World System on the Basis of Bidirectional Encoder Representations from Transformers(BERT), Categorical Network(CN) and Point-Voxel Convolutional Neural Network (Point-Voxel CNN)
New York General Group
Summary
We have a technology called "World System on the Basis of Bidirectional Encoder Representations from Transformers(BERT), Categorical Network(CN) and Point-Voxel Convolutional Neural Network (Point-Voxel CNN)" technology. Current neural network-based machine learning models lack "brains". They are capable of statistical processing of data, but cannot "truly think as an intelligence". Therefore, we define Categorical Networks (CNs) based on Categorical Theory as the "brains" in artificial intelligence systems; Transformer-based large-scale language models (we use BERT here, but GPT, Claude, or whatever) and PVCNN are defined as other neural systems that exchange information between the brain and the environment. CN allows artificial intelligence to think as true intelligence based on category theory. BERT (any Transformer-based large-scale language model, such as GPT or Claude) can process information in a multimodal way; PVCNN can process information in our real world, i.e., the three-dimensional world.
Introducing the World System: A Revolutionary Paradigm in Artificial Intelligence
We are thrilled to unveil our groundbreaking technology, the "World System," which represents a monumental leap forward in the field of artificial intelligence. This innovative approach integrates three cutting-edge components: Bidirectional Encoder Representations from Transformers (BERT), Categorical Networks (CN), and Point-Voxel Convolutional Neural Networks (Point-Voxel CNN).
This synergistic combination addresses the fundamental limitations of current neural network-based machine learning models, transcending mere statistical processing to achieve true cognitive capabilities. The Cognitive Core: Categorical Networks (CN) At the heart of our World System lies the Categorical Network (CN), a novel architecture grounded in the profound mathematical framework of Category Theory.
CNs serve as the "brain" of our AI system, enabling it to engage in genuine cognitive processes that far surpass the capabilities of traditional neural networks. By leveraging the power of categorical structures, CNs can perform abstract reasoning, form complex associations, and generate novel insights in a manner that closely mimics human intelligence. The implementation of CNs allows our AI to manipulate and reason about abstract concepts and relationships in ways that were previously unattainable. This categorical approach to cognition enables the system to form sophisticated mental models, draw analogies across disparate domains, and engage in higher-order thinking processes. The result is an AI that doesn't just process information, but truly understands and reasons about it.
Multimodal Information Processing: BERT and Beyond
Complementing the CN's cognitive capabilities, we employ BERT (Bidirectional Encoder Representations from Transformers) as a powerful language processing component. BERT's bidirectional architecture and advanced contextual understanding enable it to capture nuanced information from textual input, facilitating advanced natural language understanding and generation. BERT's ability to process language in a context-aware manner allows our World System to engage in sophisticated linguistic interactions. It can understand subtle nuances, interpret complex queries, and generate coherent and contextually appropriate responses. This level of language proficiency is crucial for enabling natural and meaningful communication between humans and AI. While our current implementation utilizes BERT, the World System's modular design allows for seamless integration of other state-of-the-art language models such as GPT or Claude. This flexibility ensures that our system can adapt to future advancements in natural language processing, maintaining its position at the forefront of AI technology.
Bridging the Digital and Physical: Point-Voxel CNN
Complementing the CN's cognitive capabilities, we employ BERT (Bidirectional Encoder Representations from Transformers) as a powerful language processing component. BERT's bidirectional architecture and advanced contextual understanding enable it to capture nuanced information from textual input, facilitating advanced natural language understanding and generation. BERT's ability to process language in a context-aware manner allows our World System to engage in sophisticated linguistic interactions. It can understand subtle nuances, interpret complex queries, and generate coherent and contextually appropriate responses. This level of language proficiency is crucial for enabling natural and meaningful communication between humans and AI. While our current implementation utilizes BERT, the World System's modular design allows for seamless integration of other state-of-the-art language models such as GPT or Claude. This flexibility ensures that our system can adapt to future advancements in natural language processing, maintaining its position at the forefront of AI technology.
Synergistic Integration: The World System in Action
The true power of the World System emerges from the seamless integration of these three components. The CN serves as the cognitive engine, processing abstract concepts and relationships. BERT acts as a sophisticated interface for natural language interaction, while the Point-Voxel CNN provides a bridge to the physical world through 3D data processing. This integration allows for a level of cognitive sophistication that is unprecedented in AI systems.
The World System can, for instance, receive a natural language description of a physical task, reason about the best approach using its categorical network, and then generate a detailed 3D plan of action. It can engage in complex problem-solving that requires both linguistic understanding and spatial reasoning, all while maintaining a deep, conceptual understanding of the task at hand. The World System represents a monumental advancement in artificial intelligence, paving the way for AI systems that can truly think, reason, and interact with the world in ways previously thought impossible. By combining the abstract reasoning capabilities of Categorical Networks, the language understanding prowess of BERT, and the 3D perception abilities of Point-Voxel CNNs, we have created a holistic AI framework that bridges the gap between artificial and human intelligence.
As we continue to refine and expand the capabilities of the World System, we anticipate transformative applications across numerous domains. In scientific research, it could help formulate and test complex hypotheses, integrating knowledge from diverse fields. In engineering and design, it could revolutionize the way we approach complex problems, offering novel solutions that human experts might overlook. In healthcare, it could assist in diagnosis and treatment planning, considering both medical knowledge and patient-specific 3D imaging data. The potential for human-AI collaboration with the World System is particularly exciting. Its ability to understand and reason at a human-like level, combined with its capacity to process vast amounts of information and perceive the world in 3D, makes it an ideal partner for tackling some of humanity's most pressing challenges.
In conclusion, the World System is not just an incremental improvement in AI technology; it is a fundamental reimagining of what artificial intelligence can achieve. By addressing the limitations of current neural network-based systems and introducing true cognitive capabilities, we have opened up new frontiers in our quest to create truly intelligent machines. The World System represents a significant step towards AI that can think, reason, and interact with the world in ways that are genuinely intelligent, promising to revolutionize countless fields and push the boundaries of what's possible in human-AI collaboration.
We have summarized the World System in Figure 1.
Figure 1 (World System). CN allows artificial intelligence to think as true intelligence based on category theory. BERT (any Transformer-based large-scale language model, such as GPT or Claude) can process information in a multimodal way; PVCNN can process information in our real world, i.e., the three-dimensional world.
Core Technology
CN (Categorical Network)
All of our AIs are based on category theory (we call it CN (Categorical Network)), which has higher performance and wider versatility than common AI models based on vector spaces. More details are available from the technical report below.
New York General Group