#openworldlearning — Public Fediverse posts

#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!

#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!

#AI #ML research/publishing operates in silos - to the detriment of making progress.

Our #IJCAI submission on #OpenWorldLearning #OWL was rejected for good and bad reasons.

The bad reason: "this is not just planning but also something similar to reinforcement learning".

Guess what - that is the point of our research! We are trying to close the gap between designed #AIPlanning systems and adaptive #Learning systems. It is a super-hard gap to push #AI #ML algorithmic research in.

#AI #ML research/publishing operates in silos - to the detriment of making progress.

Our #IJCAI submission on #OpenWorldLearning #OWL was rejected for good and bad reasons.

The bad reason: "this is not just planning but also something similar to reinforcement learning".

Guess what - that is the point of our research! We are trying to close the gap between designed #AIPlanning systems and adaptive #Learning systems. It is a super-hard gap to push #AI #ML algorithmic research in.

#AI #ML research/publishing operates in silos - to the detriment of making progress.

Our #IJCAI submission on #OpenWorldLearning #OWL was rejected for good and bad reasons.

The bad reason: "this is not just planning but also something similar to reinforcement learning".

Guess what - that is the point of our research! We are trying to close the gap between designed #AIPlanning systems and adaptive #Learning systems. It is a super-hard gap to push #AI #ML algorithmic research in.

#AI #ML research/publishing operates in silos - to the detriment of making progress.

Our #IJCAI submission on #OpenWorldLearning #OWL was rejected for good and bad reasons.

The bad reason: "this is not just planning but also something similar to reinforcement learning".

Guess what - that is the point of our research! We are trying to close the gap between designed #AIPlanning systems and adaptive #Learning systems. It is a super-hard gap to push #AI #ML algorithmic research in.

#AI #ML research/publishing operates in silos - to the detriment of making progress.

Our #IJCAI submission on #OpenWorldLearning #OWL was rejected for good and bad reasons.

The bad reason: "this is not just planning but also something similar to reinforcement learning".

Guess what - that is the point of our research! We are trying to close the gap between designed #AIPlanning systems and adaptive #Learning systems. It is a super-hard gap to push #AI #ML algorithmic research in.

@mattslocombe @cogsci @cognition

Thank you! I had a blast talking about #AI, #cognition, #analogy, #OpenWorldLearning #InteractiveTaskLearning. The forum was the very BEST: very insightful students & delightful psychologists.

Honestly, I learned quite a bit from the discussions. Amidst the world wide storm of #AI and #ML, sometimes we forget why #HumanIntelligence is so special.

New in #AI #ML that is not #chatgpt

I am STOKED about our research on #OpenWorldLearning at #AAMAS 2023.

#OWL is a novel learning paradigm. The three waves of #AI share a common design pattern. Phase 1 - program/train the inference algorithm; Phase 2 - deploy it. If deployment finds some unhandled usecases, go back to the first phase.

#OWL breaks this cycle & builds systems that can #learn like #humans - they learn autonomously AFTER they have been deployed.

https://arxiv.org/abs/2303.14272

January has been an exciting month for #AI #ML fundamental research at #PARC.

Our work on making #AIPlanning methods work/learn in an #OpenWorld -will be presented at #AAMAS2023 as well as at #ICAPS2023. AND, an #AIJ article is under works.

#OpenWorldLearning is a new challenge - the environments introduce novelties while the agent is operating in the world. The agent must detect, characterize, and accommodate novelties during run time. This research is a part of #DARPA #SAILON program

January has been an exciting month for #AI #ML fundamental research at #PARC.

Our work on making #AIPlanning methods work/learn in an #OpenWorld -will be presented at #AAMAS2023 as well as at #ICAPS2023. AND, an #AIJ article is under works.

#OpenWorldLearning is a new challenge - the environments introduce novelties while the agent is operating in the world. The agent must detect, characterize, and accommodate novelties during run time. This research is a part of #DARPA #SAILON program

January has been an exciting month for #AI #ML fundamental research at #PARC.

Our work on making #AIPlanning methods work/learn in an #OpenWorld -will be presented at #AAMAS2023 as well as at #ICAPS2023. AND, an #AIJ article is under works.

#OpenWorldLearning is a new challenge - the environments introduce novelties while the agent is operating in the world. The agent must detect, characterize, and accommodate novelties during run time. This research is a part of #DARPA #SAILON program

January has been an exciting month for #AI #ML fundamental research at #PARC.

Our work on making #AIPlanning methods work/learn in an #OpenWorld -will be presented at #AAMAS2023 as well as at #ICAPS2023. AND, an #AIJ article is under works.

#OpenWorldLearning is a new challenge - the environments introduce novelties while the agent is operating in the world. The agent must detect, characterize, and accommodate novelties during run time. This research is a part of #DARPA #SAILON program

January has been an exciting month for #AI #ML fundamental research at #PARC.

Our work on making #AIPlanning methods work/learn in an #OpenWorld -will be presented at #AAMAS2023 as well as at #ICAPS2023. AND, an #AIJ article is under works.

#OpenWorldLearning is a new challenge - the environments introduce novelties while the agent is operating in the world. The agent must detect, characterize, and accommodate novelties during run time. This research is a part of #DARPA #SAILON program