Intelligent Artifacts presents

Explain. Compute. Interpret. Trace. Edit.


In order to push the envelope, we realized we needed a sensible way to examine AI techniques and technologies. We came up with ExCITE. Now, we’re helping you evaluate AI for your use cases. ExCITE is a technology-agnostic evaluation methodology focused on transparency in Artificial Intelligence solutions.

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Intelligent Artifacts is ushering in a new category of machine learning

that raises the expectations of AI solutions to meet regulatory standards and encompasses many of the elements an AI needs to operate transparently and effectively in applications that affect life, death, and well-being.

Read our ExCITE White Paper

The ExCITE Methodology for Verification & Validation of Safety-critical Artificial Intelligence.

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Ex plain

why a prediction was made.

Our universal input format converts user data into sequences of symbols before feeding into the system. All information flowing through GAIuS is presented in human terms so users can see what is happening and why.

C ompute

why a prediction was made.

Our framework is deterministic, meaning same inputs = same outputs. Therefore, predictions are returned in a deterministic amount of time for RTOS.

T race

predictions back to
source data.

Every learned record provided for training remains intact (i.e., not modeled) and is given a unique ID.  When a prediction ensemble is returned, each prediction object contains the unique ID of the learned record the system pattern matched on to produce that prediction. A user/app can track these IDs against their application’s unique IDs to show exact training records.

E dit

source data for refinements and correction.

If a user spots a problem or something missing from the original data based on a prediction provided by GAIuS they can drill down into the sequence to make real-time, human-in-the-loop refinements. Because we do not model until prediction time, users are able to edit input data sequences without any need for retraining.

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I nterpret

how data filters through
the system.

Each algorithm and each datum can be reviewed before and after it is transformed by additional algorithms. Given that the system is deterministic, it is possible to predict specific outcomes based on adjustments in input data or parameters within the system.

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