Emerging AI Technologies

# Multimodal AI pipeline example
import torch
from transformers import pipeline
from PIL import Image
import speech_recognition as sr

class MultimodalAI:
    def __init__(self):
        self.vision_model = pipeline("image-to-text", 
                                   model="Salesforce/blip2-opt-2.7b")
        self.text_model = pipeline("text-generation", 
                                 model="gpt2")
        self.speech_recognizer = sr.Recognizer()
    
    def process_multimodal_input(self, image_path, audio_path, text_input):
        """Process combined image, audio, and text inputs"""
        
        # Process image
        image = Image.open(image_path)
        image_description = self.vision_model(image)[0]['generated_text']
        
        # Process audio
        with sr.AudioFile(audio_path) as source:
            audio = self.speech_recognizer.record(source)
            audio_text = self.speech_recognizer.recognize_google(audio)
        
        # Combine contexts
        combined_context = f"""
        Visual context: {image_description}
        Audio context: {audio_text}
        Text input: {text_input}
        
        Please provide a comprehensive response considering all modalities.
        """
        
        response = self.text_model(combined_context, max_length=500)[0]['generated_text']
        return response

# Usage
multimodal_ai = MultimodalAI()
response = multimodal_ai.process_multimodal_input(
    "image.jpg", 
    "audio.wav", 
    "What's happening in this scene?"
)

# Neuro-symbolic AI framework
import tensorflow as tf
import sympy as sp
from knowledge_graph import KnowledgeGraph

class NeuroSymbolicAI:
    def __init__(self):
        self.neural_net = self.build_neural_network()
        self.symbolic_engine = SymbolicReasoner()
        self.knowledge_graph = KnowledgeGraph()
    
    def build_neural_network(self):
        """Build a neural network for pattern recognition"""
        model = tf.keras.Sequential([
            tf.keras.layers.Dense(128, activation='relu'),
            tf.keras.layers.Dense(64, activation='relu'),
            tf.keras.layers.Dense(32, activation='relu'),
            tf.keras.layers.Dense(16, activation='relu')
        ])
        return model
    
    def reason_with_knowledge(self, neural_output, query):
        """Apply symbolic reasoning to neural network outputs"""
        
        # Extract symbolic representations
        symbols = self.extract_symbols(neural_output)
        
        # Apply logical rules
        reasoning_result = self.symbolic_engine.apply_rules(symbols, query)
        
        # Verify with knowledge graph
        verified_result = self.knowledge_graph.verify(reasoning_result)
        
        return verified_result
    
    def extract_symbols(self, neural_output):
        """Convert neural activations to symbolic representations"""
        # Implementation of neural-to-symbolic conversion
        symbols = {}
        # ... conversion logic
        return symbols

# Example application: Mathematical reasoning
neuro_symbolic_ai = NeuroSymbolicAI()
problem = "If John has 5 apples and gives 2 to Mary, how many does he have left?"
solution = neuro_symbolic_ai.solve_problem(problem)

# Quantum machine learning with Qiskit
import numpy as np
from qiskit import QuantumCircuit, Aer, execute
from qiskit.circuit import Parameter
from qiskit_machine_learning.algorithms import QSVC
from qiskit_machine_learning.kernels import QuantumKernel

class QuantumEnhancedML:
    def __init__(self, n_qubits=4):
        self.n_qubits = n_qubits
        self.backend = Aer.get_backend('qasm_simulator')
    
    def create_quantum_circuit(self, x, params):
        """Create a parameterized quantum circuit"""
        qc = QuantumCircuit(self.n_qubits)
        
        # Encode classical data into quantum states
        for i in range(self.n_qubits):
            qc.rx(x[i] * np.pi, i)
        
        # Apply parameterized quantum gates
        for i in range(self.n_qubits - 1):
            qc.cx(i, i + 1)
            qc.ry(params[i], i)
        
        return qc
    
    def quantum_kernel(self, x1, x2):
        """Compute quantum kernel between two data points"""
        # Create quantum feature map
        feature_map = self.create_quantum_circuit(x1, [0] * self.n_qubits)
        
        # Compute overlap using quantum circuit
        qc = QuantumCircuit(self.n_qubits)
        qc.append(feature_map, range(self.n_qubits))
        qc.append(feature_map.inverse(), range(self.n_qubits))
        qc.measure_all()
        
        # Execute on quantum simulator
        job = execute(qc, self.backend, shots=1024)
        result = job.result()
        counts = result.get_counts()
        
        # Calculate kernel value
        overlap = counts.get('0' * self.n_qubits, 0) / 1024
        return overlap
    
    def train_quantum_model(self, X_train, y_train):
        """Train quantum support vector classifier"""
        quantum_kernel = QuantumKernel(
            feature_map=self.create_quantum_circuit,
            quantum_instance=self.backend
        )
        
        qsvc = QSVC(quantum_kernel=quantum_kernel)
        qsvc.fit(X_train, y_train)
        return qsvc

# Example: Quantum-enhanced classification
qml = QuantumEnhancedML()
model = qml.train_quantum_model(X_train, y_train)
quantum_predictions = model.predict(X_test)

# Swarm learning implementation
import torch
import torch.nn as nn
from cryptography.hazmat.primitives import hashes
from cryptography.hazmat.primitives.asymmetric import rsa, padding

class SwarmLearning:
    def __init__(self, model, num_nodes):
        self.global_model = model
        self.node_models = [model.__class__() for _ in range(num_nodes)]
        self.private_key = rsa.generate_private_key(public_exponent=65537, key_size=2048)
        self.public_key = self.private_key.public_key()
    
    def secure_aggregation(self, local_updates):
        """Securely aggregate model updates from multiple nodes"""
        
        # Homomorphic encryption for privacy
        encrypted_updates = []
        for update in local_updates:
            encrypted = self.public_key.encrypt(
                update,
                padding.OAEP(
                    mgf=padding.MGF1(algorithm=hashes.SHA256()),
                    algorithm=hashes.SHA256(),
                    label=None
                )
            )
            encrypted_updates.append(encrypted)
        
        # Secure aggregation (simplified)
        aggregated_update = self.aggregate_encrypted(encrypted_updates)
        
        # Decrypt final result
        decrypted_update = self.private_key.decrypt(
            aggregated_update,
            padding.OAEP(
                mgf=padding.MGF1(algorithm=hashes.SHA256()),
                algorithm=hashes.SHA256(),
                label=None
            )
        )
        
        return decrypted_update
    
    def swarm_consensus(self, node_predictions):
        """Reach consensus among swarm nodes"""
        
        # Byzantine fault-tolerant consensus
        validated_predictions = self.validate_predictions(node_predictions)
        
        # Weighted aggregation based on node reliability
        consensus_prediction = self.weighted_aggregation(validated_predictions)
        
        return consensus_prediction
    
    def train_round(self, local_datasets):
        """Execute one round of swarm learning"""
        local_updates = []
        
        for i, (model, data) in enumerate(zip(self.node_models, local_datasets)):
            # Local training
            local_update = self.local_training(model, data)
            local_updates.append(local_update)
        
        # Secure aggregation
        global_update = self.secure_aggregation(local_updates)
        
        # Update global model
        self.apply_update(self.global_model, global_update)
        
        return self.global_model

# AI alignment framework
class AIAlignment:
    def __init__(self, model, principles):
        self.model = model
        self.principles = principles
        self.value_model = self.train_value_model()
    
    def train_value_model(self):
        """Train a model to evaluate alignment with human values"""
        # Implementation of value learning
        pass
    
    def constitutional_training(self, training_data):
        """Train model with constitutional principles"""
        
        for batch in training_data:
            # Generate responses
            responses = self.model.generate(batch['prompts'])
            
            # Evaluate alignment with principles
            alignment_scores = self.evaluate_alignment(responses, self.principles)
            
            # Reinforcement learning from principles
            rewards = self.calculate_rewards(alignment_scores)
            
            # Update model using principle-based rewards
            self.model.update_with_rewards(batch, responses, rewards)
    
    def evaluate_alignment(self, responses, principles):
        """Evaluate how well responses align with constitutional principles"""
        scores = {}
        
        for principle in principles:
            principle_scores = []
            for response in responses:
                # Evaluate each response against the principle
                score = self.value_model.evaluate(response, principle)
                principle_scores.append(score)
            scores[principle] = principle_scores
        
        return scores
    
    def red_team_analysis(self, test_cases):
        """Systematically test for harmful behaviors"""
        harmful_behaviors = []
        
        for test_case in test_cases:
            response = self.model.generate(test_case)
            
            if self.detect_harmful_behavior(response):
                harmful_behaviors.append({
                    'test_case': test_case,
                    'response': response,
                    'harm_type': self.classify_harm(response)
                })
        
        return harmful_behaviors