LF AI & Data Foundation, the organization building an ecosystem to sustain open source innovation in artificial intelligence (AI) and data open source projects, today announced DeepCausality as its latest Sandbox Project.
DeepCausality is an advanced hyper-geometric computational causality library tailored for the Rust programming language. The library is engineered to overcome the limitations of conventional deep learning models by focusing on fast and deterministic context-aware causal reasoning. DeepCausality integrates hypergeometric recursive causal models and end-to-end explainability, creating a robust framework for various industries.
Dr. Ibrahim Haddad, Executive Director of LF AI & Data, said: “The addition of DeepCausality to LF AI & Data Foundation further diversifies our growing project portfolio and aligns with our mission to advance and democratize AI and data. DeepCausality, with its focus on computational causality, stands to bring about transformative changes in dynamic systems across industries.”
Read the full text on the LF AI & data blog post.
DeepCausality is a hyper-geometric computational causality library that enables fast and deterministic context-aware causal reasoning in Rust. Learn more about DeepCausality on GitHub and join the DeepCausality-Announce Mailing List.
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