonomy, which are crucial for both effective AI and the construction of an ethical and economically just data and processing ecosystem (the latter being a core component of web3 as a whole) (Owocki et al 2022). In GCN, we capture the non-hierarchical, spontaneous process of co-creation that constitutes the emergence of symbols. The meaning of symbols is a consensus based on autonomous perception, in which concepts are learned based on individual utility as opposed to imitation. ³ Goodfellow, I. et al. (2014) Generative Adversarial Networks. Available at: arXiv:1406.2661 [stat.ML]