What we chart
Artificial intelligence, machine learning and deep learning systems. They rarely fail in the open. They drift, slowly, until nobody checks the stars they were steering by.
So we fix a reference first: the objective, the assumptions, and the distance a model may drift before we turn back. Then we keep taking bearings.
How we observe
- 01
Fix the reference
Write down the target and the drift we will accept before any training run begins.
- 02
Observe for long
Calibration, robustness and behaviour under shift, measured over time rather than on one night.
- 03
Publish the drift
Readings that did not fit go into the log next to the ones that did.
What we will not claim
A perfect model, a safety guarantee from a single benchmark, or a course we have quietly corrected after the fact.
Where we are
The observatory is still being built. For questions, collaboration or early access, write to brain@excyst.com, or enter the network and look at the sky we are charting.