Artificial Intelligence
We research and develop artificial intelligence (AI) on the basis of a world leading technologies "World System on the basis of Bidirectional Encoder Representations from Transformers(BERT), Categorical Network(CN) and Point-Voxel Convolutional Neural Network(PVCNN)" and "Categorical AI."
Our AI models are based on category theory, which has higher performance and wider versatility than common AI models based on statistics.
We will develop and provide this on a platform with a quantum computer as the backend and a metaverse as the frontend.
Whitepapers
Case 1:
Categorical AI (Technical Report)
Case 2:
Case 3:
In the rapidly evolving trajectory of technological advancements, three pivotal domains are poised to synergistically redefine our digital infrastructure and societal fabric: the immersive realms of the metaverse, the computational prowess of quantum computing, and the cognizant capabilities of Artificial General Intelligence (AGI), with the latter emerging as an integrative force harmonizing and maximizing the potential of both the former entities.
In recent years, the inception of the Metaverse has epitomized the zenith of technological confluence, with Artificial General Intelligence (AGI) playing a cardinal role in its evolution and amplification. This report delineates seven quintessential metaverse elements and elucidates the instrumental contributions and transformative capabilities of AGI in enhancing each facet.
Features
01 Categorical AI: A Framework Based on Category Theory
Our Categorical AI system will represent a significant advancement in artificial intelligence architecture by applying the mathematical principles of category theory to create more robust and flexible computational models. This approach will move beyond traditional neural network paradigms to establish a framework where relationships between concepts are formally represented as morphisms within categorical structures.
The Categorical Network (CN) component will serve as the foundational element of our system, enabling our AI to model complex relationships and transformations between data objects with mathematical precision. By implementing categorical structures, our system will naturally represent composition of functions, allowing for more sophisticated reasoning chains than conventional architectures permit.
This categorical foundation will enhance the AI's ability to perform abstract reasoning by leveraging universal properties and natural transformations from category theory. Rather than simply processing data through fixed pathways, our system will dynamically construct appropriate transformations between different data representations, allowing for more flexible and context-sensitive processing.
We will implement functorial semantics that will enable our AI to maintain consistent meaning across different domains and data types. This approach will allow the system to transfer knowledge between contexts while preserving the essential relationships between concepts. The categorical structure will also support more transparent reasoning processes, as the paths of inference will be explicitly represented as compositions of morphisms.
The integration of BERT's contextual language understanding capabilities with our categorical framework will create a system that can process natural language with deeper structural understanding. Meanwhile, the incorporation of PVCNN will enable efficient processing of three-dimensional data within the same categorical framework, creating a unified system capable of reasoning across text, images, and spatial information.
Our Categorical AI will be particularly effective for applications requiring complex reasoning chains, knowledge integration across domains, and transparent decision processes. The system will excel at tasks involving abstract pattern recognition, logical inference, and maintaining consistency across diverse data types and contexts.
By grounding our AI in category theory, we will create a system that combines the pattern-matching strengths of modern machine learning with the formal rigor of mathematical structures. This approach will yield an AI that not only performs effectively on specific tasks but also demonstrates greater adaptability, interpretability, and reasoning capabilities across a wide range of applications.
02 Functional Metaverse Interface
Our metaverse platform will serve as a practical frontend interface to our AI system, providing users with an intuitive digital environment to interact with our technology. We will design this interface to balance immersive experiences with functional utility, creating spaces where information and communication can be accessed more naturally.
The platform will incorporate established spatial computing technologies to create responsive digital environments that adapt to user needs. We will focus on creating stable, consistent digital spaces that maintain reliability across sessions while providing flexibility for different use cases.
Users will be able to navigate between different functional areas within the platform, from collaborative workspaces to information centers. The system will support multi-user interactions with appropriate social features, making remote collaboration more effective. Features like information displays and personalized settings will ensure that each user can optimize their experience for productivity.
Our approach to metaverse implementation will represent a measured advancement in human-computer interaction, offering practical alternatives to traditional interfaces while maintaining accessibility and focusing on genuine utility for professional and educational applications.
03 Responsible Quantum Computing Development
Supporting our system will be a quantum computing backend that we will develop with a careful, measured approach to this emerging technology. Our quantum infrastructure will explore the potential of quantum computing principles while acknowledging the current limitations and challenges in the field.
This quantum backend will be designed to complement traditional computing systems, focusing on specific computational tasks where quantum approaches show practical promise. We will implement a hybrid approach that balances quantum processing with conventional computing methods to ensure reliability and practical utility.
Our development team will work with established quantum algorithms and error mitigation techniques, prioritizing stability and accuracy in our computational processes. This measured approach will allow us to explore quantum advantages while maintaining system dependability through proven classical processing for critical operations.
Our quantum computing initiative will represent a pragmatic exploration of quantum technology's potential, focusing on incremental improvements and practical applications rather than speculative capabilities. This responsible development approach will ensure that our quantum infrastructure provides reliable computational support for our AI and metaverse systems.
New York General Group
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