L2M
E17403
L2M is a DARPA research initiative focused on developing AI systems capable of continuous, lifelong learning and adaptation.
Statements (44)
| Predicate | Object |
|---|---|
| instanceOf |
DARPA research program
→
artificial intelligence research initiative → |
| abbreviationFor |
Lifelong Learning Machines
→
|
| applicationDomain |
autonomous vehicles
→
defense systems → intelligent agents → robotics → sensor processing → |
| country |
United States
→
|
| focusesOn |
continual adaptation
→
continuous learning → lifelong learning in artificial intelligence → machine learning → robust AI systems → |
| fullName |
Lifelong Learning Machines
→
|
| fundingAgency |
DARPA Information Innovation Office
→
|
| goal |
develop AI systems capable of continuous, lifelong learning and adaptation
→
enable AI systems to learn from experience during deployment → enable AI to learn new tasks without catastrophic forgetting → improve robustness of AI in changing environments → reduce need for retraining from scratch → |
| hasProperty |
aims to operate in dynamic, non-stationary environments
→
emphasizes learning during deployment → seeks biological inspiration from natural learning systems → targets efficient adaptation with limited data → targets robustness to unexpected situations → |
| organizationTypeOfSponsor |
U.S. government research agency
→
|
| relatedTo |
DARPA Lifelong Learning Machines program
→
catastrophic forgetting mitigation techniques → continual learning research community → meta-learning → neuro-inspired computing → |
| researchArea |
adaptive control
→
artificial intelligence → autonomous systems → continual learning → lifelong learning algorithms → machine learning → neuroscience-inspired AI → online learning → robust perception → transfer learning → |
| sponsor |
DARPA
→
Defense Advanced Research Projects Agency → |
Referenced by (1)
| Subject (surface form when different) | Predicate |
|---|---|
|
Lifelong Learning Machines program
→
|
acronym |