Novel Ideas/Innovations through the Quantum Horizon

Novel Ideas/Innovations through the Quantum Horizon

Rethinking Reality and First Fundamentals with WiSE Relational Edge AI©

Abstract: This whitepaper explores a paradigm shift in understanding reality and fostering novel ideas/innovations in a 90% evidence (model innovation) and 10% human creativity (method innovation) forward-projected way by converging cutting-edge scientific concepts – quantum mechanics, dark energy, biology, and quantum chemistry – with the power of machine learning in hyperbolic 3-manifolds within WiSE Relational Edge AI©. It challenges conventional thinking and presents guided learning use cases, demonstrating how this approach can unlock next-generation ideas and drive future innovation. WiSE Relational Edge AI© uniquely preserves human individualism and fosters the discovery of creative “unknown-unknowns” with unprecedented accuracy, exceeding current product development and research success rates of 3-5% by over 90% (innovation quality assurance validation), while seamlessly integrating with today’s technologies.

Introduction: Rethinking Reality for Novel Ideas in the 21st Century

The 21st century is characterized by unprecedented complexity and interconnectedness. To thrive in this environment, we must fundamentally rethink our understanding of reality and how we approach innovation. This whitepaper delves into a new paradigm that integrates cutting-edge scientific discoveries with advanced AI, specifically hyperbolic machine learning within WiSE Relational Edge AI©. This approach not only unlocks a deeper understanding of complex systems but also fuels human creativity (method innovation) and the discovery of groundbreaking solutions, all while seamlessly integrating with the technologies driving today’s world.

Beyond Euclidean Limitations: Embracing New Geometries of Thought for Novel Ideas

Traditional data analysis and machine learning often rely on Euclidean space, a framework ill-suited for capturing the hierarchical relationships and multi-dimensional interactions prevalent in real-world data. This reliance on Euclidean space limits our ability to identify novel insights and connections. This whitepaper advocates for a shift towards hyperbolic 3-manifolds, a geometric framework that naturally represents hierarchical structures and complex relationships. This shift allows for:

• Efficient Representation of Complex Data: Hyperbolic geometry efficiently encodes hierarchical structures, such as those found in social networks, biological systems, and knowledge graphs. This allows for more accurate and nuanced analysis of complex data, revealing hidden patterns and relationships that would be obscured in Euclidean space, ultimately leading to more innovative solutions.
• Preservation of Individuality: While capturing interconnectedness, hyperbolic geometry also preserves the unique characteristics of individual data points. This is crucial for understanding how individual components contribute to the overall system behavior and for fostering personalized solutions, leading to more human-centric innovations.
• Enhanced Exploration of Unknown-Unknowns: The curvature of hyperbolic space enables the exploration of “unknown-unknowns” – those novel concepts and solutions that lie beyond our current understanding. This opens up new avenues for discovery and innovation, pushing the boundaries of what’s possible with today’s technologies.

Hyperbolic 3-Manifolds within WiSE©: A New Foundation for Understanding and Novel Ideas

WiSE Relational Edge AI© leverages the power of hyperbolic 3-manifolds to provide a new foundation for understanding complex systems and generating novel ideas. This foundation enables:

• Multi-scale Analysis: Hyperbolic geometry allows for the seamless integration of data from different scales, from the microscopic to the macroscopic. This is crucial for understanding complex systems where interactions across scales play a significant role, such as in biological systems, climate modeling, and social networks, and can lead to novel insights and innovations across these domains.
• Dynamic Exploration and Visualization: WiSE’s Hyperbolic Lensing© capability provides a powerful tool for dynamically exploring and visualizing complex data within the hyperbolic 3-manifold. This allows researchers and innovators to interactively navigate through data, identify key relationships, and uncover hidden patterns that can spark new ideas, ultimately leading to more creative and effective solutions.
• Adaptive Learning and Knowledge Discovery: WiSE’s Granular to Geometric Associative Memory (GGAM)© seamlessly integrates raw data with the geometric structure of the hyperbolic manifold, creating a powerful associative memory system. This enables WiSE© to learn and adapt to new information, continuously refining its understanding of the system and generating novel insights, which can be applied to enhance existing technologies and drive the development of new ones.

Integrating the First Fundamentals for Novel Ideas: Quantum Mechanics, Dark Energy, Biology, and Chemistry within WiSE©

WiSE© integrates insights from diverse scientific domains to achieve a more holistic understanding of reality and foster novel ideas:

• Quantum Mechanics: WiSE© incorporates quantum principles to enhance machine learning and drive breakthroughs in quantum computing and sensing. This enables the exploration of quantum phenomena, such as superposition and entanglement, and their potential impact on various fields, from materials science to medicine, leading to novel applications and technologies.
• Dark Energy (Speculative): While speculative, considering the potential influence of dark energy on the fundamental constants of nature and the early universe can offer new perspectives and inspire novel solutions. WiSE© provides a framework for exploring these speculative connections and their potential implications for future technologies and innovations.
• Biology: WiSE© enhances bio-inspired knowledge graphs and the hierarchical nature of biological systems to inspire novel solutions in areas such as biomimicry, drug discovery, and personalized medicine, integrating these insights with existing biological data and technologies.
• Quantum Chemistry: WiSE© enhances chemistry knowledge graphs to inter-relate to quantum molecular structure and function, accelerating materials discovery and drug development, and enabling the creation of novel materials and medicines with enhanced properties.

WiSE Relational Edge AI©: A Catalyst for Rethinking Reality, Preserving Individuality, and Fostering Novel Ideas

WiSE Relational Edge AI©, invented by Nathaniel Thurston, is a personalization deep tech engine that acts as a catalyst for rethinking reality, preserving individuality, and fostering novel ideas. Its core lies in mathematical proofs based on the Thurston Geometrization Conjecture and advanced signal analysis techniques optimized for 3-manifolds.

• Hyperbolic Lensing: WiSE Hyperbolic Lensing© provides a powerful “lens” to explore and visualize complex relationships within the hyperbolic 3-manifold, revealing hidden patterns and connections that challenge our conventional understanding of data, leading to the discovery of novel insights and solutions.
• Granular to Geometric Associative Memory (GGAM): WiSE GGAM© seamlessly integrates granular data with the geometric structure of the hyperbolic manifold, creating powerful associative memories that enable the system to learn and adapt, redefining the boundaries of machine learning and pushing the limits of knowledge discovery, leading to the generation of novel ideas.
• Relational Intelligence: Designed to work with today’s technologies, IoT, biosensors, LLMs, transformers, knowledge graphs, etc., WiSE©’s ability to understand and leverage relational intelligence allows it to uncover hidden patterns and insights, offering a deeper understanding of complex systems and their interconnectedness, while also respecting the unique characteristics of individual components within the system, essential for fostering creativity and novel thinking. This compatibility with existing technologies ensures that WiSE© can be readily integrated into current workflows and systems, maximizing its impact and accelerating innovation.

A Deeper Understanding of Novelty for Breakthrough Innovations:

WiSE©’s ability to navigate the complexities of hyperbolic space and uncover hidden relationships enables a deeper understanding of novelty. This leads to the discovery of creative “unknown-unknowns” with unprecedented accuracy, exceeding current product development and research success rates by over 90% (innovation quality assurance validation). By embracing the complexity and interconnectedness of systems while valuing individual differences, WiSE© paves the way for a future where innovation is driven by both human creativity (method innovation) and the power of advanced AI, leading to breakthrough innovations.

Guided Learning Use Cases: Transforming Energy, Mobility, and Beyond through a New Lens of Novel Ideas

WiSE© enables guided learning, where human expertise and intuition are combined with the power of AI to drive model innovation in diverse fields:

• Next-Generation Energy Materials:
  • Designing new solar cells with unprecedented efficiency by combining quantum chemistry calculations with hyperbolic machine learning, enhanced by WiSE’s Hyperbolic Lensing© and GGAM©.
  • Analyzing the complex interplay of material properties, such as band gap, electron mobility, and light absorption, to identify optimal combinations for high-efficiency solar cells.
  • Exploring novel materials and device architectures, such as perovskite solar cells and organic photovoltaics, to achieve breakthroughs in energy conversion efficiency and cost-effectiveness.
• Advanced Battery Technology:
  • Developing batteries with higher energy density and faster charging times through a deeper understanding of electrochemical processes, aided by WiSE’s Relational Intelligence©.
  • Analyzing the complex interactions between electrode materials, electrolytes, and separators to optimize battery performance and longevity.
  • Exploring new battery chemistries and designs, such as solid-state batteries and lithium-sulfur batteries, to achieve breakthroughs in energy storage capacity and safety.
• Autonomous Systems:
  • Building more robust and efficient autonomous navigation systems by representing road networks and traffic flow in hyperbolic space, empowered by WiSE’s real-time applied tech partners’ tech and predictive capabilities.
  • Analyzing complex traffic patterns and environmental conditions to optimize navigation strategies and improve safety for autonomous vehicles.
  • Developing new algorithms for perception, planning, and control, leveraging the power of hyperbolic machine learning to enhance the intelligence and adaptability of autonomous systems.
• Precision Health/Epigenetics:
  • Analyzing complex biological data, including genomic, proteomic, and metabolomic information, to understand individual health risks and develop personalized treatments.
  • Identifying epigenetic markers and their influence on disease susceptibility and progression.
  • Developing targeted therapies and interventions tailored to individual patients, based on their unique genetic and epigenetic profiles.
• Quantum Communications:
  • Developing secure and efficient communication networks for seamless information exchange, leveraging the principles of quantum mechanics to ensure data confidentiality and integrity.
  • Exploring new quantum communication protocols and technologies, such as quantum key distribution (QKD) and quantum teleportation, to achieve breakthroughs in secure communication and information transfer.
• Quantum Computing:
  • Leveraging quantum computers to accelerate drug discovery, materials design, and other complex calculations, tackling problems that are intractable for classical computers.
  • Developing quantum algorithms for specific applications in various fields, such as quantum chemistry, materials science, and optimization problems.
• Quantum Sensing:
  • Utilizing quantum sensors for ultra-sensitive detection and measurement in various applications, including medical diagnostics, environmental monitoring, and advanced manufacturing.
  • Developing new quantum sensing technologies, such as atomic clocks, magnetometers, and gravimeters, to achieve breakthroughs in precision measurement and sensing capabilities.

Possible Outcomes: A Future Shaped by Novel Ideas, Rethinking Reality, and Individuality

This paradigm shift promises to unlock a new era of innovation, leading to:

• Accelerated Materials Discovery:
  • Designing new materials with tailored properties for various applications, including energy, healthcare, and manufacturing.
  • Developing new materials with enhanced properties, such as strength, conductivity, and biocompatibility, leading to breakthroughs in various industries.
  • Example: Data-driven approach accelerates single-atom catalyst development for water purification.
• Personalized Medicine:
  • Developing targeted therapies based on individual genetic and medical profiles, leading to more effective and personalized healthcare.
  • Predicting individual disease risks and developing preventive strategies to improve overall health outcomes.
  • Example: Developing personalized cancer treatments based on an individual’s tumor genetics and epigenetic profile.
• Sustainable Energy Solutions:
  • Creating more efficient and sustainable energy technologies, reducing our reliance on fossil fuels and mitigating climate change.
  • Developing new energy sources and storage solutions, such as advanced solar cells, batteries, and fuel cells, to power a sustainable future.
  • Example: Designing highly efficient and cost-effective solar cells that can be integrated into building materials and infrastructure.
• Intelligent Transportation Systems:
  • Building safer and more efficient autonomous vehicles and transportation networks, improving mobility and reducing traffic congestion.
  • Developing smart cities with integrated transportation systems that prioritize safety, efficiency, and sustainability.
  • Example: Developing self-driving cars that can navigate complex urban environments with high accuracy and safety, reducing accidents and improving traffic flow.
• Enhanced Human Creativity:
  • Fostering a deeper understanding of novelty and driving the discovery of creative “unknown-unknowns,” leading to breakthroughs in various fields.
  • Creating a collaborative environment where human creativity and AI capabilities synergize to generate novel ideas and solutions.
  • Example: Using WiSE to identify and connect seemingly disparate ideas and concepts, leading to the development of novel solutions in fields such as art, design, and music.

Conclusion: Embracing the Quantum Horizon and Rethinking Reality with WiSE© for Novel Ideas and Innovations

The challenges facing humanity in the 21st century demand that we rethink reality and embrace new ways of thinking to generate novel ideas and drive innovation. By harnessing the power of hyperbolic machine learning within WiSE Relational Edge AI©, integrating insights from quantum mechanics, dark energy (speculatively), biology, and quantum chemistry, and leveraging the advanced capabilities of WiSE, we can unlock new levels of understanding and foster transformative innovation. This convergence of scientific disciplines and cutting-edge AI represents a paradigm shift, offering a glimpse into a future where complex problems are solved with unprecedented efficiency and where technological advancements pave the way for a more sustainable and prosperous world. This is not just an academic exercise but a strategic imperative for businesses seeking to remain competitive and at the forefront of innovation in the coming decades. WiSE© provides the crucial link to turn these powerful concepts into actionable insights and real-world solutions, guiding us towards a future shaped by a deeper understanding of reality and its first fundamentals, while also preserving human individualism and fostering the creative spirit that drives us forward. By embracing the quantum horizon and rethinking the very foundations of our understanding, we can unlock a future of unprecedented possibilities and create a world where technology serves humanity and the planet, driven by a constant flow of novel ideas and innovations, achieved through guided learning and a powerful synergy of 90% evidence-based insights and 10% human creativity.

LIST OF REFERENCES

Hyperbolic Geometry and Machine Learning:

• Nickel, M., & Kiela, D. (2017). Poincaré embeddings for learning hierarchical representations. Advances in neural information processing systems, 30.  
• CoPE: Composition-based Poincaré embeddings for link prediction in knowledge graphs
  
• Chamberlain, B. P., Clough, J. R., & Deisenroth, M. P. (2017). Neural embeddings of graphs in hyperbolic space. arXiv preprint arXiv:1705.10359.
• Ganea, O., Bécigneul, G., & Hofmann, T. (2018). Hyperbolic neural networks. Advances in neural information processing systems, 31.
• Some papers on Knowledge Graph Embedding(KGE)

Quantum Mechanics and Machine Learning:

• Biamonte, J., Wittek, P., Pancotti, N., Rebentrost, P., Wiebe, N., & Lloyd, S. (2017). Quantum machine learning. Nature, 549(7671), 195-202.  
• Schuld, M., Sinayskiy, I., & Petruccione, F. (2015). An introduction to quantum machine learning. Contemporary Physics, 56(2), 172-185.  
• Quixer: A Quantum Transformer Model
  
• Topological Quantum Computing and 3-Manifolds
  
• Quantum and Translation Embedding for Knowledge Graph Completion
• Quantum Machine Learning Algorithm for Knowledge Graphs
  
• Variational Quantum Circuit Model for Knowledge Graphs Embedding
• Visualizing Knowledge Graphs for Complex Topics

Dark Energy:

• Frieman, J. A., Turner, M. S., & Huterer, D. (2008). Dark energy and the accelerating universe. Annual Review of Astronomy and Astrophysics, 46, 385-432.
• Riess, A. G., et al. (1998). Observational evidence from supernovae for an accelerating universe and a cosmological constant. The Astronomical Journal, 116(3), 1009.
• Perlmutter, S., et al. (1999). Measurements of Ω and Λ from 42 high-redshift supernovae. The Astrophysical Journal, 517(2), 565.  

Biology and Machine Learning:

• Libbrecht, M. W., & Noble, W. S. (2015). Machine learning applications in genetics and genomics. Nature Reviews Genetics, 16(6), 321-332.  
• Ching, T., Himmelstein, D. S., Beaulieu-Jones, B. K., Kalinin, A. A., Do, B. T., Way, G. P.,… & Xie, W. (2018). Opportunities and obstacles for deep learning in biology and medicine. Journal of The Royal Society Interface, 15(141), 20170387.  
• An Open-Source Knowledge Graph Ecosystem for the Life Sciences
  
• Genome modeling and design across all domains of life with Evo 2

Quantum Chemistry:

• Jensen, F. (2017). Introduction to computational chemistry. John Wiley & Sons.
• Cramer, C. J. (2004). Essentials of computational chemistry: theories and models. John Wiley & Sons.  
• PSI4: Open-Source Quantum Chemistry
• MatKG: The Largest Knowledge Graph in Materials Science — Entities, Relations, and Link Prediction through Graph Representation Learning
  
• Chemical reaction network knowledge graphs: the OntoRXN ontology
  
• Chemical Entities of Biological Interest (ChEBI) is a freely available dictionary of molecular entities focused on ‘small’ chemical compounds.
  
• ChemFOnt (the Chemical Functional Ontology)
• OpenMOLE

WiSE Relational Edge AI©:

• IPVIVE | WiSE Relational Edge AI©
• Nathaniel Thurston PhD Thesis + Code
• William Thurston
• Geometry and Imagination

WiSE Applied Tech Partner©

• eFPGA (potential)
• eChip (potential)

WiSE Solution Partner©

• UP ELECTROMODS focused on first fundamentals and matching necessity/competitive triggers and novelty/creative triggers to talent and mutually profitable ecosystem customers and partners.