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RedwoodAI is the digital root structure for RedwoodAdaptive, an energy and environmental consultancy with deep domain knowledge and expertise in complex adaptive systems (CAS) like energy markets, value chains and the changing biosphere

Redwood forests, power grids and AI technologies

AI's primary strength is its ability to process very large volumes of data with unprecedented speed and autonomy which makes it an indispensable value add for highly complex and dynamic systems with many interacting components that affect their overall performance. AI can help analyze these complex systems and identify patterns and relationships that might not be apparent through traditional methods. Redwood forests and power grids are networked, data rich environments that generate  copious amounts of information such as energy and water consumption patterns that can be used to train AI models and improve decision-making. Both systems operate in environments that are subject to uncertainty and variability, such as changes in weather patterns, extreme climate events  or sudden energy disruptions and outages. AI can help manage this uncertainty by providing real-time monitoring and analysis to anticipate potential losses and identify response patterns that mitigate damages through deep adaptation strategies.

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Complex adaptive systems (CAS) foundation models

CAS require coherence under change via conditional action and anticipation through lever points where small amounts of input produce large directed change. CAS building blocks are organized around core competencies that function in diverse, dynamic patterns that create  progressive adaptations through new niches and interactions. Agents that participate in cyclic flows cause the system to retain resources which can be exploited for new niches by new kinds of agents. The parts of CAS that exploit these possibilities will thrive, and parts that fail to do so will lose their resources to those that do. AI is now a competitive advantage imperative that can embed enhanced adaptive capacities through  cleantech strategies and technologies that expand  core competencies based on a portfolio of skills.  Strong competencies create response patterns to environmental disturbances based on resilience and organizational identity. As the system changes, it refers to itself and its established identity which makes it less vulnerable to resource supply fluctuations and market volatility.

RedwoodAI Neural Network Platform (RNNP)

The RedwoodAI Neural Network Platform (RNNP) is a Composite AI architecture designed to stimulate knowledge discovery and shared cognition for energy and earth science systems in the cleantech transition. The RNNP creates synergies among innovations in AI, high performance computing (HPC) and next generation energy technologies for accelerated time-to-impact. The platform features neural networks designed to improve the economic and environmental performance of existing infrastructure as well as technology characterizations for innovative new project design and development. The RNNP relies on a complex adaptive systems (CAS) approach for improving key system attributes like adaptive capacity, resilience, emergent productive output, and risk avoidance/management. The greatest potential gains are achieved through causal reciprocal exchange between self-determined agents in self-organized networks. CAS builds adaptation through persistent, coherent, and dynamic patterns that are the product of cyclical flows in progressive adaptations in new niche activation and construction.  The RNNP team includes AI, data and compute resources from US DOE National Labs and service providers in the private sector.

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