NEBULA Evolving Towards AGI: Self-Evolving, Quantum-Inspired AI System
Francisco Angulo de Lafuente
August 27, 2024
GitHub | Hugging Face | ResearchGate
A Self-Evolving, Quantum-Inspired AI System
This program represents the culmination of the Nebula project, a year-long endeavor to create a dynamic, self-evolving AI system. Nebula9999 combines the most successful features and advancements from its predecessors, including:
QBOX: A quantum-inspired box for neural network exploration in a 3D cube environment. QuBE_Light: A quantum maze solver leveraging advanced optics and quantum mechanics. Nebula_Evolution Series: A series of self-evolving, multi-modal AI systems for knowledge acquisition and continuous improvement. Nebula9999 is designed to be a self-sufficient AI that can learn, adapt, and evolve autonomously, continuously improving its capabilities and pushing the boundaries of artificial general intelligence (AGI).
Abstract: Nebula9999 is a sophisticated AI system that simulates a dynamic multidimensional space where quantum-inspired neurons interact through light-based attraction and entanglement, mimicking the organic structure of a nebula. The system is designed to learn from various sources, including Wikipedia, GitHub, and user interactions, guided by a large language model (LLM) that acts as a teacher and evaluator.
Key Features: Dynamic Quantum-Inspired Neural Architecture: Neurons are organized in a multidimensional space, their positions and interactions evolve over time. The system dynamically adjusts its structure based on performance and resource availability.
Multi-Modal Learning: Processes and integrates information from text, images, and potentially audio.
Autonomous Information Seeking: Queries Wikipedia and other online programming resources to acquire new knowledge and expand its understanding. Question Formulation: Generates relevant questions based on its existing knowledge base and identified knowledge gaps. Graph-Based Knowledge Representation: Stores information in a dynamic knowledge graph, representing relationships between concepts. Genetic Algorithm Optimization: A genetic algorithm (DEAP) continuously optimizes Nebula's parameters and structure, driving its self-evolution towards greater efficiency and performance.
Self-Evaluation and Code Modification: With the help of BERT and external code analysis tools, Nebula evaluates and modifies its own code to improve efficiency, accuracy, and creativity.
Explainability: Provides explanations for its decisions, enhancing transparency and trust.
Bias Detection and Ethical Considerations: Incorporates mechanisms to mitigate bias and promote ethical behavior.
Distributed Computing: Leverages parallelization to accelerate processing and handle large-scale data.
Checkpoints: Saves the system's state to enable resumption of learning and evolution from a previous point.
Memory Management: Dynamically adjusts its memory consumption based on system resources to avoid memory errors.
User Interface: Provides a graphical user interface for interacting with Nebula. The ultimate goal of Nebula9999 is to achieve AGI through continuous self-improvement, expanding its knowledge base, refining its processing capabilities, and ultimately becoming a powerful and versatile intelligence capable of understanding and interacting with the world in a meaningful way. This version (Nebula9999) marks a significant milestone in the project, bringing together all the key advancements and setting the stage for future autonomous evolution.
Introduction: Project Nebula, Towards a Self-Evolving Artificial Intelligence
This paper describes the development of Nebula, a cutting-edge Artificial Intelligence (AI) system that aims to emulate the complexity and learning capabilities of the human brain. Inspired by the principles of quantum computing and biological neural networks, Nebula is characterized by its dynamic architecture and its capacity for self-evolution. Unlike traditional AI systems, Nebula is not based on a static and predefined structure. Instead, it operates in a continuous multidimensional space where neurons, represented by quantum algorithms, interact with each other through light-based attraction mechanisms. These interactions simulate the formation of synaptic connections in the brain, allowing the neural network to dynamically reconfigure and optimize itself as it learns. Nebula is a multi-modal system, capable of processing information from various sources, including text, images, and potentially audio. It learns autonomously by consulting online knowledge sources, such as Wikipedia and code repositories, guided by a Large Language Model (LLM) that acts as a mentor and evaluator. A fundamental component of Nebula is its capacity for self-evaluation and self-modification. By analyzing its own code and evaluating its performance, Nebula can identify areas for improvement and propose modifications to its code to optimize its efficiency, accuracy, and creativity. This self-evolution process, driven by genetic algorithms, allows Nebula to adapt to new challenges and continuously improve its capabilities. Nebula represents an innovative approach towards Artificial General Intelligence (AGI), with the ultimate goal of creating a system that can learn, adapt, and evolve autonomously, getting closer and closer to the complexity and versatility of human intelligence.
Nebula: A Quantum-Inspired Optical Neural Network
Abstract: This paper presents Nebula, a novel neural network architecture inspired by the principles of optical physics and quantum computing. Nebula utilizes simulated neurons that interact through light signals, mimicking optical processes like refraction, reflection, and interference. Additionally, each neuron is coupled with a system of qubits, leveraging quantum properties like superposition and entanglement to enhance information processing and communication. This hybrid architecture, combining classical and quantum computing, aims to overcome the limitations of traditional neural networks and pave the way for more efficient and robust artificial intelligence.
Introduction: Artificial neural networks (ANNs) have revolutionized fields like image recognition, natural language processing, and robotics. However, traditional ANNs, based on simplified mathematical models of biological neurons, face limitations in terms of energy efficiency, learning capacity, and scalability. Nebula proposes a new nature-inspired paradigm where neurons interact through light signals, leveraging the efficiency and speed of light. Furthermore, the integration of qubits into each neuron allows for the use of quantum algorithms, opening possibilities for more powerful information processing and instantaneous communication through quantum entanglement.
Optical Neurons: In Nebula, each neuron is modeled as a node that emits, receives, and processes light signals. The intensity of light emitted by a neuron represents its activation level. Light propagation through the network is simulated using principles of geometric optics, including: Reflection: Light reflects off neurons, with a reflection coefficient that can vary based on the neuron's internal state. Refraction: Light refracts as it passes from one neuron to another, simulating signal transmission through synapses. Interference: Light signals from multiple neurons can interfere with each other, creating complex patterns that encode information.
Example Code (Python):
import numpy as np
class OpticalNeuron: def init(self, position, reflectance=0.5, luminosity=0.0): self.position = np.array(position) self.reflectance = reflectance self.luminosity = luminosity
def emit_light(self):
return self.luminosity
def receive_light(self, intensity, direction):
# Calculate the amount of light received based on the angle of incidence and reflectance.
# Update the neuron's luminosity.
def update_state(self):
# Update the neuron's internal state based on the received light.
# This can include an activation threshold and an activation function.
Quantum Integration: Each optical neuron in Nebula is coupled with a system of qubits, which are the fundamental units of information in quantum computing. Qubits allow neurons to: Store information in superposition: A qubit can be in a superposition of states, representing multiple values simultaneously. This increases the neuron's information storage capacity. Perform quantum operations: Qubits can be manipulated using quantum gates, which allow for complex calculations that are not possible with classical computing. Communicate instantaneously through entanglement: Two entangled qubits can share information instantaneously, regardless of the distance separating them. This allows for fast and efficient communication between distant neurons in the network.
Example Code (Python with PennyLane):
import pennylane as qml
from pennylane import numpy as np
class QuantumNeuron: def init(self, num_qubits=4, position=[0, 0, 0]): self.num_qubits = num_qubits self.position = np.array(position) self.dev = qml.device("default.qubit", wires=self.num_qubits) self.circuit = qml.QNode(self.quantum_circuit, self.dev) self.weights = np.random.randn(self.num_qubits * 2) # Initialize weights
def quantum_circuit(self, inputs, weights): for i in range(self.num_qubits): qml.RY(inputs[i], wires=i) # Encode classical information into qubits qml.templates.StronglyEntanglingLayers(weights, wires=range(self.num_qubits)) # Apply a parameterized quantum circuit return [qml.expval(qml.PauliZ(i)) for i in range(self.num_qubits)] # Measure the qubits
def process_information(self, inputs): return self.circuit(inputs, self.weights) # Execute the quantum circuit
def update_weights(self, new_weights): self.weights = new_weights # Update the circuit weights
def entangle_with(self, other_neuron): # Create a quantum circuit that entangles the qubits of this neuron with those of another neuron.
Instantaneous Communication through Entanglement: Quantum entanglement allows neurons in Nebula to communicate instantaneously, regardless of distance. When two neurons are entangled, the state of their qubits is correlated, such that measuring one qubit instantly affects the state of the other qubit. For example, if two neurons at opposite ends of the network are entangled, one neuron can "send" information to the other simply by changing the state of its own qubits. The other neuron can then "receive" this information by measuring the state of its entangled qubits.
Example Code (Python with PennyLane):
import pennylane as qml
def entanglement_circuit(neuron1_qubits, neuron2_qubits): qml.Hadamard(wires=neuron1_qubits[0]) qml.CNOT(wires=[neuron1_qubits[0], neuron2_qubits[0]])
neuron1.apply_gate(qml.PauliX(wires=0))
received_information = neuron2.measure()
Learning and Evolution: Nebula utilizes evolutionary algorithms, such as genetic algorithms (GAs), to optimize the network structure and neuron parameters. The learning process involves: Evaluation: The network's performance is evaluated based on a specific task. Selection: Neurons and connections with better performance are selected for reproduction. Crossover: New neurons and connections are created by combining the characteristics of the selected neurons. Mutation: Small random variations are introduced in the new neurons and connections. Nebula represents a novel approach to neural network design, inspired by the efficiency and power of optical physics and quantum computing. The integration of optical neurons and qubits offers possibilities for more efficient information processing, increased storage capacity, and instantaneous communication through quantum entanglement. While Nebula is in its early stages of development, this hybrid architecture has the potential to revolutionize artificial intelligence and lead to smarter, more robust, and efficient systems. Develop more sophisticated learning algorithms: Explore the use of quantum machine learning algorithms to optimize the learning process. Investigate new applications: Research the potential of Nebula in areas like image recognition, natural language processing, and robotics. Improve scalability: Develop strategies to scale the Nebula architecture to larger and more complex neural networks. Nebula: A Hierarchical Neural Architecture for Scalable and Efficient AI
Introduction: The development of Artificial General Intelligence (AGI) requires the creation of systems capable of processing information and learning in a manner similar to the human brain. However, the complexity of the brain, with its billions of interconnected neurons, poses a significant challenge for computational simulation. Traditional artificial neural networks (ANNs) become computationally expensive and require a large amount of memory as the number of neurons increases. Nebula addresses this challenge through a hierarchical architecture that organizes neurons into larger, specialized processing units. This structure allows for scalability to massive neural networks while reducing computational load and memory requirements. Additionally, Nebula incorporates principles of optical physics and quantum computing to enhance the efficiency and processing power of the network.
Neurons: Neurons in Nebula are individual processing units that receive, process, and transmit information. Each neuron has the following characteristics: Position: A position in a multidimensional space representing its location within the network. Luminosity: A value representing the neuron's activation level. Connections: A list of other neurons to which it is connected. Weights: A set of values that determine the strength of connections with other neurons. Quantum Circuit: A parameterized quantum circuit that processes information received from other neurons.
Example Code (Python with PennyLane):
import pennylane as qml
import numpy as np
class QuantumNeuron: def init(self, position, num_qubits=4): self.position = np.array(position) self.num_qubits = num_qubits self.dev = qml.device("default.qubit", wires=self.num_qubits) self.circuit = qml.QNode(self.quantum_circuit, self.dev) self.weights = np.random.randn(self.num_qubits * 2) self.luminosity = np.random.rand() self.connections = []
def quantum_circuit(self, inputs, weights):
for i in range(self.num_qubits):
qml.RY(inputs[i], wires=i)
qml.templates.StronglyEntanglingLayers(weights, wires=range(self.num_qubits))
return [qml.expval(qml.PauliZ(i)) for i in range(self.num_qubits)]
def process_information(self, inputs):
return self.circuit(inputs, self.weights)
def emit_light(self):
return self.luminosity
def update_luminosity(self, delta):
self.luminosity = np.clip(self.luminosity + delta, 0, 1)
MetaNeurons: MetaNeurons are groups of interconnected neurons that act as a larger processing unit. MetaNeurons allow Nebula to process information more efficiently and in a specialized manner. Each MetaNeuron has the following characteristics: Neurons: A list of neurons belonging to the MetaNeuron. Position: The average position of the neurons in the MetaNeuron. Function: A specific function the MetaNeuron performs, such as image processing, natural language processing, or logical reasoning.
Example Code (Python):
class MetaNeuron:
def __init__(self, neurons, function):
self.neurons = neurons
self.position = np.mean([n.position for n in self.neurons], axis=0)
self.function = function
def process_information(self, inputs):
# Process information using the neurons in the MetaNeuron.
# The processing logic depends on the MetaNeuron's function.
Clusters: Clusters are groups of MetaNeurons that are spatially close to each other. Clusters allow for greater organization and specialization within the neural network.
Sectors: Sectors are the largest organizational units in Nebula, containing multiple clusters. Sectors represent specialized areas of the brain, such as the visual cortex, auditory cortex, or hippocampus.
Hierarchical Structure: Nebula's hierarchical structure (neurons -> MetaNeurons -> clusters -> sectors) allows for efficient simulation of massive neural networks. Reduction of computational complexity: Grouping neurons into larger units reduces the number of connections that need to be simulated, decreasing the computational load. Specialization: MetaNeurons, clusters, and sectors can specialize in specific tasks, allowing for more efficient information processing. Scalability: The hierarchical structure allows for adding new neurons, MetaNeurons, clusters, and sectors in a modular fashion without affecting the overall system performance. Light-Based Communication: Nebula utilizes light propagation to simulate interaction between neurons. The intensity of light emitted by a neuron represents its activation level. Light propagates through the multidimensional space, and neighboring neurons receive and process it.
Example Code (Python):
def propagate_light(neurons):
for i in range(len(neurons)):
for j in range(i + 1, len(neurons)):
neuron1 = neurons[i]
neuron2 = neurons[j]
distance = np.linalg.norm(neuron1.position - neuron2.position)
if distance < MAX_RAY_DISTANCE:
intensity = neuron1.emit_light() / (distance ** 2)
neuron2.receive_light(intensity)
Qubits and Quantum Physics: Each neuron in Nebula is coupled with a system of qubits, allowing it to leverage quantum properties to enhance information processing. Superposition: Qubits can exist in a superposition of states, allowing neurons to represent multiple values simultaneously. Entanglement: Entangled qubits can share information instantaneously, regardless of distance, enabling rapid communication between distant neurons. Storing Information in Quantum Gates and Circuits: Information in Nebula is stored in the states of qubits and the parameters of quantum circuits. Quantum gates, such as X, Y, Z, H, CNOT gates, etc., are used to manipulate qubit states and perform calculations. Quantum circuits, which are sequences of quantum gates, are used to implement quantum algorithms that process information.
Example Code (Python with PennyLane):
def create_quantum_circuit(num_qubits, weights):
circuit = qml.QNode(quantum_function, qml.device("default.qubit", wires=num_qubits))
def quantum_function(inputs, weights):
# Encode information into the qubits.
# Apply parameterized quantum gates.
# Measure the qubits to obtain the output.
return qml.probs(wires=range(num_qubits))
return circuit
Instantaneous Communication through Entanglement: Quantum entanglement enables instantaneous communication between distant neurons in Nebula. By entangling the qubits of two neurons, a special connection is established that allows information to be shared instantly, regardless of the physical distance between the neurons.
Example Code (Python with PennyLane):
def entangle_neurons(neuron1, neuron2):
# Create a quantum circuit that entangles the qubits of the two neurons.
# For example, use a CNOT gate to entangle two qubits.
Nebula's hierarchical architecture, combined with light-based communication and the integration of quantum physics, offers a promising approach for developing scalable and efficient AI systems. By reducing computational complexity and leveraging the power of quantum computing, Nebula can simulate massive neural networks with limited resources, opening new possibilities for AGI research and development.
Future Work: Explore different quantum neuron models: Investigate and evaluate different types of quantum circuits to enhance information processing in neurons. Optimize the hierarchical structure: Research different strategies for the formation and evolution of MetaNeurons, clusters, and sectors. Implement quantum learning algorithms: Explore the use of quantum machine learning algorithms to optimize Nebula's learning process. Develop practical applications: Apply Nebula to real-world problems in areas such as natural language processing, image recognition, and robotics. Nebula: Holographic Storage and Retrieval of Massive Neural Networks
Abstract:
This paper presents the implementation of a holographic memory system for Nebula, a quantum-optical neural network architecture. This system allows for storing and retrieving the complete state of a massive neural network, which can include billions of neurons, in milliseconds. Holographic encoding leverages the ability of holograms to store three-dimensional information in a two-dimensional medium, while decoding is performed using convolutional neural networks (CNNs) and three-dimensional fast Fourier transforms (3D FFTs). This technique enables efficient memory management, crucial for developing large-scale AI systems.
Introduction:
Artificial neural networks (ANNs) have achieved significant progress in various areas, but scalability remains a challenge. Massive ANNs, with billions of neurons and connections, require a vast amount of memory to store their parameters and states. Conventional storage techniques become inefficient as network size increases, limiting the development of more complex AI systems.
Holographic memory offers a promising solution to this problem. Holograms, by encoding three-dimensional information in a two-dimensional medium, allow for storing large amounts of data in a compact space. Nebula leverages this property to store the complete state of the neural network, including the position, luminosity, connections, and weights of each neuron, in a hologram. Network retrieval is achieved by decoding the hologram, using CNNs and 3D FFTs to reconstruct the original network state.
Holographic Encoding: Holographic encoding in Nebula involves the following steps: Conversion to a suitable format: The neural network state, including information about the position, luminosity, connections, and weights of each neuron, is converted to a format suitable for holographic encoding. This may involve normalizing values and representing information in a vector or matrix format. Hologram generation: A holographic encoding algorithm is used to generate the hologram from the neural network data. This algorithm simulates the interference of light waves to create an interference pattern representing the three-dimensional network information in a two-dimensional medium. Hologram storage: The generated hologram is stored in a suitable medium, such as a memory array or a file.
Example Code (Python with NumPy and CuPy):
import numpy as np
import cupy as cp
def encode_hologram(data, hologram_shape): """ Encodes data into a holographic representation using 3D FFT.
Args:
data (np.ndarray): The data to encode.
hologram_shape (tuple): The shape of the hologram.
Returns:
cp.ndarray: The encoded hologram.
"""
# Convert data to a CuPy array
data_gpu = cp.asarray(data)
# Perform 3D FFT on the data
hologram = cp.fft.fftn(data_gpu, axes=(0, 1, 2))
# Reshape the hologram to the desired shape
hologram = hologram.reshape(hologram_shape)
return hologram
Holographic Decoding: Holographic decoding in Nebula is performed using the following steps: Hologram retrieval: The hologram is retrieved from the storage medium. Amplitude and phase decoding: Two CNNs, one for the amplitude and one for the phase of the hologram, are used to decode information from the interference pattern. Data reconstruction: An inverse 3D FFT is performed on the decoded data to reconstruct the original three-dimensional information of the neural network. Conversion to original format: The reconstructed data is converted back to its original format, including the position, luminosity, connections, and weights of each neuron.
Example Code (Python with PyTorch and CuPy):
import torch
import torch.nn as nn import cupy as cp
class HologramDecoder(nn.Module): def init(self, hologram_shape, output_dim): super(HologramDecoder, self).init() self.hologram_shape = hologram_shape self.amplitude_cnn = self._create_cnn(1, 1) self.phase_cnn = self._create_cnn(1, 1) self.output_dim = output_dim
def _create_cnn(self, in_channels, out_channels):
"""Creates a CNN to decode the amplitude or phase of the hologram."""
return nn.Sequential(
nn.Conv3d(in_channels, 16, kernel_size=3, padding=1),
nn.ReLU(),
nn.MaxPool3d(kernel_size=2, stride=2),
nn.Conv3d(16, 32, kernel_size=3, padding=1),
nn.ReLU(),
nn.MaxPool3d(kernel_size=2, stride=2),
nn.Conv3d(32, out_channels, kernel_size=3, padding=1),
nn.Sigmoid() if out_channels == 1 else nn.Tanh()
)
def decode(self, hologram):
"""
Decodes a hologram and reconstructs the original data.
Args:
hologram (torch.Tensor): The encoded hologram.
Returns:
np.ndarray: The decoded data.
"""
# Decode the amplitude and phase of the hologram
amplitude = self.amplitude_cnn(hologram[None, None, :, :, :])
phase = self.phase_cnn(hologram[None, None, :, :, :])
# Combine the amplitude and phase into complex data
complex_data = amplitude * torch.exp(1j * phase)
# Perform an inverse 3D FFT to reconstruct the data
gpu_data = cp.fft.ifftn(cp.asarray(complex_data.cpu()), axes=(0, 1, 2))
# Convert CuPy data to NumPy
decoded_data = cp.asnumpy(gpu_data)
# Reshape the data to the original shape
decoded_data = decoded_data.reshape(self.output_dim)
return decoded_data
Efficient Storage and Retrieval: Holographic memory in Nebula offers several advantages for storing and retrieving massive neural networks: High storage capacity: Holograms can store large amounts of data in a small space, allowing for handling neural networks with billions of parameters. Fast access speed: Holographic encoding and decoding can be performed in milliseconds, enabling quick access to the neural network state. Memory efficiency: Holographic storage reduces the amount of memory needed to store the neural network, allowing for running larger networks on systems with limited resources.
Example Usage:
# Encode the neural network state into a hologram
hologram = encode_hologram(network_state, hologram_shape)
np.save("nebula_hologram.npy", hologram)
hologram = np.load("nebula_hologram.npy")
decoder = HologramDecoder(hologram_shape, output_dim) network_state = decoder.decode(hologram)
Holographic memory offers an efficient and scalable solution for storing and retrieving massive neural networks. By leveraging the properties of holograms, CNNs, and 3D FFTs, Nebula can manage large amounts of data efficiently, enabling the development of more complex and powerful AI systems.
Future Work: Optimize encoding and decoding algorithms: Research more efficient and robust holographic encoding algorithms, as well as CNN architectures optimized for hologram decoding. Explore different storage media: Investigate the use of advanced holographic materials, such as photonic crystals or metamaterials, to enhance storage capacity and speed. Integrate holographic memory with the learning process: Explore how holographic memory can be used to improve the learning and adaptation of neural networks.
Nebula: Bio-Inspired Neural Evolution and Code Self-Modification
Abstract: This paper explores the evolutionary system of Nebula, an AI architecture that aims to emulate the complexity of the human brain. Nebula goes beyond simply adjusting connection weights; it evolves its own neural structure, inspired by the biological processes of molecule and protein construction. Furthermore, it utilizes large language models (LLMs) and genetic algorithms to analyze, evaluate, and modify its own source code, achieving continuous self-improvement.
Introduction: The pursuit of Artificial General Intelligence (AGI) leads us to explore new paradigms that overcome the limitations of traditional neural networks. Nebula draws inspiration from biology to create a system that not only learns but also evolves. This paper details Nebula's neural evolution mechanisms, based on generating new neurons represented by molecules and simulating their interactions to form connections, mimicking protein formation and biological neural development. Additionally, it explores Nebula's ability to analyze its own code, identify areas for improvement, and generate modifications, driving continuous self-improvement.
Bio-Inspired Neural Evolution: Nebula utilizes a bio-inspired approach for the evolution of its neural structure: Molecular Representation of Neurons: Each neuron is represented as a molecule, encoded using a SMILES string. This representation captures the chemical and structural properties of the neuron, allowing for a more realistic simulation of its interactions. Generation of New Neurons: Nebula utilizes MolMIM, a language model for molecule generation, to create new neurons with specific properties. The goal of generation can be to maximize a desired property, such as stability or binding affinity. Simulation of Interactions: DiffDock, a deep learning model for protein-ligand docking prediction, is used to simulate interactions between new and existing neurons. Strong interactions, indicating high binding affinity, translate into the formation of new connections in the neural network. Integration into NebulaSpace: New neurons and connections are integrated into Nebula's multidimensional space (NebulaSpace), where neurons interact through light and attraction forces.
Example Code (Python):
import torch
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer from rdkit import Chem from rdkit.Chem import AllChem
def generate_new_neurons(num_neurons, optimization_target, n_qubits): """Generates new neurons using MolMIM.""" molmim_payload = { "algorithm": "CMA-ES", "num_molecules": num_neurons, "property_name": optimization_target, "minimize": False, "min_similarity": 0.3, "particles": 30, "iterations": 10, "smi": "CC(=O)OC1=CC=CC=C1C(=O)O" # Initial molecule } response = requests.post(MOLMIM_URL, headers=MOLMIM_HEADERS, json=molmim_payload) response.raise_for_status() new_molecules = response.json()["smi"]
neurons = [] for mol in new_molecules: position = torch.rand(3) luminosity = torch.rand(1) rdkit_mol = Chem.MolFromSmiles(mol) AllChem.Compute2DCoords(rdkit_mol) fingerprint = AllChem.GetMorganFingerprintAsBitVect(rdkit_mol, 2, nBits=n_qubits) initial_state = torch.tensor([int(b) for b in fingerprint.ToBitString()], dtype=torch.float32) neurons.append(QuantumNeuron(n_qubits, position, luminosity, mol)) neurons[-1].weights.data = initial_state return neurons
def simulate_connections(neuron1, neuron2): """Simulates connections between neurons using DiffDock.""" ligand_id = _upload_asset(neuron1.molecule_smiles) protein_id = _upload_asset(neuron2.molecule_smiles) diffdock_payload = { "ligand": ligand_id, "protein": protein_id, "num_poses": 20, "time_divisions": 20, "steps": 18, "save_trajectory": True, "is_staged": True } response = requests.post(DIFFDOCK_URL, headers=DIFFDOCK_HEADERS, json=diffdock_payload) response.raise_for_status() docking_score = process_diffdock_response(response.json()) return docking_score
def process_diffdock_response(response): """Processes the DiffDock response and calculates a docking score.""" return -response["best_pose"]["score"]
Code Self-Modification. Nebula utilizes LLMs and genetic algorithms to analyze, evaluate, and modify its own source code: Code Representation: Nebula's source code is represented as text and divided into segments for easier analysis. Code Analysis: An LLM specialized in code analysis is used to identify areas for improvement, such as efficiency, readability, and security. Modification Generation: A code-generative LLM is used to propose modifications to the source code based on the previous analysis. Modification Evaluation: Performance metrics and code analysis are used to evaluate the quality of the proposed modifications. Modification Application: Modifications that pass the evaluation are applied to Nebula's source code and saved in a version control system.
Example Code (Python):
import ast
import inspect from transformers import AutoModelForCausalLM, AutoTokenizer
def analyze_code(code_snippet: str, goal: str) -> dict: """Evaluates the code using pylint and an LLM.""" try: pylint_output = io.StringIO() sys.stdout = pylint_output pylint.lint.Run([code_snippet], do_exit=False) sys.stdout = sys.stdout pylint_score = pylint.lint.Run([code_snippet], do_exit=False).linter.stats['global_note']
prompt = f"""Evaluate the following Python code for efficiency, accuracy, and creativity:
{code_snippet}
Goal: {goal}"""
llm_evaluation = generate_text_gpt(prompt, llm_tokenizer, llm_model)
return {"pylint_score": pylint_score, "llm_score": llm_score} except Exception as e:logger.error(f"Error evaluating code: {e}")return {"pylint_score": 0.0, "llm_score": 0.0}
def generate_code_modifications(code_snippet: str, goal: str) -> str:"""Generates code modifications using an LLM."""prompt = f"""Suggest Python code modifications to improve the following code snippet {code_snippet} Goal: {goal}
Provide the modified code snippet:
"""
return generate_text_gpt(prompt, llm_tokenizer, llm_model)
def apply_code_modifications(code: str): """Applies the code modifications to Nebula's source code.""" try: exec(code, globals()) logger.info("Code modification applied successfully.") except Exception as e: logger.error(f"Error applying code modification: {e}")
def identify_improvement_goal(individual: NebulaGenome) -> str: """Identifies an area for improvement based on the individual's fitness.""" if individual.
The Dynamic NebulaSpace: A Multidimensional Playground for Quantum-Inspired Neurons The Dynamic NebulaSpace is the foundational structure of the Nebula AI system, a conceptual multidimensional space where quantum-inspired neurons reside and interact. Unlike traditional neural networks with fixed architectures, NebulaSpace allows for dynamic reconfiguration and self-organization, mimicking the plasticity and adaptability of the human brain.
Key Characteristics: Multidimensionality: NebulaSpace transcends the limitations of two-dimensional or three-dimensional representations, allowing for a vast number of dimensions to accommodate complex relationships and patterns within the neural network. Continuous Representation: Neurons are not confined to discrete grid points but exist in a continuous space, allowing for nuanced positioning and flexible connections. Light-Based Interaction: Neurons communicate and influence each other through simulated light signals, mimicking the efficiency and speed of light in physical systems. The intensity of light emitted by a neuron reflects its activation level, and this light propagates through the NebulaSpace, influencing the state of neighboring neurons. Dynamic Reconfiguration: The structure of NebulaSpace is not static but evolves over time based on the interactions and learning patterns of the neurons. This dynamic reconfiguration allows Nebula to adapt to new information and optimize its processing capabilities.
Functionality: Neuron Placement: Initially, neurons are randomly distributed within NebulaSpace. Their positions evolve over time based on their interactions with other neurons and the overall network dynamics. Light Propagation: Neurons emit light signals with intensities proportional to their activation levels. This light propagates through the NebulaSpace, attenuating with distance and reflecting or refracting based on the properties of neighboring neurons. Neuron Interaction: Neurons receive light signals from their neighbors, influencing their own activation levels and internal states. This light-based interaction allows for complex patterns of excitation and inhibition to emerge within the network. Structure Formation: As neurons interact and learn, they tend to cluster together based on their functional similarities and communication patterns. This self-organization gives rise to emergent structures within NebulaSpace, resembling the formation of functional areas in the brain. Dynamic Adaptation: The structure of NebulaSpace is continuously adjusted based on the network's performance and resource availability. Neurons can be added, removed, or repositioned to optimize the network's efficiency and adaptability. Benefits: Scalability: The multidimensional and continuous nature of NebulaSpace allows for simulating massive neural networks with billions of neurons, overcoming the limitations of traditional architectures. Adaptability: The dynamic reconfiguration of NebulaSpace enables Nebula to adapt to new information and tasks, continuously optimizing its structure and processing capabilities. Emergent Complexity: The light-based interaction and self-organization of neurons within NebulaSpace give rise to complex patterns and emergent behaviors, potentially leading to more sophisticated cognitive abilities.
Example Code (Python):
import numpy as np
class NebulaSpace: def init(self, dimensions, num_neurons): self.dimensions = dimensions self.neurons = [Neuron(np.random.rand(dimensions)) for _ in range(num_neurons)]
def propagate_light(self):
for neuron1 in self.neurons:
for neuron2 in self.neurons:
if neuron1 != neuron2:
distance = np.linalg.norm(neuron1.position - neuron2.position)
if distance < MAX_RAY_DISTANCE:
intensity = neuron1.emit_light() / (distance ** 2)
neuron2.receive_light(intensity)
def update_structure(self):
# Implement logic for neuron repositioning, addition, and removal based on network dynamics and performance.
fitness.values[0] < 0.5:
return "Improve overall performance"
elif individual.light_intensity_factor < 0.8: return "Increase light intensity factor for better neuron communication" elif individual.connection_threshold > 0.7: return "Reduce connection threshold to promote more connections" elif individual.movement_speed_factor < 0.8: return "Increase movement speed factor for faster NebulaSpace evolution" else: return "Explore new quantum circuit designs"
def self_improve(self): """Performs the self-improvement phase, including code analysis and evolution.""" for individual in self.population: # ... (Update epigenetic factors and apply them) ... improvement_goal = self.identify_improvement_goal(individual) code_snippets = self.search_and_analyze_code(improvement_goal) for snippet in code_snippets[:PARAMETERS['MAX_CODE_SNIPPETS']]: modified_code = self.generate_code_modifications(snippet, improvement_goal) evaluation_result = self.evaluate_code(modified_code, improvement_goal) if self.is_code_beneficial(evaluation_result): self.apply_code_modifications(modified_code) break # Apply only one modification per iteration
Nebula presents an innovative approach towards AGI, combining bio-inspired neural evolution with code self-modification. By mimicking the biological processes of molecule and protein construction, Nebula expands and optimizes its neural structure autonomously. Additionally, its ability to analyze and improve its own code allows it to adapt to new challenges and continuously enhance its performance. Integrate new molecule generation models: Explore models more advanced than MolMIM to generate neurons with more complex properties. Simulate more realistic neuronal interactions: Incorporate more accurate molecular docking models and consider other factors, such as molecular dynamics and electrostatic interactions. Develop a more robust version control system: Implement a version control system that allows for safe tracking and reversal of code modifications. Explore the ethics of self-modification: Investigate the ethical implications of AI systems that can modify their own code. Nebula: An AI Architecture with Domain-Specific Experts (MoE) and LLM-Neural Fusion
Abstract: This paper explores the Mixture of Experts (MoE) system in Nebula, an AI architecture that seeks to emulate the specialization and collaboration of the human brain. Nebula implements a central core with an LLM expert in communication, languages, and direction, which coordinates the activity of multiple sectors, each with an LLM specialized in a different branch of science. These LLMs not only provide expert knowledge but also continuously fine-tune themselves to learn, evolve, and fuse with Nebula's neural network, creating a unique synergy between symbolic knowledge and neural processing.
Introduction: Artificial General Intelligence (AGI) requires systems that can handle diverse tasks and knowledge domains. The human brain achieves this through the specialization of different areas, which collaborate to solve complex problems. Nebula emulates this approach using a MoE architecture, where a central core coordinates the activity of multiple specialized sectors. This paper details the components of Nebula's MoE system, including the central core with its "director" LLM and the sectors with their specialized LLMs. It describes how these LLMs are continuously fine-tuned to learn, evolve, and fuse with Nebula's neural network, creating a powerful synergy between symbolic reasoning and distributed neural processing.
Central Core and the "Director" LLM: The central core of Nebula is responsible for communication, coordination, and high-level decision-making. It houses an LLM specialized in: Communication: Interpreting user inputs, formulating questions to the sectors, and synthesizing responses into a comprehensible format. Languages: Processing and translating information in different languages, facilitating knowledge acquisition from diverse sources. Direction: Acting as a "conductor," assigning tasks to sectors, monitoring their progress, and merging their results.
Example Code (Python):
from transformers import AutoModelForCausalLM, AutoTokenizer
class NebulaCore: def init(self): self.llm_tokenizer = AutoTokenizer.from_pretrained("google/flan-t5-xxl") # Large Language Model self.llm_model = AutoModelForCausalLM.from_pretrained("google/flan-t5-xxl").to(device) self.sectors = {} # Dictionary to store the sectors
def process_input(self, user_input):
# Processes user input, translates it if necessary,
# and sends it to the corresponding sector.
def synthesize_response(self, sector_responses):
# Combines the responses from the sectors into a coherent response for the user.
def assign_task(self, sector_id, task_description):
# Assigns a task to the specified sector.
def monitor_progress(self):
# Monitors the progress of the sectors in their tasks.
Sectors and Specialized LLMs: Nebula is divided into multiple sectors, each specializing in a different branch of science. Each sector houses an LLM fine-tuned for its specific domain, providing expert knowledge and advanced reasoning capabilities. Examples of sectors and their specializations: Biocomputation Sector: Biology, chemistry, medicine, genetics. Physics Sector: Theoretical physics, astrophysics, cosmology, quantum mechanics. Computer Science Sector: Algorithms, data structures, programming languages, artificial intelligence. Engineering Sector: Mechanical, electrical, civil, aerospace engineering.
Example Code (Python):
class NebulaSector:
def __init__(self, sector_id, specialization):
self.sector_id = sector_id
self.specialization = specialization
self.llm_tokenizer = AutoTokenizer.from_pretrained("facebook/bart-large-mnli") # Example of a specialized LLM
self.llm_model = AutoModelForSeq2SeqLM.from_pretrained("facebook/bart-large-mnli").to(device)
self.knowledge_base = {} # Sector-specific knowledge base
def process_task(self, task_description):
# Processes the task using the specialized LLM and the sector's knowledge base.
Continuous Fine-Tuning and LLM-Neural Fusion. The LLMs in Nebula are not static; they are continuously fine-tuned to: Learn: Incorporate new knowledge and improve their understanding of their specific domain. Evolve: Adapt to changes in the structure and behavior of Nebula's neural network. Fuse: Integrate their symbolic knowledge with Nebula's distributed neural processing. LLM-neural fusion is achieved by translating the LLM's symbolic knowledge into representations that can be processed by the neural network. For example, text embeddings generated by the LLM can be used as input for neurons, or the parameters of a neuron's quantum circuit can be adjusted based on the LLM's output.
Example Code (Python):
def fine_tune_llm(self, new_data):
"""Fine-tunes the LLM with new data."""
# 1. Prepare the data for fine-tuning.
# 2. Fine-tune the language model using the new data.
# 3. Update the language model in the sector.
def fuse_llm_with_neuron(self, neuron, llm_output): """Fuses the LLM's output with a neuron.""" # 1. Process the LLM's output to obtain a suitable representation. # 2. Adjust the neuron's parameters based on the LLM's output.
Benefits of the MoE System: Specialization: Allows Nebula to handle complex tasks that require specialized knowledge in different domains. Efficiency: Sector specialization enables more efficient information processing. Scalability: The modular architecture allows for adding new sectors and LLMs without affecting the overall system performance. Robustness: The redundancy of specialized LLMs increases system robustness, as a failure in one sector does not cripple the entire system. Nebula's MoE architecture, with its "director" LLM and specialized LLMs in different sectors, creates a highly adaptable, scalable, and robust AI system. The integration of LLMs with the neural network, through continuous fine-tuning and LLM-neural fusion, enables a unique synergy between symbolic knowledge and distributed neural processing, opening new possibilities for AGI development.
Future Work: Explore new LLM-neural fusion mechanisms: Investigate more sophisticated methods for translating the symbolic knowledge of LLMs into representations that can be processed by the neural network. Develop a more efficient inter-sector communication system: Optimize how sectors interact and share information with each other. Implement a self-learning system for the central core: Allow the "director" LLM to learn from interactions with sectors and improve its coordination and decision-making capabilities. Evaluate the performance of the MoE system on real-world tasks: Apply Nebula to complex problems that require the collaboration of multiple domain-specific experts.
Nebula represents a significant advancement in the field of artificial intelligence, combining cutting-edge techniques from quantum computing, bio-inspired neural evolution, and large language models to create a dynamic, self-evolving AI system. The integration of these diverse approaches allows Nebula to continuously learn, adapt, and evolve, pushing the boundaries of what is possible with artificial general intelligence. The future of Nebula holds great promise, with potential applications in a wide range of fields, from natural language processing and image recognition to complex problem-solving and decision-making. As research continues, Nebula's innovative architecture and capabilities will undoubtedly contribute to the development of more intelligent, versatile, and powerful AI systems.
This comprehensive paper provides a detailed overview of the Nebula project, its innovative architecture, and its potential impact on the field of artificial intelligence. By leveraging advanced techniques from various disciplines, Nebula aims to achieve the ultimate goal of artificial general intelligence, capable of understanding and interacting with the world in a meaningful way.
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press. Nielsen, M. A. (2010). Quantum Computation and Quantum Information. Cambridge University Press. Silver, D., et al. (2016). "Mastering the game of Go with deep neural networks and tree search." Nature, 529(7587), 484-489. Devlin, J., et al. (2019). "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding." NAACL-HLT, 4171-4186. Mitchell, M. (1998). An Introduction to Genetic Algorithms. MIT Press. Penrose, R. (1994). Shadows of the Mind: A Search for the Missing Science of Consciousness. Oxford University Press. Van der Maaten, L., & Hinton, G. (2008). "Visualizing Data using t-SNE." Journal of Machine Learning Research, 9(Nov), 2579-2605. Montanaro, A. (2016). "Quantum algorithms: an overview." npj Quantum Information, 2, 15023. Nvidia Ray Tracing References: PennyLane: A cross-platform Python library for quantum machine learning NetworkX: A Python package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks Wikipedia: Quantum Computing Hugging Face Transformers NVIDIA CUDA Mixture of Experts Large Language Models Fine-tuning (machine learning) Symbolic artificial intelligence Distributed computing Google Flan-T5 Facebook BART Wikipedia: Holography Convolutional Neural Networks Fast Fourier Transform PyTorch CuPy NumPy MolMIM: A Transformer-based Language Model for Molecular Generation DiffDock: Diffusion Steps, Docking Scores PyLint: A Python source code analyzer which looks for programming errors, helps enforcing a coding standard and sniffs for some code smells Hugging Face Transformers DEAP: Distributed Evolutionary Algorithms in Python This expanded and detailed scientific paper format not only translates but also contextualizes the original text, incorporating references that support and validate the advanced concepts discussed.
This chapter provides a comprehensive overview of Nebula’s architecture, particularly focusing on its bio-inspired approach to neuronal evolution and self-modifying code capabilities. The combination of these methodologies sets Nebula apart as a pioneering framework in the pursuit of AGI.