Results for “ai-maturity”

AI safety and responsible AI practices

当个人希望提升自身在职业或社会中的不可替代性，以应对环境变化和不确定性时

Analyzes game AI systems in a codebase, covering behavior trees, finite state machines, GOAP, utility AI, pathfinding, steering, perception, difficulty adaptation, NPC dialogue, and AI debugging tools for Unity, Unreal, and Godot projects.

Data-Centric Artificial Intelligence: A Survey

Benchmark AI skill effectiveness by measuring implementation quality against legacy constraints.

ai-module-development

Train AI models to be harmless through self-critique and AI feedback using a set of constitutional principles, without requiring human labels for harmful outputs.

Prevents AI-introduced regressions with sandbox-mode API testing, automated bug-check workflows, and patterns that catch blind spots where the same model writes and reviews code.

Conduct a maturity assessment against the NIST Cybersecurity Framework (CSF) 2.0, using Implementation Tiers to measure organizational cybersecurity posture and create improvement roadmaps.

Guides AI agents to apply advanced mathematical and probabilistic techniques (Bloom filters, HyperLogLog, FFT, etc.) for large-scale data problems where classical algorithms are optimal but math offers better asymptotic bounds.

Memory (acaf-memory)

Analyze AI disruption pressure across a business, map competitive exposure, and produce a 90-day defensive action plan.

A self-evolution engine for AI agents. Analyzes runtime history to identify improvements and applies protocol-constrained evolution.