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.
Complex adaptive systems (CAS) foundational 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.
Curated content for system bound AI platforms
Curated concept and content creation is a critical first step for developing high performance generative AI platforms that are trained on the most relevant foundational large language models (LLMs) and deep learning algorithms that process and understand natural language and computer vision. Electricity market complex adaptive systems (EMCAS) simulations combine engineering techniques with quantitative market analysis, decentralized decision making, alternative company strategies and risk profiles, agent learning and adaptation, different rules for different markets, and transient market conditions and emerging behaviors. Generative AI platforms can combine EMCAS data with techno-economic and earth science indicators (TESIs) designed to retain existing resources while accelerating new niche technologies in the cleantech transition. TESIs contain economic and technology data lakes that support the due diligence process through multi-physical and earth science modelling that provides system bound time to value (TTV) sequences for simulated natural resource retention and extraction profiles as well as a broad range of environmental impacts for a given project or asset base.