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> Ingest your enterprise data, extract what matters, build a Context Graph and knowledge graph (KG), and run graph analytics and causal reasoning over all of it, with full decision provenance baked in. Explainable, traceable, and trustworthy by design.

白箱AGI架构探索：元认知（自我认知循环）、持续学习（知识飞轮）、世界模型（条件空间+语义时空图）、自我改进（自举纪律）、零LLM白箱管线与可审计信任护栏。

《李宏毅深度学习教程》（李宏毅老师推荐👍，苹果书🍎），PDF下载地址：

Build reliable customer-facing AI agents with Parlant: an interaction control harness optimized for controlled, consistent, and predictable LLM interactions.

Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!

SHAP (SHapley Additive exPlanations) is a game theoretic approach to explain the output of any machine learning model.

This is a package with state of the art methods for Explainable AI for computer vision. This can be used for diagnosing model predictions, either in production or while developing models.

This project is about explaining what machine learning classifiers (or models) are doing.

This repository provides a research-grade, unsupervised anomaly detection framework for microservice logs, combining multi-modal embeddings, graph attention networks, hybrid ensembles with GMM thresholding, streaming simulation, and explainability (SHAP + gradients).

Most calibration work asks whether a model is correct 80% of the time when it reports 80% confidence. We ask a different question: after a person sees that confidence and makes the final decision, is the human-AI team correct 80% of the time?