  This is a theoretical paper, as a companion paper of the plenary talk for the
same conference ISAIC 2022. In contrast to the author's plenary talk in the
same conference, conscious learning (Weng, 2022b; Weng, 2022c) which develops a
single network for a life (many tasks), "Deep Learning" trains multiple
networks for each task. Although "Deep Learning" may use different learning
modes, including supervised, reinforcement and adversarial modes, almost all
"Deep Learning" projects apparently suffer from the same misconduct, called
"data deletion" and "test on training data". This paper establishes a theorem
that a simple method called Pure-Guess Nearest Neighbor (PGNN) reaches any
required errors on validation data set and test data set, including zero-error
requirements, through the same misconduct, as long as the test data set is in
the possession of the authors and both the amount of storage space and the time
of training are finite but unbounded. The misconduct violates well-known
protocols called transparency and cross-validation. The nature of the
misconduct is fatal, because in the absence of any disjoint test, "Deep
Learning" is clearly not generalizable.

Machine Learning