
Machine Learning
Machine learning develops models whose behavior is fitted from data rather than specified only through fixed rules.
How it works
Training procedures adjust model parameters to reduce an objective on examples, simulations, demonstrations or interaction data.
Why it matters
Learning can help machines perceive complex scenes, predict outcomes and adapt policies to varied inputs.
Advantages
- handles high-dimensional data
- can improve with representative data
- supports perception and control
Limitations
- data and validation requirements
- distribution shift
- outputs can be difficult to interpret
See it in the Observatory
Capabilities depend on implementation, operating environment, training data, hardware and safety controls. Follow the connected records and sources before generalizing.