Emet-Labs releases DeepCausality for Rust
Emet Labs announces the availability of DeepCausality, the first hyper-geometric computational causality library for the Rust programming language.
Contemporary artificial intelligence suffers from several shortcomings in interpreting context and conducting complex multi-stage reasoning leading to limitations in high-stakes environments. Fundamentally, these shortcomings root in the correlation-based foundations of deep learning that cause, among other issues, a lack of determinism, lack of context awareness, weak reasoning capabilities, and high resource requirements.
DeepCausality contributes a novel contextualized causality reasoning engine that enables deterministic reasoning over poly-contextual, complex multi-stage causality models. DeepCausality leverages contemporary advancements in static-type systems, adding only minimal overhead, and thus is suitable for deployment on low-power (IoT) devices or real-time applications without any additional acceleration hardware such as FPGA or GPUs.
DeepCausality is publicly available on GitHub: