- “Ng says that if data is carefully prepared, a company may need far less of it than they think. With the right data, he says companies with just a few dozen examples or few hundred examples can have A.I. systems that work as well as those built by consumer internet giants that have billions of examples.
- Ng has some tips that include making sure that data is what he calls “y consistent.” In essence this means there should be some clear boundary between when something receives a particular classification label and when it doesn’t.
- The idea of thinking of the building and training of A.I. models as a continuous cycle, not a one-off project, also comes across in a recent report on A.I. adoption from consulting firm Accenture.”