Draft for AI capabilities systematic evaluation development proposal:
The core idea here is that easier visibility of AI models’ capabilities helps safety of development in multiple ways.
Clearer situation awareness of safety research – Researchers can see where we are in various aspects and modalities, they get a track record/timeline of abilities developed which can be used as baseline for future estimates.
Division of capabilities can help create better models of components necessary for general intelligence. Perhaps a better understanding of cognitive abilities hierarchy can be extracted.
Capabilities testing can be forced by regulatory policies to put most advanced systems under more scrutiny and/or safe(ty) design support. To state differently: better alignment of attention focus to emerging risk( of highly capable AIs).
Presumably smooth and well available testing infrastructure or tools are a prerequisite here.
The most obvious risks are:
Measure becoming a challenge and a goal, speeding up a furious developments of strong AI systems.
Technical difficulties of testing setup(s) and evaluation, especially handling the factor of randomness in mechanics(/output generation) of AI systems.
Draft for AI capabilities systematic evaluation development proposal:
The core idea here is that easier visibility of AI models’ capabilities helps safety of development in multiple ways.
Clearer situation awareness of safety research – Researchers can see where we are in various aspects and modalities, they get a track record/timeline of abilities developed which can be used as baseline for future estimates.
Division of capabilities can help create better models of components necessary for general intelligence. Perhaps a better understanding of cognitive abilities hierarchy can be extracted.
Capabilities testing can be forced by regulatory policies to put most advanced systems under more scrutiny and/or safe(ty) design support. To state differently: better alignment of attention focus to emerging risk( of highly capable AIs).
Presumably smooth and well available testing infrastructure or tools are a prerequisite here.
The most obvious risks are:
Measure becoming a challenge and a goal, speeding up a furious developments of strong AI systems.
Technical difficulties of testing setup(s) and evaluation, especially handling the factor of randomness in mechanics(/output generation) of AI systems.