Machine-to-Machine AI-driven experiments autonomously recording on chain.
Causality.Network is focused on solving a core problem in science: Is the data authentic?
- How do we know it wasn't falsified?
- How do we know the equipment wasn't tampered with?
- How do we know this wasn't just a data set generated by AI?
Causality.Network solves this problem by adding a blockchain-based layer to IoT devices combined with methods of data authentication (secure enclave signatures) and privacy (zK)
IoT devices are at the core of scientific research from wet lab research (e.g. bioreactors) to clinical trials (e.g. blood test machines) and more. There are weaknesses in the current system with the ability to easily tamper data or falsify it (the latter is a particular concern with AI being able to falsify large data sets that appear authentic). The inability to provide raw data is a major factor contributing to the reproducibility crisis in science.
Causality.Network is a dApp that integrates secure enclave signatures from IoT devices to prove data authenticity at its source. Data is then stored on or off-chain via zK ensuring data privacy. The dApp will run on open-source hardware such as OpenTrons and it will be used by scientists. Initial focus areas are neurotech through an open brain-computer interface device, synbio, and longevity through wearables.
As research moves towards full automation, this dApp will help usher in an era of Machine-to-Machine AI-driven experiments autonomously recording on-chain.
Note: Causality.Network is the core infrastructure project being worked on as part of the MuseMatrix Fellowship
Causality.Network History
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applied to the dApps & Apps 10 months ago which was rejected
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