Agnuxo/Nebula

Enhanced Unified Holographic Neural Network

Francisco Angulo de Lafuente

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Winner Nvidia and LlamaIndex Developers 2024

Project Overview

The Enhanced Unified Holographic Neural Network is an advanced AI system that combines holographic memory, neural networks, and optical computing principles. This project, developed by Francisco Angulo de Lafuente, aims to create a more efficient and powerful AI model capable of learning, storing, and retrieving information in a manner inspired by the human brain and holographic principles.

Key Features

• Holographic memory for efficient information storage and retrieval
• Neural network architecture for learning and pattern recognition
• Optical computing simulation for enhanced processing capabilities
• P2P network integration for distributed learning and knowledge sharing
• Real-time learning and prediction capabilities
• Integration with external LLM models for enhanced text generation
• File processing capabilities (TXT and PDF) for knowledge ingestion
• Interactive 3D visualization of the neural network

Ray Tracing and CUDA Acceleration

The EUHNN utilizes NVIDIA's Ray Tracing and CUDA technologies to simulate the optical neural network efficiently. Key aspects of the implementation include:

Ray Tracing: A Monte Carlo path tracing algorithm simulates the propagation of light through the holographic memory and neural network elements. The optical elements are modeled as a combination of refractive and diffractive surfaces. Lenses are simulated using thin lens approximations, while diffraction gratings are modeled using phase functions that alter the direction of incident rays based on their wavelength.

CUDA: CUDA kernels are implemented to accelerate complex optical operations such as convolutions and Fourier transforms. This allows for highly parallel computations on the GPU, significantly improving performance. Custom CUDA kernels are also used to simulate wave propagation effects and interference patterns critical for holographic computations.

RTX Hardware: The project takes advantage of RTX hardware features like RT Cores for accelerated ray-triangle intersection tests, Tensor Cores for matrix operations in neural network layers, and specialized hardware for denoising the Monte Carlo rendered results. This combination of features allows for real-time simulation of complex optical phenomena within the neural network architecture. The integration of these technologies enables the EUHNN to perform optical neural computations at speeds comparable to traditional electronic neural networks while maintaining the advantages of optical processing, such as reduced power consumption and increased parallelism.

Technology Stack

• React for the frontend user interface
• Three.js and React Three Fiber for 3D visualizations
• Node.js for backend processing
• WebRTC (via PeerJS) for P2P networking
• PDF.js for PDF file processing
• LocalForage for client-side storage

Installation and Setup

1. Clone the repository:

   git clone https://github.com/username/enhanced-holographic-neural-network.git
   

2. Navigate to the project directory:

   cd enhanced-holographic-neural-network
   

3. Install dependencies:

   npm install
   

4. Start the development server:

   npm run dev
   

5. Open your browser and navigate to http://localhost:3000 to view the application.

Usage

1. Chat Interface: Use the chat interface to interact with the AI. Type your messages and receive responses generated by the holographic neural network.

2. Learning: Use the learning interface to teach the AI new associations between inputs and outputs.

3. File Processing: Upload TXT or PDF files to ingest new knowledge into the system.

4. Knowledge Management: Save and load the AI's knowledge base using the provided buttons.

5. Training: Use the training button to run the AI through a series of random inputs and outputs to enhance its knowledge.

6. P2P Networking: Connect with other instances of the application to share and distribute knowledge across the network.

7. 3D Visualization: Observe the real-time 3D representation of the neural network, including neurons, connections, and context nodes.

DEMO: https://v0.dev/chat/kyvoEEtAEU2

DEMO-1: https://b_ic1rgwmt8fv.v0.build/

DEMO-2: https://b_1eghmy2q0il.v0.build/

Deploy the project and test the prototype here:

https://github.com/user-attachments/assets/4f878d32-00fd-429c-99d3-59c66f356497

DEMO 2D: https://v0.dev/chat/zxua26lZsnT?b=Nb1RXgPNUa8

DEMO 3D: https://stackblitz.com/edit/sb1-evxclo?embed=1&file=package.json

Results and Discussion

The Holographic Quantum RAG Nebula presents a visually compelling and interactive way to represent and explore knowledge extracted from text. The simulation of quantum effects enhances the retrieval process and provides a novel way to conceptualize relationships between words and concepts.

Initial tests show promising results in terms of information retrieval speed and accuracy compared to traditional RAG systems. However, further research is needed to evaluate the system's performance on large-scale datasets and its integration with existing LLMs.

Conclusion and Future Work

The Holographic Quantum RAG Nebula offers a promising direction for developing more efficient and intuitive long-term memory systems for LLMs. Future work will focus on:

1. Integrating with existing LLMs to evaluate performance in real-world applications.
2. Scaling the system to handle larger datasets efficiently.
3. Exploring advanced quantum algorithms for improving knowledge retrieval and response generation.
4. Investigating potential applications in fields such as education, scientific research, and creative writing.

References

1. Gabor, D. (1948). A New Microscopic Principle. Nature, 161(4098), 777-778.

2. van Heerden, P. J. (1963). Theory of Optical Information Storage in Solids. Applied Optics, 2(4), 393-400.

3. Pribram, K. H. (1969). The Neurophysiology of Remembering. Scientific American, 220(1), 73-86.

4. Deutsch, D. (1985). Quantum Theory, the Church-Turing Principle and the Universal Quantum Computer. Proceedings of the Royal Society of London. A. Mathematical and Physical Sciences, 400(1818), 97-117.

5. Shor, P. W. (1994). Algorithms for Quantum Computation: Discrete Logarithms and Factoring. Proceedings 35th Annual Symposium on Foundations of Computer Science, 124-134.

6. Grover, L. K. (1996). A Fast Quantum Mechanical Algorithm for Database Search. Proceedings of the Twenty-Eighth Annual ACM Symposium on Theory of Computing, 212-219.

7. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I. (2017). Attention is All You Need. Advances in Neural Information Processing Systems, 30.

8. Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv preprint arXiv:1810.04805.

9. Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., ... & Amodei, D. (2020). Language Models are Few-Shot Learners. arXiv preprint arXiv:2005.14165.

10. Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., ... & Kiela, D. (2020). Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. Advances in Neural Information Processing Systems, 33, 9459-9472.

11. Gao, L., Biderman, S., Black, S., Golding, L., Hoppe, T., Foster, C., ... & Leahy, C. (2020). The Pile: An 800GB Dataset of Diverse Text for Language Modeling. arXiv preprint arXiv:2101.00027.

12. Beltagy, I., Peters, M. E., & Cohan, A. (2020). Longformer: The Long-Document Transformer. arXiv preprint arXiv:2004.05150.

13. Zaheer, M., Guruganesh, G., Dubey, K. A., Ainslie, J., Alberti, C., Ontanon, S., ... & Ahmed, A. (2020). Big Bird: Transformers for Longer Sequences. Advances in Neural Information Processing Systems, 33, 17283-17297.

14. Borgeaud, S., Mensch, A., Hoffmann, J., Cai, T., Rutherford, E., Millican, K., ... & Sifre, L. (2022). Improving Language Models by Retrieving from Trillions of Tokens. arXiv preprint arXiv:2112.04426.

15. Izacard, G., Grave, E., Joulin, A., & Usunier, N. (2022). Few-shot Learning with Retrieval Augmented Language Models. arXiv preprint arXiv:2208.03299.

Contributing

Contributions to the Enhanced Unified Holographic Neural Network project are welcome. Please follow these steps to contribute:

1. Fork the repository
2. Create a new branch (git checkout -b feature/your-feature-name)
3. Commit your changes (git commit -am 'Add some feature')
4. Push to the branch (git push origin feature/your-feature-name)
5. Create a new Pull Request

License

This project is licensed under the MIT License. See the LICENSE file for details.

Contact

Francisco Angulo de Lafuente

Project Link: https://youtu.be/29xr5okUZ54?si=XIW2rNyYxMpRWXx-

Acknowledgments

• NVIDIA for their cutting-edge AI technologies and APIs
• The open-source community for providing invaluable tools and libraries
• All contributors and researchers in the fields of neural networks, holographic memory, and optical computing

NEBULA

NEBULA: Neural Entanglement-Based Unified Learning Architecture NEBULA is a dynamic and innovative artificial intelligence system designed to emulate quantum computing principles and biological neural networks. NEBULA.py https://github.com/Agnuxo1/NEBULA/blob/main/NEBULA.py

Abstract

This paper presents NEBULA (Neural Entanglement-Based Unified Learning Architecture), a novel artificial intelligence system that integrates principles from quantum computing and biological neural networks. NEBULA operates within a simulated continuous 3D space, populated by virtual neurons with quantum computational capabilities. These neurons interact dynamically based on light-based attraction, forming clusters reminiscent of a nebula. The system employs advanced techniques like holographic encoding for efficient state representation, parallel processing with Ray for accelerated computation, and genetic optimization for learning and adaptation. This paper outlines the architecture, key components, and potential applications of NEBULA in various domains of artificial intelligence and machine learning.

1. Introduction

The field of artificial intelligence (AI) is constantly seeking new computational paradigms that can push the boundaries of machine learning and problem-solving. NEBULA emerges as a novel approach that integrates concepts from quantum computing, neural networks, and biological systems to create a flexible and powerful learning architecture. This system is designed to learn from data, adapt to new information, and answer questions based on its internal representations.

Figure 1: Conceptual representation of NEBULA’s 3D space. The image would depict a 3D space filled with glowing points, representing neurons. These points would be clustered in groups, resembling a nebula, with brighter points indicating higher luminosity and stronger interactions.

NEBULA distinguishes itself from conventional neural network architectures through several key features:

Dynamic 3D Space: Unlike traditional neural networks with fixed structures, NEBULA operates within a simulated continuous 3D space called NebulaSpace. This allows neurons to move and interact dynamically based on their luminosity and proximity, forming clusters reminiscent of a nebula. This dynamic interaction facilitates a more organic and potentially efficient form of information processing.

Virtual Neurons and Qubits: NEBULA utilizes virtual neurons and qubits for computation. Each neuron is equipped with a QuantumNeuron object, simulating a quantum circuit using PennyLane [2]. This allows for quantum-inspired computations, leveraging the potential of quantum phenomena like superposition and entanglement to enhance learning and processing capabilities.

Holographic Encoding: NEBULA employs a novel holographic encoding scheme using Convolutional Neural Networks (CNNs) for efficient state representation and compression. This approach, implemented by the HologramCodec class, leverages the principles of holography to encode the system's state as a complex pattern, allowing for compact storage and efficient retrieval.

Figure 2: Visualization of the holographic encoding process. This image would show a 3D representation of the NebulaSpace's state being transformed into a complex holographic pattern using FFT and CNNs.

Parallel Processing: NEBULA leverages the Ray framework [4] for distributed computing, enabling parallel processing of tasks such as neuron activation, interaction updates, and genetic algorithm operations. This significantly accelerates computation, allowing NEBULA to handle larger datasets and more complex problems efficiently.

Genetic Optimization: The NebulaTrainer class implements a genetic algorithm using the DEAP library [3] to evolve the system's parameters, improving its performance over time. This optimization technique allows NEBULA to adapt to new information and optimize its structure, leading to continuous learning and enhanced problem-solving capabilities.

Figure 3: Representation of the genetic algorithm’s optimization process. The image would show a visualization of the genetic algorithm evolving the system's parameters, with a fitness landscape depicting the search for optimal solutions.

These core features combined create a unique and powerful learning architecture that holds potential for various AI applications.

2. System Components 2.1 NebulaSpace: The Dynamic 3D Environment

The NebulaSpace is the foundational component of NEBULA, providing a simulated 3D environment where virtual neurons exist and interact. It is divided into sectors, each managed by a NebulaSector object. The NebulaSpace class handles the creation and tracking of sectors, ensuring a spatial organization for the system. Neurons within each sector interact based on their proximity and luminosity, mimicking gravitational forces that lead to dynamic clustering.

2.2 Neurons: The Building Blocks of NEBULA

Neurons in NEBULA are represented by the Neuron class. Each neuron has a 3D position within the NebulaSpace, a QuantumNeuron for information processing, a luminosity value, and connections to other neurons. The QuantumNeuron class simulates a parameterized quantum circuit using PennyLane, allowing for quantum-inspired computations. The neuron's luminosity influences its interactions with other neurons, mimicking the attractive force in a nebula.

Figure 4: Structure of a single neuron in NEBULA. This image would show a schematic representation of a neuron, with its 3D position, luminosity, QuantumNeuron circuit, and connections to other neurons.

2.3 HologramCodec: Efficient State Representation

The HologramCodec class is responsible for encoding and decoding the system's state using holographic principles. This approach allows for efficient representation and compression of the network's state, utilizing Fast Fourier Transforms (FFT) and CNNs for processing. The encoding process transforms the state into a complex holographic pattern, which can be decoded back to the original state. This provides a compact and efficient way to store and retrieve the network's configuration and learned information.

2.4 Ray: Parallel Processing for Acceleration

NEBULA leverages the Ray framework for distributed computing to enhance computational efficiency. This allows for parallel processing of tasks such as neuron activation, interaction updates, and genetic algorithm operations. Ray's distributed nature enables NEBULA to scale to larger datasets and more complex problems by distributing computations across multiple processors or machines.

2.5 NebulaTrainer: Learning and Adaptation

The NebulaTrainer class implements a genetic algorithm using the DEAP library for learning and adaptation. This optimization technique is used to evolve the system's parameters, improving its performance over time. The genetic algorithm operates on a population of candidate solutions, iteratively selecting, mutating, and evaluating individuals to find those with the highest fitness. This process allows NEBULA to learn from feedback, adapt to new information, and optimize its structure for better performance.

3. Key Processes 3.1 Information Processing

NEBULA's information processing flow involves several key steps:

Input Embedding: When NEBULA receives input data, it is first converted into a numerical representation called an embedding. This embedding captures the essential features of the input in a vector format.

Neuron Activation: The input embedding is used to activate neurons in the system. Neurons with embeddings that are similar to the input embedding are activated more strongly.

Inter-Neuron Interactions: Activated neurons interact within their respective sectors based on their proximity and luminosity. The strength of interaction between two neurons is inversely proportional to the square of the distance between them and directly proportional to their luminosities.

State Update: The system's state is updated based on the interactions between neurons. This involves adjusting neuron positions, luminosities, and connection strengths.

Figure 5: Diagram of NEBULA's information processing flow. This image would show the flow of information from input data to embedding generation, neuron activation, inter-neuron interactions, and finally, state update.

3.2 Learning and Adaptation

NEBULA's learning process involves a combination of direct feedback and genetic optimization:

Question Answering: The system answers questions based on its current state. This involves activating neurons related to the question and interpreting their collective activation pattern as an answer.

Feedback Integration: Feedback on the correctness of answers is used to adjust neuron parameters. For correct answers, the system reinforces the activation patterns that led to the correct response. For incorrect answers, the system adjusts parameters to discourage those patterns.

Genetic Optimization: The genetic algorithm evolves the system's overall configuration, including neuron positions, luminosities, and connection strengths, to improve performance. This optimization process aims to find configurations that lead to more accurate and efficient question answering.

3.3 Memory and Review

NEBULA maintains a memory of past interactions, storing questions, correct answers, and associated rewards. This memory is used to reinforce learning from past experiences. The system periodically reviews its memory, re-evaluating past questions and adjusting learning parameters based on the stored rewards or feedback. This review process helps NEBULA to consolidate its knowledge and improve its performance over time.

4. Applications and Future Work

NEBULA's flexible architecture and unique combination of quantum-inspired and biological principles make it suitable for a wide range of AI applications, including:

Natural Language Processing (NLP): NEBULA can be trained on large text datasets to understand language, answer questions, and generate text. Its dynamic 3D space and quantum-inspired computations could potentially offer new ways to represent and process language information.

Pattern Recognition: NEBULA can be used to identify patterns in complex datasets, such as images, audio, or sensor data. Its ability to adapt and learn through genetic optimization makes it suitable for tasks like anomaly detection, classification, and clustering.

Simulation of Biological Neural Systems: NEBULA's dynamic 3D space and light-based attraction mechanism can be used to simulate the behavior of biological neural networks. This could provide insights into how biological brains process information and learn.

Exploration of Quantum-Classical Hybrid Algorithms: NEBULA provides a platform for exploring the potential of quantum-classical hybrid algorithms. By integrating quantum-inspired computations with classical neural network techniques, NEBULA can be used to investigate new approaches to machine learning and problem-solving.

Figure 6: Potential applications of NEBULA in various domains. This image would show a collage of different applications, such as NLP, pattern recognition, and biological system simulation, highlighting NEBULA's versatility.

Future work on NEBULA could focus on:

Enhancing Quantum-Inspired Aspects: Further research could explore the integration of more advanced quantum computing concepts, such as quantum annealing or variational quantum algorithms, to enhance NEBULA's learning and processing capabilities.

Improving Scalability: Developing techniques to improve NEBULA's scalability for larger, more complex problem domains is crucial. This could involve optimizing memory management, data structures, and parallel processing strategies.

Developing Specialized Modules: Creating specialized modules for specific application areas, such as NLP, image processing, or robotics, could enhance NEBULA's performance and applicability in those domains.

Integration with Other Frameworks: Integrating NEBULA with other AI and machine learning frameworks, such as TensorFlow or PyTorch, could provide access to a wider range of tools and resources, facilitating further research and development.

5. Conclusion

NEBULA represents a novel approach to artificial intelligence, combining principles from quantum computing, neural networks, and biological systems. Its dynamic, 3D architecture and use of advanced techniques like holographic encoding and genetic optimization offer promising avenues for future research and development in the field of AI.

While the current implementation of NEBULA is primarily a proof of concept, it demonstrates the potential for integrating diverse computational paradigms into a unified learning system. As quantum computing and AI technologies continue to advance, systems like NEBULA may play a crucial role in developing more powerful and flexible artificial intelligence solutions.

References

Angulo de Lafuente, F. (2024). NEBULA.py: Dynamic Quantum-Inspired Neural Network System. GitHub Repository. https://github.com/Agnuxo1

Bergholm, V., et al. (2018). PennyLane: Automatic differentiation of hybrid quantum-classical computations. arXiv preprint arXiv:1811.04968.

Fortin, F. A., et al. (2012). DEAP: Evolutionary algorithms made easy. Journal of Machine Learning Research, 13(Jul), 2171-2175.

Moritz, P., et al. (2018). Ray: A distributed framework for emerging AI applications. In 13th USENIX Symposium on Operating Systems Design and Implementation (OSDI 18) (pp. 561-577).

Subject: Clarification and Reflection on the Contest

Dear JoAnn,

I hope this message finds you well. I want to start by thanking you for your time and attention in reviewing my case. My intention with this email is not to request any changes to the contest results, as winning was never my goal. My main interest was simply to showcase my research, papers, and patents to NVIDIA engineers, as I deeply admire your work.

I have worked as a researcher for many years in the field of biotechnology, and you can find my patents on Google Patents: Francisco Angulo Lafuente - Patents. However, I see myself more as a science fiction writer of B-movie-style novels and as a programming and artificial intelligence enthusiast.

I would also like to apologize because I understand that part of the misunderstanding may have arisen due to my limited command of English, as I am Spanish and speak only Castilian fluently. I am sure that, if I had been able to communicate better in English, this issue could have been resolved easily from the very beginning.

After much reflection, I believe I now understand your perspective. The typical approach to integrating NVIDIA NeMo technologies would have been to download the GitHub repository and integrate it directly into my code. However, to do so, the code would need to be written in Python.

My project, although it may not appear so at first glance, is more complex because it combines various NVIDIA technologies, such as CUDA, Raytracing, AI RAG, and NVIDIA NeMo systems. Presenting it solely in Python would have posed significant challenges during deployment, as it would require adjusting the CUDA version on every machine, rewriting the kernel, and making it nearly impossible for general users to test the demo.

For this reason, I chose to implement it in JavaScript to facilitate deployment on Vercel. However, Vercel's virtual machine does not allow Python execution, which forced me to use NVIDIA's APIs to make some components work. This approach allowed me to create an interactive demo that was accessible to the general public. Below, I provide the links to the demos, which you can easily explore:

DEMO 1 DEMO 2 DEMO 2D Model Lastly, I hope you can understand my perspective. In my country, an RTX 6000 is valued at approximately 8,000 euros, and my current computer is second-hand, purchased for 300 euros. For this reason, although I never sought to win, it was inevitable for me to feel excited about the possibility of recognition at this level.

From the bottom of my heart, I hope we can move past this incident and maintain a friendly relationship based on our shared interest in technology and innovation.

Warm regards, Francisco Angulo de Lafuente