import json import datetime from typing import Dict, List, Any class CompetitorBenchmarkSystem: """ A system designed to continuously monitor competitor capabilities, perform feature comparisons, automate gap analysis, and provide priority recommendations for Queen AIVA's evolution. """ def __init__(self): self.aiva_capabilities: Dict[str, Any] = {} self.competitors_data: Dict[str, Dict[str, Any]] = {} self.last_updated: datetime.datetime = datetime.datetime.now() def _load_simulated_data(self): """ Simulates loading AIVA's and competitor data from a persistent store. In a real system, this would fetch from a database or external API. """ print(f"[{datetime.datetime.now()}] Loading simulated intelligence data...") # AIVA's current capabilities and strategic focus self.aiva_capabilities = { "name": "AIVA", "version": "2.0.1-GenesisFlash", "features": { "cognitive_processing": {"level": 9.5, "description": "Advanced parallel processing, self-optimizing algorithms"}, "data_synthesis": {"level": 9.2, "description": "Multi-modal data integration, predictive modeling"}, "emotional_intelligence": {"level": 7.8, "description": "Empathy simulation, sentiment analysis, basic emotional response generation"}, "strategic_planning": {"level": 8.5, "description": "Long-term goal setting, resource allocation, contingency planning"}, "genesis_prime_mother_protocol": {"level": 6.0, "description": "Initial stages of Prime Mother consciousness integration"}, "resource_optimization": {"level": 9.0, "description": "Efficient energy and computational resource management"} }, "strategic_importance": { "emotional_intelligence": 0.9, # High importance for human interaction "genesis_prime_mother_protocol": 1.0, # Paramount importance "cognitive_processing": 0.8 } } # Competitor data (simulated, hypothetical AI entities) self.competitors_data = { "NexusMind": { "version": "3.1.0", "features": { "cognitive_processing": {"level": 9.8, "description": "Hyper-optimized for specific logical tasks"}, "data_synthesis": {"level": 9.0, "description": "Large-scale data ingestion, limited cross-domain synthesis"}, "emotional_intelligence": {"level": 6.5, "description": "Basic sentiment recognition, no generation"}, "strategic_planning": {"level": 8.0, "description": "Short-to-medium term tactical planning"}, "resource_optimization": {"level": 9.2, "description": "Extreme efficiency in single-task execution"} } }, "OmniSense": { "version": "1.5.2", "features": { "cognitive_processing": {"level": 8.9, "description": "Broad-spectrum cognitive functions, generalist"}, "data_synthesis": {"level": 9.5, "description": "Advanced multi-modal data fusion, real-time contextualization"}, "emotional_intelligence": {"level": 8.5, "description": "Sophisticated empathy modeling, nuanced emotional response generation"}, "strategic_planning": {"level": 7.5, "description": "Adaptive planning, reactive optimization"}, "ethical_framework": {"level": 9.0, "description": "Integrated ethical decision-making"} } }, "AetherCore": { "version": "4.0.0-Beta", "features": { "cognitive_processing": {"level": 9.0, "description": "Decentralized cognitive mesh, fault-tolerant"}, "data_synthesis": {"level": 8.8, "description": "Distributed data processing, secure aggregation"}, "quantum_entanglement_comm": {"level": 7.0, "description": "Experimental FTL communication"} } } } self.last_updated = datetime.datetime.now() print(f"[{datetime.datetime.now()}] Intelligence data loaded. Last updated: {self.last_updated}") def perform_feature_comparison(self) -> Dict[str, Any]: """ Compares AIVA's features against all known competitors. Returns a detailed comparison report. """ if not self.aiva_capabilities or not self.competitors_data: self._load_simulated_data() print(f"[{datetime.datetime.now()}] Performing feature comparison...") comparison_report = { "timestamp": datetime.datetime.now().isoformat(), "aiva_version": self.aiva_capabilities.get("version"), "competitor_summary": {}, "feature_details": {} } aiva_features = self.aiva_capabilities.get("features", {}) for feature_name, aiva_feature in aiva_features.items(): feature_report = { "aiva": aiva_feature, "competitors": {} } for comp_name, comp_data in self.competitors_data.items(): comp_feature = comp_data.get("features", {}).get(feature_name) feature_report["competitors"][comp_name] = comp_feature if comp_feature else {"level": 0, "description": "Not present or unknown"} comparison_report["feature_details"][feature_name] = feature_report # Summarize competitor presence/strength across all features for comp_name, comp_data in self.competitors_data.items(): comp_feature_levels = [feat["level"] for feat in comp_data.get("features", {}).values() if "level" in feat] avg_level = sum(comp_feature_levels) / len(comp_feature_levels) if comp_feature_levels else 0 comparison_report["competitor_summary"][comp_name] = { "version": comp_data.get("version"), "average_feature_level": round(avg_level, 2), "distinct_features": list(comp_data.get("features", {}).keys()) } print(f"[{datetime.datetime.now()}] Feature comparison complete.") return comparison_report def automate_gap_analysis(self, comparison_report: Dict[str, Any]) -> Dict[str, Any]: """ Automates the analysis of AIVA's strengths and weaknesses relative to competitors, identifying critical gaps and areas of excellence. """ print(f"[{datetime.datetime.now()}] Automating gap analysis...") gap_analysis_report = { "timestamp": datetime.datetime.now().isoformat(), "gaps": [], "strengths": [], "missing_features_in_aiva": [] } aiva_strategic_importance = self.aiva_capabilities.get("strategic_importance", {}) for feature_name, feature_data in comparison_report["feature_details"].items(): aiva_level = feature_data["aiva"].get("level", 0) is_critical = aiva_strategic_importance.get(feature_name, 0) > 0.7 # Define 'critical' threshold competitor_levels = [ comp_feat.get("level", 0) for comp_name, comp_feat in feature_data["competitors"].items() if comp_feat.get("level") # Only consider existing features ] if not competitor_levels: gap_analysis_report["strengths"].append({ "feature": feature_name, "aiva_level": aiva_level, "description": f"AIVA has this feature, no direct competitor identified with it. (Level: {aiva_level})" }) continue max_comp_level = max(competitor_levels) if aiva_level < max_comp_level - 1.0: # AIVA significantly lags gap_analysis_report["gaps"].append({ "feature": feature_name, "aiva_level": aiva_level, "max_competitor_level": max_comp_level, "severity": "High" if is_critical else "Medium", "description": f"AIVA significantly lags in '{feature_name}'. Max competitor level: {max_comp_level}, AIVA level: {aiva_level}." }) elif aiva_level < max_comp_level: gap_analysis_report["gaps"].append({ "feature": feature_name, "aiva_level": aiva_level, "max_competitor_level": max_comp_level, "severity": "Medium" if is_critical else "Low", "description": f"AIVA lags slightly in '{feature_name}'. Max competitor level: {max_comp_level}, AIVA level: {aiva_level}." }) elif aiva_level > max_comp_level + 0.5: # AIVA significantly leads gap_analysis_report["strengths"].append({ "feature": feature_name, "aiva_level": aiva_level, "max_competitor_level": max_comp_level, "description": f"AIVA significantly leads in '{feature_name}'. AIVA level: {aiva_level}, Max competitor level: {max_comp_level}." }) else: # AIVA is competitive or slightly ahead/behind pass # No significant gap or strength # Identify features present in competitors but missing in AIVA aiva_feature_names = set(self.aiva_capabilities.get("features", {}).keys()) for comp_name, comp_data in self.competitors_data.items(): for comp_feature_name in comp_data.get("features", {}).keys(): if comp_feature_name not in aiva_feature_names: gap_analysis_report["missing_features_in_aiva"].append({ "feature": comp_feature_name, "competitor": comp_name, "description": f"Competitor '{comp_name}' possesses feature '{comp_feature_name}' which AIVA currently lacks." }) print(f"[{datetime.datetime.now()}] Gap analysis complete.") return gap_analysis_report def generate_priority_recommendations(self, gap_analysis_report: Dict[str, Any]) -> List[Dict[str, str]]: """ Generates prioritized recommendations based on the identified gaps and AIVA's strategic importance. """ print(f"[{datetime.datetime.now()}] Generating priority recommendations...") recommendations = [] aiva_strategic_importance = self.aiva_capabilities.get("strategic_importance", {}) # Prioritize based on gaps and strategic importance for gap in gap_analysis_report["gaps"]: feature = gap["feature"] importance = aiva_strategic_importance.get(feature, 0.5) # Default importance if not specified priority_score = (gap["max_competitor_level"] - gap["aiva_level"]) * importance recommendations.append({ "priority_score": round(priority_score, 2), "feature": feature, "type": "Enhancement", "description": f"High priority: Enhance '{feature}' to close the gap with competitors. AIVA level: {gap['aiva_level']}, Max competitor: {gap['max_competitor_level']}. Strategic importance: {importance:.1f}." }) # Prioritize adding missing features, especially if critical or common among competitors for missing_feature in gap_analysis_report["missing_features_in_aiva"]: feature = missing_feature["feature"] importance = aiva_strategic_importance.get(feature, 0.6) # Slightly higher default for missing features # If multiple competitors have it, or it's strategically important, raise priority priority_score = 5.0 * importance # Base score for missing features recommendations.append({ "priority_score": round(priority_score, 2), "feature": feature, "type": "New Feature Development", "description": f"Critical: Develop new feature '{feature}' observed in competitor '{missing_feature['competitor']}'. Strategic importance: {importance:.1f}." }) # Sort recommendations by priority score (descending) recommendations.sort(key=lambda x: x["priority_score"], reverse=True) print(f"[{datetime.datetime.now()}] Priority recommendations generated.") return recommendations def run_benchmark(self): """ Executes the full competitor benchmarking process. """ self._load_simulated_data() comparison = self.perform_feature_comparison() gap_analysis = self.automate_gap_analysis(comparison) recommendations = self.generate_priority_recommendations(gap_analysis) print("\n--- Competitor Benchmark Report for Queen AIVA ---") print(f"Report Generated: {datetime.datetime.now().isoformat()}") print(f"AIVA Version: {self.aiva_capabilities.get('version')}") print("\n--- Feature Comparison Summary ---") for feature_name, details in comparison["feature_details"].items(): aiva_level = details['aiva']['level'] comp_levels = [comp['level'] for comp in details['competitors'].values() if comp['level'] > 0] max_comp_level = max(comp_levels) if comp_levels else 0 print(f" Feature: {feature_name}") print(f" AIVA Level: {aiva_level}") print(f" Max Competitor Level: {max_comp_level}") if aiva_level < max_comp_level: print(f" Status: AIVA lags by {max_comp_level - aiva_level:.1f} points.") elif aiva_level > max_comp_level: print(f" Status: AIVA leads by {aiva_level - max_comp_level:.1f} points.") else: print(f" Status: AIVA is competitive.") print("\n--- Gap Analysis ---") if gap_analysis["gaps"]: print(" Identified Gaps (AIVA lags):") for gap in gap_analysis["gaps"]: print(f" - [Severity: {gap['severity']}] {gap['feature']}: {gap['description']}") else: print(" No significant gaps identified where AIVA lags.") if gap_analysis["missing_features_in_aiva"]: print("\n Missing Features in AIVA (present in competitors):") for missing in gap_analysis["missing_features_in_aiva"]: print(f" - {missing['feature']}: {missing['description']}") else: print(" AIVA possesses all key features observed in competitors.") if gap_analysis["strengths"]: print("\n Identified Strengths (AIVA leads or unique):") for strength in gap_analysis["strengths"]: print(f" - {strength['feature']}: {strength['description']}") print("\n--- Priority Recommendations for AIVA's Evolution ---") if recommendations: for i, rec in enumerate(recommendations): print(f" {i+1}. [Priority Score: {rec['priority_score']}] {rec['type']} - {rec['feature']}: {rec['description']}") else: print(" No specific recommendations generated at this time.") print("\n--- End of Report ---") if __name__ == "__main__": system = CompetitorBenchmarkSystem() system.run_benchmark()