No, manual feature extraction is not outdated.
In addition, manual feature extraction is hard to do-away, given, a data scientist needs business and domain logic to build a robust model to replicate and capture trend and pattern from data. Nevertheless, there are exceptions such as image data.
Depends, if its image data, yes the statement is true. There are many deep learning techniques e.g. CNN which extract features automatically. However, if your data is structures i.e. standard table format, one will need to use p_value, correlation analysis, chi-test, and feature_selection models such as PCA and dimensionality-reduction to select features.
Here are a list of feature extraction techniques (i.e.manual feature extraction techniques, requiring human intervention; these are not deep learning extraction techniques, though automated.):
- Independent component analysis
- Kernel PCA
- Latent semantic analysis
- Partial least squares
- Principal component analysis
- Multifactor dimensionality reduction
- Nonlinear dimensionality reduction
- Multilinear Principal Component Analysis
- Multilinear subspace learning
- Semidefinite embedding
Below is a list of deep-learning feature extraction techniques: