#darpahard — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #darpahard, aggregated by home.social.
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#DARPA concluded its #SAILON program on #OpenWorldLearning #OWL. I just attended its last PI meeting.
The program sought to study a very new paradigm of #ML. How to design #AI systems that can recognize, characterize, and accommodate distributional shifts, transformations, perturbations in the domains _after_ they have been deployed, _without_ retraining/reprogramming?
This learning paradigm breaks out of the train/test mode that classical ML is setup as.
The problem truly is #DARPAhard!
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#DARPA concluded its #SAILON program on #OpenWorldLearning #OWL. I just attended its last PI meeting.
The program sought to study a very new paradigm of #ML. How to design #AI systems that can recognize, characterize, and accommodate distributional shifts, transformations, perturbations in the domains _after_ they have been deployed, _without_ retraining/reprogramming?
This learning paradigm breaks out of the train/test mode that classical ML is setup as.
The problem truly is #DARPAhard!
-
#DARPA concluded its #SAILON program on #OpenWorldLearning #OWL. I just attended its last PI meeting.
The program sought to study a very new paradigm of #ML. How to design #AI systems that can recognize, characterize, and accommodate distributional shifts, transformations, perturbations in the domains _after_ they have been deployed, _without_ retraining/reprogramming?
This learning paradigm breaks out of the train/test mode that classical ML is setup as.
The problem truly is #DARPAhard!
-
#DARPA concluded its #SAILON program on #OpenWorldLearning #OWL. I just attended its last PI meeting.
The program sought to study a very new paradigm of #ML. How to design #AI systems that can recognize, characterize, and accommodate distributional shifts, transformations, perturbations in the domains _after_ they have been deployed, _without_ retraining/reprogramming?
This learning paradigm breaks out of the train/test mode that classical ML is setup as.
The problem truly is #DARPAhard!
-
#DARPA concluded its #SAILON program on #OpenWorldLearning #OWL. I just attended its last PI meeting.
The program sought to study a very new paradigm of #ML. How to design #AI systems that can recognize, characterize, and accommodate distributional shifts, transformations, perturbations in the domains _after_ they have been deployed, _without_ retraining/reprogramming?
This learning paradigm breaks out of the train/test mode that classical ML is setup as.
The problem truly is #DARPAhard!