Some scientists just want to build cool things and not have to worry about publishing. Some of my colleagues over the years have moved to Apple and startups for this reason.
Reproducibility does not necessarily mean that other people should be able to reproduce the results in question (which would depend upon them knowing the relevant information in the first place) - but that an experiment can be replicated.
I would agree that publishing results is better for several reasons (ethics, pragmatism, probably more thorough evaluation, etc.), but the definition doesn't really deal with the public at all.
It doesn't have to be "arbitrarily many people". Just people other than the original scientist. So colleagues at Apple still qualify.
There are tons of secret research behind any company (and governments), and it's still "science".
(And conversely, lots of published scientific papers are actually not reproducible, but for most of them nobody bothers -- even if other scientists cite them as accurate).
People studying machine learning are not like people running a psychology experiment. If you give the same code, data and computers to two different teams, they will get the same results, and they will understand why that happens.
That's because a machine learning system has been built up from known principles and hardware; whereas the human brain is not fully understood, so researchers must work their way down from observed evidence to induce theories.