#opg $OPG The More Confidently AI Speaks, the Less I Trust That It Knows What It Doesn't Know
Something started bothering me a few months ago that I haven't been able to set aside. The AI systems I use most confidently are also the ones that seem least capable of expressing genuine uncertainty. They answer. Fully. Fluently. Whether or not the ground beneath that answer is solid.
I assumed fluency and accuracy were roughly correlated. The more coherent the response, the more reliable the reasoning behind it.
The more I think about it, that assumption might be exactly backwards.
Fluency is a surface property. It's a function of training, not a signal of epistemic honesty. A model can sound certain while being wrong in ways it has no mechanism to flag.
What bothers me is that we're building the future of AI on infrastructure that optimizes for output confidence rather than output calibration.
This is what draws me back to what @OpenGradient is working through. Not just the decentralization angle but the deeper question of whether AI execution can eventually carry something like traceable reasoning, not just traceable computation.
$OPG is still early and I'm still working out what that distinction actually means in practice.
But I keep wondering if an AI system can't represent its own uncertainty honestly, does making its inference verifiable actually solve the right problem? #OPG
@OpenGradient
Something started bothering me a few months ago that I haven't been able to set aside. The AI systems I use most confidently are also the ones that seem least capable of expressing genuine uncertainty. They answer. Fully. Fluently. Whether or not the ground beneath that answer is solid.
I assumed fluency and accuracy were roughly correlated. The more coherent the response, the more reliable the reasoning behind it.
The more I think about it, that assumption might be exactly backwards.
Fluency is a surface property. It's a function of training, not a signal of epistemic honesty. A model can sound certain while being wrong in ways it has no mechanism to flag.
What bothers me is that we're building the future of AI on infrastructure that optimizes for output confidence rather than output calibration.
This is what draws me back to what @OpenGradient is working through. Not just the decentralization angle but the deeper question of whether AI execution can eventually carry something like traceable reasoning, not just traceable computation.
$OPG is still early and I'm still working out what that distinction actually means in practice.
But I keep wondering if an AI system can't represent its own uncertainty honestly, does making its inference verifiable actually solve the right problem? #OPG
@OpenGradient